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  • Anima Anandkumar
    Anima Anandkumar, Ph.D.
    Bren Professor at Caltech and Director of ML Research at NVIDIA, Cal Tech and NVIDIA

    Anima Anandkumar is a Bren Professor at Caltech and Director of ML Research at NVIDIA. She was previously a Principal Scientist at Amazon Web Services. She has received several honors such as Alfred. P. Sloan Fellowship, NSF Career Award, Young investigator awards from DoD, and Faculty Fellowships from Microsoft, Google, Facebook, and Adobe. She is part of the World Economic Forum's Expert Network. She is passionate about designing principled AI algorithms and applying them in interdisciplinary applications. Her research focus is on unsupervised AI, optimization, and tensor methods.

  • Limor Appelbaum
    Limor Appelbaum, M.D.
    Staff Scientist, BIDMC; Instructor in Radiation Oncology, Harvard Medical School, Beth Israel Deaconess Medical Center

    Limor Appelbaum, MD, is a Radiation Oncologist, and currently serves as Staff Scientist in the Department of Radiation Oncology, Beth Israel Deaconess Medical Center (BIDMC), and Instructor at Harvard Medical School. Dr. Appelbaum’s goal is to increase early cancer detection rates, by building and clinically implementing cancer risk prediction models across diverse populations. Towards this goal, her research focuses on developing, validating, and deploying cancer risk models using electronic health record data, in collaboration with Prof. Martin Rinard’s group from MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). Her preliminary model for Pancreatic Duct Adenocarcinoma (PDAC), developed on BIDMC data, and validated on Partners Healthcare data, was published in the European Journal of Cancer. Recent work, in collaboration with a federated network company, utilizes multi-institutional EHR data to develop and externally validate PDAC risk models. It also aims to establish a path towards clinical implementation of such models by deployment and prospective validation within the federated network platform. Dr. Appelbaum is also currently leading a study at BIDMC which is prospectively validating the PDAC model and recruiting model-assigned high-risk patients for biomarker analysis. Additional work focuses on developing cancer risk prediction models for other cancers such as Hepatocellular Carcinoma (HCC), gastric, and ovarian carcinoma, as well as prediction of surgical outcomes to help guide treatment decisions. She has recently received funding from the Joint Center for Radiation Therapy (JCRT) Harvard Medical School and the Prevent Cancer Foundation for her work.

    Dr. Appelbaum received her M.D. from Semmelweis University in Budapest Hungary, and completed her Internship year at Soroka University Medical Center, Israel. She completed her Radiation Oncology Residency at Hadassah Hebrew University Medical Center, and then served as a Senior Radiation Oncologist there before coming to Boston. She completed her clinical research fellowship at BIDMC.

  • Elham Azizi
    Elham Azizi, Ph.D.
    Assistant Professor , Columbia University

    Elham joined Columbia in 2020 as the Herbert and Florence Irving Assistant Professor of Cancer Data Research in the Irving Institute for Cancer Dynamics and Assistant Professor of Biomedical Engineering. She is also affiliated with the Department of Computer Science, Data Science Institute, and the Herbert Irving Comprehensive Cancer Center. Elham holds a BSc in Electrical Engineering from Sharif University of Technology, an MSc in Electrical Engineering and a PhD in Bioinformatics from Boston University. She was a postdoctoral fellow in the Dana Pe'er Lab at Columbia University and Memorial Sloan Kettering Cancer Center. Her multidisciplinary research utilizes novel machine learning techniques and single-cell genomic and imaging technologies to study the dynamics and circuitry of interacting cells in the tumor microenvironment. She is a recipient of the CZI Science Diversity Leadership Award, NSF CAREER Award, Tri-Institutional Breakout Prize for Junior Investigators, NIH NCI Pathway to Independence Award, an American Cancer Society Postdoctoral Fellowship, and an IBM Best Paper Award at the New England Statistics Symposium.​

  • Yoshua Bengio
    Yoshua Bengio, Ph.D.
    Scientific Director & Full Professor, Mila/U. Montreal

    Yoshua Bengio is recognized worldwide as one of the leading experts in AI, known for his conceptual and engineering breakthroughs in artificial neural networks and deep learning. He is Full Professor in the Dept of Computer Science and Operations Research at U. Montreal (1993) and the Founder and Scientific Director of Mila–Quebec Artificial Intelligence Institute, one of the largest academic institutes in deep learning and one of the three federally-funded centers of excellence in AI research and innovation in Canada.

    In 2016, he became the Scientific Director of IVADO. Co-Director of the CIFAR Learning in Machines & Brains program that funded the initial breakthroughs in deep learning, he held the Canada Research Chair in Statistical Learning Algorithms (2000-2019) and holds a Canada CIFAR AI Chair since 2019, while acting as Co-Chair of Canada’s Advisory Council on AI. He is also a member of the NeurIPS Foundation advisory board and Co-Founder of the ICLR conference.

    Yoshua Bengio was made an Officer of the Order of Canada in 2017 and a Fellow of both the Royal Society of Canada of London. He is the 2018 laureate of the A.M. Turing Award, “the Nobel Prize of Computing,” alongside Geoffrey Hinton and Yann LeCun for their important contributions and advances in deep learning. In 2022, he was appointed Knight of the Legion of Honor by France and named co-laureate of Spain’s Princess of Asturias Award for technical and scientific research.

    Concerned about the social impact of AI, he actively took part in the conception of the Montreal Declaration for the Responsible Development of AI. His goal is to contribute to uncovering the principles giving rise to intelligence through learning while favouring the development of AI for the benefit of all. In 2022, Yoshua Bengio became the most cited computer scientist in the world (h-index).

  • Tianxi Cai
    Tianxi Cai, SciD
    John Rock Professor of Population and Translational DataSciences and Professor of Bioinformatics, Harvard T.H. Chan School of PublicHealth

    Sciences and Professor of Bioinformatics at Harvard T.H. Chan School of Public Health, and Professor of Biomedical Informatics at Harvard Medical School. Dr. Cai is a major player in developing analytical tools for mining EHR data and predictive modeling with biomedical data. She directs the HMS and HSPH translational data science center for a learning health system. Cai's research lab develops novel statistical and machine learning methods for several areas including clinical trials, real world evidence, and personalized medicine using genomic and phenomic data. Cai received her ScD in Biostatistics at Harvard and was an assistant professor at the University of Washington before returning to Harvard as a faculty member in 2002.

  • Ryan Copping
    Ryan Copping, BSc (Hons)
    Global Head of Data Science Acceleration, Roche & Genentech

    Ryan is the Global Head of Data Science Acceleration at Roche & Genentech where he leads a team of data scientists, data engineers and software engineers focused on developing capabilities to generate actionable insights from clinical trial and real-world data assets. Ryan has worked for Roche for 20 years and has held multiple data science leadership roles before his current role including building and leading the personalized healthcare analytics team who generated novel insights from real world data sources including electronic medical records, omics datasets and images. Ryan also leads the Roche Advanced Analytics Network (RAAN) which is a community of over 1500 AI and machine learning enthusiasts from 40 Roche locations across the globe. Ryan established and leads Roche’s advanced analytics academic partnerships with the Alan Turing Institute in the UK and Stanford in the US developing novel methods and applications leveraging real-world data which has led to multiple impactful projects and publications. Ryan’s background is in Statistics and Computing and he has a passion for identifying new ways to have an impact with data and analytics as well as understanding & fostering team culture and engagement. Ryan was named as one of the rising stars in health tech by Fierce Healthcare last year.

  • Atul Deshpande
    Atul Deshpande, Ph.D.
    Postdoctoral Researcher, Johns Hopkins University

    Atul is a postdoctoral research fellow in quantitative sciences in the lab of Dr. Elana Fertig at Johns Hopkins University. He has a Ph.D. in Electrical Engineering from University of Wisconsin-Madison. He is currently working on methods to model the spatiotemporal dynamics of tumor growth and immunotherapy response using single-cell and spatially resolved multiomics.

  • Khaled El Emam
    Khaled El Emam, Ph.D.
    Professor, University of Ottawa

    Dr. Khaled El Emam is the Canada Research Chair (Tier 1) in Medical AI at the University of Ottawa, where he is a Professor in the School of Epidemiology and Public Health. He is also a Senior Scientist at the Children’s Hospital of Eastern Ontario Research Institute and Director of the multi-disciplinary Electronic Health Information Laboratory, conducting research on privacy enhancing technologies to enable the sharing of health data for secondary purposes, including synthetic data generation and de-identification methods.

    Khaled is a co-founder of Replica Analytics, a company that develops synthetic data generation technology, which was recently acquired by Aetion. As an entrepreneur, Khaled founded or co-founded six product and services companies involved with data management and data analytics, with some having successful exits. Prior to his academic roles, he was a Senior Research Officer at the National Research Council of Canada. He also served as the head of the Quantitative Methods Group at the Fraunhofer Institute in Kaiserslautern, Germany.

    He participates in a number of committees, number of the European Medicines Agency Technical Anonymization Group, the Panel on Research Ethics advising on the TCPS, the Strategic Advisory Council of the Office of the Information and Privacy Commissioner of Ontario, and also is co-editor-in-chief of the JMIR AI journal.

    In 2003 and 2004, he was ranked as the top systems and software engineering scholar worldwide by the Journal of Systems and Software based on his research on measurement and quality evaluation and improvement. He held the Canada Research Chair in Electronic Health Information at the University of Ottawa from 2005 to 2015. Khaled has a PhD from the Department of Electrical and Electronics Engineering, King’s College, at the University of London, England.

