The Organizing Committee

Marcus M. Noack, Chair

Research Scientist, Applied Math and Comp. Research Division, LBNL, USA

Marcus Noack got his master's degree in geophysics from Friedrich-Schiller University in Jena, Germany. Working as a Ph.D. candidate at Simula Research Laboratory in Oslo, he was able to pursue his interests in the theory of wave propagation and mathematical function optimization. There, Marcus leveraged his knowledge of theoretical and numerical physics and applied mathematics and connected it with high-performance computing to create efficient methods to model wave propagation and solve non-linear inverse problems. He graduated with a Ph.D. in applied mathematics from the University of Oslo. Starting at Lawrence Berkeley National Laboratory as a Post Doc, Marcus worked on uncertainty quantification, stochastic function approximation, and autonomous experimentations.

Now, as a Research Scientist, Marcus is continuing this line of work with a focus on stochastic processes for function approximation and dimensionality reduction, function optimization, and high-performance computing while serving the autonomous-experimentation community by providing support and practical software. This work has earned him several awards, most notably the 2022 Director’s Award for Exceptional Early-Carreer Achievements. He is the original founder of CASE and can be reached by email at MarcusNoack@lbl.gov

Aldair E. Gongora

Staff Scientist, Lawrence Livermore National Laboratory, USA

Aldair Gongora is a Staff Scientist at Lawrence Livermore National Laboratory in the Materials Engineering Division. His research work is at the confluence of machine learning, robotics and automation, and advanced manufacturing. He is interested and focused on accelerating scientific discovery by designing, building, and deploying the next generation of research laboratories that leverage modern tools and capabilities to address critical challenges in materials science, climate and energy sciences, life sciences, and sustainable manufacturing. Aldair holds a B.S. in mechanical engineering from Rockhurst University and a Master’s and Ph.D. in mechanical engineering from Boston University, where he conducted research on autonomous experimentation for mechanical design.

Bappaditya Dey

Senior R&D Engineer, imec, Belgium

Bappaditya Dey is a Senior R&D Engineer, Advanced Patterning at imec, Belgium. He is also responsible for developing and optimizing deep learning algorithms and architectures for solving challenging industrial problems in EUV/EBEAM Lithography and SEM Metrology as well as interdisciplinary research collaboration with multiple universities and research teams (across the globe) and mentoring students for their research thesis (MS/PhD) in this domain.

He joined imec in 2018 and worked in various roles since then. He received his PhD degree, majoring in Computer Engineering, from the Center for Advanced Computer Studies (CACS), University of Louisiana at Lafayette, USA, in 2022. He received his B.Sc. and M.Sc. degrees in Physics (Hons.) and Electronic Science from University of Calcutta, Kolkata, India, in 2006 and 2008, respectively, and the second M. Tech degree in Computer Sc. and Engg. from MAKAUT (formerly known as WBUT), Kolkata, India in 2010. He also received his third M.Sc. degree in Computer Engineering from the Center for Advanced Computer Studies (CACS), University of Louisiana at Lafayette, USA in 2017. He is a member of IEEE and SPIE. His research interests include VLSI, microelectronics, reconfigurable hardware, machine learning, computer vision, artificial intelligence, and semiconductor process optimization. Till now, he has authored/co-authored 40 publications and has presented at several international conferences.

Jose Lugo-Martinez

Assistant Professor, Carnegie Mellon University (CMU), USA

Jose Lugo-Martinez is an Assistant Professor in the Computational Biology Department at the School of Computer Science at Carnegie Mellon University (CMU). He also serves as co-Director of the M.S. in Automated Science at CMU. Prior to that, he was a Lane Fellow at CMU, co-hosted by Professors Ziv Bar-Joseph and Robert F. Murphy. Dr. Lugo-Martinez received a PhD degree in computer science with a minor in bioinformatics from Indiana University (IU) under the supervision of Professor Predrag Radivojac. His research aims at the development of computational approaches to accelerate biomedical knowledge discovery through automated and autonomous science.

Anudha Mittal

Various Roles

Dr. Anudha Mittal has a Ph.D. in chemical engineering from the University of Minnesota, Minneapolis, and a B.S. in chemical engineering from the University of Massachusetts, Amherst. She was a Senior Materials Engineer at the Naval Nuclear Lab, Niskayuna, NY, from 2014 to 2019. She shifted to the data science and machine learning domain and has worked in different software contract and consulting roles, 2020 - present. Her outside work interests are reading fiction, creative writing, and hiking. The mountains closest to her heart are the ones surrounding Lake George and the Adirondacks.

Jon Potter

Automation and Software Engineer, Predictive Oncology (AGPU), USA

Jonathan Potter is a Software and Automation Engineer at Predictive Oncology in Pittsburgh, PA, where he builds laboratory automation infrastructure and computational pipelines for clinical drug sensitivity assays. His work spans liquid handler orchestration - optimizing plate layouts and instrument operations across multiple instruments for parallel patient–drug experiments - to NMF/ALS-based analysis of patient drug response matrices for identifying latent biological programs predictive of drug sensitivity and resistance in ovarian cancer. He is the author of Forge, an open-source visual pipeline builder for reproducible data science workflows, compatible with AI agents via MCP.

Jon holds a Master of Science in Automated Science from Carnegie Mellon University and a Bachelor of Science in Biological Systems Engineering from the University of California, Davis, graduating Magna Cum Laude. His graduate work included a collaboration with Lawrence Livermore National Laboratory to build an experiment preparation and LIMS-updating robot using a 6 DoF Kinova arm, and a capstone with Generate Biomedicines applying machine vision to detect clogged pipette nozzles on automated liquid handling equipment. He won the 2024 Nucleate Pittsburgh Biohackathon building an LLM-powered symbolic query generator for clinical trial cohort selection in 48 hours, and was selected as an MVP Poster Presenter at SLAS 2026 for co-designing an introductory automated laboratory curriculum for CMU Pre-College scholars.

His interests lie at the intersection of agentic scientific systems, autonomous experimentation, and machine learning - and the question of how tightly coupling these with physical laboratory infrastructure can compress the cycle time of biomedical discovery.

Peneeta Wojcik

CPCB Ph.D Student, Carnegie Mellon University (CMU), USA

Peneeta Wojcik is a CPCB Ph.D. student starting Fall 2026. Before that, she completed a Masters in Automated Science at Carnegie Mellon University. She first realized her interest 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 in her coursework.

Her current research involves automated design of DNA oligos and protein structures, which combines in silico modeling and machine learning with automated lab protocols. She is also interested in frameworks for lab automation (i.e. SiLA 2) and how multiple lab instruments can be integrated and optimized for specific protocols. Outside of research, she enjoys playing guitar, crocheting, and creating small front-end web dev projects.

Sina Barazandeh

Ph.D Student, Carnegie Mellon University (CMU), USA

Sina Barazandeh is a second-year PhD student in Computational Biology at Carnegie Mellon University’s School of Computer Science, advised by Dr. Jose Lugo-Martinez. His research lies 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 developing intelligent systems for scientific discovery, with applications in biomarker discovery, protein representation learning, and generative AI for biological systems. Sina has contributed to multiple publications in computational biology and generative modeling for biological sequences. He received his M.Sc. in Computer Engineering from Bilkent University, where his work focused on generative models and LLM-based approaches for genomic sequence design.

Thank you to our past organizing committee members:

Peter Beaucage, NIST
Kevin Yager, BNL
Kristofer Reyes, BNL
Sumner Harris, ORNL
Jeyan Thiyagalingam, STFC

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