This course prepares new doctoral students for success as students, researchers, and mentors. Covers the processes, best practices, and conventions of doctoral studies and computer science research including course of study, critical surveys, experimental design, data management, proposals, publication, technical writing, and presentation. Computer science research fields and interdisciplinary applications are surveyed, with an emphasis on collaborative research design for inclusion and outreach to undergraduates and other disciplines.

This course prepares new doctoral students for success as students, researchers, and mentors. Covers the processes, best practices, and conventions of doctoral studies and computer science research including course of study, critical surveys, experimental design, data management, proposals, publication, technical writing, and presentation. Computer science research fields and interdisciplinary applications are surveyed, with an emphasis on collaborative research design for inclusion and outreach to undergraduates and other disciplines.

Selected research topics in computer science and interdisciplinary applications presented by faculty, students, and outside speakers.

Selected research topics in computer science and interdisciplinary applications presented by faculty, students, and outside speakers.

Selected research topics in computer science and interdisciplinary applications presented by faculty, students, and outside speakers.

Selected research topics in computer science and interdisciplinary applications presented by faculty, students, and outside speakers.

Examines classes of languages and computation models. The topics include finite state automata, pushdown automata, Turing machines and the Chomsky hierarchy of formal languages, decidability and undecidability theory, and computational complexity theory, which includes NP-Completeness and Reductions.

Advanced topics in data structures and algorithms, including divide and conquer method, dynamic programming, greedy method, local search, polynomial time reductions, NP-complete problems, approximation algorithms and randomized algorithms. Applications of various algorithms and data structures will be discussed and implemented.

Examines classes of languages and computation models. The topics include finite state automata, pushdown automata, Turing machines and the Chomsky hierarchy of formal languages, decidability and undecidability theory, and computational complexity theory, which includes NP-Completeness and Reductions.

Advanced topics in data structures and algorithms, including divide and conquer method, dynamic programming, greedy method, local search, polynomial time reductions, NP-complete problems, approximation algorithms and randomized algorithms. Applications of various algorithms and data structures will be discussed and implemented.

In-depth treatment of operating systems concepts. Covers process and processor management, primary and secondary storage, system performance, network considerations spanning local and wide area networks, and system security. Includes a programming project on concurrent resource management.

Covers trends and measuring and reporting of improvements in computer technology; instruction set principles that contains evolution of instruction systems, and reduced instruction set architecture (RISC); CPU design that covers pipeline system, dynamic scheduling and thread-level parallelism; and memory system that includes cache design and memory hierarchy.

Studies models, architectures, languages, and algorithms of parallel computing. Topics include parallel computing models, algorithm designs, software tools, parallel architectures, and performance evaluation.

In this course, the theory and practice of neural computation for machine learning are introduced. Starting with feed forward neural networks, more complicated multi-layered "deep" networks are then covered, including basic back-propagation, gradient descent and modern regularization techniques. The class will look at modern deep learning techniques: convolutional neural networks, deep belief networks and deep recurrent neural models. The course also provides acquaintance with some of the software libraries available for building and training deep neural networks.

This course provides an introduction to computational and statistical approaches to analyzing data. This Data Science course is designed to provide a comprehensive introduction to the field of data science. The course covers a wide range of topics including data and distributions, statistical inference, assessing pairwise relationships, and machine learning. In the first part of the course, students will learn about the basics of data and distributions, including measures of central tendency and variability, probability distributions, and statistical tests. They will also learn how to visualize data using plots and charts, and how to use statistical software to analyze data. The second part of the course focuses on statistical inference, including estimation and hypothesis testing. Students will learn about sampling distributions, confidence intervals, and p-values, and how to use these concepts to make inferences about populations based on samples. In the third part of the course, students will learn about assessing pairwise relationships, including correlation and mutual information. They will learn how to use these techniques to examine the relationship between two or more variables. Finally, the course concludes with an introduction to machine learning. Students will learn about the various types of machine learning, including supervised and unsupervised learning, and will explore the use of machine learning algorithms for prediction and classification.

As a multidisciplinary field, draws on work from areas including database technology, artificial intelligence, machine learning, neural network, statistics, information retrieval, and data visualization. Theoretical and practical methods will be presented on knowledge discovery and systems design and implementation.

This course provides an introduction to computational and statistical approaches to analyzing data. This Data Science course is designed to provide a comprehensive introduction to the field of data science. The course covers a wide range of topics including data and distributions, statistical inference, assessing pairwise relationships, and machine learning. In the first part of the course, students will learn about the basics of data and distributions, including measures of central tendency and variability, probability distributions, and statistical tests. They will also learn how to visualize data using plots and charts, and how to use statistical software to analyze data. The second part of the course focuses on statistical inference, including estimation and hypothesis testing. Students will learn about sampling distributions, confidence intervals, and p-values, and how to use these concepts to make inferences about populations based on samples. In the third part of the course, students will learn about assessing pairwise relationships, including correlation and mutual information. They will learn how to use these techniques to examine the relationship between two or more variables. Finally, the course concludes with an introduction to machine learning. Students will learn about the various types of machine learning, including supervised and unsupervised learning, and will explore the use of machine learning algorithms for prediction and classification.

As a multidisciplinary field, draws on work from areas including database technology, artificial intelligence, machine learning, neural network, statistics, information retrieval, and data visualization. Theoretical and practical methods will be presented on knowledge discovery and systems design and implementation.

Computational Geometry is the branch of Computer Science concerned with the design, analysis, and implementation of efficient algorithms for solving problems by exploiting their geometric structures. Geometric structures are often described in terms of elementary geometric objects such as points, lines, curves, polygons, polyhedra, and surfaces. The course focuses on the algorithmic complexity of problems and their applications in robotics, computer-aided design, and geographic information systems (GIS).

This research course will be based on lectures, research materials reading and projects. The lecture part includes: 1). introduction to graph model and graph measures, 2). graph deep learning methods, 3). neuroimaging data and data processing, 4). graph deep learning on neroimaging data analysis, disease predictions and other related research topics. The related reading materials will be assigned to students in class. The students should read, understand, and present the materials that they are assigned to read. Also, two discussion classes will be organized for research brain storm. A final project will be assigned to students to train their practical ability on this topic.

The course will involve reading papers and discussions, as well as independent research projects on swarm robotics, self-organized systems, and decentralized robot teams. Students from all fields, including but not limited to CS, mechanical, and electrical engineering, are encouraged to join. This is a highly interdisciplinary class, and we have very few prerequisites. Most of the discussion topics will be rooted in robotics and algorithms. Students are challenged to form unorthodox teams and conduct projects outside of their comfort zone (within the field of multi-robot and multi-agent systems).

Computation exists beyond the world of digital electronics. It is occurring in every biological cell, in the neurons in your brain, and even among most basic components of the universe. This course covers several models of computation that are each meant to capture the behavior of these molecular/nano-scale systems. Students will devise algorithms, analyze constructions, and prove theorems for each of these models. Topics will include the theory of chemical reaction networks, computing with thermodynamics, molecular self-assembly, and more.

This PhD-level course explores deep learning algorithms with a focus on applications in medical imaging. The course is organized into four parts: 1. Foundations – introduction to foundational concepts in deep learning and medical imaging, including regression models, convolutional neural networks, and transformers, etc. 2. Implementation – hands-on training in PyTorch, with in class coding assignments that build and evaluate key architectures. 3. Critical Reading – discussion of seminal and state-of-the-art research papers in medical imaging. 4. Research Project – a semester-long research project culminating in a conference-style presentation and manuscript submission. By the end of the course, students will gain theoretical knowledge, practical coding experience, and research skills necessary to advance the field of deep learning in medical imaging.

This course will teach the theory in advanced time series data mining tools. The class will first discuss two classical time series data mining problems: motif discovery and anomaly detection. In the last half of the semester, we will discuss several advanced representation learning approaches for time series. Meanwhile, we will discuss the challenges we faced when handling time series data.

This class explores the research frontier of intelligent security systems, focusing on the merger of artificial intelligence, data management, and cybersecurity. The class emphasizes a deep review of current literature, critical analysis of state-of-the-art methodologies, and the identification of open research questions. Topics include Federated Learning, AI-driven intrusion detection, advanced firewall design, malware analysis, hacking recognition, and adversarial attacks against ML systems. Students will conduct comprehensive literature reviews, lead discussions, and formulate novel research proposals.

Systems biology is a field that aims to understand the interactions and behaviors of biological systems at various levels. It combines concepts and tools from various disciplines to study the behavior and function of biological systems in a holistic manner. In a systems biology course, students will learn how to model, analyze, integrate, and interpret omics data. They will use computational and statistical approaches to identify patterns in large datasets and apply these techniques to understand biological systems and solve real-world problems such as biomarker and drug target identification. Students will learn to use tools and software packages to analyze and interpret data and will work on hands-on projects and case studies to apply their skills. They will also engage in discussions and collaboration with peers and their instructor, and develop critical thinking, problem-solving, and communication skills.

This course will teach the theory of advanced data mining and machine learning. The class consists of two components: the advanced classification and clustering approaches, and the key challenging problem in recent data mining research: scalability vs. performance tradeoff. Students will learn how to analyze and design data mining and machine learning models as well as gain some hands-on experience in how to implement the models.

This class covers the fundamentals of autonomous mobile robots and basic programming for mobile robots. The topics include locomotion, sensors, image processing, localization, motion planning, and swarm robotics. It also provides several practical sessions with hands-on algorithm development in coordinating multi-robot systems and robot swarms. You will have the chance to export your algorithms into physical robots if your algorithms perform efficiently in a simulation. The students will undertake assignments in theory, mini projects in programming, and presentations in their final projects.

This course explores the intersection of Artificial Intelligence, data management, and cybersecurity, focusing on the application of intelligent systems within modern computing. Students examine the foundations of how ML/AI methodologies, such as anomaly detection and pattern recognition, are used to build robust security applications and enhance system integrity. The curriculum also investigates how these technologies are used against systems, detailing how adversaries utilize adversarial machine learning, including data poisoning and evasion attacks, to compromise defenses. Federated learning is included as a special topic to address the security and robustness of decentralized training environments. By bridging these domains, the course prepares students to navigate the complexities of safety-critical deployments where the synergy between intelligent automation and security is essential.

In this course, we will learn and implement basic theory and practical algorithms of reinforcement learning (RL) to be capable of understanding research papers on RL and able to develop a new RL algorithm for a given problem. The course will focus on state of the art techniques including Deep Q-Learning, Policy Gradient, and Actor Critic. After studying basic algorithms, current scientific topics selected from recent Deep Reinforcement Learning research papers will be discussed.

This course provides an introduction and overview of Algorithmic Game Theory (AGT) Complexity and Combinatorial Game Theory (CGT). The topics include hardness results and complexity classes, game positions and trees, nimbers and surreal numbers, constraint logic, motion planning gadgets, and research/collaboration methodologies.

This course will be based on lectures and research materials reading. The lecture part includes deep neural networks, deep convolution neural networks, neural network optimization, training, image classification /segmentation with neural networks, attention and transformers, and medical image with deep learning special issue. The materials related to deep learning on computer vision will be assigned to students in class. The students should read, understand, and present the paper that they are assigned to read. Several outstanding researchers in the related fields will be invited to provide a zoom talk to provide research frontiers to the students in class.

This class covers the fundamentals of autonomous mobile robots and basic programming for mobile robots. The topics include locomotion, sensors, image processing, localization, motion planning, and swarm robotics. It also provides several practical sessions with hands-on algorithm development in coordinating multi-robot systems and robot swarms. You will have the chance to export your algorithms into physical robots if your algorithms perform efficiently in a simulation. The students will undertake assignments in theory, mini projects in programming, and presentations in their final projects.

Computation beyond digital electronics—molecular, biological, and physical systems. Topics include chemical reaction network theory, thermodynamic computing, and molecular self-assembly, with emphasis on algorithm development and theoretical proof.

In this course, we will learn and implement basic theory and practical algorithms of reinforcement learning (RL) to be capable of understanding research papers on RL and able to develop a new RL algorithm for a given problem. The course will focus on state of the art techniques including Deep Q-Learning, Policy Gradient, and Actor Critic. After studying basic algorithms, current scientific topics selected from recent Deep Reinforcement Learning research papers will be discussed.

This course will be based on lectures and research materials reading. The lecture part includes deep neural networks, deep convolution neural networks, neural network optimization, training, image classification /segmentation with neural networks, attention and transformers, and medical image with deep learning special issue. The materials related to deep learning on computer vision will be assigned to students in class. The students should read, understand, and present the paper that they are assigned to read. Several outstanding researchers in the related fields will be invited to provide a zoom talk to provide research frontiers to the students in class.

This course covers the basic techniques used in acquiring, processing and displaying of digital images and video. Topics include image acquisition, spatial and frequency domain representation, image filtering, image compression, image analysis, morphological image processing and image understanding. Efficient implementation of image processing algorithms in a structured computer language is emphasized.

Examines the creation and development of advanced information and computational techniques for problems in the biosciences, including biology, biochemistry, biotechnology, and medicine. Presents advanced concepts and techniques of bioinformatics and computational biology tools to solve problems in topics such as sequence alignment, gene and motif finding, restriction mapping, microarray data analysis and gene expressions.

Focuses on distributed database systems. Includes file allocation, directory systems, deadlock detection and prevention, synchronization, query optimization, and fault tolerance. The course will include one or more programming projects demonstrating implementation of concepts introduced.

This course covers introductions to both cryptography and network security. Foundations of cryptography include block ciphers, public key encryption, key management, and hashing functions. The foundation part of network security includes an in-depth review of commonly-used security mechanisms and techniques, security threats, applications of cryptography, authentication, security protocols, web security, etc.

This course covers the basic techniques used in acquiring, processing and displaying of digital images and video. Topics include image acquisition, spatial and frequency domain representation, image filtering, image compression, image analysis, morphological image processing and image understanding. Efficient implementation of image processing algorithms in a structured computer language is emphasized.

Focuses on distributed database systems. Includes file allocation, directory systems, deadlock detection and prevention, synchronization, query optimization, and fault tolerance. The course will include one or more programming projects demonstrating implementation of concepts introduced.