  • Elana Fertig
    Elana J Fertig, PhD
    Professor, Johns Hopkins University

    Dr. Fertig advances a new predictive medicine paradigm for oncology by converging systems biology with translational technology development. Her wet lab develops time course models of therapeutic resistance and single cell technology development for analysis of clinical biospecimens. Her computational methods blend mathematical modeling and artificial intelligence to determine the biomarkers and molecular mechanisms of therapeutic resistance and disease progression from multi-platform genomics data. These techniques have broad applicability to the analysis of clinical biospecimens, developmental biology, and neuroscience.

    Dr. Fertig is a Professor of Oncology and Division and Associate Cancer Center Director in Quantitative Sciences, co-Director of the Convergence Institute, and co-Director of the Single-Cell Training and Analysis Center at Johns Hopkins University. She has secondary appointments in Biomedical Engineering and Applied Mathematics and Statistics, affiliations in the Institute of Computational Medicine, Center for Computational Genomics, Machine Learning, Mathematical Institute for Data Science, and the Center for Computational Biology and is a Daniel Nathans Scientific Innovator. Prior to entering the field of computational cancer biology, Dr Fertig was a NASA research fellow in numerical weather prediction. Dr. Fertig's research is featured in over numerous peer-reviewed publications, R/Bioconductor packages, and competitive funding portfolio as PI and co-I. Notably, she led the team that won the HPN-DREAM8 algorithm to predict phospho-proteomic trajectories from therapeutic response in cancer cells and was elected to the College of Fellows American Institute for Medical and Biomedical Engineering (AIMBE) in 2022. She serves on the editorial boards of the pre-eminent computational biology journals PLoS Computational Biology, Cell Systems, ImmunoInformatics, eLife, and Cancer Research Communications, and as a steering committee member for the NCI Informatics Technology for Cancer Research Consortium.

  • Olivier Gevaert
    Olivier Gevaert, PhD
    Associate Professor, STANFORD UNIVERSITY


    Dr. Olivier Gevaert is an associate professor at Stanford University focusing on developing machine-learning methods for biomedical decision support from multi-scale data. He is an electrical engineer by training with additional training in artificial intelligence, and a PhD in bioinformatics at the University of Leuven, Belgium. He continued his work as a postdoc in radiology at Stanford and then established his lab in the department of medicine in biomedical informatics. The Gevaert lab focuses on multi-scale biomedical data fusion primarily in oncology and neuroscience. The lab develops machine learning methods including Bayesian, kernel methods, regularized regression and deep learning to integrate molecular data or omics. The lab also investigates linking omics data with cellular and tissue data in the context of computational pathology, imaging genomics & radiogenomics.

  • Trey Ideker
    Trey Ideker, PhD
    Professor, UC San Diego

    Trey Ideker, PhD, is a UC San Diego Professor of Medicine, Bioengineering and Computer Science, and former Chief of Genetics. Dr. Ideker is the Director of the Cell Maps for AI initiative under the Bridge2AI program. Additionally, he is Director or Co-Director of the the Cancer Cell Map Initiative, the National Resource for Network Biology, the Psychiatric Cell Map Initiative and the UC San Diego PhD Program in Bioinformatics and Systems Biology, all NIH-funded efforts. Dr. Ideker received BS and MEng degrees in Computer Science from MIT and his PhD in Genome Sciences from the University of Washington under Drs. Lee Hood and Dick Karp. He is a pioneer in genomic, transcriptomic, and proteomic analysis and in the theory and practice of Systems Biology. He founded and continues to develop the widely used Cytoscape network analysis platform (>30,000 citations). His lab also created the Hannum epigenetic clock, the first to measure human aging rates using DNA methylation. Dr. Ideker is on the Board of Scientific Advisors to the National Cancer Institute and formerly the National Human Genome Research Institute. He serves on the editorial boards of Cell, Cell Systems and PLoS Computational Biology. He was named a Top 10 Innovator by Technology Review and a Web of Science Highly Cited Researcher (top 1% by citations). Dr. Ideker received the 2009 ICSB Overton Prize and is an AAAS, AIMBE and ISMB Fellow.

  • Livnat Jerby
    Livnat Jerby, PhD
    Assistant Professor, Department of Genetics, Stanford University

    Livnat Jerby is an Assistant Professor in the Department of Genetics at Stanford University, a Chan Zuckerberg Biohub Investigator, and an Allen Distinguished Investigator. Her research focuses on decoding determinants of cell function and multicellular dynamics at scale towards new interventions for disease treatment and prevention. Leveraging emerging technologies, her multidisciplinary lab at Stanford Genetics is integrating genetic engineering with high content screens, machine learning, and large patient-focused studies to uncover immune response mechanisms and develop new (multi)cell engineering strategies.

    Prior to joining Stanford, Livnat was a postdoctoral fellow in Aviv Regev's lab at the Broad Institute of MIT and Harvard, where she used single cell genomics to identify regulators of T cell exclusion and dysfunction and demonstrated the potential of epigenetic reprogramming as a therapeutic modality to sensitize tumors to immune checkpoint blockade. Livnat holds a B.Sc. in Computer Science and Biology and obtained her PhD in 2016 from Tel Aviv University, where she worked with Prof. Eytan Ruppin, studying genetic interactions in cancer. Her research has been generously supported by the Cancer Research Institute (CRI), the Burroughs Wellcome Fund (BWF), Schmidt Family Foundation, Rothschild Foundation, Ovarian Cancer Research Alliance (OCRA), Paul G. Allen Family Foundation, Bill and Melinda Gates Foundation, and Chan Zuckerberg Biohub Initiative.

  • Sean Khozin
    Sean Khozin, MD, MPH
    Affiliate , MIT

    Sean Khozin, MD, MPH, a physician-executive, oncologist, and data scientist, has over 15 years of leadership experience in therapeutic development, drug regulation, and the utilization of artificial intelligence (AI) and machine learning in biomedical research. As the founder of Phyusion, he is currently focusing on advancing innovations at the intersection of biology, technology, and AI. Dr. Khozin is the former CEO at CancerLinQ, a precision oncology enterprise focused on transforming cancer care and research with real-world data and advanced analytics. He also served as the Global Head of Data Strategy and Data Science Innovation at Johnson & Johnson, leading a worldwide multidisciplinary team charged with the design and implementation of pioneering data science solutions to support the development of innovative new medicines and vaccines

    Prior to these roles, Dr. Khozin was a founding member of the US FDA's Oncology Center of Excellence and established INFORMED, the FDA's inaugural data science and technology incubator. As an entrepreneurial sandbox within the agency regulating nearly a third of the US economy, INFORMED was crucial in shaping the FDA's policies on real-world evidence and spurring the adoption of data science solutions in drug discovery and clinical development.

    Before his tenure with the US federal government, Dr. Khozin co-founded Hello Health, an innovative TechBio company that developed telemedicine, point-of-care data visualization, and advanced analytical systems as an integrated approach to optimizing drug discovery, patient care, and clinical research.

    Dr. Khozin is a Research Affiliate at the MIT and has over a decade of clinical and basic science research experience at the US National Cancer Institute. He advises and serves on the boards of various organizations, including Owkin, the Society for Translational Oncology, Alliance for Artificial Intelligence in Healthcare, and the Life Sciences Council of the CEO Roundtable on Cancer.

  • Arjun Krishnan
    Arjun Krishnan, Ph.D.
    Associate Professor, University of Colorado Anschutz Medical Campus

    Arjun Krishnan is an Associate Professor in the Department of Biomedical Informatics and the Center for Health Artificial Intelligence at the University of Colorado Anschutz Medical Campus. Before CU, he was an Assistant Professor at Michigan State University. Arjun received his PhD in Computational Biology at Virginia Tech and conducted his postdoctoral research at Princeton University. In addition to biomedical data science research, he is passionate about open science, research training, and creating diverse and inclusive learning environments. He is a recipient of the Maximizing Investigators' Research Award from National Institutes of Health and the CAREER award from the National Science Foundation.

    The goal of his research group (https://www.thekrishnanlab.org/) is to enable biomedical researches to effectively reuse these data — e.g., omics profiles, molecular networks, knowledgebases, unstructured text corpora, and genetic associations — to gain nuanced insights into the heterogeneous traits/disease. They develop integrative machine learning approaches and tools that work to improve every stage of data-driven biology: harmonizing and integrating heterogeneous genomic and genetic data, reconstructing genome-scale networks for data and knowledge representation, transferring information across species, natural language processing to annotate omics data, and developing open software and interactive webservers. The approaches that they develop are highly general, thus applicable to a wide range of biological phenomena in both human and model organisms.

  • Smita Krishnaswamy
    Smita Krishnaswamy, Ph.D.
    Associate Professor, Yale

    Smita Krishnaswamy is an Associate Professor in the departments of Computer Science (SEAS) and Genetics (YSM). She is part of the programs in Applied Mathematics, Computational Biology & Bioinformatics and Interdisciplinary Neuroscience. She is also affiliated with the Yale Institute for the foundations of data science, Wu-Tsai Institute, Yale Cancer Center. Smita's lab works on fundamental deep learning and machine learning developments for representing and learning from big data. Her techniques incorporate mathematical priors from graph spectral theory, manifold learning, signal processing, and topology into machine learning and deep learning frameworks, in order to denoise and model the underlying systems faithfully for predictive insight. Currently her methods are being widely used for data denoising, visualization, generative modeling, dynamics. modeling, comparative analysis and domain transfer in datasets arising from stem cell biology, cancer, immunology and structural biology (among others).

    Smita teaches several courses including: Deep Learning Theory and Applications, Unsupervised learning, and Geometric and Topological Methods in Machine Learning. Prior to joining Yale, Smita completed her postdoctoral training at Columbia University in the systems biology department where she focused on learning computational models of cellular signaling from single-cell mass cytometry data. She obtained her Ph.D. from EECS department at University of Michigan where her research focused on algorithms for automated synthesis and probabilistic verification of nanoscale logic circuits. Following her time in Michigan, Smita spent 2 years at IBM's TJ Watson Research Center as a researcher in the systems division where she worked on automated bug finding and error correction in logic. Smita's work over the years has won several awards including the NSF CAREER Award, Sloan Faculty Fellowship, and Blavatnik fund for Innovation.

  • Jure Leskovec
    Jure Leskovec, Ph.D.
    Associate Professor of Computer Science, Stanford University

    Jure Leskovec (http://cs.stanford.edu/~jure) is Associate Professor of Computer Science at Stanford University, and a co-Founder of Stanford Data Science Initiative. He co-founded several machine learning start-ups and served as Chief Scientist at Pinterest. Leskovec pioneered the field of Graph Neural Networks and co-authored PyG, the most widely-used graph neural network library. Leskovec's research area is machine learning and data science for complex, richly-labeled relational structures, graphs, and networks for systems at all scales, from interactions of proteins in a cell to interactions between humans in a society. Applications include commonsense reasoning, recommender systems, social network analysis, computational social science, and computational biology with an emphasis on drug discovery. This research has won several awards including a Lagrange Prize, Microsoft Research Faculty Fellowship, the Alfred P. Sloan Fellowship, and numerous best paper and test of time awards. It has also been featured in popular press outlets such as the New York Times and the Wall Street Journal. Leskovec received his bachelor's degree in computer science from University of Ljubljana, Slovenia, PhD in machine learning from Carnegie Mellon University and postdoctoral training at Cornell University.

  • Ziad Obermeyer
    Ziad Obermeyer, M.D.
    Associate Professor, Blue Cross of California Distinguished Professor, Berkeley

    Ziad Obermeyer is Associate Professor and Blue Cross of California Distinguished Professor at UC Berkeley, where he works at the intersection of machine learning and health. He is a Chan Zuckerberg Biohub Investigator, a Faculty Research Fellow at the National Bureau of Economic Research, and was named an Emerging Leader by the National Academy of Medicine. Previously, he was Assistant Professor at Harvard Medical School, and continues to practice emergency medicine in underserved communities.

  • Sergey Ovchinnikov
    Sergey Ovchinnikov, Ph.D.
    Fellow, Harvard University

    My research aims to understand the origins and evolution
    of life, from single cells to complex multicellular organisms,
    through a combination of computational, structural, and
    evolutionary biology approaches. In the past, I've worked
    on optimizing and developing new techniques for contact
    prediction of proteins and interactions, and structure
    determination. Today, my group is pursuing the
    development of a unified statistical model of protein
    evolution and exploring the potential of deep-learning
    models for this goal. This includes the use of structure
    prediction models for refining alignment, generating decoys
    for the prediction of structure from a single sequence, and
    protein design.

  • Dana Pe'er
    Dana Pe'er, Ph.D.
    Chair, Computational & Systems Biology Program, Sloan Kettering Institute

    Dana Pe’er is an HHMI Investigator, Chair of the Computational and Systems Biology Program and Director of the Gerry Metastasis and Tumor Ecosystems Center and Single Cell Analytics and Innovation Lab at the Sloan Kettering Institute. The Pe’er lab combines single-cell technologies, genomic sequencing and imaging data, and machine learning techniques to address fundamental questions in biomedicine. Dr. Pe’er pioneered foundational approaches to derive biological cell states, trajectories and future cell fates from single-cell data. Her group has applied these methods to learn how cells reach healthy and aberrant states in development, immunity and cancer, with a focus on tumor heterogeneity, plasticity and tumor-immune interactions. Dr. Pe'er earned her PhD at the Hebrew University and completed a postdoctoral fellowship at Harvard University. She is an ISCB Fellow and has received the Burroughs Wellcome Fund Career Award, NSF CAREER award, Stand Up To Cancer Innovative Research Grant, NIH Director's New Innovator and Pioneer awards, Packard Fellowship in Science and Engineering, Ernst W. Bertner Memorial Award and ISCB Overton Prize. She serves on the editorial board of Cell, leads the MSK Center within the NCI Human Tumor Atlas Network, and co-heads computational analysis for the Human Cell Atlas project.

