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 .
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.
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. Rong Fan is the Harold Hodgkinson Professor of Biomedical Engineering and Professor of Pathology at Yale University. His research focuses on developing and applying single-cell and spatial omics technologies to study immune systems, cancer, and aging. His lab has pioneered technologies like the IsoCode microchip for high-throughput protein profiling, and spatial multi-omics platforms (e.g., DBiT-seq, spatial-ATAC-seq) to analyze tissue complexity at cellular resolution. He co-founded IsoPlexis, Singleron Biotechnologies, and AtlasXomics to commercialize these innovations. Education: PhD in Chemistry from UC Berkeley (2006), B.S. in Applied Chemistry from University of Science and Technology of China (1999). Postdoctoral training at Caltech before joining Yale in 2010. Research interests include CAR-T cell therapy optimization, spatial epigenomics, and multi-omics integration. Key achievements include discovering biomarkers predictive of CAR-T efficacy and defining spatial genomic landscapes in cancer and neuroinflammation. Awards: NSF CAREER Award, Packard Fellowship, election to AIMBE, CASE, and NAI. Serves on advisory boards for Bio-Techne and Yale Ventures. Active in training future scientists via the Yale Biomedical Engineering and Yale School of Medicine programs.
Vitaly Kheyfets, PhD, serves as Associate Professor in the Department of Pediatrics-Critical Care Medicine at the University of Colorado Anschutz Medical Campus School of Medicine, where he directs research at the intersection of pediatric critical care and cardiopulmonary pathophysiology with emphasis on pulmonary arterial hypertension (PAH). His primary research focuses on right ventricular adaptation to pulmonary hypertension, utilizing machine learning-driven multi-omics analysis to identify disease biomarkers and molecular networks. He pioneers computational fluid dynamics approaches for hemodynamic modeling in congenital heart conditions like Glenn physiology, while also investigating sleep oscillatory patterns as neurodegenerative biomarkers. His methodology integrates proteomics, spatial transcriptomics, and pressure waveform analysis to dissect vascular remodeling mechanisms. Publication trends reveal a strong emphasis on translating computational models into clinical applications for PAH prognostication, with recent work developing AI-cooperative diagnostic platforms and characterizing microvascular changes in the right ventricle. Cross-disciplinary collaborations span proteomics, imaging, and sleep neuroscience, demonstrating consistent innovation in both pulmonary hypertension and neurodegenerative disease biomarker discovery.
Dr. Christina Leslie is a Research Professor and Member of the Computational & Systems Biology Program at Memorial Sloan Kettering Cancer Center (MSK). She leads an active research laboratory focused on developing computational approaches to understand complex biological systems. Dr. Leslie earned her PhD from the University of California, Berkeley and has established herself as a leading computational biologist in cancer research and immunology. Computational & Systems Biology Program, Memorial Sloan Kettering Cancer Center Gerstner Sloan Kettering Graduate School of Biomedical Sciences Dr. Leslie's research focuses on developing novel computational methods to study cellular biological systems from a global and data-driven perspective. Her lab exploits diverse high-throughput functional and genomic data to understand molecular networks underlying fundamental cellular processes, including transcription regulation, pre-mRNA processing, signaling, and post-transcriptional gene silencing. Her algorithmic methods draw heavily on machine learning to build accurate predictive models from noisy and high-dimensional biological data. Key areas of interest include modeling cell-type specific transcriptional programs and dissecting co- and post-transcriptional regulation, particularly microRNA-mediated gene regulation. Analysis of Dr. Leslie's publication record over the last five years reveals a strong focus on computational approaches to cancer genomics, immunology, and epigenetics. Her work bridges multiple disciplines, with a particular emphasis on developing machine learning methods to interpret complex biological data. The publications demonstrate increasing sophistication in integrating multiple data types (genomic, transcriptomic, epigenomic) to understand cancer biology and immune responses. Recent work shows a growing emphasis on single-cell technologies and spatial analysis of tumor microenvironments. Introduction of string kernel methodology for SVM classification of biological sequences Development of algorithms for predictive modeling of gene regulation First systems-level analyses of competition between microRNAs and between target transcripts Dr. Leslie actively mentors numerous graduate students and research associates, with current lab members including Vianne Gao, Alireza Karbalaghareh, Erik Ladewig, and several others. Her lab has received significant research funding to support their work on computational approaches to cancer biology and immunology. The Leslie Lab maintains close collaborations with multiple experimental groups at MSK, facilitating the translation of computational insights into biological understanding. The Leslie Lab operates within the Computational & Systems Biology Program at MSK, with strong ties to both the research and clinical missions of the institution. The lab maintains state-of-the-art computational infrastructure for analyzing large-scale genomic and proteomic datasets and collaborates extensively with wet-lab researchers to validate computational predictions experimentally.
