CSC 400-1
Daniel Gildea
TR 4:50PM - 6:05PM
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An introduction to the technical, communal, and societal aspects of graduate education in computer science at Rochester. Majority Most class meetings will consist of group discussions and presentations that focus on a broad range of topics, and are intended to improve the critical analysis, technical writing, presentation, and problem-solving skills of students and in to preparing for scientific research. The course also offers a forum for individual department faculty members to discuss their research interests and recent results. Satisfactory performance is required of all first-year graduate students.
- Location
- Hylan Building Room 203
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CSC 412-1
Ehsan Hoque
MW 10:25AM - 11:40AM
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This course will explore the design, implementation, and evaluation of user interfaces. Students will study the theoretical methods for interface design and evaluation, including requirements gathering, usability heuristics, user interface inspections, usability studies, information visualization, and prototyping. Case studies of interface successes and failures will augment theory with practical experiences. Students will apply this methodology to assignments in the design, implementation, and evaluation cycle. Students taking this course at the graduate level will have additional readings and assignments.
- Location
- Hylan Building Room 202
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CSC 413-1
Mujdat Cetin
MW 2:00PM - 3:15PM
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This course provides a broad introduction to augmented and virtual reality (AR/VR) systems. The course involves lectures covering an overview of all aspects of the AR/VR domain, as well as individual work performed by each student aimed at providing more intensive training on various aspects of AR/VR. Topics covered in the lectures include history, conceptual origins, and design/evaluation principles of AR/VR technologies; overview of visual/auditory/haptic AR/VR interfaces and applications; visual perception; optics/platforms/sensors/displays; auditory perception and spatial audio; silicon hardware architecture and materials; graphics and computation; interfaces and user experience design; data processing and machine intelligence for AR/VR; introduction to AR/VR programming tools; societal implications and ethical aspects. At the end of the course, students will have gained familiarity with the techniques, languages, and cultures of fields integral to the convergent research theme of AR/VR. This course is co-instructed by Mujdat Cetin, Michele Rucci, Ross Maddox, Jannick Rolland, Yuhao Zhu, Andrew White, Chenliang Xu, and Zhen Bai.
- Location
- Goergen Hall Room 109
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CSC 427-1
Maria Helguera
MW 10:25AM - 11:40AM
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This course will introduce the students to the basic concepts of digital image processing, and establish a good foundation for further study and research in this field. The theoretical components of this course will be presented at a level that seniors and first year graduate students who have taken introductory courses in vectors, matrices, probability, statistics, linear systems, and computer programming should be comfortable with. Topics cover in this course will include intensity transformation and spatial filtering, filtering in the frequency domain, image restoration, morphological image processing, image segmentation, image registration, and image compression. The course will also provide a brief introduction to python (ipython), the primary programming language that will be used for solving problems in class as well as take-home assignments. prerequisites: ECE 242 and ECE 440 & 446 are recommended or permission of instructor
- Location
- Harkness Room 114
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CSC 440-2
Jiebo Luo
TR 3:25PM - 4:40PM
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Fundamental concepts and techniques of data mining, including data attributes, data visualization, data pre-processing, mining frequent patterns, association and correlation, classification methods, and cluster analysis. Advanced topics include outlier detection, stream mining, and social media data mining. CSC 440, a graduate-level course, requires additional readings and a course project.
- Location
- Gavett Hall Room 202
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CSC 442-1
Hangfeng He
TR 11:05AM - 12:20PM
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Introduces fundamental principles and key applications of artificial intelligence, including heuristic search, automated reasoning, probability, and machine learning. Programming projects will be assigned. This course provides a foundation for advanced AI courses.
- Location
- Gavett Hall Room 206
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CSC 444-1
Lenhart Schubert
TR 9:40AM - 10:55AM
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Representing knowledge and implementing reasoning and goal-directed planning in a computer, using first-order logic as well as specialized and uncertain methods.
- Location
- Hylan Building Room 102
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CSC 445-1
Chenliang Xu
TR 9:40AM - 10:55AM
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Much of the recent advances in machine learning and artificial intelligence have been dominated by neural network approaches broadly described as deep learning. This course provides an overview of the most important deep learning techniques covering both theoretical foundations and practical applications. The applications focus on problems in image understanding and language modeling utilizing state-of-the-art deep learning libraries and tools, which are introduced in the course. Prerequisites: MATH 164, MATH 165, and CSC 172. Programming in Python.
- Location
- Morey Room 321
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CSC 446-1
Daniel Gildea
TR 11:05AM - 12:20PM
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Mathematical foundations of classification, regression, and decision making. Supervised algorithms covered include perceptrons, logistic regression, support vector machines, and neural networks. Directed and undirected graphical models. Numerical parameter optimization, including gradient descent, expectation maximization, and other methods. Introduction to reinforcement learning. Proofs covered as appropriate. Significant programming projects will be assigned. Prerequisites: This course involves a lot of math and algorithms. You should know multivariable calculus, linear algebra, and some algorithms. No formal prerequisites but MATH 165, MATH 164, and CSC 242 strongly recommended.
- Location
- Meliora Room 221
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CSC 452-1
John Criswell
MW 3:25PM - 4:40PM
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Introduction to computer architecture and the layering of hardware/software systems. Topics include instruction set design; logical building blocks; computer arithmetic; processor organization; the memory hierarchy (registers, caches, main memory, and secondary storage); I/O buses, devices, and interrupts; microcode and assembly language; virtual machines; the roles of the assembler, linker, compiler, and operating system; technological trends and the future of computing hardware. Several programming assignments required. Prerequisites: MATH 150 Discrete Mathematics and CSC 172 Data Structures and Algorithms
- Location
- Wegmans Room 1400
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CSC 453-1
Chen Ding
MW 3:25PM - 4:40PM
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Modern software is complex and more than a single person can fully comprehend. This course teaches collaborative programming which is multi-person construction of software where each person's contribution is non-trivial and clearly defined and documented. The material to study includes design principles, safe and modular programming in modern programming languages including Rust, software teams and development processes, design patterns, and productivity tools. The assignments include collaborative programming and software design and development in teams. Students in CSC 453 and TCS 453 have additional reading and requirements. Prerequisites: CSC 172 Data Structures and Algorithms, or equivalent for CSC 253. CSC 172 and CSC 252 or equivalent for CSC 453 and TCS 453.
- Location
- Hylan Building Room 202
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CSC 454-1
Michael Scott
MW 10:25AM - 11:40AM
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Design and implementation of programming languages, with an emphasis on imperative languages and on implementation tradeoffs. In-depth examination of 'how programming languages work.' Topics include fundamental language concepts (names, values, types, abstraction, control flow); compilation and interpretation (syntactic and semantic analysis, code generation and optimization); major language paradigms (imperative, object-oriented, functional, logic-based, concurrent). Course projects include assignments in several different languages, with an emphasis on compilation issues. Prerequisites: CSC 173 Computation and Formal Systems, and 252 Computer Organization, or equivalent.
- Location
- Wegmans Room 1400
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CSC 457-1
Adam Purtee
MW 4:50PM - 6:05PM
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Introduction to computer networks and computer communication: Architecture and Protocols:. Design of protocols for error recovery, reliable delivery, routing and congestion control. Store-and-forward networks, satellite networks, local area networks and locally distributed systems. Case studies of networks, protocols and protocol families. Emphasis on software design issues in computer communication.
- Location
- Goergen Hall Room 108
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CSC 461-1
Eustrat Zhupa
MW 12:30PM - 1:45PM
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This course presents the fundamental concepts of database design and use. It provides a study of data models, data description languages, and query facilities including relational algebra and SQL, data normalization, transactions and their properties, physical data organization and indexing, security issues and object databases. It also looks at the new trends in databases. The knowledge of the above topics will be applied in the design and implementation of a database application using a target database management system as part of a semester-long group project.
- Location
- Harkness Room 115
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CSC 462-1
Anson Kahng
TR 4:50PM - 6:05PM
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This course will cover foundational concepts in descriptive analyses, probability, and statistical inference. Topics to be covered include data exploration through descriptive statistics (with a heavy emphasis on using R for such analyses), elementary probability, diagnostic testing, combinatorics, random variables, elementary distribution theory, statistical inference, and statistical modeling. The inference portion of the course will focus on building and applying hypothesis tests and confidence intervals for population means, proportions, variances, and correlations. Non-parametric alternatives will also be introduced. The modeling portion of the course will include ANOVA, and simple and multiple regression and their respective computational methods. Students will be introduced to the R statistical computing environment. PREREQUISITES: MTH 150 or MTH 150A; AND MTH 142 or MTH 161 or MTH 171 (or equivalent)
- Location
- Gavett Hall Room 206
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CSC 464-1
Zhiyao Duan
TR 12:30PM - 1:45PM
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Computer audition is the study of how to design a computational system that can analyze and process auditory scenes. Example problems in this field include source separation (splitting audio mixtures into individual source tracks), pitch estimation (estimating the pitches played by each instrument), timbre modeling (finding features to distinguish different kinds of instruments), and source localization (finding where the sound comes from). This course will cover both fundamentals and state-of-the-art research in this field, which applies various kinds of signal processing and machine learning techniques. Multiple programming assignments will help students practice what they learn, and a final research project will lead students through the entire research process. Prerequisites: ECE 246/446 or ECE 272/472 or other equivalent signal processing courses, and Python/Matlab programming. Knowledge of machine learning techniques such as Markov models, support vector machines and neural networks is also helpful, but not r
- Location
- Computer Studies Room 601
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CSC 480-1
Eustrat Zhupa
MW 2:00PM - 3:15PM
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This course studies fundamental computer models and their computational limitations. Finite-State machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages , undecidability. Prerequisites: CSC 173 Computation and Formal Systems, and MTH 150 Discrete Mathematics.
- Location
- Morey Room 321
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CSC 480-10
Eustrat Zhupa
R 3:25PM - 4:15PM
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No description
- Location
- Lechase Room 184
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CSC 480-2
Eustrat Zhupa
M 4:50PM - 6:05PM
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No description
- Location
- Wegmans Room 1005
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CSC 480-3
Eustrat Zhupa
W 4:50PM - 6:05PM
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No description
- Location
- Wegmans Room 1005
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CSC 480-4
Eustrat Zhupa
W 6:15PM - 7:30PM
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No description
- Location
- Hylan Building Room 306
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CSC 480-5
Eustrat Zhupa
R 4:50PM - 6:05PM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability.
- Location
- Dewey Room 4162
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CSC 480-6
Eustrat Zhupa
R 6:15PM - 7:30PM
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No description
- Location
- Dewey Room 4162
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CSC 480-7
Eustrat Zhupa
T 6:15PM - 7:30PM
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No description
- Location
- Dewey Room 4162
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CSC 481-1
MW 9:00AM - 10:15AM
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The modern study of cryptography investigates techniques for facilitating interactions between distrustful entities. In this course we introduce some of the fundamental concepts of this study. Emphasis will be placed on the foundations of cryptography and in particular on precise definitions and proof techniques. The main skills that will be assumed from these courses are: the ability to understand and write formal mathematical definitions and proofs and 2) comfort with reasoning about algorithms, such as proving their correctness and analyzing their running times. It is also important that you are familiar with basic probability. CSC 280 (or equivalent), MATH150 (or mathematical maturity), or permission of instructor.
- Location
- Hylan Building Room 105
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CSC 481-2
R 3:25PM - 4:40PM
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No description
- Location
- Meliora Room 205
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CSC 482-1
Daniel Stefankovic
TR 11:05AM - 12:20PM
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How does one design programs and ascertain their efficiency? Greedy algorithms, dynamic programming, divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Introduction to NP-completeness and linear programming. Students taking this course at the 400 level may be required to complete additional tests, readings or assignments. Prerequisites: (CSC 172 and MATH 150) or MATH172. Students MUST register for the recitation when registering for this course.
- Location
- Wegmans Room 1400
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CSC 482-2
Daniel Stefankovic
T 6:15PM - 7:30PM
|
How does one design programs and ascertain their efficiency? Greedy algorithms, dynamic programming, divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Introduction to NP-completeness and linear programming. Students taking this course at the 400 level may be required to complete additional tests, readings or assignments. Prerequisites: (CSC 172 and MATH 150) or MATH172. Students MUST register for the recitation when registering for this course.
- Location
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CSC 482-3
Daniel Stefankovic
W 6:15PM - 7:30PM
|
How does one design programs and ascertain their efficiency? Greedy algorithms, dynamic programming, divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Introduction to NP-completeness and linear programming. Students taking this course at the 400 level may be required to complete additional tests, readings or assignments. Prerequisites: (CSC 172 and MATH 150) or MATH172. Students MUST register for the recitation when registering for this course.
- Location
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CSC 482-4
Daniel Stefankovic
M 6:15PM - 7:30PM
|
How does one design programs and ascertain their efficiency? Greedy algorithms, dynamic programming, divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Introduction to NP-completeness and linear programming. Students taking this course at the 400 level may be required to complete additional tests, readings or assignments. Prerequisites: (CSC 172 and MATH 150) or MATH172. Students MUST register for the recitation when registering for this course.
- Location
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CSC 482-5
Daniel Stefankovic
W 7:40PM - 8:55PM
|
How does one design programs and ascertain their efficiency? Greedy algorithms, dynamic programming, divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Introduction to NP-completeness and linear programming. Students taking this course at the 400 level may be required to complete additional tests, readings or assignments. Prerequisites: (CSC 172 and MATH 150) or MATH172. Students MUST register for the recitation when registering for this course.
- Location
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CSC 484-1
Kaave Hosseini
TR 12:30PM - 1:45PM
|
Advanced study of design and analysis of algorithms. Topics typically include: growth of functions; recurrences; probabilistic analysis and randomized algorithms; maximum flow; sorting networks; expander graphs; matrix operations; linear programming; discrete Fourier transform; number-theoretic algorithms; string matching; computational geometry; NP-completeness; approximation algorithms. Students taking this course at the 400 level may be required to complete additional tests, readings or assignments.
- Location
- Hylan Building Room 203
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CSC 487-1
Daniel Stefankovic
MW 2:00PM - 3:15PM
|
The course will focus on algorithmic sampling from probability distributions using Markov chains, perfect sampling methods (coupling from the past, Lovasz Local Lemma), determinantal techinques (spanning trees, matchings in planar graphs), and using correlation decay. For MCMC we will study methods to bound convergence speed (coupling, canonical paths, conductance, log-concavity) and show how they apply to classical problems (colorings, permanent, volume, matroids). Background material on probability theory will be covered.
- Location
- Harkness Room 210
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CSC 494-3
Lenhart Schubert
|
Blank Description
- Location
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CSC 495-01
James Allen
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- Location
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CSC 495-02
John Criswell
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- Location
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CSC 495-03
Zhen Bai
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- Location
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CSC 495-04
Chen Ding
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CSC 495-05
Sandhya Dwarkadas
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CSC 495-06
Daniel Gildea
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CSC 495-07
Lane Hemaspaandra
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CSC 495-08
Ehsan Hoque
|
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CSC 495-09
Henry Kautz
|
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CSC 495-10
Jiebo Luo
|
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CSC 495-11
Fatemeh Nargesian
|
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CSC 495-12
Randal Nelson (Terminated)
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CSC 495-13
Sreepathi Pai
|
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CSC 495-14
Lenhart Schubert
|
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CSC 495-15
Michael Scott
|
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CSC 495-16
Daniel Stefankovic
|
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CSC 495-17
Muthuramakrishnan Venkitasubramaniam
|
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CSC 495-18
Chenliang Xu
|
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CSC 495-19
Yuhao Zhu
|
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CSC 495-20
Aaron White
|
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CSC 495-21
Thomas Howard
|
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CSC 578-1
Chenliang Xu
TR 9:40AM - 10:55AM
|
Much of the recent advances in machine learning and artificial intelligence have been dominated by neural network approaches broadly described as deep learning. This course provides an overview of the most important deep learning techniques covering both theoretical foundations and practical applications. The applications focus on problems in image understanding and language modeling utilizing state-of-the-art deep learning libraries and tools, which are introduced in the course. Prerequisites: MTH 164, MTH 165, and CSC 172. Programming in Python.
- Location
- Morey Room 321
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CSC 595-1
James Allen
|
Blank Description
- Location
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CSC 595-10
Jiebo Luo
|
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CSC 595-11
Fatemeh Nargesian
|
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CSC 595-12
Ji Liu
|
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CSC 595-13
Sreepathi Pai
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CSC 595-14
Lenhart Schubert
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CSC 595-15
Michael Scott
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CSC 595-16
Daniel Stefankovic
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CSC 595-17
Muthuramakrishnan Venkitasubramaniam
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CSC 595-18
Chenliang Xu
|
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CSC 595-19
Yuhao Zhu
|
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CSC 595-2
John Criswell
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CSC 595-20
Aaron White
|
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CSC 595-21
Gonzalo Mateos Buckstein
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CSC 595-22
Thomas Howard
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CSC 595-23
Gregory Heyworth
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CSC 595-3
Zhen Bai
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CSC 595-4
Chen Ding
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CSC 595-5
Sandhya Dwarkadas
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CSC 595-6
Daniel Gildea
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CSC 595-7
Lane Hemaspaandra
|
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CSC 595-8
Ehsan Hoque
|
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CSC 595-9
Henry Kautz
|
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CSC 595A-5
Sreepathi Pai
|
Blank Description
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CSC 597-2
M 11:50AM - 1:05PM
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Blank Description
- Location
- Wegmans Room 1400
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CSC 890-1
|
Blank Description
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CSC 895-1
|
Blank Description
- Location
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CSC 897-02
Sandhya Dwarkadas
|
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- Location
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CSC 897-03
Daniel Gildea
|
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CSC 897-04
Chen Ding
|
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- Location
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CSC 897-05
John Criswell
|
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CSC 897-06
Muthuramakrishnan Venkitasubramaniam
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CSC 897-07
Lenhart Schubert
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CSC 897-08
Zhen Bai
|
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CSC 897-09
Chenliang Xu
|
Blank Description
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CSC 897-1
Michael Scott
|
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CSC 897-10
Sreepathi Pai
|
Blank Description
- Location
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CSC 897-11
Daniel Stefankovic
|
Blank Description
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CSC 897-12
James Allen
|
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CSC 897-13
Yuhao Zhu
|
Blank Description
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CSC 897-14
Jiebo Luo
|
Blank Description
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CSC 897-15
Ehsan Hoque
|
Blank Description
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CSC 897-16
Henry Kautz
|
Blank Description
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CSC 897-17
Adam Purtee
|
Blank Description
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CSC 899-01
James Allen
|
Blank Description
- Location
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CSC 899-02
John Criswell
|
Blank Description
- Location
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CSC 899-03
Zhen Bai
|
Blank Description
- Location
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CSC 899-04
Sandhya Dwarkadas
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- Location
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CSC 899-05
Chen Ding
|
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- Location
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CSC 899-06
Daniel Gildea
|
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CSC 899-07
Lane Hemaspaandra
|
Blank Description
- Location
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CSC 899-08
Ehsan Hoque
|
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- Location
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CSC 899-09
Henry Kautz
|
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- Location
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CSC 899-10
Jiebo Luo
|
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CSC 899-11
Fatemeh Nargesian
|
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CSC 899-12
Randal Nelson (Terminated)
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- Location
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CSC 899-13
Sreepathi Pai
|
Blank Description
- Location
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CSC 899-14
Lenhart Schubert
|
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CSC 899-15
Michael Scott
|
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CSC 899-16
Daniel Stefankovic
|
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CSC 899-17
Muthuramakrishnan Venkitasubramaniam
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- Location
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CSC 899-18
Chenliang Xu
|
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CSC 899-19
Yuhao Zhu
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CSC 995-1
|
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- Location
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CSC 997-02
Sandhya Dwarkadas
|
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CSC 997-03
Daniel Gildea
|
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- Location
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CSC 997-04
Chen Ding
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- Location
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CSC 997-05
John Criswell
|
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- Location
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CSC 997-06
Muthuramakrishnan Venkitasubramaniam
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- Location
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CSC 997-07
Lenhart Schubert
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- Location
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CSC 997-08
Zhen Bai
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CSC 997-09
Chenliang Xu
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Blank Description
- Location
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CSC 997-1
Michael Scott
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CSC 997-10
Sreepathi Pai
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CSC 997-11
Daniel Stefankovic
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CSC 997-12
James Allen
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CSC 997-13
Yuhao Zhu
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CSC 997-14
Jiebo Luo
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CSC 997-15
Ehsan Hoque
|
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CSC 997-16
Henry Kautz
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CSC 997A-3
Sreepathi Pai
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CSC 999-01
James Allen
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CSC 999-02
John Criswell
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CSC 999-03
Zhen Bai
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CSC 999-04
Chen Ding
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CSC 999-05
Sandhya Dwarkadas
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CSC 999-06
Daniel Gildea
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CSC 999-07
Lane Hemaspaandra
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CSC 999-08
Ehsan Hoque
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CSC 999-09
Henry Kautz
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CSC 999-10
Jiebo Luo
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CSC 999-11
Fatemeh Nargesian
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CSC 999-12
Ji Liu
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CSC 999-13
Sreepathi Pai
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CSC 999-14
Lenhart Schubert
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CSC 999-15
Michael Scott
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CSC 999-16
Daniel Stefankovic
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CSC 999-17
Muthuramakrishnan Venkitasubramaniam
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CSC 999-18
Chenliang Xu
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CSC 999-19
Yuhao Zhu
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CSC 999-20
Aaron White
|
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CSC 999-21
Gonzalo Mateos Buckstein
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CSC 999-22
Thomas Howard
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CSC 999A-10
Daniel Gildea
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CSC 999A-11
Yuhao Zhu
|
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- Location
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