Tom Bersano, PhD. - Scientist, Instructor, Software Engineer at Google

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Teaching
Research
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My Degrees and Academics:

I_received my PhD and several Masters from the University of Michigan (ranked 3rd best Grad School in the U.S.).

Academic Achievements:

I have both conventional accomplishments (usual PhD, MsE, BsE, graduated with honors, Dean's List,etc.) and above average accomplishments: close to 10 Masters Degrees I earned by taking a much larger amount of classes per semester than any student ever did at this university.

Extracurricular Activities

While earning my degrees I was also working full-time as an engineer (to pay for classes and to earn my dual EU-US citizenship), teaching and doing research.

- Post-Doctoral Training in
  MED, EECS, BME, PHYS
  (Completed in '05, '08, '10)
  Int.Medicine/Oncology:'10-'11
  Cancer Cell Capture Technologies.
  
  Electrical Engineering:'08-'10
  MEMS for Single-Cell Biology.
  
  Biomed. Engineering:'05-'08
  Microfluidic Automation.
  
  Physics:'04-'05
  BioInformatics, Quantum Computing.
- Doctorate (Ph.D.) in
  Computer Engineering - AI
  (Graduated Dec 2003)
  My PhD Dissertation was:
  "Computer evolution of gene circuits for cell-embedded computation, biotechnology, and as a model for evolutionary computation". Links to abstract [A], table of contents [T], ProQuest[P]
  
  My PhD Advisor was:
  John H. Holland. He authored several books in Artificial Intelligence and Complex Systems in which he established widely used optimization and AI techniques such as Genetic Algorithms (computer evolution of designs and solutions). He was awarded the prestigious MacArthur Genius Grant
- Master (M.S.) in
  BioChemistry
  (Graduated Dec 2001)
- Master (M.S.) in
  Kinesiology
  (Graduated May 2001)
- Master (M.S.E.) in
  BioMedical Engineering
  (Graduated Dec 2001)
- Master (M.Eng.) in
  Aerospace Engineering
  (Graduated May 1999)
- Cert. of Grad Studies in
  Complex Systems
  (Graduated May 1999)
- Master (M.S.E.) in
  Computer Engineering
  (Graduated May 1997)
- Master (M.S.) in
  Philosophy
  (needed just 3 more term papers)
- Master (M.S.E) in
  Elec.Eng. - Circuits & BioSys.
  (needed just 3 more classes)
- Master (M.S) in
  Psychology - Cog. Neurosci.
  (needed just 1 more project report)
- Bachelor (B.S.E) in
  Computer Engineering
  (Graduated with Honors May 1996)
- Undergraduate Honors in
  Computer Engineering
  (Dean's List 1995-1996)

My Teaching Experience: 3 Engineering Classes I Taught at UofM

I was a graduate student instructor for 3 engineering courses for junior and senior engineers at the University of Michigan: a programming course, a digital logic circuits design lab course and a computer architecture course, and I was ranked in the top quartile among instructors at the University. I was also a project mentor in a biomedical engineering design course (BME 450).

Teaching Philosophy

I have been exposed to a wide array of teaching styles throughout my extensive academic career, which allowed me to observe, through first-hand experience, which styles and methods are most engaging and effective.
Specifically, I believe that good teaching requires communicating excitement about the subject and a deep understanding that goes beyond stating a list of facts and rather tries to tie them into interesting and memorable stories and intuitive analogies. ...

2001:Logic Design EECS270
My UofM Ranking as Teacher:
95%
2001:C++ Progr. EECS280
My UofM Ranking as Teacher:
94%
1997:Computer Arch. CS370
My UofM Ranking as Teacher:
N/A

My Travels: 21 World Nations I Visited.

19 of my Research Projects:

My Research Interests span several related areas of Computer Science, Molecular Biology and Biomedical Engineering.

My Main Research Focus

I am expert at engineering and assembling microscale systems that use networks of microchannels, biological cells and computerized systems or artificial intelligence to automate or improve upon various biological experiments, to study and extend technologies like genetic engineering, human genome analysis, cancer biology, stem cell therapies, modification of cellular aging or extension of synthetic biology.

New Interdisciplinary Areas

By combining multiple fields of science and technology in new ways, I am working toward the goal of automated science, the development of a new technology and assembly of new devices that design and perform scientific experiments on their own. I also work on improving Synthetic Biology through Artificial Intelligence, and the reverse: improving Evolutionary Computation by imitating Molecular Genetics.

Scientific Peer Review Activities

An important duty of a scientist is peer review, evaluating the work of other research and deciding if it is conclusive and worthy of publication or if it needs corrections or additional experiments. I played this official role 15 times over the years, as a Reviewer and Board Member for the following Scientific Journals and International Conferences:

BioTechniques Journal (2009)
IEEE Transactions on Evolutionary Computation (1999, 2000, 2003)
Bio-Techno Conference (2010, 2011, 2012)
Genetic and Evolutionary Computation Conference (2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011)
Evolutionary Computation (2000)
Genetic Programming Conference(1997-1998)

2010
Bio+Micro
Circulating Tumor Cells
- - Fig1
    Figure 1: An SEM image of blood¹, showing normal circulating cells that need to be separated and distinguished from metastatic cancer cells.
  - Fig2
    Figure 2: An SEM image of a cancer cell², anchoring itself on a substrate.
  - Fig3
    Figure 3: A zoomed-out view of a Circulating Tumor Cell (green) in one of my devices, about to be separated from human blood cells (gray).
  - Video1
    Video 1: Since this project is still ongoing, this video just shows some unrelated data I collected on cancer cells rather than the final device.
  Project Year: 2010.
  Location: Univ. of Michigan, USA.
  Collaborators:Hayes (Medical Oncology) Lab.
  Separating Circulating Tumor Cells from Blood.
  The Problem: Metastasis is a dangerous stage of cancer progression in which cancer cells start to shed into the bloodstream and can eventually form multiple secondary tumors. The number of these Circulating Tumor Cells (CTC) in a patient can be used successfully to predict cancer outcome and there are already a few commercial and clinical systems to do this. If we could isolate these cells we could test what treatments might be most effective against them but attempts to do this have not yet been successful. In addition, the existing systems are large and expensive and rely on antibodies or immunomagnetic particles against very specific types of cancer cells. Thus they are not able to detect important cells like cancer stem cells that do not express those antigens, and they only work with fixed (dead) cells, which makes it impossible to do useful tests like drug-resistance on these cells.
  To address these problems, I developed a new technology that can separate and capture circulating tumor cells from blood in real time and without relying on antibodies or magnetic particles or fixation techniques, leaving cells alive for further tests, and can be fabricated in microchip size and inexpensive materials. We are currently testing this technology with human blood and cancer cells and with cancer patients' blood.
2009
Bio+Micro
Cancer Stem Cells
- - Fig1
    Figure 1: Isolated cancer cells (green) in our device, enabling a new level of drug discovery.
  - Fig2
    Figure 2: After several days, differences in growth speed between microchambers reveal cell subtypes and drug resistance.
  - Fig3
    Figure 3: A close-up of our microfluidic device, hundreds of microchambers in just a few millimeters in size.
  - Video1
    Video 1: Cells are hydrodynamically separated and captured, one clone per chamber.
  Project Year: 2009-2010.
  Location: Univ.of Michigan, USA.
  Collaborators:Integrated Micro-systems Lab and UM Cancer Center.
  Single-Cell Device to Identify Cancer Stem Cells
  The Problem: Recent evidence in Cancer research strongly indicates that cancers often consist of a mixture of normal cancer cells and a low number of special and more drug-resistant "cancer stem cells". When isolated and reimplanted, cancer stem cells are much more likely to form new tumors and cause relapses.
  This means that most of our cancer therapeutics were selected by the wrong criteria, looking at the total average number of cancer cells killed, while the difference between a drug that killed none of the cancer stem cells and one that killed all of them could be lost in the average.
  Another problem is that we can currently only identify cancer stem cells in a few specific types of cancers and lack the markers and tools to identify them in a more general way.
  To address these problems, we devised a microassay chip that allows us to separate a population of cancer cells into arrays of isolated single cells, identify which of them might be a cancer stem cell by its speed and ability to grow, and then test different drug treatments on these identified subgroups, which might help us find drug combinations that kill both the cancer stem cells and the normal cancer cells.
2009
Bio+Micro
Cells Interactions
- - Fig1
    Figure 1: A cancer cell (green) and muscle cell interact chemically without direct contact on chip (amplified by close positioning and micro scale).
  - Fig2
    Figure 2: We screen for both contact and non-contact interactions through cell pairs in various arrangements across hundreds of microchambers.
  - Fig3
    Figure 3:Single cells from different tissue types are captured one at a time and precisely placed in direct contact of each other.
  Project Year: 2009-2010.
  Location: Univ.of Michigan, USA.
  Collaborators:Integrated Micro-systems Lab and UM Cancer Center.
  Observing Interactions Between Specific Cell Pairs
  The Problem:Cells in our body communicate and organize themselvesboth by contact interactions and by chemical signals. For example, when a salamander has to regrow its tail, its stem cells will coordinate which of them regrows which part. Another example of this is cancer, which often will communicate with the surrounding heathy cells and cause them to grow new blood vessels to feed the tumor. But studying these signals with conventional methods is difficult as it relies on averaged measurement of millions of cells, which hides imortant details and makes high-throughput testing problematic.
  To address these problems, we proposed a new microfluidic device which can analyze pairwise cell interactions in isolated microchamber arrays, enabling hundreds of simultaneous tests and potential high-throughput screening of useful cell pairings. Soluble factors secreted by cells in each pair can quickly reach high physiological concentrations when trapped in a small chamber in proximity to each other, rather than being diluted in larger amounts of media as in conventional methods. Media exchange for cell viability is achieved by quick flow-through washes and media perfusion alternated with media isolation phases to expose cells to accumulated signals.
2008
AI+Bio+Micro
Automated Cell Biology.
- - Video1
    Video 1: Cells rearranging over several days under constant observation and guidance of my automatic cell biology system.
  - Video2
    Video 2: My automated system makes it possible to observe rare events such as cells dividing 3way instead of the conventional 2way.
  - Fig1
    Figure 1: Cells growing, dividing and crawling after weeks of computer-controlled experiments inside my automated microchips.
  Project Year: 2008-2009.
  Location: Univ.of Michigan, USA.
  Collaborators:Micro/Nano/Mol. Lab.
  Fully-Automated Cell Biology
  The Problem: While biotechnologies like genome sequencing and combinatorial chemistry have benefited from great advances in automation, molecular cell biology experiments involving mammalian cell culture still require continuous manual operation and ad-hoc setup by human researchers. If for example scientists wanted to look at the effect of an anti-aging treatment on cells, they would need to be prepared to carefully handle and maintain cells every other day for several months.
  To address these problems, I designed, fabricated and developed a new type of fully automated microfluidic device. Through remote communication these fully-programmable systems can perform long-term cell-passaging outside of incubators, allowing real-time continuous monitoring and tracking of individual cells. My system can also be controlled remotely to make changes to the experimental script in real time, and it sends me email reports at programmed intervals complete with cell microscopy images and video updates.
2010
Micro
Fluid Micro-Circuits
- - Fig1
    Figure 1: A microscale fluidic circuit generating precise switching and oscillating of multiple reagents by design, with no external control.
  - Video1
    Video 1: A Fluidic Circuit that is able self-regulate the sequence and timing release of multiple reagents.
  - Video2
    Video 2:A Fluidic Circuit capable of alternating continuously and autonomously between different states.
  Project Year: 2009-2010.
  Location: Univ.of Michigan, USA.
  Collaborators:Micro/Nano/Mol. Lab.
  Fluid-Based Intelligent MicroCircuits
  The Problem: High-throughput biological experiments require a method to control the movement, mixing and incubation timing of microscopic amounts of reagents. While the current technology successfully miniaturized channels and chambers for these tasks, the control lines for these microchips are still very large external machines, often expensive and difficult to program and operate.
  To address these problems, we have developed a new fluidic circuit technology capable to perform biological experiments on a microscale without the need for complex external machinery. Quoting the Science Daily news article on our research:
  "A microfluidic device, or lab-on-a-chip, integrates multiple laboratory functions onto one chip just centimeters in size. The devices allow researchers to experiment on tiny sample sizes, and also to simultaneously perform multiple experiments on the same material...
  Just as electronic circuits intelligently route the flow of electricity on computer chips without external controls, these microfluidic circuits regulate the flow of fluid through their devices without instructions from outside systems..."
2010
BioInfo
Deciphering Synthetic DNA code
- - Fig1
    Figure 1:A small section of the DNA containing encrypted information, embedded by the Craig Venter Institute into the first self-replicating synthetic bacterial cell.
  Project Year:2010
  Location: Univ. of Michigan, USA.
  Collaborators: None.
  Breaking Craig Venter's Embedded Code in First Synthetic Cell.
  The Problem: This was not a research project in the traditional sense, but still something interesting and fun. A friend of mine pointed me to this article that describes the first synthetic living cell. That project was interesting both technologically and in terms of philosophical questions, since it shows that we can create our own design of life essentially just out of information. In the article there was a mention of an embedded encrypted code in the DNA, and a challenge to people to try to decode it.
  So I decided to give it a try, got the appropriate DNA sequences (about 5000 base pairs), and a few hours later I had decyphered their new code embedded in the DNA, and found a website and email address for anybody to contact who could read messages in their DNA. After that, I got a nice email back from some people at the Venter Institute congratulating me for breaking their code and informing me that I was the 17th person in the world to have solved it so far, which is pretty good considering I didn't find out until 2 days after their original announcement so I had to start two days behind others.
2005
BioInfo
Bio-Informatics / Genomics
- - Fig1
    Figure 1: New technologies enable us to reconstruct the complex interactions between genes in action rather than just learning their sequence.
  - Fig2
    Figure 2: A new technology called CAGE lets us look at more gene transcripts than ever before.
  - Fig3
    Figure 3: Gene regulation starts with DNA transcription at specific sites but also continues with alternative splicing and RNA interference.
  Project Year: 2004-2005.
  Location: Tokyo, JAPAN.
  Collaborators:RIKEN Research Institute.
  Genomic Activity Data Analysis.
  The Problem: Knowing the full sequence of the Human Genome is important, but even more useful is to be able to see what parts of it are transcribed and work together at any secific time (looking at RNA instead of just DNA). To do that we relied on technology that needed us to specify both the start and end of gene transcripts. However many of our genes actually generate different splicing or alternative ending transcripts of the same gene in different situations, and would therefore go undetected.
  To address these problems, the RIKEN Genomic Sciences Center in Tokyo, Japan, developed a new technique called CAGE that allowed us to look at all transcripts based just on their starting sequence, and through the work of a large consortium of international scientists, this technology was used to generate a more comlete picture of the mammalian Genome in action (the" Transcriptome"). My research revolved around the bioinformatics analysis of this new type of data.
2003
AI+Bio+GenEng
Computers Re-Program Live Cells
- - Fig1
    Figure 1: Elements of Genetic Eng. by AI: cell simulation, mapping of biological parts (bacterial flagella etc) and translation into precise recombinant DNA steps.
  - Video1
    Video 1: Simulation of the best evolved bacterial circuit (goal: more time on red areas by sensing gradients only).
  - Video2
    Video 2: In contrast to my research, evolved A-Life creatures like this are not implementable in real biological systems, but they remain impressive demonstrations.
  Project Year: 1999-2003 (PhD).
  Location: Univ. of Michigan, USA.
  Collaborators:John H. Holland (PhD Advisor).
  Computer Evolution of Gene Circuits to Re-Program Live Cells.
  The Problem: Living cells have many useful abilities that we cannot yet reproduce in electronic or robotic systems, such as the ability to self-repair, power themselves from their environment, replicate, and generate chemicals, light, or motion at microscopic scales. They control these abilities through sets of interconnected genetic instructions called "gene circuits". Through genetic engineering we can already modify the DNA in cells and alter these gene circuits, but their complexity has limited us to small changes designed by hand rather than entire new circuits and behaviors. Computer scientists had success with much more complex systems in fields like Artificial Life, but lacked biological realism or a way to directly implement them in living systems.
  To address these problems, I developed a mapping between real DNA sequences and data structures that enabled computers to simulate and evolve complex new gene circuits, and then produce a precise set of recombinant DNA procedures to embed the new gene circuits in both bacterial and human cells. My implementation takes advantage of the ability of qualitative simulations to predict overall behavior without requiring extensive and often difficult-to-measure parameters required by more quantitative simulations, and still produce robust and rapid prototyping of gene circuits from available components.
2003
AI+BioInfo
Molecular Genetics Improve Computers
- - Fig1
    Figure 1: Through computational experiments I show that search performance in computer evolution can be improved by more accurate molecular encoding.
  Project Year: 1999-2003 (PhD).
  Location: Univ. of Michigan, USA.
  Collaborators:John H. Holland (PhD Advisor).
  Using Ideas from Molecular Genetics to Improve Artificial Intelligence.
  The Problem: Biological systems evolve to create clever designs for solving problems, using mechanisms that do not explicitly involve knowledge or intelligence, and yet are often superior to the best human designs. Evolution reuses and recombines previously discovered structures, building blocks, to generate more complex designs, reducing exponential problems to simpler hierarchical problems. AI search techniques like Genetic Algorithms attempt to emulate this mechanism and harness it for automated programming and problem solving and have been successfully applied in a vast number of areas as a powerful black-box design and optimization tool.
  But current implementations still require significant human input in the initial problem formulation, whereas real evolution uses the same structures and mechanisms (DNA) to solve problems as different as flying or optimizing metabolic reactions.
  To address these problems, I examined more carefully the molecular genetics encoding of living cells and identified and tested key mechanisms and features responsible for the success of evolution from a computational perspective. I then used them to further AI theory on evolutionary computation and build a single all-purpose implementation that does not require human intervention to setup each problem and instead uses the same sub-symbolic building blocks (gates and circuit patterns like loops, counters) for all possible problems.
