Niraj Jha

Niraj Jha is a Professor of Electrical and Computer Engineering at Princeton University, affiliated with the School of Engineering and Applied Science. He joined Princeton in 1987 and became a full professor in 1998. His research focuses on smart healthcare, machine learning algorithms, IoT security, and neuro-symbolic AI. He leads projects in wearable medical sensors for disease detection, transformer synthesis, and cybersecurity for cyber-physical systems. Jha has authored/co-authored over 480 papers, 25 patents, and multiple books, including Testing and Reliable Design of CMOS Circuits and Nanoelectronic Circuit Design. He has received prestigious awards like the ACM and IEEE Fellowships, and the Distinguished Alumnus Award from IIT Kharagpur.

Education:

• Ph.D., University of Illinois at Urbana-Champaign, 1985
• M.S., State University of New York at Stony Brook, 1982
• B.Tech., Indian Institute of Technology Kharagpur, 1981

Research Interests: Jha’s work bridges AI, hardware design, and healthcare. Key areas include:

• Predictive healthcare via wearable sensors and ML ensembles
• Transformer acceleration for edge computing
• Cybersecurity for IoT and 5G systems
• Neuro-symbolic AI for small-data learning
• Optimization via reinforcement learning

His lab explores body-area networks, synthetic control for clinical trials, and energy-efficient architectures.

Publications & Awards: Over 20 papers have won best paper awards. Notable recognitions include the Princeton Graduate Mentoring Award (2004) and the 2025 ACM recognition for Learning Interpretable Differentiable Logic Networks.

Advising & Labs: Jha advises graduate students on topics like neural architecture search (e.g., FlexiBERT) and healthcare AI (e.g., DOCTOR framework). He directs the Andlinger Center for Energy and the Environment and has led the Center for Embedded System-on-a-Chip Design. His lab collaborates on projects like SHF: Small grants for transformer synthesis and NSF-funded initiatives.

Labs & Teams: Active in Princeton’s Global Health Initiative and collaborates across disciplines, including robotics and data science. His research group develops tools like CODEBench (co-design frameworks) and TUTOR (decision-rule-based ML).