Harri Lähdesmäki is an Associate Professor (tenured) at the Department of Computer Science, Aalto University, where he leads the Computational Systems Biology research group. His work focuses on probabilistic machine learning and deep generative models with applications in biomedicine and molecular biology. Key Research Interests: Probabilistic machine learning, deep generative models, computational biology, bioinformatics, longitudinal data modeling Contact: harri.lahdesmaki@aalto.fi | Konemiehentie 2, 02150 Espoo, Finland His recent publications highlight advancements in: Gaussian process priors for scalable deep generative models Single-cell analysis of immune repertoires in leukemia and diabetes Probabilistic deconvolution methods for RNA-seq data Epigenetic analysis using hidden Markov and mixed models Transformer-based survival prediction and missing data handling Harri’s work integrates mechanistic modeling with Bayesian inference, particularly applied to immunology, cancer biology, and early disease prediction.
Professor David Grainger is a faculty member at the University of Birmingham's School of Biosciences, specializing in Molecular Microbiology. He leads the Grainger Lab, focusing on bacterial chromosome biology, pathogenicity, and antibiotic resistance. His research integrates high-throughput techniques and single-molecule analysis to study gene regulation and bacterial pathogenesis. Education: PhD (2004), PGCE (2000), BSc (1999) in Biochemistry from the University of Birmingham. Affiliations: Part of the Institute of Microbiology and Infection (IMI), collaborating with experts in genomics, proteomics, and structural biology. Research Interests: Deciphering chromosome biology of pathogenic bacteria, including transcriptional regulation, toxin production control, and antibiotic resistance pathways. Utilizes cutting-edge methods like Hi-C for 3D chromatin analysis and single-molecule microscopy. Recent Articles: Focused on transposon capture mechanisms, bacterial promoter diversity, and quorum sensing signaling. Highlights include studies on Salmonella regulons and Vibrio cholerae biofilm suppression. Awards: Wellcome Trust Career Development Fellowship (2008), Runner-up in 'Science Snaps' competition for scientific communication. Grants: Career Development Fellowship-funded establishment of his research group at the University of Warwick (2008). Labs/Teams: Grainger Lab at the University of Birmingham, part of the IMI network. Engages in public science outreach via Twitter and lab website.
Rhenish Friedrich Wilhelm University of BonnGermany
Prof. Waldemar Kolanus leads the Molecular Immunology and Cell Biology department at the University of Bonn's Life & Medical Sciences Institute (LIMES) . His research bridges immunoregulation , stem cell dynamics , and metabolic stress responses in immune cells. Unit 2 member at LIMES Principal investigator in SFB 704 and ImmunoSensation Cluster Leads a multidisciplinary lab with postdocs, PhD students, and technical staff His work focuses on intracellular signaling pathways connecting immune activation to tissue homeostasis, particularly through: Cytohesin proteins in integrin-mediated adhesion and migration TRIM71 in stem cell regulation and congenital hydrocephalus High-salt environments affecting macrophage function Publication trends show expertise in immune cell migration , genetic models , and chemical inhibition , with frequent use of mice and zebrafish for in vivo studies. Key articles explore: TRIM71's dual role in auditory development and germ cell maintenance Cytohesin family's Golgi regulation and insulin signaling Ruxolitinib's off-target migration inhibition of dendritic cells Contact details: Address: LIMES Institute, Carl-Troll-Straße 31, Bonn Email: kolanus.sekretariat@uni-bonn.de Phone: +49 228 73-62788
