Director and Founder, Intelligent Systems Lab
Professor, Department of Engineering and Computer Science
Sonoma State University
Collaborations & inquiries: shrestsu@sonoma.edu
Sudhir builds intelligent systems for the real world, with a focus on robots that learn new skills from human demonstration and simulation.
He is a Professor in the Department of Engineering and Computer Science at Sonoma State University, where he founded the Intelligent Systems Lab (ISL) in 2017 and serves as its director. His research centers on robot learning and manipulation, including sim-to-real transfer, imitation learning, and vision-language-action models, with related work in intelligent sensing and embedded AI.
He created SSU's robotics and computer vision curriculum and developed the Engineering Robotics and AI Lab, which opened in Fall 2026. Through ISL, he has mentored more than 100 students.
He holds seven U.S. patents, has authored more than 35 peer-reviewed publications, and is an IEEE Senior Member. His work has been funded by NSF and CSU grants. He earned his Ph.D. at Louisiana Tech University, then completed postdoctoral training and served on the research faculty at Purdue University in Indianapolis before joining SSU.
Students, researchers, and industry partners interested in collaborations are welcome to get in touch.
The ISL at SSU conducts research in robot learning and manipulation, computer vision, embedded AI, and intelligent sensing, mentoring undergraduate and graduate students on applied research projects.
Visit sonomaisl.ai →Sim-to-real transfer learning, reinforcement learning for robotic arms, imitation learning, and vision-guided control.
Deep learning for image classification, diabetic retinopathy detection, autonomous navigation via natural language commands, and vision-language-action integration.
Microcontroller-based sensor platforms, RTOS implementations, on-device inference, and low-power systems for real-world health and environmental monitoring.
Non-invasive blood glucose monitoring using breath volatile organic compounds (VOCs), wearable sensor devices, machine learning models deployed on-device, and clinical data collection pipelines for diabetes management.
Research and professional highlights
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