Joel Weadge is an Associate Professor in the Biology Department at Wilfrid Laurier University , Waterloo, Ontario. His research focuses on bacterial biofilms, glycobiology, and protein structure-function relationships. Contact: jweadge@wlu.ca , Office: BA425 (Bricker Academic). Education: PhD in Microbiology (University of Guelph, 2006) BSc (Hons) in Microbiology (University of Guelph, 2000) Research Interests center on bacterial biofilms as virulence factors in pathogens like E. coli and Salmonella . Key areas include: Structural and functional characterization of biofilm proteins (cellulose, curli fimbriae) Enzymology of carbohydrate modifications (acetylation, phosphoethanolamine transfer) Developing therapeutics targeting biofilm synthesis Biopolymer applications for medical/industrial use Publications highlight studies on Pseudomonas and Salmonella biofilm mechanisms, glycosyltransferases, and carbohydrate-active enzymes, with methodologies spanning X-ray crystallography to high-throughput biofilm profiling. Labs and Teams: The Weadge Lab investigates biofilm roles in food/water security and oral health, utilizing enzymology, mass spectrometry, and structural biology. Current members include graduate students, technicians, and research assistants.
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 .
Josh Atkinson is an Assistant Professor in the Department of Civil and Environmental Engineering and the Omenn-Darling Bioengineering Institute at Princeton University. His research focuses on using synthetic biology and protein engineering to control electron transport in microbes for environmental applications, such as bioelectronic sensors and bioremediation. The Atkinson Lab investigates microbial energy processing, biofilm-electronic interfaces, and sustainable biotechnologies. Affiliations: Princeton University, Omenn-Darling Bioengineering Institute Research Interests: Microbial electron transport, bioelectronic systems, environmental monitoring, sustainable catalysis His work bridges disciplines like electrochemistry, bioengineering, and environmental science to engineer living materials for real-world challenges. The lab recruits students across levels, emphasizing diversity and interdisciplinary collaboration. Recent projects include real-time contaminant sensors and light-controlled biofilm patterning. Articles highlight innovations in bioelectronics and microbial systems engineering. The lab’s future directions involve scaling-up bioelectronic devices and enhancing microbial community understanding.
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 .
Angel Xuan Chang is an Associate Professor at Simon Fraser University's School of Computing Science, where she leads research at the intersection of natural language processing, computer vision, and 3D scene understanding. She holds the prestigious Canada CIFAR AI Chair position and is affiliated with multiple research groups including 3DLG, GrUVi, SFU NatLang, SFU AI/ML, and VINCI. PhD in Computer Science, Stanford University MSc in Computer Science, Stanford University M.Eng in Electrical Engineering and Computer Science, MIT BSc in Computer Science and Engineering, MIT Professor Chang's research primarily focuses on connecting language to 3D representations of shapes and scenes, with particular emphasis on grounding language for embodied agents in indoor environments. Her work spans natural language processing and understanding, linking natural language with visual and 3D representations, multimodal grounding of language, embodied AI, and machine learning applications for biodiversity monitoring through the BIOSCAN project. She has developed methods for synthesizing 3D scenes and shapes from natural language and created various datasets for 3D scene understanding. Her recent publications reveal a strong trend toward integrating language understanding with 3D scene generation and manipulation, with increasing focus on practical applications in embodied AI and biodiversity monitoring. The research shows progression from foundational work on text-to-3D scene generation to more sophisticated approaches for evaluating semantic coherence in generated scenes and developing efficient methods for zero-shot scene modeling. Canada CIFAR AI Chair TUM-IAS Hans Fischer Fellow (2018-2022) Best paper award at 3DV 2025 for 'An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion' Professor Chang actively advises numerous graduate students who appear as first authors on her publications, indicating a strong mentoring program. Her research is supported through multiple channels including the CIFAR AI Chair position and likely various research grants supporting her BIOSCAN-related work and 3D scene understanding projects. She has been involved in organizing multiple workshops at major conferences including ICML, CVPR, and ICLR. Her research is conducted through several interconnected groups: 3DLG (3D Language and Graphics), GrUVi (Graphics, Vision, and Interaction), SFU NatLang (Natural Language Processing), SFU AI/ML, and VINCI. These groups work collaboratively on problems spanning language grounding, 3D scene understanding, embodied AI, and biodiversity applications, creating a rich interdisciplinary research environment.
Professor Colin Semple is a leading researcher at the University of Edinburgh's Institute of Genetics and Cancer (IGC), where he serves as Group Leader and Head of Bioinformatics. His work is conducted within the MRC Human Genetics Unit, focusing on computational genomics and the analysis of structural mutations in both germline and cancer contexts. Professor Semple's research investigates the origins and impacts of structural mutations in the human genome, with particular emphasis on how these alterations affect gene function in developmental contexts and drive cancer progression. His group studies complex structural rearrangements in challenging cancer types including ovarian cancer, glioblastoma, and mesothelioma, where tumor genomes undergo dramatic reorganization. The research is guided by four key questions: What are the origins of structural mutations? How do they impact gene function? How does structural complexity drive disease progression? How do diverse mutational constellations combine to create adaptations and vulnerabilities? Analysis of Professor Semple's publications reveals a consistent focus on structural variation in cancer genomics, with particular attention to ovarian cancer mechanisms, lesion segregation in tumor evolution, and the functional consequences of genomic rearrangements. His work frequently employs whole genome sequencing approaches to uncover previously hidden layers of genomic variation that affect more of the genome than traditional short variants. Professor Semple leads a substantial research team including bioinformaticians and PhD students, and oversees the Bioinformatics Analysis Core which provides collaborative expertise to over 500 researchers at the IGC. His group maintains strong collaborations with both local researchers at the University of Edinburgh and international consortia, working closely with clinicians to translate genomic findings into potential diagnostic and therapeutic approaches. The Semple Lab is funded by major organizations including the Medical Research Council (MRC), Cancer Research UK (CRUK), and the Chief Scientist Office (CSO).
Simone Fior is a Lecturer at the Department of Environmental Systems Science , ETH Zürich , focusing on ecological genetics and plant adaptation. Their research integrates genomic, quantitative genetics, and ecological field experiments, particularly on Dianthus (Caryophyllaceae) along altitudinal and climatic gradients. Recent work explores climate-induced range shifts, local adaptation, and genomic responses to environmental changes. Professional experience includes roles at ETH Zürich since 2013 (Senior Assistant, Postdoc) and prior positions at the Edmund Mach Foundation (2009-2012) and University of Insubria (2007-2008). Education spans a PhD in Plant Biology (University of Milan, 2007) and an MSc in Natural Sciences (University of Milan, 2003). Simone co-organizes the Bioinformatics for Adaptation Genomics Winter School . Key research areas include adaptive divergence , polygenic adaptation , climate change biology , and phylogenomics . Articles emphasize genomic selection signatures, functional-structural modeling, and ecological-genetic interactions. Notable collaborations involve Jake Alexander, Alex Widmer, and interdisciplinary teams at ETH Zurich.
George Perry is a Professor of Anthropology at Pennsylvania State University, with research intersections in Biology, Evolutionary Medicine, and Genomics. He is affiliated with the Huck Institutes' Center for Infectious Disease Dynamics, Ecology, Molecular Cellular and Integrative Biosciences, and Bioinformatics and Genomics programs. Perry directs the Anthropological Genomics Lab , focusing on paleogenomics and evolutionary adaptation. Research areas: anthropological genomics, parasite evolution, human body size transitions, and evolutionary medicine Key collaborations: international teams in Madagascar, Europe, and Africa Leadership: Bioinformatics and Genomics Chair (2019–2023) His 2025–2022 publications span evolutionary responses to invasive species, human migration health impacts, chemosensory gene adaptation, and primate genomic diversity. Notable methodological contributions include ancient DNA recovery and comparative paleogenomics. Perry advises graduate students like Vanessa Garcia and Annette Mercedes, with grants including NIH support for Cuban health disparity studies. Scientific leadership includes tenure-line promotions (2023) and NASA Space Grant collaborations.
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.
Ana Damjanovic is an Assistant Research Professor in the Thomas C. Jenkins Department of Biophysics at Johns Hopkins University (JHU), affiliated with the Zanvyl Krieger School of Arts & Sciences. Her research focuses on ion channels, protein and membrane electrostatics, and computational biophysics. She holds a Ph.D. in Physics from the University of Illinois at Urbana-Champaign, where she studied quantum physics of photosynthetic light harvesting under Prof. Klaus Schulten. Subsequent postdoctoral research included work on photosynthesis with Prof. Graham Fleming at UC Berkeley, and molecular dynamics studies of protein ionization at JHU. Her current lab investigates ion channel mechanisms, protonation dynamics, and electrostatic effects in biological systems using advanced computational tools. Group members include graduate student Nauman Sultan (co-supervised with NIH's Bernard Brooks) and undergraduates Marianne Ri and Vivek Booshan. Past advisees include Ada Chen (now a NIH postdoc) and Maggie Li. Key research contributions include developing pH replica exchange methods, protein pKa prediction using machine learning, and structural-functional studies of voltage-gated sodium channels. Her work has been published in high-impact journals like Proceedings of the National Academy of Sciences and Biophysical Journal . Lab affiliations include the Computational Biophysics Group at JHU, with access to cutting-edge simulation techniques and experimental validation platforms. Ongoing projects explore ion channel selectivity, membrane protein dynamics, and computational modeling of protonation-dependent phenomena.