Ron Dror is the Cheriton Family Professor of Computer Science at the Stanford Artificial Intelligence Lab , with courtesy appointments in Structural Biology and Molecular & Cellular Physiology . He also holds affiliations with Bio-X, the Institute for Human-Centered Artificial Intelligence (HAI), the Institute for Computational and Mathematical Engineering (ICME), Sarafan ChEM-H, and the Wu Tsai Neurosciences Institute. Education: PhD in Electrical Engineering and Computer Science, MIT MPhil in Biological Sciences, University of Cambridge (Churchill Scholar) BS in Mathematics and Electrical & Computer Engineering, Rice University (summa cum laude) Ron leads a multidisciplinary research group that combines molecular simulation and machine learning to study biomolecular structure, dynamics, and function. His work focuses on developing computational methods to accelerate drug discovery by predicting molecular interactions and designing more effective therapeutics. Current projects include the PENSA software library for analyzing biomolecular ensembles and FRAME framework for structure-based ligand design. His research has produced groundbreaking work on G-protein-coupled receptors (GPCRs) , RNA structure prediction , and mitochondrial transport mechanisms . Key publications highlight applications of geometric deep learning and molecular dynamics simulations in structural biology. Scientific Awards: Cheriton Family Professorship (2023) Two Gordon Bell Prizes (2014, 2009) Best Paper Awards at NeurIPS (2021), IPDPS (2013), SC11 (2011), SC09 (2009), SC06 (2006) Science Magazine Top 10 Breakthrough (2010) Fulbright Scholarship , NSF Fellowship , DoD Fellowship , Whitaker Foundation Fellowship Ron has advised numerous doctoral and master’s students including EJ Fine , Masha Karelina , and Briana Sobecks . His lab collaborates with experimentalists across academia and industry, applying computational methods to diverse biomedical problems such as RNA structure prediction , GPCR signaling , and mitochondrial metabolism .
Pengtao Xie is an Associate Professor (with tenure as of June 2025) in the Department of Electrical and Computer Engineering at the University of California San Diego. He also serves as Associate Adjunct Professor in the Division of Biomedical Informatics, Department of Medicine, and holds affiliate appointments with the Halıcıoğlu Data Science Institute, School of Biological Sciences, Shu Chien-Gene Lay Department of Bioengineering, Skaggs School of Pharmacy and Pharmaceutical Sciences, and multiple research institutes including the AI Group, Center for Machine-Intelligence, Computing and Security, Institute of Engineering in Medicine, and Institute for Genomic Medicine. Education: PhD in Machine Learning, School of Computer Science, Carnegie Mellon University Research Interests: His research focuses on machine learning inspired by human learning skills, such as self-explanation, small-group learning, and learning by teaching. He applies these techniques to large language models, foundation models, healthcare, and biomedicine. His work spans generative AI, medical imaging, protein modeling, and drug discovery. Recent Research Trends: His 2024–2025 publications emphasize generative AI for ultra-low-data medical image segmentation, multimodal large language models for biomedical applications, protein function prediction, and novel training strategies like task-adaptive pretraining and bi-level optimization for model adaptation. Scientific Awards: NIH MIRA Award (2025) NSF CAREER Award (2024) Best Graduate Teacher Award – UCSD ECE (2023) ICLR Notable-Top-5% Paper (2023) Global Top-100 Chinese Young Scholars in AI (2022) UCSD Faculty Career Development Award (2022) Tencent Faculty Award (2021) Outstanding Reviewer – ICLR (2021) AMIA Doctoral Dissertation Award Finalist (2020) Amazon AWS Research Award (2020) Tencent AI-Lab Faculty Award (2020) Innovator Award – Pittsburgh Business Times (2018) Siebel Scholarship (2014) Advising and Grants: He currently advises PhD students, postdocs, and master’s students. He has received major grants including the NIH MIRA and NSF CAREER awards, and actively mentors Schmidt AI in Science postdocs and graduate students. Teaching and Labs: He teaches ECE285 Deep Generative Models and ECE175B Probabilistic Reasoning and Graphical Models . His lab focuses on foundational and translational AI research with applications in biomedicine and healthcare.
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.
