CSE 599 Research Miniproject

Presentation: You will give an in-class presentation about your project on either March 1 or March 8 (using Powerpoint, overheads, slides or the whiteboard). You are free to pick the date you wish to present, but those who present on the later date (March 8) will be expected to give a more in-depth presentation with results and/or demos, while presentations given on March 1 can be in the form of project proposals with introduction, problem statement/review, and preliminary results/analysis. Sign up for a date and time using the link below:

Sign up for a presentation time

Report: You must hand in a project report that clearly and precisely describes the problem and your results. Last date to hand in reports: March 15.

Notation: D - project must include a demonstration/simulation with results; report should be about 5-10 pages, with about 10 references.

A - project consists of a theoretical analysis of the problem, with optional demonstration/simulation with results; report should be about 10 pages, with about 10 references.

R - project is a comprehensive review/survey of the problem; report should be about 20-25 pages, with 20 or more references.

The above numbers for pages and references are intended to provide rough guidelines as to what we expect; the actual numbers will of course vary depending on the topic chosen. Various combinations of the A/D/R categories are also possible.

Note: A given review topic can only be chosen by one student, so they will be assigned on a first-come, first-serve basis. Other topics can be pursued by multiple students.

Topics already taken are listed in italics.

Click here to see the current list of topic assignments

• DNA/Molecular Computing
  1. Memory stored in molecules (D)
     • Given a set of bits, how do we encode/retrieve those bits using DNA and molecular operations?
  2. Scaling issues in DNA computation (A)
     • Analysis--show how exponential complexity may overwhelm the advantages of DNA-based massive parallelism (that scales as cubic volume)
  3. DES cracking and DNA computers (A/D)
     • Explain the DES algorithm--why is it designed the way it is?
     • Show how the surface-based method can be used to crack DES
  4. Error correction--how does DNA do it? How reliable is it? How can you make DNA computing more reliable using error correction? (A/D)
  5. Nanotech/self-assembly--what are some upcoming technologies? How will they help us build computing devices? Demo should be high-level. (A/D)
  6. 3D protein folding & computing--How can we predict 3D structure of proteins from genetic code/primary structure of protein? Can this be used to build computational devices? (A/D)
• Neural Computing
  1. Neural coding: Firing rates versus spike timing (A/D)
     • Why is the topic important?
     • Demo should show differences between 2 approaches
  2. Neural Synchrony and the Binding problem (A/D)
  3. Synaptic dynamics-- short-term plasticity, LTP/LTD (R/D)
  4. Relate specific brain structures to models of networks and learning (e.g. in hippocampus) (R)
  5. Backpropagation of spikes in neurons and computational significance (A/D)
  6. Evolving neural topologies (D)
  7. Development/synaptogenesis (D/R)
  8. Relate SOM or other learning algorithms to development of sensory maps (R/D)
• Quantum Computing
  1. Adiabatic/reversible computation (A/R or D)
     • Adiabatic computing: should be a review/quantitative analysis
     • Reversible computation should be a demo showing how a fairly complex circuit (e.g. instruction decoder for a simple processor) could be realized using reversible gates
  2. Quantum cryptography (A/R)
  3. Error correction in quantum computing--demo should be high-level (A/D)
  4. Quantum computer implementation approaches--what looks promising, what looks infeasible, and why? (R)
  5. Single electron transistors/quantum dots--what are they and how might they help us? How much progress have we made in manufacturing these technologies? What remains to be done? (A/R)
• Multi-Paradigm Projects (compare and contrast)
  1. Adaptive computing using DNA/Neural/Quantum systems(A/D or A/R)
  2. Data compression (A/D or A/R)
     • Classical
     • DNA/molecular
     • Neural
     • Quantum
  3. Information theory (A/D or A/R)
     • Compute bounds on maximum information transfer in neural/molecular systems
     • Compare quantum/classical information theory
     • Classical/quantum definitions of Kolmogorov complexity--how might they differ?
     • Limits on the maximum possible density of information
     • Quantum effects (e.g. teleportation)