The people behind CASE

Marcus Noack is the original founder of CASE. His background spans geophysics, applied mathematics, function optimization, stochastic processes, dimensionality reduction, and high-performance computing. At Berkeley Lab, his work supports the autonomous experimentation community through practical software and support, and his contributions earned him the 2022 Director's Award for Exceptional Early-Career Achievements.

Aldair Gongora works in the Materials Engineering Division at LLNL. His research sits at the confluence of machine learning, robotics, automation, and advanced manufacturing, with a clear focus on accelerating scientific discovery by designing and deploying modern research laboratories for materials, climate, energy, life sciences, and sustainable manufacturing.

Bappaditya Dey develops and optimizes deep learning algorithms and architectures for challenging industrial problems in EUV/EBEAM lithography and SEM metrology. He also supports interdisciplinary collaboration with universities and research teams across the globe while mentoring students working on research theses.

Jose Lugo-Martinez is an Assistant Professor in the Computational Biology Department at CMU and co-Director of the M.S. in Automated Science. His research develops computational approaches that accelerate biomedical knowledge discovery through automated and autonomous science, building directly on his training in computer science and bioinformatics.

Anudha Mittal holds a Ph.D. in chemical engineering from the University of Minnesota and a B.S. in chemical engineering from the University of Massachusetts, Amherst. She previously worked as a Senior Materials Engineer at the Naval Nuclear Lab, then moved into data science and machine learning through contract and consulting work.

Jonathan Potter builds laboratory automation infrastructure and computational pipelines for clinical drug sensitivity assays. His work spans liquid-handler orchestration and NMF/ALS-based analysis of patient drug response matrices, and he is the author of Forge, an open-source visual pipeline builder for reproducible data science workflows compatible with AI agents via MCP.

Peneeta Wojcik first became interested in laboratory automation while developing protocols for liquid handlers such as the OpenTrons OT-2 and designing closed-loop experiments in the integrated Thermo Fisher Momentum system. Her current research focuses on automated design of DNA oligos and protein structures, alongside frameworks for lab automation such as SiLA 2.

Sina Barazandeh works at the intersection of machine learning, reinforcement learning, simulation, and foundation models, with a focus on advancing automated science and cloud laboratories. He is particularly interested in applications such as biomarker discovery, protein representation learning, and generative AI for biological systems.