James Zou is an Associate Professor of Biomedical Data Science at Stanford University, with courtesy appointments in Computer Science and Electrical Engineering. His research focuses on advancing machine learning methodologies for healthcare applications, emphasizing reliability, fairness, and statistical rigor. He holds a Ph.D. from Harvard University and has held positions at Microsoft Research, Cambridge University (as a Gates Scholar), and UC Berkeley (Simons Fellow). Zou leads the Stanford Data4Health hub and is a Chan-Zuckerberg Investigator. His work spans AI-driven diagnostics, spatial transcriptomics, and ethical AI frameworks. Key achievements include the EchoNet AI system for echocardiography and foundational contributions to data valuation (e.g., Data Shapley). Awards include the Sloan Fellowship, NSF CAREER Award, and Google/Tencent AI awards. Education: Ph.D., Harvard University (2014); Postdoctoral roles at Microsoft Research, Cambridge, and Berkeley. Research Interests: Machine learning for healthcare, algorithmic fairness, interpretable AI, spatial omics, and translational bioinformatics. His lab develops tools like TextGrad (PyTorch for text agents) and frameworks for evaluating medical AI systems. Recent work addresses LLMs in peer review and clinical decision-making. Grants/Grants: Supported by NSF, Sloan Foundation, Chan-Zuckerberg Initiative, and industry partnerships (Google, Amazon, Adobe). Advises on over 20 doctoral students, many contributing to high-impact papers in Nature , Science , and top conferences (NeurIPS, ICML). Leads collaborations in cardiology, oncology, and veterinary medicine. Labs/Teams: Stanford AI Lab, Stanford Data4Health, and interdisciplinary groups in precision medicine. Active in open-source projects like FrugalML and MetaViz.
Ben Cosgrove is an Associate Professor in the Meinig School of Biomedical Engineering at Cornell University, serving as Director of Graduate Studies. His research focuses on systems bioengineering approaches to understand muscle stem cell dysfunction in aging and disease. He leads the Cosgrove Lab, a multidisciplinary group integrating biomedical engineering, stem cell biology, and systems biology to study microenvironmental signaling in muscle regeneration. His work includes developing biomimetic microenvironments for stem cell manufacturing and improving regenerative medicine therapies. Dr. Cosgrove holds a B.Eng. from the University of Minnesota (2003) and a Ph.D. in Bioengineering from MIT (2009). Postdoctoral training at Stanford University (with Dr. Helen Blau) followed. His research is supported by NIH grants (including R01, R21), the Glenn Medical Research Foundation, and others. He has been recognized with awards such as the BMES Graduate Research Award (2008), Rising Star Award (2015), and Swanson Teaching Excellence Award (2019). Research interests span bioengineering, biomechanics, computational science, and systems biology. His lab's innovations include spatial transcriptomic mapping and high-yield stem cell expansion platforms. Current projects aim to decode stem cell-niche interactions to treat muscle degeneration and aging. Grants: NIH K99/R00, R01, R21; Glenn Medical Research Foundation Labs/Teams: Cosgrove Lab (Cornell University) Future Work: Expanding applications of spatial transcriptomics and engineering regenerative therapies for muscle diseases
Maizie Zhou is an Assistant Professor in Biomedical Engineering and Computer Science at Vanderbilt University’s School of Engineering. She holds dual PhDs in Computer Science (Stanford University) and Neuroscience (Wake Forest School of Medicine), with additional degrees from Wake Forest University and Huazhong University of Science and Technology. Her research focuses on computational genomics, bioinformatics, and machine learning applied to problems in cancer genomics, single-cell and spatial transcriptomics, and computational neuroscience. She leads the Zhou Lab, which develops algorithms for structural variant detection, neural circuit analysis, and integrative omics approaches. Recent work includes tools like VolcanoSV and stDyer, and she has received grants from NIH, Vanderbilt Brain Institute, and industry partnerships. Key achievements include VUSE Best Paper Awards, Global Engagement Travel Grants, and mentoring students in prestigious programs like the Provost’s Pathbreaking Discovery Award. Her lab also explores the neural underpinnings of cognitive maturation in primates, combining computational and experimental neuroscience. Education: PhDs in Computer Science (Stanford) and Neuroscience (Wake Forest), MS (Computer Science, Wake Forest), BS (Biotechnology, Huazhong). Research interests span computational genomics (e.g., structural variant detection, haplotype phasing), spatial transcriptomics (clustering, integration), and computational neuroscience (neural circuit dynamics, prefrontal cortex plasticity). Her lab’s tools address challenges in precision medicine, cancer genomics, and understanding adolescent brain development. Recent projects include NIH-funded work on spatial transcriptomics and collaborations with Dr. Meltzer’s lab on cancer genomics. Publications highlight advancements in bioinformatics tools and neural mechanisms, with trends toward multi-omics integration and algorithmic innovation in genomics. Awards include the Global Engagement Travel Grant and CCSB Accelerator Fund. Students under her mentorship have excelled in qualifying exams and travel grants, reflecting her impactful training program.