2001
Aging+Bio+GenEng
Genetic Engineering to Slow Cells' Aging
- - Fig1
    Figure 1: The brighter cells are secreting a fusion of immortalizing and viral proteins, which spreads into the nucleus of the surrounding normal cells.
  Project Year: 2001.
  Location: Univ. of Michigan, USA.
  Collaborators:Howard Hughes Medical Institute Lab.
  Interrupting Cellular Aging with Translocating Nuclear Proteins.
  The Problem:Normal human cells age and eventually stop growing and reproducing, reaching a state known as senescence. There are however special situations where this aging process is undone or bypassed, for example in certain cancers and in the process of making offsprings. Being able to stop cells from aging could be very useful even when considering simple applications like tissue engineering and stem cell therapies, where we might need to grow large amounts of cells that are however limited in lifespan. But while we have long known how to make cells immortal, this is done by permanently altering genes that also turn those cells into cancer cells, and therefore can no longer be used in humans.
  To address these problems, I explored ways to create a similar immortalization effect in a temporary and reversible way without direct genetic modifications, by fusing these genes to viral genes capable of transporting nuclear proteins into the nucleus of new cells. These proteins would activate the same cellular immortalization pathways in the new cells, but would degraded and disappear after a few days, returning the cell to its normal state.
2008
Bio+Micro
Altering ZebraFish in MicroChips
- - Fig1
    Figure 1: An example of double-headed fish caused by altering embryonal development.
  - Fig2
    Figure 2: One of the zebrafish embryos grown within our microdevice, showing healthy development.
  Project Year: 2008-2009.
  Location: Univ.of Michigan, USA.
  Collaborators:Micro/Mol. Lab and Barald (Neuronal Dev.) Lab.
  Growing Altered ZebraFish in microchips
  The Problem: Zebrafish is a small fish, just a few centimeters in length, that has been used extensively to study vertebrate development. It matures from egg to larva in just 3 days and its morphogenesis can be altered just by adding specific growth factors directly to the surrounding water, which however produces a uniform distribution in the embryo. Non-uniform distribution would be desirable as it has been used in other organisms to produce altered body development like flies with two sets of wings or two heads. The tools to direct specific growth factors or gene regulators just to specific target systems in an embryo in vertebrates rather than insects however had not been developed yet.
  To address these problems, and as part of a student design project, we mentored students to design a microfluidic system to deliver cytokines to only selected parts of a zebrafish embryo, and demonstrated not only that such as system can be produced and used to grow viable adult vertebrates but also that this type of project can be used to teach students about both biology and engineering principles.
2009
Micro+Neuro
C-Nanotubes in Neural Interfaces
- - Fig1
    Figure 1: Neuron cells I cultured (green), growing on the silicon substrate of the UM neural probe.
  - Fig2
    Figure 2: I verified that Carbon nanotubes improve the interface between cells and circuitry of the neural probe.
  - Fig3
    Figure 3: Neurons I cultured, shown on a transparent substrate.
  Project Year: 2008-2009.
  Location: Univ.of Michigan, USA
  Collaborators:SSEL lab and Integrated Micro-Systems Lab.
  Interfacing Electronic Circuits with Neuron Cells using Carbon Nanotubes.
  The Problem: Advances in neuroscience depend on our ability to measure complex neural patterns through microscopic implantable electronic neural probes. The University of Michigan Wireless Integrated Microsystems Center has been at the forefront in this type of research. However these devices are not very compatible with brain tissue and neural cells tend to suffer damage at the interface with the probe even with adhesion protein coating.
  To address these problems, I tested the effect of using carbon nanotubes (grown at one of the University's laboratories) as a coating and interface material between neuron cells and the neural probe. The results show that carbon nanotubes produced a better interface between cells and the UM's neural probe.
1997
AI+Robotics
Evolved Robotics
- - Fig1
    Figure 1:How to best control various body shapes in different robots is not trivial and is often best left to computer evolution.
  - Video1
    Video 1: Example: evolved behaviors from another researcher, a robotic spider with evolved self-balancing (it is not glued, it just 'surfs')
  Project Year: 1996-1997.
  Location: Univ. of Michigan, USA.
  Collaborators:Adaptive Computation Lab..
  Evolving Smarter Robotic Controllers.
  The Problem: Programming even just the motor controls of actual robots can be quite complex, as it involves mapping various sensors and coordinating appropriate responses as well-timed and precisely coordinated movements in various robotic motor units. There are different aproaches to this, including complex muathematical analysis in 3D space. A simpler and often effective approach however is to let a computer evolve the ideal control behavior within a virtual simulation until an effective solution is found. Think of newborn baby trying to learn how to move around and walk, trying many different ways over time and learning which movements work better and how much force to apply to balance, except that the baby is a robot and instead of waiting years it tries millions of simulations within just hours. This approach however can run into problems depending on how the simulation and evolution is done, for example creating solutions that seem to work within the simplified testing environment but then break down in more realistic ones.
  To address these problems, we evolved a robotic controller to follow walls within a room and showed it to be much more robust, compact and effective than those evolved by previous groups, thanks to specific details in the way we handled the simulation, evolution criteria and evolution components for evolved robotic controllers.
1997
AI+Image
Automated AI Image Analysis
- - Fig1
    Figure 1: This technology was successfully applied to the automated analysis of large amounts of satellite data for geoscience research.
  - F2
    Figure 2: My software implementation was tested on geoscience satellite images and its results were validated against ground data and human analysis.
  - F3
    Figure 3: This project later inspired similar technologies, like the GENIE project from Los Alamos National Laboratory, here identifying streets or military targets by example.
  - F4
    Figure 4: Technology inspired by our research is even used for commercial automated analysis of histological sections of tumors by example and then automatically.
  - Video1
    Video 1: Shown an example of what to call 'edges' in an image, the AI evolves algorithms to recognize them automatically.
  Project Year: 1997.
  Location: Univ. of Michigan, USA.
  Collaborators:Adaptive Computation Lab.
  Artificial Intelligence that Learns by Example to Analyze Image Data.
  The Problem: Imaging systems on satellites, telescopes and microscopes collect massive amounts of information that requires expert human eyes to interpret correctly, yet we collect far more information than experts could ever analyze on their own. But while automating this interpretation process would be desirable, experts are generally required because there is no simple rule that can replace expert interpretation. If you asked some art critics how they can measure the quality of a painting for example you are not likely to be able to write an algorithm based on their responses.
  To address these problems, we designed a system that used artificial intelligence to write automated image classification algorithms not based on rules but based on examples. Take an image, ask someone to identify correctly certain features on it, and our system would evolve algorithms until they were able to take the same image and identify it in the same way as the user. Those evolved algorithms could then be used on thousands of other images to identify the same features in them.
1996
AI+Music
Computers Composing Music
- - Fig1
    Figure 1:In the background is one of the best performing musical compositions created through computer evolution without human interference.
  Project Year: 1996-1997.
  Location: Univ. of Michigan, USA.
  Collaborators:Adaptive Computation Lab..
  Computers Composing Classical Music.
  The Problem: Music has often been considered strictly the domain of humans, and computers are often not considered 'creative' enough to compose music. Yet computers are clearly capable of finding creative solutions in areas like engineering design and problem-solving in general. In addition we can often see music that is generated by what looks like straightforward formulas, so it should be possible to obtain at least a similar level of creativity and tolerable melody from computers and artificial intelligence.
  To address these problems, we used computer evolution and combined it with rules to score the partially random but continuously improving evolved output (rather than having human input, which would also work but would be much much more time-consuming). For that purpose we used the rules of 16th century classical counterpoint so that we would obtain solutions that scored better as they satisfied more and more of the rules of melodic arrangement and counterpoint.
2009
Micro+Laser
Photonic Bio-Molecule Detection
- - Fig1
    Figure 1: Molecules binding to a substrate can cause slight but measurable deflections to laser beams.
  - Fig2
    Figure 2: Our system combines microfluidics, photonic-crystals and lasers to accurately measure biomolecules.
  Project Year: 2008-2009.
  Location: Univ.of Michigan, USA.
  Collaborators:Ultrafast Optical Sciences Lab.
  Photonic Biomolecule Detection
  The Problem: Detecting the presence of small amounts of complex molecules is both an important and difficult task. Often this is done by attaching other molecules to them that are either fluorescent or magnetic particles, but even more useful are methods that are label-free, and can detect molecules without modifying them by permanently binfding something to them and interfering with later tests. A very successful technique has relied on changes in gold-film surface plasmon resonance to achieve this, but these systems are very expensive and their sensitivity could be further improved. In addition it is also useful to be able to measure not just binding but also the dissociation rates of molecules in their natural state.
  To address these problems, we have developed a low-cost and highly-sensitive biomolecular detection mechanism that uses lasers and a photonic crystal in a microfluidic device, which enables for much more sensitive measurements of molecules based on the refractive index changes around the detection field.
2005
Quantum+Crypto.
Unforgeable IDs with Quantum Physics
- - Fig1
    Figure 1: Documents, fingerprints, money can be forged, but Quantum Mechanics can be used to overcome this problem.
  Project Year: 2004-2005.
  Location: Tokyo, JAPAN
  Collaborators:Digital Materials Lab.
  Making Unforgeable Cryptography Certificates with Quantum Physics.
  The Problem:With enough resources, all our current ID documents and even biometrics can be forged. And making documents that are increasingly difficult to copy or forge requires increasing effort (holograms etc). But there is a physical limit to what can be copied and how accurately when we enter the realm of quantum mechanics, and these limitations could be used there are specific limitations to what cen be done in the physical world Text
  To address these problems, I considered the ultimate limits to what can be physically done to copy something, which is given by Quantum Mechanical laws. This concept has been already used successfully to build working systems that perform Quantum Cryptography, which is the transmission of information across a communication channel in a manner that cannot be intercepted. My research instead considered using quantum uncertainty and chaotic formations to make complex materials with unique and unforgeable properties that could be used as certificates.
2008
Bio+Micro
Cells Mechano-Biology
- - Fig1
    Figure 1: The effect of periodic cycle microstretching on cell organization and alignment, in muscle and skin cells.
  - Fig2
    Figure 2: The mechanism of our programmable surface cell microstretcher.
  - Fig3
    Figure 3: An example of why mechanical signals are important (from another lab¹): Stem cells grappling softer microposts will change into different tissue types.
  Project Year: 2008.
  Location: Univ. of Michigan, USA.
  Collaborators:Micro/Nano/Mol. Lab.
  Cells Aligning to Mechanical Signals.
  The Problem: Cells in the body are not static and they are not isolated. Instead, they sit anchored to other tissues of various elasticity and rigidity in complex 3D structures. They expect to be periodically stretched or contracted as part of their nature. But conventional cell biology just isolates them inside static and non-physiological flasks.
  One example of why this is a big problem is the recent discovery that stem cells, useful for their potential to regrow and replace damaged tissue, will actually change into different cell types depending on the type of mechanical signals they receive from their surroundings.
  To address these problems, and to recreate these mechanical signals, we built a microdevice capable of exposing cells to very complex patterns of mechanical stretching over time, and observed different amounts of cell organization and restructuring as a result.
2010
Bio+Micro
Tissue Engineering & Cell Migration
- - Fig1
    Figure 1:We positioned different 'columns' of cells, with one cell type generating or absorbing gradients of chemo-attractants to guide the other.
  - Fig2
    Figure 2:We can measure the movement of certain cells (in blue), like stem cells or cancer cells, toward specific tissues (red vs green).
  - Fig3
    Figure 3:An example of more complex patterning (from a different research project in our lab) using the same method used in our chemotaxis experiment.
  Project Year: 2010.
  Location: Univ. of Michigan, USA.
  Collaborators:Micro/Nano/Mol. Lab.
  Controlling Cell Migration with MicroGradients and Multiple Cell Types.
  The Problem: Cells in our body do not always stay in one place. During infection, cancer, healing and even during fetal development or growth, different cells send signals to each other to migrate to different locations. In the body, the signals spread slowly through tissues and form a gradient (higher concentration near the source cell) that tells cells in which direction to go. These signals are important (modifying them can generate creatures with abnormal development of body structures, block or facilitate cancer or muscle growth etc), but they have been difficult to study with conventional means (dishes of cells) because these signals would just get diluted and diffuse out rather than forming gradients that cells could sense and follow.
  To address these problems, we developed microscopic devices capable of patterning cells to generate gradients inside microchannels and then observe and accurately measure the effect of different factors, including more complex gradients formed by both generator and sink cells for these signals.

My 28 Publications: Articles I wrote for Scientific Journals and Books about my Research

10 Scientific Journals
4 Books and Invited Talks
8 BioTechnology Conferences
7 Computer Science Articles

My Research Skills: 24 Technologies and Techniques I Mastered

Computational Technology:

Artificial Intelligence

Simulation and modeling

Software Engineering

Image Processing

Automation and Scripting

Genetic Programming

Languages:

English, Italian

Spanish, French

Programming: C#, C++, Java, Perl, Javascript, CSS3, HTML5, PHP...

Clean Room and Microfabrication:

Microprocessors design and fabrication

MEMS Micro-Electro-Mechanical Systems

Nano-Technology and Robotics

Soft-Lithography and MicroFluidics

Molecular and Cell Biology:

Mammalian Cell and Tissue Culture

Transfection and Viral Vectors

Primary and Stem Cell Cultures

Co-Culture, Single-Cell Measurements

Genetic Engineering:

Bacterial Transformation

Recombinant DNA Sub-Cloning

Polymerase Chain Reaction Methods

Chimeric Protein Design&Synthesis

Biochemical and Lab Technology:

Immuno-Fluorescent Microscopy

Purification of DNA and Proteins

Electrophoresis and Blots (Western, etc)

DNA Synthesis

My Grants: 10 Funding/Research Proposals I wrote for the University

Grants and Funding Proposals I wrote for the University of Michigan.

Writing grants is an important part of doing research and paying for equipment, reagents and especially the time of scientists, engineers and students that will actually do much of the work. I wrote grants for very different funding agencies, some focused on science, some on technology advancement, and others even connected to entrepeneurship and developing new businesses based on new technology, and I have learned a lot about this important task in the process.

2010 CCG

2010 CCG: Center for Chemical Genomics

Part (of this grant) that I wrote:

90%

Budget: ~$100,000 (1 year)

Status:

Successfully Funded

Collaborators/PIs:

Yoon

Title:

"A High-Throughput Low-Cost Portable Technology for Single-Cell and Cell-Cell Interaction Screening"

Goal:

Research to advance combinatorial testing of cell-cell interactions

2009 NIH

2009 NIH
ID:PA-09-164

Part (of this grant) that I wrote:

90%

Budget: ~$200,000 (R21)

Status:

Ranked in Top 22%

Collaborators/PIs:

Pienta, Yoon

Title:

"Microchip for screening single-cell progenies from heterogeneous sources"

Goal:

Study how cell-to-cell differences affect disease and biology.

2009 IMAT

2009 NCI IMAT
ID:RFA-CA-09-008

Part (of this grant) that I wrote:

70%

Budget: ~$500,000 (R21)

Collaborators/PIs:

Pienta, Yoon

Title:

(protected)

Goal:

Detect and isolate hard-to-detect cancer stem cells based on their growth dynamic.

2009 NIST

2009 NIST
ID:NIST-ARRA-MSE-01

Part (of this grant) that I wrote:

50%

Budget: ~$1 million

Collaborators/PIs:

Kopelmann,Yoon

Title:

High-throughput Microscope-free Real-time Cellular Monitoring Devices: Combining Nanoprobes and Microfluidics

Goal:

Using nanoprobes in microdevices for new high-throughput cell assays

2009 NCI

2009 NCI SPORE
ID:REF#

Part (of this grant) that I wrote:

90%

Budget:

Collaborators/PIs:

Pienta, Yoon

Title:

(protected)

Goal:

(protected)

2009 SBIR

2009 NSF-SBIR
ID:REF#

Part (of this grant) that I wrote:

10%

Budget:

Collaborators/PIs:

Yoon, Wise

Title:

"Wireless Cellular Microsystems: A New In-Vitro Interface Testbed"

Goal:

(protected)

2009 CCNE

2009 CCNE
ID:REF#

Part (of this grant) that I wrote:

20%

Budget:

Collaborators/PIs:

Baker and 5 others

Title:

(protected)

Goal:

(protected)

2009 NCI

2009 NCI
ID:REF#

Part (of this grant) that I wrote:

10%

Budget: ~$300,000/yr(R01)

Collaborators/PIs:

Takayama and 12 others

Title:

(protected)

Goal:

(protected)

2008 NSF

2008 NSF
ID:REF#

Part (of this grant) that I wrote:

30%

Budget:

Collaborators/PIs:

Burns, Takayama

Title:

"Circuit Logic and Design for Microfluidic Flow Control"

Goal:

(protected)

2000 DARPA

2000 DARPA
ID:DARPA-RA-00-14

Part (of this grant) that I wrote:

Budget: ~$10 Millions

Collaborators/PIs:

Nori and 17 others

Title:

"Fundamental Research at the interface between biology, computing, and micro-technology: networks and computing in biological and artificial systems"

Goal:

(protected)

My Coursework: 125+ Courses that I completed:

Biotechnology [3]

Course Title:

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MicroBiology and Immunology [9]

BIO 436

Immunology

BIO 436 - Introductory Immunology:

This course is intended to introduce pre-professional and biology concentrators to the theoretical and experimental principles of immunology. Topics covered include: a detailed study of the molecules, cells, and organs that constitute the immune system; the innate and adaptive immune responses; and the role of the immune system in host defense, allergy, and organ transplantation. Topics will be illustrated with clinical case studies. Grades are based on three exams. The course is appropriate for concentrations in biology, microbiology, and cell and molecular biology.

BIOCHEM 597

Critical Analysis

BIOCHEM 597 - Critical Analysis:

A course designed to help first-year graduate students in the Ph.D. program improve their skills in reading, analyzing, discussing, and writing about the biochemical literature.

MICROBIO 604

Microbial. Genetics

MICROBIO 606

Microbial Physiology and metabolism

MICROBIO 606 - Microbial Physiology and Metabolism:

This module focuses on the bacterial cell as evolution's most successful biological product in terms of growth, survival, and niche colonization. Topics include the metabolism and physiology of growth as well as selected environmental response strategies, such as directed motility, resistance, mechanisms, regulation of genome expression above the operon level, and differentiation into non-growth states.

MICROBIO 553

Cancer Cell Biology

MICROBIO 553 - Cancer Cell Biology:

This course will cover a broad range of subjects relating to cancer biology. Emphasis is on the relationship between basic science and clinical aspects of cancer. Topics to be covered include carcinogenesis, cancer progression, tumor pathology, oncogenes, cellular growth control, tumor suppressor genes, oncogenic viruses, apoptosis, tumor immunology, clinical oncology, and therapeutics. The course consists of lectures by faculty in the Cancer Center who are experts on various topics.

MICROBIO 615

Mol. Cellular Viral Pathogenesis

MICROBIO 615 - Mol. Cellular Viral Pathogenesis:

Will focus on interactions of selected viral pathogens with cells to successfully infect, replicate and spread in hosts. Emphasis will be on how virus replication and host cellular receptors, regulatory proteins and components of the immune system determine pathogenesis and the outcome of infection. Format combines critical evaluation of primary literature with some lecture/discussions.

MICROBIO 616

DNA Tumor Viruses

MICROBIO 616 - DNA Tumor Viruses:

This course will cover the molecular biology of small DNA viruses, with an emphasis on tumor viruses. Topics to be discussed include replication, gene expression, oncogenic transformation, and the virus-host interactions that contribute to these processes such as viral oncoprotein-tumor suppressor protein interactions. The format will be a combination of lectures by the instructor and reading and discussion of the primary literature by the class.

MICROBIO 617

RetroViruses

MICROBIO 617 - RetroViruses:

This course will consist of lectures and class discussion of primary literature and other materials about retroviruses. Students will be required to read assigned materials and display critical understanding of them, and to participate in exercises intended to strengthen their skills in the preparation and criticism of scientific manuscripts. Materials covered will include the following: a) The biology and replication of retroviruses, including the organization of their genomes, the functions of their genes, their replication cycles, and viral population dynamics. b) The RNA tumor virus class of retroviruses, including their role in the discovery of cellular oncogenes and how they cause animal tumors. c) Human retroviruses with a focus on HIV, including the biology of HIV, the roles of HIV accessory proteins, the evidence that HIV causes AIDS, and strategies for the treatment of HIV disease. d) The use of retrovirus derivatives as vectors for gene therapy.

MICROBIO 812

Graduate Seminar

MICROBIO 812 - Graduate Seminar:

An analysis of advances at the frontiers of microbiology. Every graduate student is required to enroll in this course every semester.

BioMedical Engineering [8]

MSE 250

Fundamentals of Material Science Engineering

MSE 250 - Fundamentals of Material Science Engineering:

Properties (mechanical, thermal and electrical) of metals, polymers, ceramics, and electronic materials. Correlations of these properties with: (1) their internal structures (atomic, molecular, crystalline, micro-and macro); (2) service conditions (mechanical, thermal, chemical, electrical, magnetic and radiation); and (3) processing.

NE&RS 400

Elements of Nuclear Engineering

BIOMED 410

BioMedical Materials

BIOMED 410 - BioMedical Materials:

Biomaterials and their physiological interactions. Materials used in medicine/dentistry: metals, ceramics, polymers, composites , resorbable smart, natural materials. Material response/degradation: mechanical breakdown, corrosion, dissolution, leaching, chemical degradation, wear. Host responses: foreign body reactions, inflammation, wound healing, carcinogenicity, immunogenicity, cytotoxicty, infection, local/systemic effects.

BIOMED 483

MRI Magnetic Resonance Imaging

BIOMED 483 - MRI Magnetic Resonance Imaging:

Introduction to the physics, techniques and applications of magnetic resonance imaging (MRI). Basics of nuclear magnetic resonance physics, spectral analysis and Fourier transforms, techniques for spatial localization, MRI hardware. Applications of MRI including magnetic resonance properties of biological tissues and contrast agents, imaging of anatomy and function.

