The Program
Expert Panelists
Jun Allard
Jun Allard is a Professor in Physics & Astronomy and Mathematics. His research is in developing mathematical models of cell and molecular biophysics, including fluid dynamics of immune cells, mechanics of the 3d genome, and signaling by intrinsically disordered proteins. He has served as former Director of Graduate Gateway Program in Mathematical, Computational and Systems Biology (2017-2024) and former Executive Committee member and co-PI of the NSF Simons Center for Multiscale Cell Fate (2018-2022) at UC Irvine; has supervised 12 PhD students; and has received sole-PI funding and served on review panels for NIH and NSF. Allard earned his PhD in Applied Mathematics from the University of British Columbia.
Neda Bagheri
Neda Bagheri earned her doctorate in Electrical Engineering from the University of California in Santa Barbara. Her focus on control theory and dynamics piqued her interest in biology. Bagheri believes that engineering principles can be employed to better understand, predict, and control complex biological functions, and that these principles need to be informed by biology.
After completing a postdoc in Biological Engineering at MIT, she joined the Chemical & Biological Engineering faculty at Northwestern University where she started the Modeling Dynamic Life Systems (MoDyLS) Lab. In 2019, she was recruited to both the University of Washington where she holds a joint position in Biology and Chemical Engineering and the Allen Institute for Cell Science. In recognition for her research accomplishments and vision, Bagheri was awarded a National Science Foundation CAREER Award (2017), and was honored as a Distinguished Speaker for the Accelerated Discover Forum at IBM Research-Almaden (2018) and for the Mindlin Foundation (2019). She serves on multiple scientific advisory and editorial boards, guiding the frontier of multidisciplinary research.
Bikash Sabata
Bikash joined Altos Labs from Roche Diagnostics, where he was the VP of Software Systems at Roche Sequencing Solutions, leading the software development for the sequencing platform. He worked for Roche in several positions, including those of Head of Imaging and Software for Digital Pathology, Head of Advanced Workflow and Analytics, Head of Software for Ariosa Labs (NIPT CLIA Lab in Roche Sequencing), and Head of Architecture for Personalized Health Care (program in Roche Pharma and Genentech).
Before joining Roche, Bikash was the Chief Technology Officer of BioImagene, where he oversaw the development of artificial intelligence (AI) algorithms for digital pathology and led software engineering. Bikash started his career as an assistant professor of Computer Science at Wayne State University, then moved to SRI International, where he was a Senior Scientist while simultaneously serving as a part-time Research Associate at Stanford University. In addition, he has held positions at multiple startups, including those he helped found, such as Primitive Root and Aginova. His background gives him a wide-ranging viewpoint on the nexus between AI and the life sciences.
Bikash is driven to translate research ideas into marketable products that can change the world. To this end, he draws on his extensive experience and a deep understanding of technology and solutions in the areas of AI/ML, computer vision, medical informatics, omics, medical imaging, digital pathology, and intelligent distributed systems. Bikash holds a PhD from the University of Texas at Austin with a focus on computer vision and AI/ML.
David Stone
David E. Stone is a professor of Biological Sciences at the University of Illinois at Chicago. He earned his PhD from the University of Wisconsin-Madison and holds a BA from Wesleyan University. Dr. Stone's research career is distinguished by his contributions to cellular and molecular biology, particularly focusing on mechanisms of cellular signaling and polarization in yeast.
His academic work has garnered attention for its impact on understanding cellular responses to environmental cues, with significant publications on yeast signal transduction pathways and protein interactions. Dr. Stone's research group has been recognized by the National Science Foundation for their discoveries related to chemical gradient sensing and receptor polarization in yeast. He has published extensively in leading scientific journals, with notable studies on heat shock proteins, mitogen-activated protein kinase (MAPK) pathways, and the molecular basis of chemotropic responses in yeast.
Throughout his career at the University of Illinois at Chicago, Dr. Stone has been an active mentor and collaborator, contributing both to departmental teaching and to the broader scientific community through research and outreach.
Belinda Akpa
Belinda S. Akpa is an Associate Professor in the University of Tennessee's Department of Chemical and Biomolecular Engineering and Director of Data Sciences & AI at the National Institute for Modeling Biological Systems. She holds a BA, MEng, and doctorate in Chemical Engineering from the University of Cambridge (UK). A highly interdisciplinary researcher, her current interest is in developing mathematical frameworks that integrate scarce and heterogeneous data to connect molecular phenomena to dynamic physiological outcomes. Akpa is broadly interested in computational biology, but more specifically in how mechanistic mathematical models can be used to inform targeted experimental strategies and support biological decision making under mechanistic uncertainty. To date, her work has touched the fields of pharmacology/toxicology, membrane biophysics, plant physiology, and forensic anthropology.
Invited Speakers
Christina Hueschen
Christina Hueschen received her Ph.D. from the University of California, San Francisco (UCSF), where she worked with Sophie Dumont on cytoskeletal organization and mechanics and fell in love with physical biology. During her postdoctoral research in the group of Dr. Alex Dunn at Stanford University, she studied the gliding motility of unicellular parasites. Christina is a co-author of the book The Restless Cell: Continuum Theories of Living Matter, the result of a joy-filled adventure into the world of active matter with Rob Phillips. She joined the UCSD faculty in 2024.
Justin Kinney
Dr. Kinney completed his BA in Physics and Math at Cornell University in 2002. He then pursued his PhD work in Physics at Princeton University, where he pursued research in string theory (with Juan Maldacena) and biophysics (with Curtis Callan and Edward Cox), graduating in 2008. In 2010 Dr. Kinney became a Fellow at Cold Spring Harbor Laboratory and has worked there ever since.
Research in the Kinney Lab develops next-generation DNA sequencing as a tool for dissecting the biophysical mechanisms of gene regulation. As a graduate student, Kinney co-invented a widely used technique now known as the massively parallel reporter assay (MPRA). Kinney and colleagues further showed how, using ideas from information theory, MPRA data could be used to infer quantitative biophysical models of gene expression. The Kinney lab continues to leverage a tightly knit combination of mathematical theory, machine learning, and experiments in order to illuminate the mechanisms of gene regulation in two diverse contexts: bacterial transcriptional regulation and alternative mRNA splicing in humans. This latter context is highly relevant to understanding and treating human diseases like Spinal Muscular Atrophy. The Kinney lab also develops algorithms and software for the analysis of MPRAs and other multiplex assays of variant effect (MAVEs).
Peter Koo
Dr. Peter Koo is an Associate Professor at the Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory, where he leads a research group developing robust and interpretable deep learning methods for genomics. His lab has pioneered biologically grounded model architectures, rigorous training strategies for genomic deep learning models, and interpretable AI tools to understand and dissect cis-regulatory mechanisms. Their work bridges machine learning and biology to accelerate scientific discovery and guide precision medicine, with ongoing efforts focused on improving model generalization across genomic contexts and developing generative AI to better understand and design regulatory sequences.
Arthur Lander
Dr. Lander received a B.S. in Molecular Biophysics and Biochemistry from Yale, followed by an M.D. and Ph.D. (Neuroscience) from UCSF. After postdoctoral research at Columbia, he joined MIT where he later received tenure. He moved to UCI in 1995 and is currently the Donald Bren Professor of Developmental & Cell Biology with joint appointments in Biomedical Engineering and Logic & Philosophy of Science. Dr. Lander founded and directs the UCI Center for Complex Biological Systems, which fosters interdisciplinary research, training, and outreach at the interface between biology and the physical, computational, and engineering sciences. Honors include a Packard Fellowship and a RARE Champion of Hope in Science Award. He was elected to membership in the American Society for Clinical Investigation and fellowship in the American Association for the Advance of Science. He also serves on the Board of Directors of the Cornelia de Lange Syndrome Foundation USA. Dr. Lander enjoys doing Systems Biology, fusing biology with mathematics, physics, engineering and computer science, seeking design principles that explain the complexity of life. He asks how cells know their locations; how tissues and organs maintain precise sizes; how selection for control sows the seeds of birth defects and cancer; and how to make inferences from biological “big” data more accurate.
Molly Maleckar
Molly Maleckar, a research professor of computational physiology at Simula Research Laboratory (Oslo, Norway), centers her work on multiscale mechanisms within cardiac and other human physiological systems, from the subcellular to the systemic level.
While trained primarily in the field of mechanistic, biophysically-based mathematical modeling in biology and physiology, her more recent work over the last decade has also incorporated AI and hybrid modeling approaches to uncover new strategies for insight into cardiac disease.
She is the Simula director and a work package lead for the ProCardioCentre for Innovation, a Norwegian Centre of Excellence focused on using AI and computational models to advance cardiovascular health. Her experience also includes the development and leadership of the Simula-University of Oslo-University of California San Diego Doctoral Programme and her prior tenure as Director of Modeling at the Allen Institute for Cell Science.
Garegin Papoian
Dr. Papoian received his Ph.D. at Cornell University under guidance of Dr. Roald Hoffmann, a Nobel Laureate. He continued with postdoctoral work with Dr. Michael Klein (National Academy of Sciences member) and Dr. Peter Wolynes (National Academy of Sciences member), studying quantum and protein physics. He has held faculty positions at the University of North Carolina at Chapel Hill and subsequently at the University of Maryland, College Park, where he was the first Monroe Martin Professor in the Department of Chemistry and Biochemistry and Institute for Physical Science and Technology. He received numerous awards, including Beckman Young Investigator, Camille Dreyfus Teacher-Scholar and National Science Foundation CAREER Award. He uses computational chemistry, physics and machine learning to study biological processes at multiple scales, from protein dynamics and epigenetics to cellular level processes, such as immune cell activation and neuronal dynamics. He is also the cofounder and CSO of DeepOrigin Inc, a startup company that accelerates drug discovery through pioneering combination of physics-informed AI, quantum methods and molecular dynamics.
Detailed Agenda
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