University of Texas Southwestern Medical CenterUnited States
Masato Kato serves as Professor in the Department of Biochemistry at the University of Texas Southwestern Medical Center since 2020 and concurrently as Team Leader at Japan's National Institutes for Quantum and Radiological Science and Technology. His academic trajectory includes progressive appointments from Assistant Professor (2004-2014) to Associate Professor (2014-2020) at UT Southwestern, with prior postdoctoral training at Harvard Medical School and Nara Institute of Science and Technology. 2020-present: Professor, Department of Biochemistry, UT Southwestern 2020-present: Team Leader, National Institutes for Quantum and Radiological Science and Technology, Japan 2014-2020: Associate Professor, Department of Biochemistry, UT Southwestern 2010-2014: Assistant Professor, Department of Biochemistry and Internal Medicine, UT Southwestern 2004-2010: Assistant Professor, Department of Internal Medicine, UT Southwestern 1999-2004: Postdoctoral Fellow, Ellenberger Lab, Harvard Medical School 1998-1999: Postdoctoral Fellow, Hakoshima Lab, Nara Institute of Science and Technology Dr. Kato's research pioneers the biophysical characterization of protein phase separation, particularly focusing on low-complexity domains (LCDs) in neurodegenerative disease contexts. His work establishes fundamental mechanisms of biomolecular condensate formation, including hydrogel polymerization, liquid-solid transitions, and mutation-induced dysregulation in ALS/FTD. Key contributions demonstrate how C9orf72-encoded poly-dipeptides disrupt nucleocytoplasmic transport and how redox states regulate Ataxin-2 phase behavior, bridging structural biochemistry with pathological mechanisms. Analysis of his 22 publications reveals a cohesive research program centered on LCD-driven phase transitions. The most recent 15 articles (2012-2019) systematically investigate pathological aggregation in neurodegeneration, structural basis of condensate formation, and regulatory mechanisms like phosphorylation and oxidation. This body of work establishes LCDs as central players in both physiological RNA granule assembly and disease-associated solidification, with strong emphasis on C9orf72-related ALS/FTD mechanisms. Dr. Kato maintains active leadership within the McKnight Laboratory at UT Southwestern, where his team employs integrated approaches spanning structural biology, cell biology, and biophysics to dissect phase separation mechanisms. His collaborative network includes prominent neuroscience and biochemistry groups, with co-authorship on key studies in Cell, Science, and PNAS.
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 .
Dieter Braun is a Professor in the Faculty of Physics at Ludwig Maximilian University of Munich (LMU), leading the Functional NanoSystems research group. He serves as speaker of the CRC 235 Emergence of Life and coordinates the Molecular Origins component of the Origins Cluster. Dr. Braun holds an ERC Synergy Grant (starting April 2025), leads the CRC 392 Molecular Evolution (starting April 2024), and is a Fellow in the Max Planck School Matter to Life (since October 2023). His research focuses on understanding the physical mechanisms that could have led to the emergence of Darwinian evolution from prebiotic molecules on early Earth. Braun's laboratory investigates non-equilibrium settings, particularly asymmetrically heated open cracks in rocks, which create intricate wet-dry cycles, temperature gradients, and fluidic effects that could drive molecular evolution. His work bridges physics, chemistry, and biology to explore how dead molecules might combine through physical forces into autonomous mechanisms of evolution. Analysis of Braun's recent publications reveals a strong focus on thermal gradients and non-equilibrium physics in prebiotic environments. His research demonstrates how heat flows can concentrate molecules, drive polymerization, create pH gradients, and enable non-enzymatic replication of nucleic acids. The publications span high-impact journals including Nature, Nature Physics, and Nature Chemistry, showing interdisciplinary work connecting physics, chemistry, geology, and biology in the context of life's origins. Klung-Wilhelmy Weberbank Price (2011) Technology Transfer Price of the DPG (with LMU and NanoTemper) Deutscher Innovationspreis (2012) Step Award (2012) Dr. Braun has successfully mentored numerous PhD students, including Stefan Duhr and Philipp Baaske who founded the award-winning startup NanoTemper Technologies. His research is supported by multiple prestigious grants including ERC Starting, Advanced, and Synergy Grants, as well as funding from the Simons Collaboration on the Origins of Life. His laboratory collaborates extensively with other researchers across disciplines and institutions, particularly with Hannes Mutschler in the new ERC Synergy project. The Braun laboratory operates within the CRC 235 Emergence of Life and the Origins Cluster at LMU Munich, with strong connections to the Max Planck Society through the Max Planck School Matter to Life. The research group maintains active collaborations with geochemists, biophysicists, and molecular biologists to create comprehensive experimental models of prebiotic environments.
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 .
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.
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.
Jens S. Andersen is a Professor in the Department of Biochemistry and Molecular Biology at the University of Southern Denmark, where he leads research in Biomedical Mass Spectrometry and Systems Biology. His work is centered on the development and application of quantitative mass spectrometry and microscopy-based proteomics to study human cell biology, particularly the structure and function of organelles such as centrosomes, cilia, autophagosomes, and mitochondria. His research focuses on determining the protein composition and dynamic properties of cellular organelles, the roles of specific protein groups, and their contributions to biological processes and diseases. He investigates cell signaling mediated by post-translational modifications, especially within the DNA damage response, autophagy, and immune systems. His lab, the Jens S. Andersen Lab, is part of the Research Section of Biomedical Mass Spectrometry. The analysis of his recent publications reveals a strong interdisciplinary trend combining proteomics, structural biology, and cell signaling. His work spans cilia biology, RNA metabolism, DNA repair, and cancer mechanisms, with frequent use of advanced techniques like mass spectrometry, CRISPR, and live-cell imaging. The integration of systems biology approaches is evident across his research outputs. Professor, Department of Biochemistry and Molecular Biology, University of Southern Denmark Head of Research, Biomedical Mass Spectrometry and Systems Biology Principal Investigator, Jens S. Andersen Lab ORCID: 0000-0002-6091-140X While no specific scientific awards are mentioned in the provided texts, his extensive publication record in high-impact journals such as Science , Nature Communications , Molecular Cell , and EMBO Journal reflects significant scholarly contributions. He has supervised research projects and collaborated widely across Europe, though specific names of students are not listed. His research is supported by multiple ongoing projects, reflecting sustained funding and academic leadership. The Jens S. Andersen Lab operates at the intersection of proteomics and cell biology, contributing to fundamental understanding of organelle dynamics and disease mechanisms. The lab's work is highly collaborative, involving partnerships with groups in structural biology, RNA research, and cancer biology.
Peter A. Jones is President and Chief Scientific Officer at the Van Andel Institute (VAI) in Grand Rapids, Michigan, where he leads the Department of Epigenetics. He previously served as Director of the USC Norris Comprehensive Cancer Center from 1993 to 2011 and has been a central figure in advancing epigenetics research, particularly in cancer. His laboratory investigates DNA methylation, chromatin dynamics, and epigenetic therapies. Research Interests: Dr. Jones's work centers on epigenetic mechanisms in cancer, including DNA methylation, histone modifications, nucleosome positioning, and the therapeutic potential of epigenetic drugs. His research has pioneered the use of DNA methylation inhibitors like 5-azacytidine and explored viral mimicry as a mechanism for immune activation in cancer. He also studies transposable elements and their role in gene regulation and immune response. Publication Trends: His recent publications (2021–2024) reveal a strong focus on the interplay between epigenetics and immunotherapy, particularly how DNA methyltransferase inhibitors (DNMTi) induce viral mimicry, enhance immune recognition, and improve responses to checkpoint blockade. Studies span hematological malignancies, solid tumors, and T cell biology, with frequent collaboration with Stephen Baylin and others. Scientific Awards: Member, National Academy of Sciences Member, National Academy of Medicine Fellow, AACR Academy Fellow, AAAS Fellow, American Academy of Arts and Sciences Kirk A. Landon Award for Basic Cancer Research (2009) Medal of Honor, American Cancer Society (2011) Outstanding Investigator Grant, NCI Harvey Prize (2024) Advising and Grants: Dr. Jones mentors multiple postdoctoral fellows, graduate students, and research scientists. His lab is supported by major grants, including the VAI-SU2C Epigenetics Dream Team, which has launched 15 clinical trials. He has received sustained funding from the National Cancer Institute and collaborates with institutions worldwide to advance epigenetic therapies. Labs and Teams: He leads the Peter Jones Laboratory at VAI, a multidisciplinary team investigating epigenetic regulation in cancer. The lab includes computational biologists, clinical researchers, and molecular biologists, working on both basic mechanisms and translational applications. The team is part of larger collaborative initiatives such as the VAI-SU2C Epigenetics Dream Team and the International Linked Clinical Trials Program.
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 .
