Special Topics

Special Topics courses cover some of our most innovative and promising research directions.  They are often prototypes of new courses that we are developing.

Special Topics courses offer variable course content, so each semester's offerings are unique.

Spring 2022

CSE 510 Software Security (Lecture)
Section: ZHAO
Instructor: Ziming Zhao
Description: This course is designed to provide students with good understanding of the theories, principles, techniques and tools used for software and system hacking and hardening. Students will study, in-depth, binary reverse engineering, vulnerability classes, vulnerability analysis, exploit and shellcode development, defensive solutions, etc. to understand how to crack and protect native software. In particular, this class covers offensive techniques including stack-based buffer overflow, heap security, format string vulnerability, return-oriented programming, etc. This class also covers defensive techniques including canary, shadow stack, address space layout randomization, control-flow integrity, etc. A key part of studying security is putting skills to the test in practice. Hacking challenges known as Capture The Flag (CTF) competitions are a great way to do this. In this class the progress of students are evaluated by lab assignment and in-class Capture-The-Flag (CTF) competitions.
URL: https://zzm7000.github.io/teaching/2022springcse410510/index.html
Instruction Mode: In person
Class #: 24287
Dates: 01/31/2022 - 05/13/2022
Days, Time: MW, 5:00PM-6:20PM
Location: Obrian 109, North Campus
Credit Hours: 1.00-3.00
Enrollment: 48/49 (47/48 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 510LEC registration number 24287 calendar icon | Course Catalog: CSE 510LEC orange catalog icon
CSE 610 Learning for Autonomous Systems (Lecture)
Section: DANT
Instructor: Karthik Dantu
Description: Robots and autonomous systems are expected to play increasing roles in our daily lives – from manufacturing the cars that drive themselves, performing boring household chores such as cleaning and mowing the lawn, providing search & rescue support while keeping humans out of harm’s way, to building extra-terrestrial habitats. Nevertheless, most of the current robotic systems are programmed to perform specific tasks in typically structured environments, with very limited ability to adjust to a variety of scenarios, environments, and human users/partners. To address this critical gap in the development of intelligent robots and autonomous systems, we need to innovate a form of adaptable robot autonomy, where akin to natural systems, robots understand the world through the lens of its perception, make informed decisions accordingly, and can adapt their behavior in response to significant changes in their environment or user needs. The goal of this course is to introduce students to a wide array of contemporary machine learning algorithms and AI frameworks for generating perception, control and planning models to address the above-stated critical gap. Note that we will not delve into the fundamentals of these techniques, which are otherwise addressed in other courses such as CSE 568. We will mainly consider the broad class of problem settings where optimum decisions are not known a priori, which can be loosely termed as reinforcement learning or RL, including but not limited to traditional gradient-based RL algorithms and algorithms based on evolutionary computing. While a summary background of RL and related basics (e.g., Markov Decision Processes) will be covered, the focus will be on learning a suite of different algorithms and their implementations for solving real-world decision-making problems in embodied autonomous systems. To this end, we will acquire knowledge of how to interface learning algorithms with simulators based on or compatible with the Robotic Operating System (ROS) and benchmarking environments such as the Open AI Gym. Here, the expectation is that students come in with a background in programming as well as some prior experience in ROS – this class will focus the use of learning algorithms in modern robot simulators. In light of moving towards robust and adaptable autonomy, we will also spend some time on methods that are designed to tackle challenges such as meaningful state abstraction, reward decomposition, and policy transferability and scalability. Key topics to be covered in this course include: 1. Markov Decision Processes (MDP) and Partially Observable MDP (POMDP) 2. Introduction to Reinforcement Learning (RL) a. Bandits b. Exploration/Exploitation c. Classes of Approaches d. Value Iteration & Policy Iteration 3. Introduction to Deep Neural Networks 4. Deep Q-Learning 5. Policy Optimization/Gradient Methods 6. Solving Open AI Gym Problems 7. Connecting RL Libraries with Robot Simulators 8. Hierarchical Reinforcement Learning 9. Multi-agent Reinforcement Learning 10. Neuroevolution 11. Advanced State Abstraction 12. Solving Planning and Control Problems in Mobile Robotics/UAVs The course will be a combination of lectures by the instructors, paper reading and project discussions. We will likely interleave the lectures and the paper readings. The emphasis of the course will be on the group project. The expectation is for each group to do novel research – ideally finishing in a workshop or conference paper. All projects are expected to need a degree of implementation on realistic simulators. Evaluation Approach The primary learning objective is to be able to demonstrate hands-on problem-solving capability by performing an assigned course project of real-world complexity. Assessment of student performance will occur through: • 2 Homework assignments in the first 1/3rd of the semester, covering the use of state-of-the-art RL algorithms • Student-driven Seminar series, where each student presents 2 seminar talks (over the semester) on contemporary research articles – these articles must be chosen based on the project that the student is assigned. A list of 4-5 well-defined projects will be offered in the course. • Group Course Project (with 3 students in each group) that involves applications of learning to robot/multi-robot control and planning problems. Project topics will be assigned within the first few weeks of class. Each group must present their project outcomes at the end of the semester, and submit a project report (at a level of detail and articulation similar to papers in leading Robotics conferences such as ICRA and RSS), and associated coding package/data.
Notes: - This course will be a joint CSE/MAE course taught by me as well as Prof Souma Chowdhury - While the CSE class says capacity is 30 students, I will only be able to accommodate 15 students given the room size and us accepting some MAE students - The class will be held at Bell 250 on MW 11:30-12:50 pm
Prereqs: Machine Learning, Robotics, Good programming background
Instruction Mode: In person
Class #: 24832
Dates: 01/31/2022 - 05/13/2022
Days, Time: F, 11:00AM-1:40PM
Location: Unknown, North Campus
Credit Hours: 3.00
Enrollment: 11/30 (11/30 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 610LEC registration number 24832 calendar icon | Course Catalog: CSE 610LEC orange catalog icon
CSE 610 Automated Analysis of Sporting Event Videos (Lecture)
Section: DOER
Instructor: David Doermann
Description: This course is an introduction to those areas of Artificial Intelligence that deal with fundamental issues and techniques of analysis of sports events using multimedia analysis, including computer vision and image processing. The emphasis is on physical, mathematical, and information-processing aspects of the media and sensor data analytics. Topics to be covered include video and sensor data collection, analysis for coaches, player feedback for performance enhancement and injury prevention, game highlights, and video summarization. All forms of media: text, sensors, video, and non-visual spectrum sensing Most of the material is based on recently published research papers. Students will be required to read technical research papers, prepare summaries, comment on the state-of-the-art, and present one or more research papers throughout the semester. Students are expected to participate in discussions and be an active part of the class. A significant amount of work will be required in contributing to an analysis pipeline that will process video content of American football games. Various data will be provided, and students will be assigned multiple tasks according to their strengths. The project will evolve throughout the semester, but the goal is to be able to ingest an entire football game and provide an indexing and retrieval environment at the end. Students will work individually and in groups to define interfaces, requirements, and evaluation metrics.
Notes: Projects will be done independently, not in groups, although there are portions where others will rely on your work.
Prereqs: Excellent Programming Skills; Computer Vision, Machine Learning and/or Natural Language Processing; Permission of the instructor
URL: https://cse.buffalo.edu/~doermann/Teaching
Instruction Mode: In person
Class #: 24948
Dates: 01/31/2022 - 05/13/2022
Days, Time: MW, 5:00PM-6:20PM
Location: Davis 113A, North Campus
Credit Hours: 3.00
Enrollment: 22/30 (0/30 seats reserved: force registration only) (Active)
Links: Registration: CSE 610LEC registration number 24948 calendar icon | Course Catalog: CSE 610LEC orange catalog icon
CSE 610 Security in Emerging Cyber Physical Systems (Lecture)
Section: HU
Instructor: Chunming Qiao
Description: A cyber physical system (CPS) typically includes a cyber subsystem with both hardware and software for sensing, computing, communications/networking, and control, and a physical subsystem used at home, and in the industries for manufacturing, medical, transportation, energy, environment, and others. Examples of CPS include but are not limited to smart appliances, smart grids, robots, and autonomous vehicles. A CPS is considered emerging if it recently started getting deployed in the real-world or is deemed promising for wide-scale deployment in the near future. The security issues surrounding such emerging systems, however, may prevent end-users from utilizing their full potential, or, even worse, may rule out the chances of their deployment in the future. Currently, these emerging systems are built based on technologies ranging from Internet of Things (IoT) and deep-learning systems to edge and 5G/Next-G systems. In this seminar course, we will discuss some of the latest work in the area of securing emerging CPS, including emerging network technologies and security (NFV, SDN, Edge, 5G/Next-G, etc.), IoT security and privacy (smart home, connected and autonomous vehicles, voice assistant platforms - Amazon Alexa and Google Assistant, etc.), and machine learning for security and privacy (adversarial attacks and defenses on deep learning, backdoor attacks and defenses on deep learning, etc.). The main goal of the special topic course is to help students understand the state of the art in a variety of security topics in emerging CPS. As a secondary goal, students will learn how to read research papers and how to communicate technical material effectively. The special topic course is suitable for students who have a strong interest in network and system security and intent to pursue a career in the area, e.g., Ph.D. students already working in cybersecurity or MS students interested in pursuing a Ph.D. or doing research in the field (in the form of independent studies and/or MS Thesis). One of the goals of this seminar is to identify, by the end of the semester, a set of open research problems on which students can work during the next semester, e.g., in the form of independent studies.
Instruction Mode: In person
Class #: 24625
Dates: 01/31/2022 - 05/13/2022
Days, Time: TR, 12:00PM-1:20PM
Location: Davis 113A, North Campus
Credit Hours: 3.00
Enrollment: 25/30 (24/30 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 610LEC registration number 24625 calendar icon | Course Catalog: CSE 610LEC orange catalog icon
CSE 610 Multimodal Affective Computing (Lecture)
Section: NWOG
Instructor: Ifeoma O. Nwogu
Description: This course explores affective computing techniques for analyzing human perception data such as faces, voice signals, galvanic skin response data and others. It also includes preprocessing techniques, which involve the use of open source toolkits for feature extraction. The core of the course focuses on dynamic data modeling which deals with models for understanding time series data. We explore different types of dynamic perception data and use these as homework and project examples throughout the semester. The course work involves reviewing papers, team based projects and presentations. Students work with different signals and toolkits, and also develop their own models as needed. A prior course in machine learning/deep learning or one related to probabilistic methods will be helpful, although not required.
Notes: In addition to standard AI models, the course will involve reviewing and presenting relevant papers in social psychology, where many of the concepts we will discuss in class originated.
Prereqs: CSE 574 or CSE 676 or with the Instructor's permission
Instruction Mode: In person
Class #: 23662
Dates: 01/31/2022 - 05/13/2022
Days, Time: MWF, 10:00AM-10:50AM
Location: Clemen 103, North Campus
Credit Hours: 3.00
Enrollment: 16/30 (16/30 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 610LEC registration number 23662 calendar icon | Course Catalog: CSE 610LEC orange catalog icon
CSE 610 advanced multimedia system (Lecture)
Section: YUAN
Instructor: Junsong Yuan
Description: Upon completion of this course, students should understand basic concepts of multimedia system, and the fundamental theories and design principles behind multimedia coding, processing, content analysis, retrieval, and standards. Emphases will be put on the visual data such as images and videos, as well as audio data such as music and speech signals, which are dominating the world’s internet traffic. Students will also be introduced to the recent research progresses in deep learning based approaches for multimedia content analysis. Students are also expected to acquire problem solving skills through course projects.
Notes: Students should be familiar with python, and have taken computer vision courses. The second half of the course could be research oriented. Students are encouraged to transfer the course project into publications.
Prereqs: CSE 473/573 computer vision and image processing
Instruction Mode: In person
Class #: 23663
Dates: 01/31/2022 - 05/13/2022
Days, Time: TR, 9:30AM-10:50AM
Location: Park 440, North Campus
Credit Hours: 3.00
Enrollment: 18/30 (18/30 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 610LEC registration number 23663 calendar icon | Course Catalog: CSE 610LEC orange catalog icon
CSE 701 Neurosymbolic Artificial Intelligence (Seminar)
Section: A
Instructor: Sargur N. Srihari
Description: Today's successful Artificial Intelligence models are largely based on neural models which draw their inspiration from networks in the biological brain and implemented using deep learning. They have eclipsed the knowledge-based approach in which the world is represented in the form of pre-determined symbols with inference based on logic and probabilistic reasoning. However the symbolic approach can better address current limitations of deep learning, e.g., adaptability, generalizability, robustness, explainability, abstraction, common sense, causal reasoning, etc. The use of both approaches in the same system has cognitive support. Such as fast and slow thinking, wherein deep learning plays the role of fast thinking and the symbolic approach plays the role of slow thinking. The seminar course covers cognitive theories of fast and slow thinking, robust artificial intelligence, parallel and sequential use of deep learning and causal reasoning and implementation issues such as attention and co-operating mulitagents. We will study current papers on these topics.
Notes: This seminar was first offered in Spring 2021. Topics covered at that time can be found at https://cedar.buffalo.edu/~srihari/CSE701/index.html. The 2022 edition will cover papers that have since appeared. Seminar participants will be expected to study one such paper and share their understanding with the others. There will be no compulsory projects or tests.
Prereqs: CSE 4/574
URL: https://cedar.buffalo.edu/~srihari/CSE701/index.html
Instruction Mode: Remote: real time and recorded
Class #: 22041
Dates: 01/31/2022 - 05/13/2022
Days, Time: W, 9:30AM-11:30AM
Location: Remote
Credit Hours: 1.00-3.00
Enrollment: 115/30 (112/30 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 701SEM registration number 22041 calendar icon | Course Catalog: CSE 701SEM orange catalog icon
CSE 705 Deep Learning on Graphs (Seminar)
Section: SARI
Instructor: Ahmet Erdem Sariyuce
Description: Graphs are everywhere. Their scale, rate of change, and the irregular nature pose many new challenges. Deep learning has been shown to be successful in a number of domains, ranging from images to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics. This seminar course covers recent papers in the last few years about deep learning on graphs. We will consider graph embeddings, knowledge graphs, graph kernels, graph neural networks, graph convolutional networks, graph adversarial methods. Students will learn the literature on deep learning on graphs, understand the state-of-the-art algorithms on various problems, and be familiar with the recent trends.
Notes: (The website is from last year and there'll be changes to the grading policy and the paper list)
Prereqs: It is assumed that students have a solid background on discrete mathematics and algorithms. Basic research skills like paper reading, critical thinking, problem solving, report writing, communication, and presentation are important as well.
URL: https://sariyuce.com/F20-701.html
Instruction Mode: In person
Class #: 24193
Dates: 01/31/2022 - 05/13/2022
Days, Time: W, 10:00AM-12:40PM
Location: Grein 134C/135C, Ellicott Complex
Credit Hours: 1.00-3.00
Enrollment: 48/48 (48/48 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 705SEM registration number 24193 calendar icon | Course Catalog: CSE 705SEM orange catalog icon
CSE 706 Emerging Biometrics and Mobile Authentication (Seminar)
Section: JIN
Instructor: Zhanpeng Jin
Description: This seminar course concentrates on the discussion of emerging biometrics and user authentication technologies on mobile and wearable devices. Given the increasing advances of mobile and wearable technologies in people’s daily life, many new biometrics and authentication techniques are being explored and developed. Specifically, this seminar will review, discuss, and characterize a set of emerging biometrics (e.g., behavioral patterns, user profiling, heart beats, brain activity, etc.). The seminar will also explore recent research efforts and commercially available techniques on mobile user authentication and discuss their applicability and limitations (e.g., fingerprint, face, iris, and voice, etc.). By the end of the seminar, students will learn how to evaluate (i.e., performance metrics) and design user authentication systems using biometrics. The following topics will be covered: 1. Emerging biometrics (physiological, bio-signals, behaviors, etc.). 2. Mobile user authentication techniques. 3. Comparison and evaluation of all available technologies. 4. Brainstorming and exploration of possible future biometric and authentication approaches.
Notes: This class will need a general background (and interest) in biometrics, mobile computing, cyber physical security, and Internet-of-Things (IoTs).
Prereqs: None
Instruction Mode: In person
Class #: 17405
Dates: 01/31/2022 - 05/13/2022
Days, Time: F, 10:00AM-12:40PM
Location: Park 440, North Campus
Credit Hours: 1.00-3.00
Enrollment: 40/40 (40/40 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 706SEM registration number 17405 calendar icon | Course Catalog: CSE 706SEM orange catalog icon
CSE 707 Quantum Simulations and Applications (Seminar)
Section: REGA
Instructor: Kenneth W. Regan
Description: The seminar will have twin purposes: to further the development of a quantum circuit simulator in C++ and to highlight the determining of whether "quantum advantage" in particular applications is inherent. A famous recent example where it was not inherent is described in the 2019 Communications of the ACM article "The Algorithm That Changed Quantum Machine Learning" (https://cacm.acm.org/magazines/2019/8/238339-the-algorithm-that-changed-quantum-machine-learning/fulltext). The latter subject will open the floor to presenting various postulated application areas in ML, computational finance, communication security, high-precision measurement devices, systems simulation, and computation overall. The possible synthesis with the former subject is whether the circuit simulator can be used to effect classical heuristic solutions in a generalizable and scalable manner.
Notes: Quantum gate and systems background will be supplied in the first month via excerpts from my textbook with Richard J. Lipton, Quantum Algorithms Via Linear Algebra. Purchase of the whole textbook will not be required. The theory of the simulator is fully expounded in the paper Regan-Chakrabarti-Guan, "Algebraic and Logical Emulations of Quantum Circuits" (https://www.semanticscholar.org/paper/Algebraic-and-Logical-Emulations-of-Quantum-Regan-Chakrabarti/e553affcdb26b54101c33f553d9399dea8b76945
Prereqs: College linear algebra, knowledge of C++.
URL: Look for updates at http://www.cse.buffalo.edu/~regan/
Instruction Mode: In person
Class #: 23199
Dates: 01/31/2022 - 05/13/2022
Days, Time: W, 4:10PM-6:50PM
Location: Norton 216, North Campus
Credit Hours: 1.00-3.00
Enrollment: 38/38 (38/38 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 707SEM registration number 23199 calendar icon | Course Catalog: CSE 707SEM orange catalog icon
CSE 709 Software Engineering Seminar (Seminar)
Section: A
Instructor: Weihang Wang
Description: In this seminar, we will discuss research papers on software engineering, including dynamic program analysis, static program analysis, symbolic execution, and software testing. Topics that will be covered include program tracing, program profiling, slicing, delta debugging, software model checking, symbolic execution, fuzzing, and concolic testing, etc. Each student will be presenting a paper or conducting a literature study. The workload for all students would be the same, regardless of the number of credits enrolled. However, students are free to choose to enroll in 1, 2, or 3 credits for the seminar.
Instruction Mode: In person
Class #: 21938
Dates: 01/31/2022 - 05/13/2022
Days, Time: F, 2:00PM-4:50PM
Location: Obrian 102, North Campus
Credit Hours: 1.00-3.00
Enrollment: 51/51 (51/51 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 709SEM registration number 21938 calendar icon | Course Catalog: CSE 709SEM orange catalog icon
CSE 710 Selected Topics in Computer Vision (Seminar)
Section: YUAN
Instructor: Junsong Yuan
Description: As a key research area of artificial intelligence (AI), Computer vision has great progress in the past decade, and is now providing exciting solutions for many real world problems, such as autonomous driving, video surveillance, e-commerce, robots. This seminar is a dive into the state-of-the-arts in computer vision, with a focus on deep learning and visual analytics. The course will be beneficial to students interested in understanding the recent literature in computer vision.
Notes: Grading Only satisfactory (S) and unsatisfactory (U) will be given. Students taking one credit requires to complete a survey of a specified computer vision topic. Students taking two credits need to complete a course project. Students taking three credits need to complete both a research survey and a course project. Project representation will be on the final two weeks. All students are required to attend the weekly seminar and participate the discussions on class.
Prereqs: Students should be very familiar with programming in python, and have taken the following courses before joining this seminar. Students should have background knowledge in both computer vision and deep learning. CSE 473/573 Computer Vision and Image Processing CSE 474/574 Introduction to Machine Learning CSE 410 Introduction to Deep Learning (or equivalent)
Instruction Mode: In person
Class #: 24197
Dates: 01/31/2022 - 05/13/2022
Days, Time: T, 2:00PM-4:30PM
Location: Davis 338A, North Campus
Credit Hours: 1.00-3.00
Enrollment: 35/34 (35/34 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 710SEM registration number 24197 calendar icon | Course Catalog: CSE 710SEM orange catalog icon
CSE 712 Deep Learning for Visual Recognition with Applications to Medical Imaging Analysis (Seminar)
Section: GAO
Instructor: Mingchen Gao
Description: Deep learning provides exciting solutions for visual recognition problems and has become a critical method for many applications. As a unique application of computer vision to provide computer-aided analysis, medical imaging analysis will be a focus of the course. There are still several vital challenges while deploying deep learning models in this life-critical area, such as the limited training data, distributed medical data, trustworthiness, fairness, transparency, and patient privacy. The course will be beneficial to students interested in understanding the massive amount of literature in medical imaging analysis, computer vision, and deep learning. Only satisfactory (S) and unsatisfactory (U) will be given. Each student will be expected to present 1-2 papers and lead discussions. Students taking two credits need to write a literature review report about a selected topic. Students taking three credits need to complete a course project in a group of up to three. The project proposal will be on the 7th week, and project representation will be on the final week.
Prereqs: Require fundamental knowledge about machine learning, computer vision, and deep learning. Prior knowledge about medical imaging is not necessary.
Instruction Mode: In person
Class #: 24200
Dates: 01/31/2022 - 05/13/2022
Days, Time: R, 2:00PM-4:40PM
Location: Obrian 209, North Campus
Credit Hours: 1.00-3.00
Enrollment: 48/48 (46/48 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 712SEM registration number 24200 calendar icon | Course Catalog: CSE 712SEM orange catalog icon
CSE 713 Wireless Networks Security, Principles and Practices (Seminar)
Section: UPAD
Instructor: Shambhu J. Upadhyaya
Description: The course includes several instructor presentations and student presentations. Further, students can investigate research problems or engage in projects - simulation based or hands-on experiments. Topics included are: Overview of Security Issues in Wireless Networks, WEP Security, WPA and RSN, Bluetooth Security, Security of MANETs, Security of Sensor Networks, Wireless Mesh Networks and Security, Trust in Wireless Networks, Vehicular Networks Security, Smart Grid Security and Security of Internet of Things (IoT). Most of the topics will be from research papers and Internet documents. Topics will be assigned to or selected by students who are required to study them, prepare presentations and discuss and critique them in the class.
Notes: The course webpage will be at: http://www.cse.buffalo.edu/~shambhu/cse71322/ and will be launched end of December.
Prereqs: Basic knowledge of computer security and networking concepts
Coreqs: None
URL: http://www.cse.buffalo.edu/~shambhu/cse71322/
Instruction Mode: In person
Class #: 20719
Dates: 01/31/2022 - 05/13/2022
Days, Time: T, 10:00AM-12:00PM
Location: Davis 113A, North Campus
Credit Hours: 1.00-3.00
Enrollment: 15/15 (15/15 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 713SEM registration number 20719 calendar icon | Course Catalog: CSE 713SEM orange catalog icon
CSE 715 Special Topics in Biometrics and IoT Security (Seminar)
Section: A
Instructor: Wenyao Xu
Description: This seminar course concentrates on the discussion of emerging biometrics and IoT security. After reviewing traditional biometrics, a set of emerging biometrics (e.g., soft biometrics, behavioral patterns, etc.) and IoT security technologies will be in-depth discussed. Also, the seminar will discuss recent research work on mobile user authentication, particularly focusing on biometrics-based approaches. By the end of the seminar, students will learn how to evaluate (i.e., performance metrics) and design user authentication systems using biometrics and IoT technologies.
Notes: If you are interested in biometrics, user identification and pattern recognition, this seminar is highly recommended.
Instruction Mode: In person
Class #: 22160
Dates: 01/31/2022 - 05/13/2022
Days, Time: M, 4:10PM-6:50PM
Location: Clemen 322, North Campus
Credit Hours: 1.00-3.00
Enrollment: 58/58 (58/58 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 715SEM registration number 22160 calendar icon | Course Catalog: CSE 715SEM orange catalog icon
CSE 730 Graph Orientations and their Applications (Seminar)
Section: HE
Instructor: Xin He
Description: • Seminar Title: Graph Orientations and its Applications • Pre-req: CSE 531 (with grade at least B+) or instructor's permission • Topics: Graph algorithms and applications. An orientationof an undirected graph G is an assignment of direction to the edges of G. We require orientation to satisfy certain conditions. These special orientations result in interesting combinatorial structures, which lead to elegant and efficient algorithms for solving certain problems from Graph theory, computational geometry, graph drawing. The seminar is research oriented and fairly theoretical in nature, focused on the problems I have been working on in the past few years. We will discuss a few open problems. • Time: Each Tue. 10:00 am - 12:00 noon. Place: Davis 338A. • Format: During the first half, I'll present material. Students read papers. Students will present papers in the second half. There might be few Homeworks. No projects, nor exams.
Prereqs: CSE531 with grade at least B+, or permission of instructor.
Instruction Mode: In person
Class #: 18606
Dates: 01/31/2022 - 05/13/2022
Days, Time: T, 10:00AM-12:00PM
Location: Davis 338A, North Campus
Credit Hours: 1.00-3.00
Enrollment: 20/20 (20/20 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 730SEM registration number 18606 calendar icon | Course Catalog: CSE 730SEM orange catalog icon