BIOMED 550

Ethics & Enterprise

BIOMED 550 - Ethics & Enterprise:

Ethics, technology transfer, and technology protection pertaining to Biomedical Engineering are studied . Ethics issues range from the proper research conduct to identifying and managing conflicts of interest. Technology transfer studies the process and its influences on relationships between academia and industry. Technology protection covers legal issues such as patents, copyrights, and contracts.

BIOMED 599-105

Micro-/Nano-Technology for Biology

BIOMED 599-105 - Micro-/Nano-Technology for Biology:

Course Goals

Develop micro- and nanointuition. (lectures/exams on scaling laws, surfaces, and specific examples)

Learn basic methods of micro- and nanofabrication, especially the methods most useful for biological applications. (lectures on photolithography, soft lithography, self-assembly)

Familiarization with micro- and nanotechnology literature and applications. (presentation, literature research, proposals)

Learn how to design, evaluate, and analyze micro- and nanodevices. (lectures, discussions, presentations, proposals)

Sharpen critical thinking and analysis (discussions, presentations, proposals)

Practice effective communication of ideas (presentations, proposals)

Cultivate innovative thinking (proposals, discussions)

Learn how to learn new fields (literature research, presentations, proposals)

Learn teamwork (proposal projects)

Learn what is available/goinghere at Michigan (field trips, guest lectures)

BIOMED 590

Biomedical Engineering Research

BIOMED 800

Biomedical Engineering Graduate Seminar

Molecular and Cell Biology [5]

ANAT 504

Cellular Biotechnology

ANAT 504 - Cellular Biotechnology:

The course will combine lectures, literature discussion and student presentations on topics relevant to biotechnology. This class covers a wide range of topics in key scientific areas and methodologies, such as immunotherapy, RNAi, stem cells, systems biology, pharmacokinetics, commercialization strategies and more. During the class, students will also be exposed to concepts relevant to product development and commercialization, as well as scientific topics. Grades are determined by class participation, short take-home assignments, student presentations and a final small-group proposal.

ANAT 530

Molecular Cell Biology

ANAT 530 - Molecular Cell Biology:

This graduate course is designed to present basic information as well as the most recent developments in key areas of cell biology. Course consists of both lectures by faculty in their areas of expertise and small discussion groups that delve more deeply into lecture material and discuss primary literature. Both will expose students to current experimental approaches in cell biology. Students will be expected to demonstrate their knowledge of course material by participation in discussion groups and by examinations.

CDB 680

Organogenesis of Complex Tissues I:Gut

CDB 680 - Organogenesis of Complex Tissues I:Gut:

The course will cover multiple aspects of organogenesis, including: morphological and molecular events underlying organ formation; quantitative aspects of gradient formation, tissue modeling and cell behavior; in vitro and in vivo experimental systems; parallel pathways for organ formation in various model organisms; adult organ structure and pathology; organ regeneration/repair; stem cell systems: cell and tissue engineering; and carcinogenesis.

CDB 681

Org. of Complex Tissues II:Neural Crest

CDB 680 - Organogenesis of Complex Tissues II:Neural Crest:

CDB 682

Org. of Complex Tissues III:Skeletal Muscle

CDB 682 - Org. of Complex Tissues III:Skeletal Muscle:

Bioinformatics and Genetics [5]

HGEN 541

Gene Structure and Regulation

HGEN 541 - Gene Structure and Regulation:

A combination of classic and current papers in molecular genetics will be selected to accompany the lecture material (1-2 papers per lecture). The foundations of modern genetics will launch the course, including both the fundamentals and current research methods for analysis of gene structure and gene expression. The gene expression component will include positive and negative regulation of transcription, and mRNA splicing and turnover. The basics of DNA recombination, repair and transposition will be covered in relationship to cancer, evolution and mutagenesis. Strategies for developmental regulation will be presented. Parallels between prokaryotes and eukaryotes will be drawn, and comparisons will be made between the temporal and spatial control of gene expression in vertebrates and invertebrates. Genetic engineering topics will include gene targeting and transgenesis, with applications to understanding tissue specific control of gene expression. The course will close with discussion of the Genome Project, identification of disease genes and an introduction to the medical application of molecular genetics including gene therapy. Prerequisites: Foundations in basic biochemistry and genetics are both required for this course.

BIOCHEM 511

BioInformatics Seminar

BIOCHEM 511 - BioInformatics Seminar:

This is a seminar course consisting of attendance at the Parke-Davis/University of Michigan seminar series, "Advances in Genome Sciences", critical reading of related manuscripts prior to the seminar, and participation in roundtable discussions with each of the speakers.

BIOCHEM 526

Survey in BioInformatics

BIOCHEM 526 - Survey in BioInformatics:

This course consists of a series of integrated lectures providing an introduction to bioinformatics and functional genomics. The course will focus on the basic knowledge required in this field, including the theory and design of databases, access to genome information, sources of data, and tools for data mining. The course will also cover identification of both lower order and higher order informational patterns in DNA and approaches to linking genome data to information on gene function. The participation of nationally recognized scientists having expertise in all aspects of Bioinformatics will be solicited.

BIOCHEM 600

Lab rotation (genetic regulation lab)

BIOCHEM 600 - Lab rotation (genetic regulation lab):

Intensive independent study laboratory course for first-year biochemistry Ph.D. students. Laboratory.

BIOCHEM 701

Signal Transduction, Regulation, and Development

BIOCHEM 701 - Signal Transduction, Regulation, and Development :

Discussion of papers covering recent advances in the control of developmental processes by signal transduction and the regulation of gene expression. Topics from both vertebrate and invertebrate systems will be covered, with the main focus on studies of the developing nervous system. Specific topics and papers vary from year to year.

Computer Engineering [4]

Course Title:

No course selected. Please move the cursor over a course title to select one.

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Hardware Design and Circuits [10]

EECS 216

Circuit Analysis

EECS 216 (EECS 211) - Circuit Analysis:

Introductory electrical engineering topics, continued:basic circuit analysis; elementary transistor and diode circuits. Equivalent transformations of electric circuits. Transient analysis of circuits. Introduction to diode and transistor circuits. Amplifiers, limiters, filters and logic circuits. Laboratory experience with electrical signals and circuits.

EECS 270

Logic Design

EECS 270 - Introduction to Logic Design:

Binary and non-binary systems, Boolean algebra digital design techniques, logic gates, logic minimization, standard combinational circuits, sequential circuits, flip-flops, synthesis of synchronous sequential circuits, PLA's, ROM's, RAM's, arithmetic circuits, computer-aided design. Laboratory includes hardware design and CAD experiments.

EECS 317

Transistors and Digital Electronics

EECS 317 (EECS 313) - Solid-State Devices and Electronic:

Introduction to semiconductor characteristics and to active devices (diodes, field-effect transistors, and bipolar junction transistors). Large signal circuit analysis and design. Computer-aided design and circuit simulation. Digital logic families. Memory circuits (SRAM, DRAM, and ROM). Lectures, written homework sets and computer-based homework.

EECS 370

CPU Organization, Architecture & Design

EECS 370 - Introduction to Computer Organization:

Computer organization will be presented as a hierarchy of virtual machines representing the different abstractions from which computers can be viewed. These include the logic level, microprogramming level, and assembly language level. Lab experiments will explore the design of a microprogrammed computer.

EECS 373

Design of MicroProcessor Systems

EECS 373 - Design of Microprocessor-Based Systems:

Principles of hardware and software microcomputer interfacing; digital logic design and implementation. Experiments with specially designed laboratory facilities. Introduction to digital development equipment and logic analyzers. Assembly language programming. Lecture and laboratory.

EECS 423

Solid State Devices Lab

EECS 423 - Solid State Devices Lab:

Semiconductor material and device fabrication and evaluation: diodes, bipolar and field-effect transistors, passive components. Semiconductor processing techniques: oxidation, diffusion, deposition, etching, photolithography. Lecture and laboratory. Projects to design and simulate device fabrication sequence.

EECS 427

VLSI Design (Very Large Scale Integration)

EECS 427 - VLSI Design I:

Design techniques for rapid implementations of very large scale integrated (VLSI) circuits, MOS technology and logic. Structured design. Design rules, layout procedures. Design aids:layout, design rule checking, logic and circuit simulation. Timing. Testability. Architectures for VLSI. Projects to develop and lay out circuits.

EECS 478

Switching Theory and Sequential Systems

EECS 478 - Logic Circuit Synthesis and Optimization:

Advanced design of logic circuits. Technology constraints. Theoretical foundations. Computer-aided design algorithms. Two-level and multilevel optimization of combinational circuits. Optimization of finite-state machines. High-level synthesis techniques:modeling, scheduling, and binding. Verification and testing.

EECS 498-1

MEMS:Micro Electro Mechanical Systems

EECS 598-1

Nano-Electronics and Nano-Fabrication

Comp. Theory and Programming

EECS 280

C Programming and Data Structures

EECS 280 - Programming and Introductory Data Structures:

Techniques and algorithm development and effective programming, top-down analysis, structured programming, testing, and program correctness. Program language syntax and static and run-time semantics. Scope, procedure instantiation, recursion, abstract data types, and parameter passing methods. Structured data types, pointers, linked data structures, stacks, queues, arrays, records, and trees.

EECS 303

Discrete Structures

EECS 303 - Discrete Structures:

Fundamental concepts of algebra; partially ordered sets, lattices, Boolean algebras, semigroups, rings, polynomial rings. Graphical representation of algebraic systems; graphs, directed graphs. Application of these concepts to various areas of computer engineering.

EECS 380

Data Structures and Algorithms in C++

EECS 380 - Data Structures and Algorithms:

Abstract data types. Recurrence relations and recursions. Advanced data structures:sparse matrices, generalized lists, strings. Tree-searching algorithms, graph algorithms general searching and sorting. Dynamic storage management. Analysis of algorithms O-notation. Complexity. Top-down program development:design, implementation, testing modularity. Several programming assignments.

EECS 400

Linear Spaces & Matrix Theory

EECS 400 - Linear Spaces & Matrix Theory:

Finite dimensional linear spaces and matrix representations of linear transformations. Bases, subspaces, determinants, eigenvectors, and canonical forms. Structure of solutions of systems of linear equations. Applications to differential and difference equations. The course provides more depth and content than Math. 417. Math. 513 is the proper election for students contemplating research in mathematics.

EECS 401

Probabilistic Methods in Engineering

EECS 401 - Probabilistic Methods in Engineering:

Basic concepts of probability theory. Random variables:discrete, continuous, and conditional probability distributions; averages; independence. Introduction to discrete and continuous random processes:wide sense stationarity, correlation, spectral density.

EECS 476

Foundations of Theoretical Comp.Sci.

EECS 476 - Foundations of Computer Science:

An introduction to computation theory:finite automata, regular languages, pushdown automata, context-free languages, Turing machines, recursive languages and functions, and computational complexity.

EECS 574

Theorethical Computer Science

EECS 574 - Theorethical Computer Science:

Fundamentals of the theory of computation and complexity theory. Computability, undecidability, and logic. Relations between complexity classes, NP-completeness, P-completeness, and randomized computation. Applications in selected areas such as cryptography, logic programming, theorem proving, approximation of optimization problems, or parallel computing.

EECS 587

Parallel Programming and Algorithms

EECS 587 - Parallel Algorithms:

The design and analysis of efficient algorithms for parallel computers. Fundamental problem areas, such as sorting, matrix multiplication, and graph theory, are considered for a variety of parallel architectures. Simulations of one architecture by another.

EECS 598-4

Quantum Computing

EECS 880

Software Engineering Technical Seminars

Computer Applications [8]

EECS 482

Operating Systems

EECS 482 - Operating Systems:

Operating system functions and implementations:multi-tasking; concurrency and synchronization; deadlock; scheduling; resource allocation; real and virtual memory management; input/output; file systems. Students write several substantial programs dealing with concurrency and synchronization in a multitask environment.

EECS 489

Computer Networks

EECS 489 - Computer Networks:

Protocols and architectures of computer networks. Topics include client-server computing, socket programming, naming and addressing, media access protocols, routing and transport protocols, flow and congestion control, and other application-specific protocols. Emphasis is placed on understanding protocol design principles. Programming problems to explore design choices and actual implementation issues assigned.

EECS 498-2

Virtual Reality

EECS 498-3

Web and Internet

EECS 547

Electronic Commerce

EECS 547 - Electronic Commerce:

The Internet is rapidly changing the way we trade with one another, conduct businesses, and organize financial institutions. In this course, we will cover a range of important principles -- drawn from computer science, economics, and other disciplines -- that influence the design and analysis of Internet commerce systems. We will begin with an introduction to design and analysis methods to make e-Commerce systems robust against failures, malicious attackers, and strategic manipulation. We will then study three aspects of electronic commerce systems: locating buyers and sellers (search), setting terms of trade (negotiation), and verifying and consummating the deal (exchange). The goal is to develop a mastery of the fundamental concepts and approaches through examples, rather than an exhaustive survey of the field. The course is a semester-long, 3-credit course.

EECS 582

Advanced Operating Systems

EECS 582 - Advanced Operating Systems:

The course catalog states, "Course discusses advanced topics and research issues in operating systems. Topics will be drawn from a variety of operating systems areas such as distributed systems and languages, networking, security, and protection, real-time systems, modeling and analysis, etc."
This semester, we will take a slightly broader view of systems research and consider many common issues that emerge across operating systems, database systems, networked systems, distributed systems, mobile systems, and embedded systems.

The design of computer systems -- whether building-size or hand-held, file system or database -- faces many common challenges and pitfalls. Fortunately, many of the principles and practices are often common as well. This class will focus on identifying and understanding the enduring principles and practices in computer systems design and implementation, and will prepare students to carry our substantial independent systems research projects.

EECS 584

Advanced Databases

EECS584 - Advanced Databases:

Survey of advanced topics in database systems. Distributed databases, query processing, transaction provessing. Effects of data models:object-oriented and deductive databases; architectures:main-memory and parallel repositories; distributed organizations:client-server and heterogeneous systems. Basic data management for emerging areas:internet applications, OLAP, data mining. Case studies of existing systems. Group projects.

EECS 598-2

Cryptography

Machine Learning and A.I. [8]

EECS 492

Artificial Intelligence

EECS 492 - Introduction to Artificial Intelligence:

Fundamental concepts of AI, organized around the task of building computational agents. Core topics include search, logic, representation and reasoning, automated planning, decision making under uncertainty, and machine learning.

EECS 499

Genetic Programming

EECS 543

Knowledge Systems

EECS 543 - Knowledge-Based Systems:

Techniques and principles for developing application software based on explicit representation and manipulation of domain knowledge, as applied to computer vision, robotic control, design and manufacturing, diagnostics, autonomous systems, etc. Topics include:identifying and representing knowledge, integrating knowledge-based behavior into complex systems, reasoning, and handling uncertainty and unpredictability.

EECS 592

Advanced Artificial Intelligence

EECS 592 - Advanced Artificial Intelligence:

Advanced topics in artificial intelligence. Issues in knowledge representation, knowledge based systems, problem solving, planning and other topics will be discussed. Students will work on several projects.

EECS 594

Adaptive Systems

EECS 594 - Introduction to Adaptive Systems:

Programs and automata that "learn" by adapting to their environment; programs that utilize genetic algorithms for learning. Samuel strategies, realistic neural networks, connectionist systems, classifier systems, and related models of cognition. Artificial intelligence systems, such as NETL and SOAR, are examined for their impact upon machine learning and cognitive science.

EECS 599

Intelligent Agents Plan Recognition

EECS 695

Neural Models

EECS 695 - Neural Models: Mechanisms of Learning:

Learning is not only what education is about but also a basic activity of enormous importance to our species. Unfortunately even many whose work depends upon it do not fully grasp the way the mind and environment interact in acquiring, understanding, and storing information. The course focuses on these themes both at a practical level and by exploring the behind-the-scenes activities of neurons that make learning possible. Four major challenges will be studied:

Managing potentially overwhelming input (even before e-mail there was too much going on)

Selective Learning (so how should one decide what to remember?)

Knowledge integration (and how does one do that without destroying what's already there?)

Control mechanisms (need different ways to deal with important vs. unimportant, relevant vs. irrelevant)

PSYC 808

Cognitive Modeling

PSYCH 808 - Cognitive Modeling:

Computing in Cognitive Science.
ARCHITECTURES/APPROACHES:

PDP

Classifier systems and genetic algorithms

Soar and Unified Theories of Cognition

EPIC

ACT-R and rational analysis

Symbolic vs. subsymbolic approaches

Alternative views

DOMAINS

HCI

Case-based reasoning

Deductive reasoning

Skill acquisition

Language acquisition

Semantic memory

Categorization

Attention

Working Memory

Explicit learning and memory

Vision

Neural organization

FINAL DISCUSSION AND PROJECTS PRESENTATIONS

Other Fields

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Exercise Physiology and Kinesiology [6]

KIN 503

Legal Aspects of Sport

KIN 503 - Legal Aspects of Sport:

This is a comprehensive review of legal aspects affecting sport, recreation, and fitness industries. The range of review includes civil procedure; contracts: employment, leases, waivers; tort liability for coaches, administrators, employees, and independent contractors; 14th Amendment Due Process and Equal Protection; product liability; and statutory regulation including Title VII, Title IX, ADA, Anti-Trust, and IRS code

KIN 520

Seminar in Motor Control

KIN 520 - Seminar in Motor Control:

Focuses on current issues in movement control from either a neurophysiological or behavioral viewpoint. Students will present assigned readings and will write a paper on an approved topic

MVS 542

Exercise Physiology and Nutrition

MVS 542 - Exercise Physiology and Nutrition:

Biochemical and physiological processes of fuel mobilization and storage in response to exercise and the modification of those processes by nutritional variables

KIN 610

Current Issues in Kinesiology

KIN 615

Phil. of Science and Res. in Kines.

KIN 615 - Phil. of Science and Res. in Kines.:

Topics include the nature of scientific inquiry, theories of knowledge acquisition; empirical vs. theoretical research; basic vs. applied research; induction and deduction; doubts and alternatives; objectivity of science; facts, laws and theories; pseudo-science; causation and mechanism; formulation of problems, research design and use of statistics

KIN 682

Muscle Growth, Strength & Training Strategies

Aerospace Engineering [5]

AERO 453

Prob. Methods in Engineering

AERO 453 - Probabilistic Methods in Engineering:

Basic concepts of probability theory. Random variables: discrete, continuous, and conditional probability distributions; averages; independence. Statistical inference: hypothesis testing and estimation. Introduction to discrete and continuous random processes.

TECH-COMM 498

Technical Writing & Comm.

TECH-COMM 498 - Technical Writing and Communication:

Technical and Professional Writing for Industry, Government, and Business. Senior or graduate standing.

AERO 582

Spacecraft Technology

AERO 582 - Spacecraft Technology:

Systematic and comprehensive review of spacecraft and space mission design and key technologies for space missions. Discussions on project management and the economic and political factors that affect space missions. Specific space mission designs are developed in teams.

AERO 590

Aerospace Research

AERO 590 - Aerospace Research:

Study of advanced aspects of aerospace engineering directed by an Aerospace faculty member. Primarily for graduates. The student will submit a final report.

AERO 597

Space Plasma Physics

AERO 597 - Space Plasma Physics:

Basic plasma concepts, Boltzmann equation, higher order moments equations, MHD equations, double adiabatic theory. Plasma expansion to vacuum, transonic flows, solar wind, polar wind. Collisionless shocks, propagating and planetary shocks. Fokker-Planck equation, quasilinear theory, velocity diffusion, cosmic ray transport, shock acceleration. Spacecraft charging, mass loading.

