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.  


Jeyan Thiyagalingam

Head of AI for Science,  Science and Technology Facilities Council (STFC), UK

Jeyan Thiyagalingam is the Head of AI for Science at the Scientific Computing Department at the Rutherford Appleton Laboratory (RAL), Science and Technology Facilities Council (STFC), UK. In this role, he leads AI initiatives across the campus and various STFC laboratories, including Diamond Light Source, Central Laser Facility, and ISIS Neutron and Muon Source. Previously, he was the Head of the Scientific Machine Learning Group and a Principal Data Scientist at RAL-STFC, a position he held since 2018. Prior to joining STFC, Jeyan was a faculty member at the University of Liverpool, within the School of Electrical and Electronic Engineering and Computer Science, following his return from industry roles, including at MathWorks UK. His research interests and expertise lie in applying AI to a range of scientific problems, developing analytical and data processing algorithms, and advancing signal and image processing techniques. He is a Fellow of the British Computer Society (BCS) and a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE).






Martin Homec

Independent

Martin Homec graduated from UC Berkeley with a degree in Physics. His career path led him to pursue experimental studies with tritium and superconductivity during his time at Berkeley. He also has a background in law.  After graduating with a JD degree and acquiring credentials enabling him to practice in California and Federal courts, he worked for the Bar Association, taking cases that judges deemed in need of representation but which practicing members of the California Bar had declined due to financial concerns.

He became interested in autonomous experimentation while attending the Quantum Matter in Mathematics and Physics seminar at the CMSA (Harvard) in 2023-24. He observed a disconnect between theoretical mathematical descriptions of Fermi liquids (using bosons and first-generation leptons) and experimental results in superconductivity, strange metals, and bad metals. This sparked a desire to experimentally investigate topological superconductors and compare them with traditional multi-dimensional materials. While initially unable to conduct this research independently, he became involved with CASE, which seeks to advance scientific discovery through automation.





Bruno Jacob

Advanced Computing, Mathematics and Data Division at the Pacific Northwest National Laboratory, USA

Bruno Jacob completed his PhD in Computational Science and Engineering in 2021 at the University of California, Santa Barbara, under the supervision of Professor Linda Petzold. His doctoral research focused on the development of numerical methods for stiff and complex dynamical systems with applications to engineering and scientific computing. He is currently a Data Scientist in the Advanced Computing, Mathematics and Data Division at the Pacific Northwest National Laboratory.

His current research focuses on the intersection of scientific machine learning and computational mathematics, where he develops innovative methods for solving partial differential equations (PDEs), ordinary differential equations (ODEs), and differential-algebraic equations (DAEs). His work encompasses techniques such as physics-informed neural networks (PINNs), DeepONets, and other differentiable frameworks, alongside the design of novel deep learning architectures, including separable networks, multiscale layers in MLPs, and Kolmogorov-Arnold Networks, to enhance the expressivity and efficiency of neural models. He leverages these advancements to address applications in computational fluid dynamics, such as turbulence modeling of wind farms and multi-fidelity learning






Our Past Organizing Committee Members: