Mona Singh is a Professor of Computer Science at Princeton University, with affiliations to the Lewis-Sigler Institute for Integrative Genomics and the Department of Molecular Biology. She has been a faculty member since 1999. Ph.D., Massachusetts Institute of Technology, 1995 A.B. and S.M. degrees in Computer Science from Harvard University Her research focuses on computational molecular biology, integrating machine learning and algorithms to analyze biological networks, protein interactions, and mutational impacts. Key areas include DNA/RNA binding prediction, protein structure analysis, and network-based disease gene discovery. Her recent work highlights trends in protein language models, kinase-substrate prediction, and equitable MHC binding algorithms. These span sub-fields like structural bioinformatics, network biology, and functional genomics. Scientific Awards: Presidential Early Career Award for Scientists and Engineers (PECASE) Rheinstein Junior Faculty Award ACM Fellow (2019) ISCB Fellow (2018) She has taught an introductory computational biology course with Professor Coleen Murphy, covering sequence analysis, phylogenetics, and network reconstruction. Her group has developed tools like dPUC , nCOP , and DiffMut . Her lab collaborates with institutions including Carnegie Mellon, Duke University, and the Broad Institute, advancing applications in cancer genomics, metabolic disease, and precision medicine.
Mike Kirby is a Professor at the Kahlert School of Computing, University of Utah. He also holds adjunct professorships in the Department of Bioengineering and the Department of Mathematics. His current roles include leadership in scientific computing and informatics initiatives, including former directorships of the Utah Informatics Initiative (2019-2023) and the Multi-Scale Multidisciplinary Modeling of Electronic Materials (MSME) Collaborative Research Alliance (2016-2022). He has extensive experience in strategic research initiatives, including serving as Assistant Vice President for Research (2024-2025). Education: Dr. Kirby earned a PhD in Applied Mathematics (2002) and MS in Computer Science (2001) from Brown University, and a BS in Applied Mathematics and Computer Science from Florida State University (1997). Research Interests: Focus on large-scale scientific computing, physics-informed machine learning, computational science and engineering, high-order numerical methods, and visualization. His work bridges applied mathematics and computer science to address real-world engineering challenges. Publications: Over 150 peer-reviewed articles, including high-impact contributions in journals like Journal of Computational Physics and SIAM Journal on Scientific Computing . Recent work emphasizes machine learning for differential equations, topology optimization under uncertainty, and multi-fidelity modeling. Awards: Recognized for leadership in computational science and informatics, including contributions to University of Utah’s Clery Compliance Program. Advising & Grants: Supervised over 50 graduate students and postdocs. Secured funding from NSF, DOE, and industry partnerships, totaling millions in research grants. Active in interdisciplinary collaborations across engineering, materials science, and medicine. Labs/Teams: Scientific Computing and Imaging (SCI) Institute, Utah Informatics Initiative, and the Center for Multiscale Modeling of Electronic Materials (MSME).
Brian Hie is an Assistant Professor of Chemical Engineering at Stanford University , a Dieter Schwarz Foundation Stanford Data Science Faculty Fellow , and an Innovation Investigator at Arc Institute . He leads the Laboratory of Evolutionary Design , focusing on the intersection of biology and machine learning . His prior roles include a Stanford Science Fellow in the Stanford University School of Medicine and a Visiting Researcher at Meta AI . Education: Ph.D. , Electrical Engineering and Computer Science , Massachusetts Institute of Technology (2021) Bachelor’s Degree , Stanford University Research Interests: Brian’s work bridges machine learning and computational biology , with a focus on protein engineering , single-cell RNA sequencing , and viral evolution . His Evolutionary velocity framework predicts protein evolutionary dynamics across timescales, while his Scanorama algorithm enables efficient integration of heterogeneous single-cell datasets. He also develops structure-informed language models for antibody optimization and uncertainty-aware ML for biological discovery. Publication Trends: His recent work (2023) emphasizes structure-based inverse folding for antibody evolution, evolutionary scale modeling , and unsupervised optimization . Earlier studies (2022-2021) cover evolutionary velocity , multi-modal single-cell analysis , and viral escape prediction using natural language analogies. Scientific Awards: Stanford Science Fellow (2021) National Defense Science and Engineering Graduate Fellowship (2019) Advising: He mentors doctoral students including Brandon Ameglio , Garyk Brixi , and Chang M. Yun , with a focus on biological design and computational methods . Labs & Collaborations: His lab collaborates with Bio-X and the Institute for Human-Centered Artificial Intelligence (HAI) , and he maintains affiliations with Sarafan ChEM-H and Stanford Data Science .
Dr. Michael Baym is an Associate Professor of Biomedical Informatics at Harvard Medical School with affiliate appointments in Microbiology and the Laboratory of Systems Pharmacology, and as an Associate Member of the Broad Institute. He leads the Baym Lab, which studies microbial evolutionary genomics and antibiotic resistance through a hybrid of experimental, computational, and theoretical approaches. His research focuses on: Antibiotic Resistance Evolution and practical interventions Mobile Genetic Elements (plasmids, phages, transposons) Computational Genomic Algorithms for big data analysis Synthetic Biology tools and technologies Key recent publications explore phage discovery systems , phylogenetic compression of microbial genomes, and RNA-guided gene drives in plasmids. His work is supported by multiple NIH/NIGMS and NSF grants including a MIRA award. Scientific honors include: Packard Fellowship (2018) Pew Biomedical Scholarship (2020) Sloan Research Fellowship (2020) A. Clifford Barger Excellence in Mentoring Award (2021) SSQBio Mentorship Award (2022) The lab actively trains PhD students and postdoctoral fellows with alumni occupying academic and industry positions globally. Current team members include researchers from interdisciplinary backgrounds working at the intersection of experiment, computation, and theory .
Marina Blanton is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, and Faculty Director of Women in Science and Engineering within the School of Engineering and Applied Sciences. She holds a PhD in Computer Science from Purdue University (2007), along with multiple advanced degrees in Computer Science and Electrical Engineering from prestigious institutions in the US and Russia. Her research focuses on applied cryptography, information security, and privacy-preserving computation and outsourcing. She has pioneered work on secure multi-party computation protocols, privacy-preserving biometric authentication, and secure data analytics across distributed systems. Her contributions include foundational frameworks like PICCO, a compiler for private distributed computation, and advancements in protocols for genomic data analysis and floating-point secure computation. Blanton has been recognized with numerous awards, including IEEE and ACM Senior Membership (2016/2015), the ACM CCS Test of Time Award (2015), and the AFOSR Young Investigator Award (2013). Her research has been supported by grants such as NSF SaTC awards and AFOSR funding. Her work emphasizes practical implementations of secure computation, with applications in healthcare, biometrics, and distributed data systems. She has advised numerous students and contributed to educational initiatives promoting women in STEM through her leadership roles.
Andrea Montanari is a Professor of Mathematics and Statistics at Stanford University, affiliated with the Department of Mathematics and Statistics. His research focuses on high-dimensional statistics, machine learning theory, optimization algorithms, and statistical physics, with applications to neural networks and complex systems. He has contributed extensively to understanding generalization in overparametrized models, spin glass theory, and algorithmic methods like approximate message passing. His work bridges theoretical computer science and mathematical physics, addressing challenges in data analysis and learning from high-dimensional datasets. Notable themes include analyzing neural network dynamics, optimizing high-dimensional landscapes, and developing efficient algorithms for sparse and low-rank matrix estimation. Montanari’s publications explore topics such as the interplay between statistical and computational limits, the behavior of gradient-based methods, and the theoretical foundations of modern machine learning. His recent research demonstrates a focus on fundamental questions in learning theory, including the study of phase transitions in statistical estimation, the role of overparametrization in generalization, and the mathematical underpinnings of contemporary algorithms. While no specific awards are listed here, his contributions reflect significant impact in interdisciplinary fields.
Tengyao Wang is a Professor in the Department of Statistics at the London School of Economics and Political Science (LSE), serving as the MSc Statistics (Financial Statistics) Programme Director. Prior to LSE, he held positions as a Lecturer at University College London and a Research Fellow at the Cantab Capital Institute for the Mathematics of Information, University of Cambridge. His research focuses on high-dimensional statistics, computational efficiency, and statistical limitations imposed by computational constraints. Education: PhD in Statistics under Prof Richard Samworth at the University of Cambridge, with earlier studies including a Part III Essay in Empirical Process Theory. Research interests include sparse signal detection, change-point analysis, dimension reduction, robust statistics, and applications in medical statistics, financial data analysis, and material discovery. Key contributions include methodologies for handling missing data, high-dimensional change-point detection algorithms, and statistical learning techniques. Publications span theoretical advancements and applied innovations, with recent work emphasizing deep learning with missing data, residual permutation tests, and semi-supervised learning via random projections. His work has been recognized with awards such as the Royal Statistical Society Research Prize (2019) and the Guy Medal in Bronze (2023). He is an Associate Editor of the Journal of the Royal Statistical Society, Series B (JRSS B), and actively contributes to open-source tools like the 'ocd' and 'MissInspect' R packages for changepoint detection and missing data analysis.
Yan Huang is an Associate Professor in the Department of Computer Science at Indiana University Bloomington, focusing on security and cryptography. His work bridges theoretical foundations with practical systems, emphasizing cryptographic protocols with strong security guarantees for generic computation. University: Indiana University Bloomington School: College of Arts and Sciences Department: Computer Science Academic Rank: Associate Professor His research combines theoretical computer science , program analysis , artificial intelligence , and software engineering to address real-world security problems. Key areas include secure computation , zero-knowledge proofs , and privacy-preserving technologies . Recent publications, such as Phecda (SP'25) and Dubhe (USENIX Security'23), highlight advancements in post-quantum cryptography , AES verification , and privacy-preserving deep packet inspection , reflecting his focus on scalable and practical cryptographic solutions. He has advised notable students including Changchang Ding (PhD, Computer Science) and Ruiyu Zhu (PhD, now at Facebook), and served on program committees for top conferences like ACM CCS and CRYPTO .
Ameet Talwalkar is an Associate Professor in the Machine Learning Department at Carnegie Mellon University and Chief Scientist at Datadog. He holds a PhD from the Courant Institute at NYU (2010) where he received the Janet Fabri Prize for Best Thesis. His professional achievements include co-founding Determined AI (acquired by HPE), creating MLlib in Apache Spark, co-authoring the textbook 'Foundations of Machine Learning,' and spearheading the MLSys conference. Talwalkar's research focuses on fundamental challenges in machine learning systems, including distributed ML, federated learning, neural architecture search, and human-AI interaction. His work bridges theoretical foundations with practical applications across domains like computational biology, PDE solving, and code generation. Current interests include AI for science, specialized model development, and agent-based systems. His publications demonstrate strong focus on ML systems optimization, foundation model evaluation, and interpretable AI. Recent works investigate specialized foundation models, PDE-solving frameworks, code generation tools, and human-AI interaction paradigms. The research consistently targets efficiency, scalability, and practical deployment challenges. Best Paper Award at EAAMO 2023 Best Student Paper at NYAS ML Symposium 2009 Runner-up for Best Real-world Application at Socal ML Symposium 2017 Janet Fabri Prize for Best PhD Thesis (2010) Talwalkar leads the CMU MLSys Lab focused on scalable ML systems and has served as Board President for the MLSys conference series. His educational contributions include developing courses like 'Machine Learning with Large Datasets' and creating the LEAF benchmark for federated learning and NAS-Bench-360 for neural architecture search.
Lior S. Pachter is the Bren Professor of Computational Biology and Computing and Mathematical Sciences at the California Institute of Technology (Caltech). He holds a B.S. from Caltech (1994) and a Ph.D. from MIT (1999). His affiliations include the Division of Biology and Biological Engineering at Caltech. Roles: Faculty member, Principal Investigator Departments: Computational Biology and Computing and Mathematical Sciences Research interests span computational and experimental genomics, with a focus on single-cell sequencing technologies and RNA biology. His lab develops tools like kallisto, sleuth, and gget for genomic analysis. Key contributions include methods for quantifying RNA-Seq data and analyzing high-dimensional genomic datasets. Publications highlight advancements in spatial genomics, bioinformatics tools, and genomic data retrieval. His work emphasizes open-source software, with repositories hosted on GitHub.
Mithuna S. Thottethodi is a Professor and Interim Associate Head of Teaching and Learning at the Elmore Family School of Electrical and Computer Engineering at Purdue University. He holds a B.Tech. from the Indian Institute of Technology, Kharagpur (1996), and a Ph.D. in Computer Science from Duke University (2002). His research focuses on computer architecture, interconnection networks, distributed systems, and security, with notable work on sparse tensor accelerators and processing-near-memory architectures. His work has been funded by the NSF, AT&T, and SK Hynix. Research interests include security, interconnection networks in multicores, storage performance optimization, and memory hierarchies. Notable contributions span hardware accelerators for machine learning, secure speculative execution, and datacenter network congestion control. He has advised over 20 graduate students, many of whom have joined top tech firms like Google, Microsoft, and Intel. Awards: NSF CAREER Award (2007) Wilfred Hesselberth Teaching Excellence Award (2021) Multiple Eta Kappa Nu Outstanding Professor Awards Teaching includes undergraduate courses like EE 437 (Computer Design) and graduate courses such as ECE 666 (Advanced Computer Architecture). He also advises VIP and EPICS student teams.
Dana Pe'er is a Professor and Chair of the Computational and Systems Biology Program at the Sloan Kettering Institute (SKI) of Memorial Sloan Kettering Cancer Center. She is also an Investigator of the Howard Hughes Medical Institute and holds the Alan and Sandra Gerry Endowed Chair. Dr. Pe'er leads an interdisciplinary research group that combines advanced genomics approaches with machine learning to address fundamental questions in biomedical science, with particular focus on cancer biology, developmental biology, and immunology. Dr. Pe'er earned her PhD from Hebrew University in Jerusalem, Israel. Her academic journey includes a postdoctoral fellowship with George Church at Harvard Medical School. Before joining Memorial Sloan Kettering Cancer Center in 2016, she held faculty positions at Columbia University. Dr. Pe'er's research focuses on understanding cellular plasticity, the consequences of intra-tumor heterogeneity, cancer evolution and metastasis, and the mechanisms by which regulatory circuits go awry in disease. Her lab combines single-cell and spatial profiling technologies with machine learning approaches to investigate gene regulation, cellular plasticity, and cell-cell communication in the contexts of cancer, immunity, and development. They are particularly interested in how organisms develop from a single cell to generate diverse cell types, how epigenetic control rewires during development, and how cells communicate to execute multicellular responses. Analysis of Dr. Pe'er's recent publications reveals a strong focus on developing computational methods for single-cell and spatial genomics data analysis. Her work spans cancer types including pancreatic, prostate, colorectal, and breast cancer, with emphasis on tumor heterogeneity, metastasis mechanisms, and cellular plasticity. A significant portion of her research involves creating novel algorithms and tools like CellRank, REUNION, and SEACells that enable researchers to extract meaningful biological insights from complex genomic datasets. 2023 Class of 2023 Inductee - American Academy of Cancer Research (AACR) Academy 2023 Innovator Award - International Society for Computational Biology (ISCB) 2021 Fellow - International Society for Computational Biology (ISCB) Howard Hughes Medical Institute Investigator (2021) 2019 Ernst W. Bertner Memorial Award - University of Texas MD Anderson Cancer Center 2016 Lenfest Distinguished Faculty Award - Columbia University 2014 Director's Pioneer Award - National Institutes of Health 2014 Overton Prize - International Society for Computational Biology (ISCB) Dr. Pe'er is known for her dedicated mentorship approach, describing herself as "a mama bear" who cares deeply about her trainees while expecting independence, innovation, and hard work. She mentors numerous PhD students and postdocs in her lab. Her HHMI Investigator award provides approximately $9 million over seven years, enabling ambitious research directions. She also collaborates extensively with the Single-cell Analytics and Innovation Lab (SAIL) at MSK to generate new data from emerging technologies, working closely with wet-lab collaborators at MSK and beyond to apply computational methods to cutting-edge datasets across multiple disease areas. The Pe'er Lab is an interdisciplinary group of computational biologists with diverse backgrounds ranging from pure mathematics to clinical medicine. They work closely with wet-lab collaborators to apply their computational methods to cutting-edge datasets across cancer, immunology, and developmental biology. The lab is described as open, supportive, collaborative, and fun, with access to world-class facilities at the Sloan Kettering Institute. Dr. Pe'er's work continues to push the boundaries of computational biology and cancer research, with the ultimate goal of developing more effective, personalized therapies for cancer patients.
Dr. Igor V. Pivkin is a Full Professor at the Institute of Computing within the Faculty of Informatics at the Università della Svizzera italiana (USI) in Lugano, Switzerland. His academic journey includes degrees from Novosibirsk State University (B.Sc./M.Sc. Mathematics), Brown University (M.Sc. Computer Science and Ph.D. Applied Mathematics), and postdoctoral research at MIT's Department of Materials Science and Engineering. His research focuses on multiscale/multiphysics modeling , numerical methods , and large-scale simulations of biological and physical systems. Key areas include biophysics, cellular/molecular biomechanics, stochastic modeling, and coarse-grained molecular simulations. He leverages high-performance computing (HPC) and particle-based methods to address complex biological phenomena. His work spans diverse applications, from understanding cellular mechanosensitivity and biofilm engineering to modeling cancer cell behavior and red blood cell dynamics in the spleen. His contributions bridge computational science, biotechnology, and biomedical research. He has published extensively in top-tier journals, with recent work advancing automated biofilm analysis, deep learning for microbial classification, and systems biology approaches to metal bioleaching. His lab collaborates on interdisciplinary projects, emphasizing computational innovation for real-world biological challenges.
Dr. Gabriele Schweikert is a Senior Lecturer and Principal Investigator with a joint appointment between the Division of Computational Biology in the School of Life Sciences at University of Dundee and Cyber Valley in Tuebingen. Her research focuses on applying machine learning techniques to understand epigenetic mechanisms and molecular processes in living cells. Dr. Schweikert completed her PhD at the Max Planck Institute Tuebingen working with Schoelkopf, Weigel, and Raetsch labs on machine learning for computational gene finding. She subsequently joined Adrian Bird's lab at the Wellcome Trust Center for Cell Biology in Edinburgh, a pioneer in epigenomic research. Prior to her current position, she held prestigious Marie Curie and EMBO Fellowships at the School of Informatics, University of Edinburgh. Her research interests center on using machine learning to decode epigenetic mechanisms that determine cellular identity and function. She investigates how cells with identical DNA can differentiate into specialized cell types through epigenetic regulation, with particular focus on applications in understanding tumorigenesis where epigenetic machinery malfunctions. Her work combines high-throughput epigenomic data with advanced computational approaches to address complex biological questions. Analysis of her recent publications reveals a strong focus on epigenomic data analysis, machine learning applications in biology, and computational approaches to understanding gene regulation. Her work spans from fundamental epigenetic mechanisms to practical applications in disease research, with growing emphasis on individual-specific epigenomic analysis and explainable AI in biomedical contexts. UKRI Future Leaders Fellowship (2020, £1.6 million) Marie Curie Fellowship EMBO Fellowship Dr. Schweikert actively supervises PhD students and has received significant research funding for projects including 'Machine Learning Methods to Re-Annotate Histone Modifications,' 'Unlocking The Alternative Splicing Code,' and 'GPU-Based Machine Learning System For Fundamental Biological Research.' She is involved in multiple interdisciplinary collaborations and frequently presents her work at major conferences including ELLIS Health program retreat, Epigenetics Meetings, and RECOMB workshops. She maintains active research laboratories in both Dundee and Tuebingen, fostering international collaboration between computational biologists, machine learning experts, and experimental biologists to advance our understanding of epigenetic regulation in health and disease.