  • Lily Peng
    Lily Peng, MD, PhD
    Director, Product Management, Verily

    Dr. Peng is a physician-scientist and a director of product management at Verily, where she works on accelerating evidence generation. Before Verily, she co-led Google Health AI in applying AI to enable better and more equitable care, particularly for diabetic eye disease, cardiovascular disease, and cancer. Her resulting papers have been published in JAMA, Nature, Nature Medicine, and Nature Biomedical Engineering.

    She graduated with an MD/PhD in Bioengineering from the University of California, San Francisco and a B.S. in Chemical Engineering from Stanford. She co-founded Nano Precision Medical, a drug delivery device start-up, and was a product manager at Doximity.

    In recognition of Dr. Peng’s contributions, she has been named in Fortune Magazine’s 40 under 40 in
    Health, and Wired magazine’s list of 20 People Who Are Creating the Future.

  • Chris Probert
    Chris Probert, Ph.D.
    Senior Machine Learning Scientist, insitro, inc.

    Chris Probert is a Senior Machine Learning Scientist at insitro, a machine learning-driven drug discovery company based in South San Francisco, where he is focused on uncovering novel therapeutic insights from multi-modal clinical datasets in oncology. Prior to insitro, Chris earned a Ph.D. at Stanford, working with Anshul Kundaje and Christina Curtis on developing methods for decoding cancer functional genomics and inferring tissue of origin in cfDNA. Prior to Stanford, Chris worked in Illumina’s computational R&D group, and also holds a B.S. and M.S. in computer science.

  • Marianna Rapsomaniki
    Marianna Rapsomaniki, Ph.D.
    Staff Research Scientist and Group Leader, IBM Research Europe

    Marianna is a Staff Research Scientist and a Group Leader of the AI for single-cell research team at IBM Research Europe in Zurich. The overarching goal of her research is understanding spatiotemporal tumor heterogeneity across different scales of biological organization and its links to cancer initiation, progression, and response to drug perturbations. Her team develops new artificial intelligence and machine learning approaches to extract biologically meaningful patterns from large-scale, multimodal, and noisy (spatial) single-cell data. Marianna holds a Diploma in Computer Science and Engineering and a Master's in Bioinformatics, both from the University of Patras, Greece. Her PhD research was carried out working jointly in the Cell Cycle lab of the University of Patras and the Automatic Control Lab of ETH Zurich and involved stochastic hybrid modeling of biological systems. Her research has been supported by a Swiss Government Excellence Scholarship, the State Scholarships Foundation of Greece, and the Swiss National Science Foundation (SNSF).

  • Martin Rinard
    Martin Rinard, Ph.D.
    Professor, Massachusetts Institute of Technology

    Martin Rinard is a Professor in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. His research focuses on software systems, including machine learning, computer security, approximate computing, software robustness and reliability, probabilistic programming, and program analysis. He is an ACM Fellow and his research has received many awards and honors.

  • Donna Rivera
    Donna Rivera, PharmD., MSc.
    Associate Director for Pharmacoepidemiology, Oncology Center of Excellence, US FDA

    Donna R. Rivera, PharmD., MSc., FISPE is the Associate Director for Pharmacoepidemiology in the Oncology Center of Excellence at the U.S. Food and Drug Administration. She leads the Oncology Real World Evidence (RWE) Program, focused on the use of Real World Data (RWD) and RWE for regulatory purposes, management of the RWD research portfolio strategy, engagement and education, and development of regulatory policy to support the OCE mission.

    As a pharmacist and pharmacoepidemiologist, Dr. Rivera has interests in the use of RWD to advance health equity, pragmatic study designs and novel methodological approaches, and appropriate uses of RWD for drug development to increase access of effective therapies to patients. She is also a Scientific Executive Committee member for the COVID-19 and Cancer Consortium (CCC19) and leads Project Post COVIDity. She has published over 50 peer-reviewed publications and presented at various scientific conferences nationally and internationally. Dr. Rivera is a Fellow of the International Society for Pharmacoepidemiology and was honored as a 40 Gators Under 40 by the University of Florida.

    In her previous role at the National Cancer Institute (NCI), she led a strategic RWD initiative to facilitate large scale, longitudinal treatment data linkages with SEER through collaborative public private partnerships. She also has previous experience in clinical trials from Stiefel, a GlaxoSmithKline company. Dr. Rivera earned her Doctor of Pharmacy and Master of Science in Pharmaceutical Sciences with a specialization in Pharmacoepidemiology from the University of Florida College of Pharmacy. She completed an NCI postdoctoral fellowship in Pharmacoepidemiology and Pharmacogenomics.

  • Andrej Sali
    Andrej Sali, PhD
    Professor, University of California, San Francisco

    Andrej Sali received his BSc degree in chemistry from the University of Ljubljana, Slovenia, in 1987, working on the sequence-structure-function relationship of stefins and cistatins under the supervision of Professor Vito Turk; and his PhD from Birkbeck College, University of London, UK, in 1991, developing the MODELLER program for comparative modeling of protein structures under the supervision of Professor Tom L. Blundell. He was then a postdoc with Professor Martin Karplus at Harvard University as a Jane Coffin Childs Memorial Fund fellow, studying lattice Monte Carlo models of protein folding. From 1995 to 2002, he was first an Assistant Professor and then an Associate Professor at The Rockefeller University. In 2003, he moved to University of California, San Francisco, as a Professor of Computational Biology in the Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences (QB3). He was recognized as Sinsheimer Scholar (1996), an Alfred P. Sloan Research Fellow (1998), an Irma T. Hirschl Trust Career Scientist (2000), the Zois Award of Science Ambassador of Republic of Slovenia (2007), a Fellow of International Society for Computational Biology (2014), Jubilee Professor of Indian Academy of Sciences (2017), Bijvoet Medal recipient (2018), and member of National Academy of Sciences of USA (2018). He was an Editor of Structure from 2002 to 2021. He is also a Founder of Prospect Genomix that merged with Structural Genomix (2001) and was acquired by Eli Lilly Inc. in 2008; and of Global Blood Therapeutics (2012) that was acquired by Pfizer Inc. in 2022. Dr. Sali develops, applies, and disseminates computational methods for determining and modulating structures and functions of proteins and their assemblies.

  • G.V. Shivashankar
    G.V. Shivashankar, PH.D.
    Professor, ETH Zurich

    G.V.Shivashankar is currently a Full Professor of Mechano-Genomics at the Department of Health Sciences and Technology, ETH Zurich. He also heads the Laboratory of Nanoscale Biology at the Paul Scherrer Institute, Switzerland. Shivashankar lab is interested in understanding the mechano-genomics of cellular ageing and how ageing cells and ageing related disease cells, such as cancer and fibrotic cells, can be reprogrammed and rejuvenated by mechanotransduction. Towards this, his group also develops novel imaging-AI based mechano-genomic biomarkers as fingerprints for cells health and disease. Shivashankar carried out his PhD at the Rockefeller University (1994-1999) and Postdoctoral research at NEC Research Institute, Princeton USA (1999-2000). He was a tenured faculty at the National Center for Biological Sciences, NCBS-TIFR- Bangalore, India (2000-2009) before relocating to the National University of Singapore (NUS) in 2010. He was the Deputy Director of the Mechanobiology Institute at NUS (2011-2019) and was the IFOM-NUS Chair Professor (2014-2019) before joining ETH. His scientific awards include the Birla Science Prize in 2006, the Swarnajayanthi Fellowship in 2007, and he was elected to the Indian Academy of Sciences in 2010 and to the EMBO membership in 2019.

  • Rick Stevens
    Rick Stevens, B.S. Applied Mathematics
    Associate Laboratory Director - Computing, Environment and Life Sciences / Professor of Computer Science, Argonne National Laboratory / The University of Chicago

    Rick Stevens is a Professor of Computer Science at the University of Chicago as well as the Associate Laboratory Director of the Computing, Environment and Life Sciences (CELS) Directorate and Argonne Distinguished Fellow at Argonne National Laboratory. In these, and in numerous other roles, he is responsible for ongoing research in the computational and computer sciences from high-performance computing architecture to the development of tools and methods for bioinformatics, cancer, infectious disease, and other problems in science and engineering. Recently, he has focused on developing AI methods for a variety of scientific and biomedical problems, and also has significant responsibility in delivering on the U.S. national initiative for Exascale computing and developing the DOE’s AI for Science national initiative.

    Stevens is currently the PI of the Bacterial / Viral Bioinformatics Resource Center (BV-BRC) which is developing comparative analysis tools for infectious disease research and serves a large user community; the Exascale Deep Learning and Simulation Enabled Precision Medicine for Cancer project through the Exascale Computing Project (ECP), which focuses on building a scalable deep neural network application called the CANcer Distributed Learning Environment (CANDLE); the Innovative Methodologies and New Data for Predictive Oncology Model Evaluation (IMPROVE) project which is building a comprehensive framework and Exascale workflow to compare deep learning models that are aimed at solving critical problems; and the Exploration of the Potential for Artificial Intelligence and Machine Learning to Advance Low-Dose Radiation Biology Research (RadBio-AI) project to investigate the opportunity to understand the impact of low doses of radiation on biological systems, including humans.

    Stevens is a member of the American Association for the Advancement of Science and has received many national honors for his research, including being named a Fellow of the Association of Computer Machinery (ACM) for his continuing contributions to high-performance computing.

  • Fabian Theis
    Fabian J. Theis, Ph.D.
    Prof. , Helmholtz Munich

    Fabian Theis is the Director of Helmholtz Munich Computational Health Center and Scientific Director of Biomedical AI at Helmholtz Pioneer Campus HPC at Helmholtz Munich and Scientific Director of HelmholtzAI. He is a Full Professor at the Technical University of Munich, holding the chair ‘Mathematical Modelling of Biological Systems’, and Associate Faculty at the Wellcome Trust Sanger Institute.

    After parallel studies in mathematics and physics, Fabian Theis received his Ph.D. in biophysics from the University of Regensburg in 2002 and in information technology from the University of Granada in 2003. This was followed by short research stays at the Brain Institute RIKEN (Japan) and the Helsinki University of Technology, among others. From 2006, he was a Bernstein Fellow at the Max Planck Institute for Dynamics and Self-Organization, where he received the Heinz Maier-Leibnitz Prize for his work in machine learning. In 2008, he habilitated in biophysics at the University of Regensburg.

    In 2007, Fabian joined Helmholtz Zentrum München as a junior research group leader and has headed the Institute of Computational Biology there since 2013. He received a W2 professorship at the Technical University of Munich (TUM) in 2009 and was appointed as the Chair of Mathematical Modeling of Biological Systems in 2013. He focused his work on the description of cellular processes using AI-based methods and expanded it after obtaining an ERC Starting Grant in 2010.

    Theis is a renowned protagonist of one of the most important areas in Data Science: application-oriented analysis and modeling in biomedicine. His work on single-cell genomics, for which he is developing AI-based analysis and modeling approaches, has gained worldwide recognition and dissemination. Fabian Theis' exceptional achievements have been recognized amongst others with the Science Prize of the City of Hamburg (2021), an ERC Advanced Grant (2022), and the Gottfried Wilhelm Leibniz Prize (2023).

  • Caroline Uhler
    Caroline Uhler, Ph.D.
    Professor, Department of Electrical Engineering and Computer Science and Institute for Data, Systems and Society, MIT and Broad

    Caroline Uhler is a core institute member of the Broad Institute of MIT and Harvard, where she co-directs the Eric and Wendy Schmidt Center, and she is a Full Professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society at MIT. Uhler obtained her Ph.D. in statistics from UC Berkeley and then spent three years as an assistant professor at IST Austria before joining the faculty at MIT in 2015. Uhler develops machine learning methods for integrating and translating between vastly different data modalities and inferring causal or regulatory relationships from such data. She is particularly interested in using these methods to gain mechanistic insights into the link between genome packing and regulation in health and disease. She has received multiple career prizes including an NIH New Innovator Award, a Simons Investigator Award, a Sloan Research Fellowship, and an NSF Career Award.

  • Mihaela van der Schaar
    Mihaela van der Schaar, PhD
    John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine, University of Cambridge

    Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Fellow at The Alan Turing Institute in London. In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM).

    Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.

    Mihaela is personally credited as inventor on 35 USA patents (the majority of which are listed here), many of which are still frequently cited and adopted in standards. She has made over 45 contributions to international standards for which she received 3 ISO Awards. In 2019, a Nesta report determined that Mihaela was the most-cited female AI researcher in the U.K.

  • Byung-Jun Yoon
    Byung-Jun Yoon, Ph.D.
    Scientist (BNL), Associate Professor (TAMU), Brookhaven National Laboratory, Texas A&M University

    Dr. Byung-Jun Yoon received a B.S.E. (summa cum laude) degree from the Seoul National University (SNU), Seoul, Korea, in 1998, and M.S. and Ph.D. degrees from the California Institute of Technology (Caltech), Pasadena, CA, in 2002 and 2007, respectively, all in Electrical Engineering. In 2008, he joined the Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA, where he is currently an Associate Professor. Dr. Yoon also holds a joint appointment at Brookhaven National Laboratory (BNL), Upton, NY, where he is a Scientist in Computational Science Initiative (CSI). His honors include the National Science Foundation (NSF) CAREER Award, the Best Paper Award at the 9th Asia Pacific Bioinformatics Conference (APBC), the Best Paper Award at the 12th Annual MCBIOS Conference, and the SLATE Teaching Excellence Award from the Texas A&M University System. Dr. Yoon’s main theoretical interests include objective-based uncertainty quantification, optimal experimental design (OED), machine learning, and signal processing. Application areas of interest include bioinformatics, computational network biology, and AI-driven drug/materials discovery.

  • James Zou
    James Zou, PhD
    Assistant Professor, Stanford University

    James Zou is an assistant professor of Biomedical Data Science, CS and EE at Stanford University. He develops machine learning methods for biology and medicine. He works on both improving the foundations of ML–-by making models more trustworthy and reliable–-as well as in-depth scientific and clinical applications. He has received a Sloan Fellowship, an NSF CAREER Award, two Chan-Zuckerberg Investigator Awards, a Top Ten Clinical Achievement Award, several best paper awards, and faculty awards from Google, Amazon, Tencent and Adobe.