Our lab welcomes motivated students and collaborators interested in computational neuroscience, neural engineering, physiological systems, machine learning, and feedback control.
Lab members work on interdisciplinary problems that combine mathematical modeling, data analysis, scientific computing, experimentation, and the development of adaptive therapeutic technologies.
Undergraduate students may participate through research projects, independent study, senior design, volunteer research, or funded opportunities when available.
Projects may involve scientific programming, data analysis, computational modeling, experimental measurements, literature review, and hardware development.
Graduate students may conduct thesis, project, or independent research in computational neuroengineering, physiological control, neural stimulation, machine learning, and related areas.
Students are expected to develop strong technical, analytical, and scientific communication skills.
We welcome collaborations with researchers, clinicians, educators, and engineers working in neuroscience, biomedical systems, control, artificial intelligence, physiological modeling, and experimental neurotechnology.
Closed-loop neurostimulation
Computational neuroscience
Neural synchronization and seizure control
Deep brain stimulation
Physiological modeling and control
EEG and neural-signal processing
Machine learning for biomedical data
Gait-event prediction
System identification and state estimation
Data-driven and model predictive control
Available projects depend on current lab priorities, student preparation, funding, and faculty capacity.
Successful lab members are curious, dependable, willing to learn, and comfortable working across disciplinary boundaries.
Interest in neuroscience, engineering, computation, or control
Commitment to regular research participation
Ability to communicate progress and challenges
Willingness to learn new mathematical and computational tools
Careful documentation and responsible research practices
Ability to work independently and collaboratively
Prior research experience is helpful but not required for every project. Preparation in programming, mathematics, statistics, signals and systems, control, machine learning, or physiology may be useful depending on the project.
Prospective students should send a concise email to gautam.kumar@sjsu.edu with the subject line:
Prospective Lab Member — [Your Name]
Please include the following details in the email:
Current degree program and expected graduation date
Research interests and why they are interested in the lab
Relevant coursework, programming, laboratory, or project experience
Weekly time commitment and anticipated duration
Résumé or curriculum vitae
Unofficial transcript, when relevant
Links to a portfolio, GitHub profile, publication, or project report, when available
Please review the Research and Publications pages before contacting the lab. Personalized messages that identify a specific area of interest are more helpful than general inquiries.
Learn about our current research, review our publications, and contact the lab with a focused description of your interests and preparation.