Stanford University
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Nitish Ranjan Sarker
Postdoctoral Scholar, Civil and Environmental Engineering
BioNitish Ranjan Sarker is a Postdoctoral Scholar at Stanford University, where he contributes to the design, execution, and evaluation of an Industrial, Agricultural, and Water FlexHub Demonstration Pilot Project. His current research focuses on developing data-driven decision-support tools for sustainable water and energy systems, integrating experimental and pilot-scale data with technoeconomic analysis (TEA) to guide system design, deployment strategies, and policy recommendations.
Nitish earned his Ph.D. in Mechanical and Industrial Engineering from the University of Toronto, where his work combined laboratory-to-pilot experimentation, systems modeling, and field validation to advance resilient and affordable water technologies. Prior to that, he completed his M.Sc. in Mechanical Engineering at the University of Alberta and his B.Sc. in Mechanical Engineering at the Bangladesh University of Engineering and Technology (BUET). His research portfolio spans off-grid solar desalination, oil-water separation and spill response technologies, and distributed water quality monitoring tools for decentralized systems. Beyond research, Nitish has engaged in interdisciplinary training and global capacity-building initiatives in Canada, Mexico, Kenya, Bangladesh, India, and France, advancing the water‑energy‑health nexus and sustainable technology adoption from lab to field. He also co-founded FRODO, a venture translating foam-based oil-water separation research into deployable spill response and produced water treatment solutions, bridging lab innovation and early commercialization. -
Moritz Schaefer
Postdoctoral Scholar, Immunology and Rheumatology
Current Research and Scholarly InterestsI build AI systems that can be asked a question about biomedical data in plain language and show their work well enough to be checked. CellWhisperer (Nature Biotechnology, 2025) links a million RNA-sequencing profiles to the text describing them, so single-cell data can be explored by asking rather than coding. SpatialWhisperer (ICML 2026) carries this into histopathology. I pair these models with AI agents reasoning across imaging, genomics and the clinic to explain why CAR T therapy fails.