Economics

ECON 501

Micro-Economic Theory

ECON 501 - Applied Microeconomic Theory:

A course designed for students in the MAE program. Basic models in the principal areas of microeconomic theory are covered: consumer demand, production and costs, product markets, factor markets, allocative efficiency, and corrections for market failure. Most of the course is spent studying the use of these tools in the analysis of specific microeconomic policy problems. Application of theory to current policy problems is stressed, and a substantial amount of class time is devoted to exercises based on such problems.

Science and Philosophy [5]

Course Title:

No course selected. Please move the cursor over a course title to select one.

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Math [9]

MATH 115

Calculus I

MATH 115 - Calculus I:

The sequence Math 115-116-215 is the standard complete introduction to the concepts and methods of calculus. It is taken by the majority of students intending to concentrate in mathematics, science, or engineering as well as students heading for many other fields. The emphasis is on concepts and solving problems rather than theory and proof. All sections are given a uniform midterm and final exam. The course presents the concepts of calculus from three points of view: geometric (graphs); numerical (tables); and algebraic (formulas). Students will develop their reading, writing, and questioning skills.

MATH 116

Calculus II

MATH 116 - Calculus II:

Topics include the indefinite integral, techniques of integration, introduction to differential equations, infinite series.

MATH 215

Calculus III

MATH 215 - Calculus III:

Topics include vector algebra and vector functions; analytic geometry of planes, surfaces, and solids; functions of several variables and partial differentiation; line, surface, and volume integrals and applications; vector fields and integration; Green's Theorem and Stokes' Theorem.

MATH 216

Differential Eq.

MATH 216 - Differential Equations:

After an introduction to ordinary differential equations, the first half of the course is devoted to topics in linear algebra, including systems of linear algebraic equations, vector spaces, linear dependence, bases, dimension, matrix algebra, determinants, eigenvalues, and eigenvectors. In the second half these tools are applied to the solution of linear systems of ordinary differential equations. Topics include: oscillating systems, the Laplace transform, initial value problems, resonance, phase portraits, and an introduction to numerical methods.

STAT 402

Statistics and Data Analysis

STAT 402 - Statistics and Data Analysis:

In this course students are introduced to the concepts and applications of statistical methods and data analysis. Examples of applications are drawn from virtually all academic areas and some attention is given to statistical process control methods. The course format includes lectures (3 hours per week) and a laboratory (l.5 hours per week). The laboratory section deals with the computational aspects of the course and provides a forum for review of lecture material. For this purpose, students are introduced to the use of a statistical analysis-computer package.

MATH 419

Honors Vector Spaces and Matrix Theory

MATH 419 - Honors Vector Spaces and Matrix Theory:

Linear equations, Gauss-Jordan elimination, linear transformations and their inverses, matrix algebra, subspaces, linear independence, bases, orthogonality, Gram-Schmidt, orthogonal transformations and matrices, least squares, determinants, eigenvalues and eigenvectors, coordinate systems, diagonalization, and quadratic forms.

MATH 425

Probability

MATH 425 - Introduction to Probability:

This course introduces students to useful and interesting ideas of the mathematical theory of probability and to a number of applications of probability to a variety of fields including genetics, economics, geology, business, and engineering. The theory developed together with other mathematical tools such as combinatorics and calculus are applied to everyday problems. Concepts, calculations, and derivations are emphasized. The course will make essential use of the material of Math 116 and 215. Math concentrators should be sure to elect sections of the course that are taught by Mathematics (not Statistics) faculty. Topics include the basic results and methods of both discrete and continuous probability theory:conditional probability, independent events, random variables, jointly distributed random variables, expectations, variances, covariances.

AERO 453

Prob. Methods in Engineering

AERO 453 - Prob. Methods in Engineering:

MATH 550

Intro Adaptive Systems

MATH 550 - Intro Adaptive Systems:

Intro to Adaptive Systems (aka "Introduction to Evolutionary Dynamical Systems") centers on the construction and use of agent-based adaptive models study phenomena which are prototypical in the social, biological and decision sciences. These models are "agent-based" or "bottom-up" in that the structure placed at the le vel of the individuals as basic components; they are "adaptive" in that individuals often adapt to their environment through evolution or learning. The goal of these models is to understand how the structure at the individual or micro level leads to emerg ent behavior at the macro or aggregate level. Often the individuals are grouped into subpopulations or interesting hierarchies, and the researcher may want to understand how the structure of development of these populations affects macroscopic outcomes. The course will start with classical differential equation and game theory approaches. It will then focus on the theory and application of particular models of adaptive systems such as models of neural systems, genetic algorithms, classifier system and cel lular automata. Time permitting, we will discuss more recent developments such as sugarscape and echo.

Philosophy [9]

PHIL 1x1

General Philosophy I

PHIL 2x1

General Philosophy II

PHIL 356

Issues in Bio-Ethics

PHIL 356 - Issues in Bio-Ethics:

This course will focus on moral problems that arise in biomedical ethics. Biomedical ethics is composed of two separate fields: bioethics and medical ethics. Bioethics is the study of the ethics of life (and death), and includes familiar topics such as abortion, cloning, stem cell research, allocation of scarce medical resources, and euthanasia. We shall spend approximately the first two-third of the course on these issues. For the last third of the course, we shall discuss topics in medical ethics, which is concerned with "micro" issues such as the moral underpinnings of doctor-patient relationships as well as "macro" issues such as the structures of medical institutions or the duties that societies have to provide health care for those in need. No previous coursework in philosophy is required for this course and fundamental concepts in moral philosophy (e.g., consequentialism and deontology) will be explained as they become relevant. This is a course on theoretical (as opposed to clinical) bioethics.

PHIL 423

Space, Time & Einstein's Relativity

PHIL 423 - Problems of Space and Time:

Traditional philosophical questions about the nature of time and space have been strikingly influenced in the twentieth century by the results of contemporary physical science. At the same time, the important current physical theories of space and time rest explicitly or implicitly on deep-rooted philosophical assumptions. The purpose of this course is to study the mutual interaction between science and philosophy as illustrated in problems about space and time. Typical topics to be considered include the status of knowledge about the structure of space and time, substantial versus relational theories of space-time, spatio-temporal order and causal order, and the so-called problem of the direction of time. This course can be appreciated by students who have either a background in philosophy - especially logic and philosophy of science, metaphysics, epistemology - or background in physical science or mathematics. An attempt is made in this course to introduce the fundamental ideas of both philosophy and science at a level which can be understood by those without extensive background so students need not be proficient in both science and philosophy to benefit from the course. The primary text is L. Sklar Space, Time, and Spacetime. There are additional readings from such authors as Reichenbach, Poincaré, Grunbaum, Smart, Wheeler, and others.

PHIL 429

Ethical Analysis

PHIL 429 - Ethical Analysis:

Questions about the nature and standing of morality arise in both theory and practice. Moreover, in recent years morality has served as a central example in wide philosophical debates about the nature of normativity and the relation of theory to practice. In this course we will critically investigate several of the most influential philosophical conceptions of morality, including historical as well as contemporary writings. Among the questions we will consider:

In what sense, if any, is there a need for theory in morality?

How are we to understand the meaning of moral terms?

Are moral judgments capable of truth and falsity?

In what sense, if any, can moral claims be objective?

How is morality related to relationality?

What is the relation of "ought" to "is"?

And, why be moral?

Midterm and final examinations; a term paper.

PHIL 433

History of Ethics

PHIL 433 - History of Ethics:

The modern period in moral philosophy began with Thomas Hobbes, whose Leviathan (1651) shook the traditional foundations of ethics and forced those who would defend ethics against (what they saw to be) Hobbes' nihilism to do so in a broadly naturalistic framework that took serious account of recent advances in science. Thus began a period of exciting and fruitful moral philosophy that stretched through the end of the eighteenth century and into the nineteenth. Indeed, even debates now current in moral philosophy almost always can be traced back to origins in this period. This course will be a study of several of the central writers and texts of this "enlightenment" and post-enlightenment period. In addition to Hobbes, we shall read some of Hutcheson, Butler, Hume, Kant, Bentham, Rousseau, Nietzsche, Fichte, and Hegel. We shall end with a radical critic of this broad tradition: Nietzsche. Course requirements: short paper, long paper, final exam.

PHIL 602

Philosophy of Science

PHIL 602 - Philosophy of Science:

Our fundamental physical theories are models of mathematical rigor and precision. But how do they actually function in our attempts to give descriptions and explanations of our complex and messy world? Are the lawlike assertions of these theories 'true, ' or falsehoods that carry with them clauses restricting their applicability that can never be explicitly filled out? Or are the laws intended to be true not of the world but only of idealized 'models' of it? Are the basic laws applicable to all physical systems, or only to very limited domains of specially prepared systems in the laboratory? Are we supposed to believe the theories, or, rather, only to believe them to be false but somehow useful stepping stones in an ongoing process in which each successive theory is ultimately rejected as a transient falsehood? Can we understand our theories at face value, or must theories always be accompanied by 'interpretations' that are outside the theory but essential to grasp its import? Are these interpretations themselves part of the scientific process or are they imposed upon the theories somehow from the 'outside?' Do the answers to all of these questions force us to an 'instrumentalist' as opposed to 'realist' understanding of our basic theories?

PHIL 611

Current Philosophy - The Self

PHIL 615

Philosophy of Language

Chemistry and BioChemistry [11]

CHEM 125

General Chemistry and Reactivity

CHEM 126

General Chemistry and Reactivity Lab

CHEM 130

General and Inorganic Chemistry I & Lab

CHEM 130 - General and Inorganic Chemistry I:

Chemistry 130 provides an introduction to the major concepts of chemistry, including the microscopic picture of atomic and molecular structure, periodic trends in the chemical reactivity, the energetics of chemical reactions, and the nature of chemical equilibria. Students will be introduced to the fundamental principles of modern chemistry, the descriptive chemistry of the elements, and to the underlying theories that account for observed macroscopic behavior. In Chem 130, students will learn to think critically, examine experimental data, and form generalizations about data as chemists do.

CHEM 210

Organic Chemistry

CHEM 210 - Organic Chemistry:

Chemistry 210 is the first course in a two-term sequence in which the major concepts of chemistry are introduced in the context of organic chemistry. Emphasis is on the development of the capacity of students to think about the relationship between structure and reactivity and to solve problems in a qualitatively analytical way. This course is a particularly good first course for students with AP credit in chemistry, Honors students, and other students with a strong interest in chemistry and biology.

CHEM 211

Organic Chemistry Lab

CHEM 210 - Organic Chemistry Lab:

Chemistry 211 is a laboratory introduction to methods of investigation in inorganic and organic chemistry. Students solve individual problems using microscale equipment and a variety of techniques such as thin layer chromatography, titrations, and spectroscopy. The course consists of a four-hour laboratory period with a teaching assistant under the supervision of the professor.

BIOCHEM 451

Intro Biochemistry

BIOCHEM 451 - Intro Biochemistry:

A rigorous introduction to biochemistry with a chemical emphasis. Designed for undergraduates in the Biochemistry Concentration Program but open to graduate students with a strong background in chemistry. Prerequisites: Chem 215, Biol. 152 or 195, and Math 115.

BIOCHEM 516

Biochemistry Lab

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BIOCHEM 570

Protein Structure

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BIOCHEM 571

DNA & Nucleic Acids

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BIOCHEM 572

Gene Expression

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BIOCHEM 573

Enzyme Kinetics

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BIOCHEM 574

Catalysis

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Neuroscience and Cognitive Psychology [10]

PSYC 2x1

General Psychology

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BIOPHYS 417

Cellular Neurophysiology

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PSYC 594

Adaptive Systems

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PSYC 600

Psychology Graduate Proseminar

NEUROSCI 615

NeuroBiology of Learning

NEUROSCI 616

Cognition and Integration

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PSYC 619

Master's Thesis Project

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PSYC 640

Neural Models

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PSYC 653

Personality Psychology

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PSYC 808

Cognitive Modeling

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Physics and Chaos Theory [10]

PHYS 140

General Physics I

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PHYS 141

General Physics I Lab

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PHYS 240

General Physics II

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PHYS 241

General Physics II Lab

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PHYS 390

Quantum Mechanics & Particle Physics

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PHYS 413

Physics of Nonlinear Dynamical Systems

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APHYS 514

Applied Physics Grad Seminar

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PSCS 520

Empirical analysis of Non-Linear Systems

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PSCS 530

Computer Modeling

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The purpose of this course is to introduce students to the basic concepts, tools and issues which arise when using computers to model complex (adaptive) systems (CAS). The emphasis will be on agent-based, bottom-up computer models. (We will only briefly look at other approaches.) The bulk of the course will involve "learning by example", i.e., students will:

read, discuss, evaluate a number of models from a variety of disciplines.

Modify and run experiments with exisiting models.

Design, implement, run, write-up results from their own models.

The course will cover all aspects of the modeling process itself, from model design through implementation to analyzing, documenting and communicating results.

PSCS 541

Dynamical Complex Systems

PSCS 541 - Introduction to Nonlinear Dynamics and the Physics of Complexity:

An introduction to nonlinear science with an elementary treatment from the point of view of the physics of chaos and fractal growth

Timeline: Industry and Science Labs: My Employment History [13]

1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011

RESEARCH
JOBS

UG Researcher, UM AOSS Dept.
Grad Student Researcher, UM AI Lab
Research Lab Rotation (Gene Regulation Lab), BioChemistry Dept.
PhD Research, UM AI Lab

Visiting Scientist, Riken Institute (Tokyo, Japan) and UM Physics Lab
PostDoc Researcher, UM BioMed Lab
PostDoc, UM Microsystems Lab
Researcher, UM Oncology Lab

INDUSTRY
JOBS

Internship, Pfizer (Parke-Davis)
- My Position: Internship
  Employment Year: 1995
  Location: Ann Arbor,MI,USA.^{Map Street View}
  Company:Parke-Davis Pharmaceutical Research (now Pfizer¹) was America's oldest and largest drug maker.
  Job Description - Area of work:
  There is a big component of data management to pharmaceutical companies. It takes many years and hundreds of millions of dollars to get new drugs approved and to the market, and the company must prove that experimental data has not been compromised in any way, or risk facing great added costs.
  Job Description - My Contributions:
  I was in charge of supervising the servers containing this valuable information. To protect and support years of new drug development data I developed new programs, applications and scripts for servers kept locked beyond 3 levels of security doors, and helped migration from older systems (VAX) to more recent ones (Windows). I also developed automatic back-up scheme and supervised data back-ups and restores for this data and my scripts controlled all the password accounts and password changes to this data, from ordinary workers to executives.

Senior Engineer at General Inspection Inc.
- My Position: Senior Engineer
  Employment Year: 1998-2000
  Company:General Inspection, produces laser-sorting equipment to serve the automotive, aerospace and gauging industries.
  Job Description - Area of work:
  Cars, Airplanes, and most machines and tools need screws, bolts and other small parts, generally made by 3rd party manufacturers. The crucial part of all this is that if a screw is ever slightly defective, your car, airplane etc could break down and whoever made them will get a bad reputation or lose money by forced recalls. And despite how sophisticated your fabrication methods are, there is always a very small fraction of parts that end up with fabrication defects. So companies have to inspect their parts, and bolt makers have to inspect the millions of bolts they make for almost invisible defects.
  Job Description - My Contributions:
  I was in charge of developing and improving new high-speed laser scanning systems to find and remove these 'needle in a haystack' defective parts. Some of these defects could only be found by a combination of laser, ultrasound, and eddy currents so I had to program and integrate these raw sensor data into a coherent laser gauging technology and software capable to analyze, measure, and sort parts with variable specifications. In addition, in order to achieve high-speed sorting of 300 parts per minute, I had to devise ways to compensate for variable speed/ bouncing/friction, effects which would otherwise disrupt the instrument's precision and render readings useless. And when looking at high precision, everything is more difficult: even small changes in temperature and environment would make the laser signal fluctuate and become unreliable, so I had to build in calibrations, interpolations high speeds (~300 parts per minute). I then built-in methods for automated statistical data collection and automatic shape recognition/analysis/fitting and measurement of relevant features, and for 3D reconstruction and display.
SoftwareEngineer at Netarx.com
- My Position: Software Engineer
  Employment Year: 2001
  Location: Auburn Hills, MI, USA
  Company:Netarx, a company that provides Managed IT Services.
  Job Description - Area of work:
  Larger companies with their own internal network need to keep track of all their servers, detect security threats, deploy updates and perform upgrades and repairs. One solution is to pay people to physically go around and check for any problem and maintain all these infrastructure components. But an arguably better solution is to automate and centralize this process, so that each component communicates if there are any problems that might later lead to crashes or security attacks and can be updated remotely, and technicians are only deployed when absolutely needed and knowing exactly what to replace.
  Job Description - My Contributions:
  As a Software Engineer in the Research and Development department, I worked on integrating all the incoming information from monitored network components into a coherent situational analysis to predict or deduce possible causes for any network problem, and to generate automatic notifications to support and service people. I then developed a web-based tool that allowed corporate partners to monitor and navigate through a dynamic visual representation of their entire network in which any network congestion, security breach, or problem is displayed in real time.

Senior Engineer at Lesynski.com
- My Position: Senior Engineer
  Employment Year: 2003
  Location: Seattle, WA,USA.^{Map Street View}
  Company:Leszynski Group, Inc., developed several Pen-Based technologies and APIs for Microsoft.
  Job Description - Area of work:
  Several years before the iPhone, the iPad, the Nintendo-DS and other touch and pen-based devices, the technology for these new types of interfaces was still being developed and the main new platform to allow touch and pen input was the TabletPC (a touchscreen PC laptop). That's what I had to work with at the time.
  Job Description - My Contributions:
  My focus was on important applications that only become possible by combining artificial intelligence and pen-based input. For example, while inspecting and repairing military vehicles and planes, it can be important to look up the blueprint schematics of some part, but often people could sketch the part but not know its exact name and model. Before my work there was no way to search blueprints in a database based on partial sketches, so I devised new shape recognition algorithms and new conceptual and visual pattern-matching AI to perform that task.
  Additionally I developed a biometric recognition algorithm that extracted speed and pressure information from signatures written with a tablet PC pen and used them to perform authentication and cryptography. While the looks of a signature can be forged, the general pattern of pressure and acceleration as it is written is almost impossible to guess and reproduce.
  Then, through a collaboration with Microsoft, I developed a shape-recognition SDK for tablet-PCs and other pen-based applications that used artificial intelligence and complex pattern recognition to interpret and correct hand-drawn diagrams and sketches over a variety of applications and domains. This new technology was featured in multiple keynote talks by Bill Gates and other Microsoft Executives, and allowed applications like search of part databases from partial rough sketches and animating sketches into physical simulations (in collaboration with MIT) automatically.

My Position: Science/Technology Consultant
Employment Year: 2006-2011
Company Type: Independent Contractor
Job Description:
I Developed solutions to scientific problems for industrial companies, on a project basis. I have found working solutions to problems that internal engineers had tried to solve and determined to be 'unsolvable', such as noise-free Hex and trilobe laser reconstructions.

Publications

TEACHING

EECS370

EECS270,280

ACADEMICS

BSE in CS

MS in CS

PhD Quals

PhD in Computer Engineering, Artificial Intelligence

MS in Aerospace Eng.

MS in BioChemistry

(95% MS Psychology)

CGS in Complex Systems

MS in BioMed Eng.

(85% MS Philosophy, MS EE)

2004
2005
2006
2007
2008
2009
2010
2011

RESEARCH
JOBS

Visiting Scientist, Riken Institute (Tokyo, Japan) and UM Physics Lab
PostDoc Researcher, UM BioMed Lab
PostDoc, UM Microsystems Lab
Researcher, UM Oncology Lab

INDUSTRY
JOBS

My Position: Science/Technology Consultant
Employment Year: 2006-2011
Company Type: Independent Contractor
Job Description:
I Developed solutions to scientific problems for industrial companies, on a project basis. I have found working solutions to problems that internal engineers had tried to solve and determined to be 'unsolvable', such as noise-free Hex and trilobe laser reconstructions.

Publications

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