Dr. David Burton is a Professor in the Department of Plant, Food, and Environmental Sciences at Dalhousie University's Faculty of Agriculture. He serves as Director of the Centre for Sustainable Soil Management and leads initiatives in soil health, greenhouse gas emissions, and sustainable agricultural practices. His teaching spans undergraduate and graduate courses in soil science, nutrient management, and climate change. Research focuses on microbial metabolism in soil, nitrogen cycling, and the environmental impacts of agricultural practices. He co-founded the Atlantic Soil Health Lab and manages the Greenhouse Gas Analysis Lab. Key affiliations include the Canadian Society of Soil Science (Fellow), Soil Conservation Council of Canada, and Fertilizer Canada's 4R Research Network. Recent work emphasizes soil's role in climate resilience, including presentations on regenerative farming and soil carbon sequestration. He collaborates with government and industry to develop climate-smart soil management policies and tools for nitrogen management optimization. Awards: Fellow of the Canadian Society of Soil Science Labs: Centre for Sustainable Soil Management, Greenhouse Gas Analysis Lab, Atlantic Soil Health Lab Grants: NSERC CREATE Climate Smart Soils
Sudin Bhattacharya is an Associate Professor at the BioMolecular Science Gateway, Michigan State University, with affiliations in the Genetics & Genome Sciences Program and Cell & Molecular Biology Program. His research bridges computational biology and toxicology to understand complex biological systems. Email: sbhattac@msu.edu Research Interests Dr. Bhattacharya specializes in systems toxicology, focusing on computational modeling of gene regulatory networks, single-cell transcriptomics, and molecular dynamics in response to environmental toxicants. His work examines how chemical exposures disrupt cellular pathways and contribute to disease mechanisms. Article Trends His recent publications emphasize: Single-cell and single-nucleus RNA sequencing for toxicological profiling Computational models of circadian rhythms and intercellular communication Dose-dependent responses to environmental chemicals like TCDD and heavy metals Mechanistic studies of adipose tissue remodeling and hypertension Applications of machine learning in chemical risk assessment Integrative approaches to liver metabolism and disease modeling Scientific Contributions Dr. Bhattacharya has pioneered multiscale modeling of biological systems, particularly in hepatic and vascular contexts. His work on the aryl hydrocarbon receptor and PPARα signaling networks has advanced predictive toxicology frameworks.
Ueli Grossniklaus is an Ordinary Professor at the University of Zurich within the Faculty of Mathematical and Natural Sciences , affiliated with the Department of Plant and Microbiology . His work focuses on plant developmental biology, particularly epigenetic and genetic mechanisms governing reproduction and adaptation. Key Courses: Epigenetics, Plant Biology Workshop, Group Seminars on Current Research Laboratory Techniques: Advanced methods in plant cell mechanics, transcriptomics, and genome editing Research Interests span plant epigenetics, reproductive biology, and the interplay between environmental stress and genetic regulation. He investigates: Mechanistic control of gametogenesis and fertilization Epigenetic contributions to plant adaptation Evolutionary implications of asexual reproduction Biophysical forces in plant cell growth Publication Trends (2025–2018) reveal expertise in: Arabidopsis and fern model systems Epigenetic regulation (DNA methylation, histone dynamics) Apomixis and hybrid seed failure mechanisms Biomechanics of pollen tubes and carnivorous plants Genome editing tools (CRISPR) and long-read sequencing Scientific Collaborations include interdisciplinary projects on: Microfluidic devices for plant cell analysis Gene drive ecology and ethics 3D imaging of plant reproductive structures Advising and Grants focus on mentoring through research internships in developmental biology, genetics, and systems biology. His lab engages in: Epigenetic response to environmental stress Cell wall mechanics in reproduction Computational modeling of plant growth Laboratory Teams integrate plant biologists, bioengineers, and computational scientists to study: Mechanistic gene regulation Evolutionary developmental biology Microrobotics for cellular force measurement
Nathan G. Swenson is a Professor in the Department of Biological Sciences at the University of Notre Dame and serves as the Gillen Director of the Environmental Research Center (UNDERC). He has held prior academic positions at the University of Maryland and Michigan State University, advancing from Assistant to full Professor. His research integrates genomics, ecology, and evolutionary biology to understand forest biodiversity and dynamics. Research Interests: Ecology and Environmental Biology Evolutionary Biology Genetics and Genomics Global Change Biology Forest Ecology Functional and Phylogenetic Ecology Tree Physiology and Demography His research focuses on leveraging intra- and interspecific variation in tree performance to predict forest biodiversity patterns. He employs integrative approaches from genomes to forest canopies and utilizes large-scale global datasets. Recent publications emphasize intraspecific trait variation, transcriptomic responses to drought, ecological forecasting, and functional group dynamics in tropical and temperate forests. Scientific Awards: Winner of 2017 British Ecological Society John Harper Prize (awarded to J. Zambrano; Swenson was senior author) Advising and Grants: Dr. Swenson has mentored numerous researchers, including M.N. Umana, S.J. Worthy, and J. Yang, who frequently co-author high-impact papers. His lab receives substantial research funding, evident from large collaborative projects and participation in global networks like ForestGEO and the TRY plant trait database. He has secured support for long-term ecological research, genomic studies, and international fieldwork. Labs and Teams: He leads the Swenson Lab, which conducts research on woody plant ecology in dynamic environments. The lab emphasizes community transcriptomics, functional trait analysis, and large-scale ecological modeling. It collaborates widely across institutions and is involved in major initiatives such as ForestGEO and NEON.
Thomas Walter is a Professor at Mines ParisTech and Director of the Centre for Computational Biology (CBIO) , a research group affiliated with the Institut Curie and INSERM . His work focuses on applying Machine Learning and Computer Vision to biomedical image analysis, particularly in high-content screening and computational pathology . He also serves as Deputy Director of the Computational Oncology (U1331) unit and leads the Statistical Learning and Modeling of Biological Systems team. PhD in Medical Image Analysis (2003, Mines ParisTech) Postdoctoral work at EMBL (European Molecular Biology Laboratory) Director of CBIO since 2018 Holder of a PRAIRIE Chair (Paris Artificial Intelligence Research Institute) since 2019 Dr. Walter's research bridges biomedical imaging , machine learning , and cancer genomics . Key areas include: Statistical reconstruction of biological networks Prediction of tumor progression at genomic/transcriptomic levels Development of deep learning methods for cell cycle analysis Integration of multi-omics data for precision oncology Tools for spatial transcriptomics (e.g., autoFISH, RNA2seg) Recent publications highlight his work in spatial transcriptomics , immunotherapy outcome prediction , and deep learning for digital pathology . His team has developed open-source tools like FISH-quant and pyHiM for single-molecule RNA imaging analysis. Scientific Honors: PRAIRIE Chair (2019) for AI research in life sciences Dr. Walter actively contributes to teaching deep learning for image analysis in multiple graduate programs across France, including courses at Mines ParisTech , Université Paris-Saclay , and Institut Curie . His software tools (FISH-quant, pyHiM) and methodological frameworks (e.g., Cut-Detector, PointFISH) have become standard resources in bioimage informatics.
Facundo M. Fernandez is a Regents' Professor and Vasser-Woolley Chair in Bioanalytical Chemistry at the Georgia Institute of Technology, where he leads the Fernandez Research Group within the School of Chemistry and Biochemistry in the College of Sciences. His research spans multiple cutting-edge areas of analytical chemistry with significant applications in medicine, forensics, and basic science. Dr. Fernandez earned his M.Sc. in Chemistry (1996) and Ph.D. in Analytical Spectrometry/Metallomics (1999) from the Facultad de Ciencias Exactas y Naturales at Buenos Aires University, Argentina. His research program focuses on Bioanalytical Mass Spectrometry with particular emphasis on Ambient Sampling/Ionization & Molecular Imaging, Ion Mobility Spectrometry, Metabolomics, and Pharmaceutical Forensics. His work has pioneered new approaches in ambient ionization techniques that enable direct analysis of complex samples without extensive preparation. His recent publications reveal a strong trend toward spatial metabolomics, particularly in traumatic brain injury and ovarian cancer research, with increasing integration of machine learning approaches for data analysis. His work also extends to pharmaceutical quality control, exercise physiology through the MoTrPAC consortium, and prebiotic chemistry investigations. The interdisciplinary nature of his research is evident in collaborations across Georgia Tech's campus and with external institutions. NSF CAREER Award (2007) 3M Non-tenured Faculty Award (2008) CETL/BP Junior Faculty Teaching Excellence Award (2009) Ron A. Hites Award for Outstanding Research Publication (2010) Sigma Xi (GT Chapter) Best Faculty Paper Award (2010) Vasser-Wooley Faculty Fellow (2012) Dr. Fernandez has secured significant funding for his research, including NSF CAREER support, and leads projects related to metabolomics for ovarian cancer detection, pharmaceutical forensics through the CODFIN network, and participation in the large-scale Molecular Transducers of Physical Activity Consortium (MoTrPAC). His laboratory develops advanced instrumentation for mass spectrometry applications and maintains strong collaborations with the Integrated Cancer Research Center, the College of Computing, and the Center for Chemical Evolution at Georgia Tech.
