Winter 2022 Course Offerings
All courses offered in Winter 2022 will be delivered in-person (unless historically offered online).
For delivery method and date and time of offer please look at the schedule of classes.
Instructor |
Recommended Background |
Area |
Number |
Course Title |
|
Ali Abedi |
CS 631 |
Data-Intensive Dist. Analytics |
|||
Noura Limam |
CS 636 |
Intro to Networks & Distr Syst |
|||
Gregor Richards |
Familiarity with parsing and compiler basics, e.g. as taught in CS 241, is expected. Basic familiarity with object oriented and functional languages, e.g. as taught in CS 246 and CS 135 respectively, is recommended. | Programming Languages |
CS 642 |
Principles of Prog Languages |
|
Yizhou Zhang |
Programming Languages |
CS 644 |
Compiler Construction |
||
Joanne Atlee |
Knowledge of finite-state machines (eg as used in CS 241 or ECE 351); experience with object-orientation (eg experience with OO programming languages like Java or C#); knowledge of propositional and predicate logic (eg as used in CS 245 or ECE 108) | Software Engineering |
Sotware Req Specif & Anylysis |
||
Shane McIntosh |
Experience with team collaboration tools for software development (eg Git, GitHub, Issue Tracking), data sturctures, algorithms, and basic UML diagrams | Sotware Engineering |
CS 646 |
Software Design, Architectures |
|
Arie Gurfinkel |
Software Engineering |
Software Test/Qual Assur/Maint |
|||
TBA |
Hardware and Software Systems |
CS 650 |
Computer Architecture |
||
Ali Abedi |
Hardware and Software Systems |
CS 651 |
Data-Intensive Distrib Comput |
||
Martin Karsten |
C/C++ programming (CS 246); operating systems (CS 350) |
Hardware and Software Systems |
Real-Time Programming |
||
Samer Al Kiswany |
Hardware and Software Systems |
CS 654 - 001 |
Distributed Systems |
||
Samer Al Kiswany |
Hardware and Software Systems |
CS 654 - 002 |
Distributed Systems |
||
Noura Limam |
Hardware and Software Systems |
CS 656 - 001 |
Computer Networks |
||
Noura Limam |
Hardware and Software Systems |
CS 656 – 002 |
Computer Networks |
||
Hans Liljestrand, Meng Xu |
Hardware and Software Systems |
Computer Security and Privacy |
|||
Hans Liljestrand, Meng Xu |
Hardware and Software Systems |
Computer Security and Privacy |
|||
Jeffrey Shallit |
Algorithms and Complexity |
CS 662 |
Formal Languages & Parsing |
||
Yuying Li |
Good background in linear algebra, basic statistics, and calculus, and to have taken an introduction course in numberical computing (similar to CS 370 or 371 at UW) | Scientific and Symbolic Computing |
CS 676 |
Num Comp for Fin Modeling |
|
Jeff Orchard |
Numerical analysis, basic statistics | SciCom or AI |
Neural Networks |
||
Ali Ayub |
Pre-requisites: CM 339/CS 341 or SE 240; Computer Science students only | Computational Statistics |
CS 680 – 001 |
Intro to Machine Learning |
|
Ali Ayub |
Pre-requisites: CM 339/CS 341 or SE 240; Computer Science students only | Computational Statistics |
CS 680 – 002 |
Intro to Machine Learning |
|
Bin Ma |
Bioinformatics |
CS 682 |
Comp Tech: Biol Sequence Anal |
||
Yuri Boykov |
Artificial Intelligence |
CS 684 |
Computational Vision |
||
Blake Paul Allen Vanberlo |
Basic calculus, probability, and algorithms | Artificial Intelligence |
Intro to Artificial Intelligence |
||
Blake Paul Allen Vanberlo |
Basic calculus, porbability, and algorithms | Artificial Intelligence |
Intro to Artificial Intelligence |
||
Eugene Zima |
Scientific and Symbolic Computing |
CS 687 |
Intro to Symbolic Computation |
||
Gladimir Baranoski |
Graphics and User Interfaces |
CS 688 |
Intro to Computer Graphics |
||
Prabhakar Ragde |
Open |
Programming Languages |
Software Verification Using Proof Assistants |
||
Hans De Sterck |
Scientific and Symbolic Computing |
CS 778 |
Numerical Sol of PDEs |
||
Helen Chen |
Health Informatics |
CS 792 - 001 |
Data Structures and Standards |
||
Helen Chen | Health Informatics | CS 792 - 081 | Data Structures and Standards | ||
Raouf Boutaba |
Computer Networks, Operating Systems |
Hardware and Software Systems |
Network Softwarization: Principles and Foundations |
||
Raouf Boutaba |
Computer Networks, Operating Systems |
Hardware and Software Systems |
Network Softwarization: Technologies and Enablers |
||
Shai Ben-David | Prereauisite of CS 485 or CS 685; undergraduate students that have the prerequisite could participate with the instructor's permission | Computational Statistics | CS 798 - 003 | Theory of Common ML Tools | |
J. Ian Munro |
A strong undergraduate course in data structures like CS 240 is required. A more advanced algorithms course such as CS 466 is helpful but not necessary |
Algorithms |
CS 840 |
Time and Space Efficiency |
|
Chengnian Sun |
Open |
Software Engineering |
Advance Topics in Software Testing and Debugging |
||
Shane McIntosh |
Open background, but a basic understanding of software release practices, inferential statistics, and machine learning will help | Software Engineering |
SW Analytics for Release Pipe |
||
Gordon Cormack |
Undergrad CS |
Databases |
CS 848 |
Information Retrieval |
|
Florian Kerschbaum |
Undergrad security course, familiarity with databases and machine learning |
Artificial Intelligence, Databases |
Security & Privacy in Data Science |
||
Diogo Barradas |
Open |
Hardware and Software Systems |
Internet Censorship and Surveillance |
||
N. Asokan |
Good understanding of programming in C/C++ required, prior undergraduate level courses in security (equivalent to CS 458) and operating systems (equivalent to CS 350) strongly recommended, but not mandated |
Systems |
Selected Topics in Systems Security |
||
Lap Chi Lau |
Linear algebra, probability, multivariate calculus, optimization |
Algorithms and Complexity |
Eigenvalues and Polynominals |
||
Beni Yoshida, Debbie Leung, Michael Vasmer |
QIC 710 / CS 768 / CO 681 or CS 467 / CO 481 | Quantum Information and Computation |
CS 867 |
Qtm Error Corr & Flt Tolerance |
|
Pascal Poupart |
Undergrad courses in machine learning, statistics and linear algebra |
Computational Statistics |
CS 885 |
Reinforcement Learning |
|
Ming Li |
Basics in deep learning, know basic biology |
AI, Learning, Bioinformatics, Health Science |
Deep Learning for Biotechnology |
||
Gladimir Baranoski |
Open | Computer Graphics, Scientific Computing, Systems, HCI |
CS 898 - 001 |
Synergy Between CS and Biology |
|
Yuri Boykov | Artificial Intelligence | CS 898 - 002 | TBA |