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.
Nathan (Nati) Linial is a Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, where he has been a faculty member since completing his postdoctoral period at UCLA. He earned his undergraduate degree in mathematics from the Technion and his PhD in graph theory from the Hebrew University. His research spans multiple areas of theoretical computer science and mathematics, with primary focus on combinatorics, theoretical computer science, and bioinformatics. Linial's work has made significant contributions to high-dimensional combinatorics, expander graphs, metric embeddings, and computational molecular biology. His research often bridges geometry, analysis, and combinatorial structures, demonstrating deep connections between seemingly disparate mathematical fields. Linial's recent publications reveal a strong trend toward high-dimensional combinatorial structures, including simplicial complexes, hypertrees, and high-dimensional permutations. His work frequently employs probabilistic methods, linear programming techniques, and geometric approaches to solve fundamental combinatorial problems. The breadth of his research is evident in both pure mathematical contributions and applications to computational biology. Fellow of the American Mathematical Society ISI Highly Cited Researcher Conant Prize (2008) for the influential survey paper "Expander graphs and their applications" Linial has served on the editorial boards of several prestigious journals including the Israel Journal of Mathematics (as Chief Editor 2013-2017), Random Structures and Algorithms, and Combinatorica. His academic leadership extends to organizing conferences and workshops in combinatorics and theoretical computer science. He has mentored numerous students whose work spans theoretical computer science, combinatorics, and computational biology. Linial is associated with research projects including ProtoNet (for protein sequence classification) and EVEREST (for evolutionary conserved protein domains), demonstrating his commitment to interdisciplinary research that bridges computer science with molecular biology.
James C. Gumbart is an Adjunct Professor in the School of Physics at Georgia Institute of Technology, with additional affiliation to the School of Chemistry and the Institute for Bioengineering and Bioscience . His research leverages molecular dynamics simulations to decode the atomic-level mechanisms of bacterial proteins and cellular structures. B.S., Physics and Mathematics, Western Illinois University, 2003 Ph.D., Physics, University of Illinois at Urbana Champaign, 2009 Dr. Gumbart's work bridges computational biophysics and biochemistry to understand: Mechanisms of bacterial membrane protein insertion and nutrient import Structural dynamics of cell wall mechanics SARS-CoV-2 spike protein interactions with ACE2 Free-energy calculations for protein-ligand binding Applications of machine learning in biomolecular simulations His publications reflect trends in membrane protein biophysics , viral dynamics , and computational drug design , with a strong emphasis on interdisciplinary techniques. Awards include multiple fellowships and grants from NSF , DOE , and NIAID . He has mentored numerous PhD students, including Zijian Zhang , David Ryoo , and Andrew Pang , whose work has advanced understanding of bacterial systems and viral proteins. The Gumbart Lab integrates high-powered supercomputing and advanced software to model biomolecular processes, fostering collaborations with institutions like the National Institutes of Health and Argonne National Laboratory .
Celeste Sagui is a Professor in the Department of Physics at North Carolina State University (NC State), affiliated with the College of Sciences. She holds additional roles as a faculty affiliate in Genomics Sciences at NC State and is a member of the Center for High Performance Simulation. Her research focuses on computational biophysics, biomolecular simulations, and free energy methods applied to nucleic acid structures, protein dynamics, and nanotechnology systems. She has contributed to the AMBER simulation package development, co-authoring versions from 10 to 14. Education: Doctorate in Physics, University of Toronto (1995) Licentiate degree, National University of San Luis, Argentina Research Interests: Sagui’s work explores DNA/RNA structure and phase transitions, electrostatic interactions, and methodologies for large-scale molecular simulations. Recent studies include nucleic acid hairpin instabilities linked to neurodegenerative diseases, polyglutamine aggregation mechanisms, and novel DNA motifs like the eGZ structure in Z-DNA. She employs quantum chemistry, density functional theory, and phase-field models to investigate systems ranging from biomolecules to nanomaterials. Publications: Her recent work emphasizes nucleic acid dynamics, free energy landscapes, and computational methods for studying diseases such as Friedreich’s ataxia and polyglutamine disorders. Key contributions include advancements in laser-driven simulations and infrared spectroscopy analysis of protein structures. Labs/Teams: Active in the Center for High Performance Simulation, focusing on high-throughput computational modeling and collaborative software development for biomolecular research.
