Kelly Arnold is an Associate Professor in the Department of Biomedical Engineering at the University of Michigan. Her research integrates systems engineering principles with immunology to investigate variability in immune responses across infection, vaccination, and injury, with a focus on computational modeling and clinical translation. Research Focus Systems-level immune response modeling Vaccination and antibody functionality Vaginal microbiome-host interactions Chronic lung disease progression Computational serology and proteomics Recent Work Her 2025 studies examine SARS-CoV-2 vaccination responses in cancer patients and computational frameworks for vaginal probiotics. Earlier works (2024-2007) span COPD progression, lupus fibrosis, HIV susceptibility, and tissue engineering for fertility preservation. Methodologies include proteomic profiling, network modeling, and microfluidic systems.
Sushmita Roy is a Professor at the University of Wisconsin–Madison, affiliated with the Department of Computer Sciences and the College of Letters and Science. Her research focuses on developing computational methods in statistical machine learning to understand gene regulatory networks in living cells, particularly under environmental, developmental, disease, and evolutionary contexts. She explores bulk and single-cell genomic data integration to study processes like cell fate specification, host-microbe interactions, and diseases such as cancer and neurodevelopmental disorders. Her work emphasizes three key areas: inference of genome-scale transcriptional networks, evolutionary analysis of regulatory networks, and 3D genome organization dynamics. Roy’s lab collaborates across disciplines, leveraging genomic data from plant and mammalian systems. She has contributed to methodologies for analyzing chromatin accessibility, single-cell profiling, and network-based models of pathogen systems. Her affiliations include Wisconsin Institutes for Discovery, and she is a leader in computational biology and systems genomics research.
David S. Eisenberg is a Professor of Chemistry and Biochemistry and Biological Chemistry at the University of California, Los Angeles, where he also serves as Director of the UCLA-DOE Institute for Genomics and Proteomics and as an HHMI Investigator. His research focuses on protein interactions, particularly the structural basis for conversion of normal proteins to the amyloid state and conversion of prions to the infectious state. Dr. Eisenberg earned his undergraduate degree in biochemical sciences from Harvard College and his D.Phil. degree in theoretical chemistry from Oxford University on a Rhodes Scholarship. His postdoctoral research was on ice and water with Walter Kauzmann at Princeton and in protein crystallography with Richard Dickerson. He joined the UCLA faculty after his postdoctoral studies. Dr. Eisenberg and his research group focus on protein interactions in amyloid and prion diseases. These diseases involve protein aggregation where normal functional proteins convert to abnormal aggregated forms. Systemic amyloid diseases like dialysis-related amyloidosis result from fiber accumulation until organ failure, while neurodegenerative diseases like Alzheimer's, Parkinson's, ALS, and prion conditions appear to be caused by smaller oligomers. In 2005, his team determined the atomic-level structure for the amyloid fiber spine, revealing a 'steric zipper' of two parallel beta sheets packed across a dry interface. Since then, they've determined approximately 90 amyloid spines from 15 disease-related proteins. In 2010, they identified the structure of a toxic amyloid-related oligomer consisting of six anti-parallel beta strands forming a cylindrical barrel. His recent publications demonstrate continued innovation in amyloid research, with focus areas including structural prediction of amyloid formation, mechanisms of tau fibril disassembly in Alzheimer's disease, cryo-EM analysis of amyloid polymorphism, and structure-based design of inhibitors for amyloid toxicity. His work integrates computational, structural, and biochemical approaches to understand protein aggregation across multiple disease contexts. Dr. Eisenberg has received numerous prestigious awards and honors: National Academy of Sciences Member American Philosophical Society Member Institute of Medicine Member Howard Hughes Medical Institute Investigator Biophysical Society Emily M. Gray Award Harvard Westheimer Medal UCLA Seaborg Medal Technion - Israel Institute of Technology Harvey Prize in Human Health As Director of the UCLA-DOE Institute for Genomics and Proteomics and an HHMI Investigator, Dr. Eisenberg leads significant research initiatives in protein structure and aggregation. His laboratory combines X-ray crystallography, bioinformatics, and biochemical techniques to investigate protein interactions, with particular emphasis on amyloid-forming proteins and their role in disease. The Eisenberg Lab, located in Boyer Hall at UCLA, maintains an active research program investigating the structural basis of protein aggregation. The lab continues to build on its landmark discoveries of amyloid structures while exploring new frontiers in understanding protein misfolding diseases and developing potential therapeutic interventions.
Dr. Lourdes Pena-Castillo is a Professor jointly appointed in the Departments of Computer Science and Biology at Memorial University of Newfoundland's Faculty of Science. Her research focuses on applying machine learning and bioinformatics to study bacterial gene regulation, with emphasis on transcriptomics, gene expression pathways, and microbiology. She leads the Bioinformatics Lab at MUN, developing computational tools like Promotech for promoter prediction and sRNARFTarget for sRNA target identification. Education: BSc in Information Systems Engineering, ITESM-Mexico MSc in Computer Science, University of Alberta PhD in Computer Science (Doktoringenieurin), Otto-von-Guericke Universität Magdeburg Postdoc in Bioinformatics, University of Toronto Research Interests: Bioinformatics, Genomics, Machine Learning, Artificial Intelligence, Transcriptomics, Gene Regulation, Microbiology Her work integrates computational methods with biological data to address challenges in molecular biology, including analyzing bacterial sRNA functions, promoter recognition, and disease diagnostics using machine learning. She has advised numerous graduate students, including PhD candidates Purvikalyan Pallegar and Bonita McCuaig, and MSc students like Ruben Chevez-Guardado and Kratika Naskulwar. Her lab focuses on translational research with applications in both basic science and clinical contexts. Publications span computational methods for bacterial gene regulation, bioinformatics tool development, and interdisciplinary projects in VR and healthcare informatics. Her research has contributed to understanding symbiotic relationships in marine organisms, inflammatory bowel disease diagnostics, and clavulanic acid production in Streptomyces. