Machine Learning and AI
Monica Bugallo
Professor
Bayesian Estimation and Inference, Monte Carlo Methods, Machine Learning and Applications, Computational Methods for Data Analysis, Engineering Education and Workforce Development, K–12 STEM Education, Educational Access and Opportunities in STEM
Diego Cuji
PRODIG+ Postdoctoral Fellow
diego.cujidutan@stonybrook.edu
Adaptive Signal Processing and Communication Systems, Underwater Acoustics, Communication in High Doppler-distorted Channels, Algorithms for Machine Learning
Petar Djuric
Distinguished Professor
Prof. Djurić's research has primarily focused on investigating the fundamentals of machine learning and signal and information processing with a diverse range of interests within them including causal inference theory, information fusion, Bayesian optimization, Monte Carlo methods, distributed signal processing, autonomous agents, open world recognition, signal modeling, as well as detection and estimation. He applies the newly developed knowledge across various disciplines, including medicine and radio frequency identification.
Alex Doboli
Professor
Neural architectures; prompt engineering; AI-based engineering, including code generation (C, VHDL, Verilog); AI-based circuit design; real-time edge computing.
Ji Liu
Associate Professor
Distributed Control and Optimization, Decentralized Reinforcement Learning, Distributed Quantum Computing, Network Epidemiology, Multi-Agent Systems, Network Science
Vibha Mane
Assistant Professor of Practice
Machine learning methods, with interests in statistical and probabilistic modeling, topic modeling, and real-world applications.
Emre Salman
Professor
Reliable AI systems for space and other extreme environments, energy-efficient AI hardware.
Yuanyuan Yang
Distinguished Professor
Investigate the recovery capabilities of large language models (LLMs) for mathematical reasoning, focusing on how LLMs can learn from incorrect reasoning steps and recover by generating appropriate corrections. The resulting insights of our work will advance the development of LLMs that are more robust, self-correcting, and reliable for mathematical reasoning.
Hyeongrak "Chuck" Choi
Assistant Professor
AI-enabled autonomous quantum experiments, physics-aware digital twins, AI-native quantum sensing and inference.
Jorge Mendez-Mendez
Assistant Professor
jorge.mendezmendez@stonybrook.edu
Lifelong learning, embodied AI, reinforcement learning.
Andrew Singer
Dean, College of Engineering and Applied Sciences & Professor
Andrew.C.Singer@stonybrook.edu
Signal Processing and Communication Systems, Algorithms for Machine Learning and Sequential Decision Making, Underwater Sensing and Signal Processing, Audio and Speech Signal Processing, Augmented Listening Systems
Murali Subarrao
Professor
murali.subarrao@stonybrook.edu
Research on Computer vision and Robot vision. Physics informed neural nets (PINNs).
Shan Lin
Associate Professor
Machine learning algorithms, generative models, and LLM applications to extract actionable insights from multi-modal data. Learning digital biomarkers, emotional cues, and closed-loop treatment policies. Physics-informed, human-centered AI for digital health monitoring and urban service analytics.
Wendy Tang
Associate Professor & Associate Chair
Parallel and distributed processing; massively parallel systems; computer architecture; neural networks
Lav R. Varshney
Della Pietra Infinity Professor / Director, AI Innovation Institute
Foundations, applications, governance, and ethics of AI.
Fan Ye
Professor & Graduate Program Director
AI algorithms and infrastructure for health sciences.
Peter Milder
Associate Professor
Reliable AI in extreme environments. Efficient hardware and software for AI.
Ji Liu
Associate Professor
Developing distributed multi-agent algorithms for learning, decision-making, and resilient coordination, as well as AI methods for mathematical discovery and reasoning.
Peng Zhang
SUNY Empire Innovation Professor
AI-enabled, programmable smart grids.
Yifan Zhou
Assistant Professor
Verifiable and physics-informed AI for power grids.
Anton Selitskiy
Professor of Practice
anton.selitskiy@stonybrook.edu
Mathematically grounded artificial intelligence; robust, reliable, interpretable, and secure AI; speech and multimodal AI; generative models for audio; synthetic and manipulated media detection; speaker verification and biometric authentication.