Timo Minssen is Professor of Law at the University of Copenhagen (UCPH) and the Founding Director of UCPH's Center for Advanced Studies in Bioscience Innovation Law (CeBIL). He also holds affiliations as an LML Research Affiliate at the University of Cambridge and an Inter-CeBIL Research Affiliate at Harvard Law School's Petrie-Flom Centre. With extensive expertise in Intellectual Property, Competition, and Regulatory Law, Minssen focuses on the legal aspects of emerging health and life science technologies, including genome editing, big data, artificial intelligence, and quantum technology. His educational background includes a German law degree (Staatsexamen) from Georg-August-University in Göttingen, and Swedish biotech & IPR related LL.M., LL.Lic., and LL.D. degrees from Lund University and Uppsala University. His PhD thesis on the patentability of biopharmaceutical technology in the US & Europe received the prestigious Swedish King Oscar award. 2024: TUM Global Visiting Professor, Technical University of Munich (Germany) 2016: Visiting Research Fellow, University of Cambridge (UK) 2014: Visiting Research Fellow, University of Oxford (UK) 2013-14: Visiting Scholar, Harvard Law School (US) 2012: LL.D. - Doctor of Laws (Swedish "juris doktor"), EU/US patent law, Lund University, Sweden Minssen's research spans AI & Big Data in Health & Life Sciences, Sustainable and responsible innovation & tech transfer, Pharmaceutical-, Life Science- & Biotech Law, Comparative European & US Patent Law, Intellectual Property Law & Open Innovation, and EU Competition- & US Antitrust Law. His work addresses legal issues throughout the lifecycle of health and life science products and processes, from R&D regulation to technology transfer and commercialization. His extensive publication record includes 7 books and over 200 articles and book chapters published in leading journals such as Science, Nature Biotechnology, JAMA, and Harvard Business Review. His research has been featured in The Economist, Financial Times, and other major media outlets. Minssen's recent work shows a strong focus on AI regulation, quantum technology law, and data governance in health contexts, reflecting the evolving landscape of technology and law. Scientific Awards and Recognition King Oscar award for best Jur. Dr. thesis (2014) Jorcks Fonds Forsknings Pris (Jorck's Foundation Research Prize) (2017) Awapatent Research Prize (2009) Max Planck Research Scholarship (2005) Visiting Scholar appointments at Harvard Law School, University of Oxford, and University of Cambridge Recipient of a Novo Nordisk Foundation Grant for a "Collaborative Research Program in Biomedical Innovation Law" (2018) As an advisor, Minssen serves international organizations including the WHO, WIPO, and EU Commission. He has supervised numerous PhD students in areas including pharmaceutical law, biotechnology patents, and antimicrobial resistance. His current research projects include the Novo Nordisk Foundation's International Collaborative Bioscience Innovation & Law (Inter-CeBIL) Programme (50 million DKK), CLASSICA: EU Horizon Project on AI-assisted surgery, and AI@Care: Law and Ethics and Algorithmic Bias in Healthcare. Minssen leads the Center for Advanced Studies in Bioscience Innovation Law (CeBIL), which serves as a hub for interdisciplinary research on the intersection of law, technology, and innovation in the health and life sciences. The center collaborates with institutions worldwide to address pressing legal challenges in emerging technologies.
