Ying Wu College of Computing is Honored to Announce Six New Faculty for 2025-2026
Ying Wu College of Computing (YWCC) proudly welcomes six recently appointed assistant professors to the departments of Computer Science and Data Science for the 2025-2026 academic year. These individuals all bring unique expertise that will transform research and experiential learning during a time of change amidst rapidly evolving technology.
NJIT-YWCC has a mission to keep pace with rapid advancements in the technology revolution across all computing disciplines and is poised to continue its forward trajectory with the addition of these new members of the faculty.
Their combined experience has innovated areas of software and systems security, AI/ML, computer vision, information theory, and human-robot interaction. They have been published in premier venues, including AAAI, IEEE, ICML and USENIX, received funding from DARPA, NSF and AFOSR to support their research, and received award recognition through ACM and PLDI, among many others.
Dean Jamie Payton said: "We are pleased to welcome six new faculty to the Ying Wu College of Computing. Their expertise—ranging from AI and machine learning to robotics and cybersecurity—expands our strengths in both emerging and foundational areas of computing. They will be key contributors to advancing research discoveries that have meaningful societal impacts, creating transformative learning experiences for our students, and strengthening our role as a leader in the rapidly evolving tech landscape."
YWCC looks forward to what these newly minted assistant professors will contribute to pioneering research and discovery across disciplines, expanding opportunities for practice-based learning, and joining its faculty and students at all degree levels in providing real-world solutions to some of modern society’s most urgent challenges.
Find out more about the newest members of the YWCC community:
Computer Science
Shivvrat Arya (Ph.D., University of Texas at Dallas)
His research lies at the intersection of Artificial Intelligence and Machine Learning, with emphasis on neuro-symbolic AI, probabilistic reasoning, explainable AI, and combinatorial optimization. The overarching goal of his work is to develop interpretable and trustworthy AI systems that combine structured domain knowledge with powerful data-driven learning. His research spans graph-based inference and symbolic reasoning, with applications in computer vision and natural language processing, particularly video understanding, activity recognition, and language reasoning.
Research Highlights:
- Developed NeuPI, a neural inference engine for probabilistic models
- Built real-time AR guidance systems for complex physical tasks
- Released CaptainCook4D, an egocentric 4D dataset for procedural task understanding
Tsung-Chi Lin (Ph.D., Worcester Polytechnic Institute))
Tsung-Chi Lin was a Postdoctoral Fellow in the Department of Computer Science at Johns Hopkins University, affiliated with the Malone Center for Engineering in Healthcare. He completed his Ph.D. in Robotics Engineering at Worcester Polytechnic Institute. His research focuses on advancing human- robot interaction to enable the seamless integration of robots into daily life. By developing algorithms and interfaces that empower end-users with diverse abilities, his work contributes to the development of adaptive and collaborative robots for real-world applications. He has published in top Robotics (e.g., RA-L, ICRA, IROS) and Human-Robot Interaction (e.g., THRI) venues and actively serves the community as a workshop organizer (e.g., RSS) and referee.
Completed Research:
- Wearable Walking Assistive Exoskeleton Robot – Won an R&D 100 Award in 2016
- Hip Joint Based Walking Device
Current Research:
- Designing Versatile Human-Robot Interfaces
- Modeling User States for Workload Adaptation
- Developing Effective Training Protocols for End-Users
Kieran Murphy (Ph.D., University of Chicago)
Kieran Murphy recently completed his postdoc at the University of Pennsylvania, where his research spanned the intersection of machine learning, information theory and physics.
Before this, he studied the physics of amorphous materials during a Ph.D. at the University of Chicago, which included 3D-printing around 100,000 bespoke grains of sand. He also spent 1.5 years in New York City as part of Google's AI Residency program, where he developed methods in computer vision and representation learning.
My research focuses on designing information processing systems around lossy compression. I use representation learning as a foundation and information theory as a guide, building algorithms that distill high-dimensional data into reduced descriptions.
This design-first perspective bridges machine learning and complex systems: by engineering tools that compress data in principled ways, I aim both to build new interpretability into AI and to uncover organizing principles in fields ranging from biology to physics.
Haotian Zhang (Ph.D., University of Texas at Arlington)
Haotian Zhang is a researcher with expertise in software and systems security. His research focuses on binary code analysis, malware detection, and the application of machine learning techniques to cybersecurity challenges. He has published his work in premier venues, including ASPLOS, USENIX Security, and CCS, and has received multiple awards, such as recognition in the ACM and PLDI Student Research Competitions. His work bridges foundational program analysis with advanced machine learning methods to tackle pressing issues in software security and reliability.
I am fueled by code, curiosity, and a dash of optimistic nihilism.
Data Science
Yingcong Li (Ph.D., University of Michigan)
Prior to doctoral work at UMich, Yingcong Li worked with Samet Oymak at the University of California, Riverside (UCR) starting in Fall 2020. She earned her bachelor’s degree from the University of Science and Technology of China (USTC) in 2019 and her master’s degree from UCR in 2020.
My research focuses on developing impactful machine learning methods and uncovering their underlying mechanisms. I currently focus on the mathematical understanding of sequence models and exploring the emergent behaviors in generative models. I am always open to new research directions in advancing AI and reducing human labor.
In my free time, I enjoy hiking, rock climbing, and trying out new activities (and, of course, researching).
Thanh Nguyen-Tang (Ph.D., Deakin University, Australia)
Prior to joining NJIT, Thanh Nguyen-Tang was a postdoc at Johns Hopkins University, did his M.Sc. in Computer Science and Engineering at Ulsan National Institute of Science and Technology, South Korea, and his B.Eng. in Electronic and Communication Engineering (Talented Engineering Program) at Danang University of Science and Technology, Vietnam. He was awarded the Alfred Deakin Medal for Doctoral Theses, 2022.
I am mostly interested in the theoretical and algorithmic aspects of machine learning motivated by real-world problem settings. I like to seek a mathematical understanding of the underlying algorithmic principles for learning with strong adaptivity to problem structures and thereby design efficient machine learning algorithms with strong theoretical guarantees. The current topics include sequential decision-making (RL, bandits, games), responsible AI and reasoning.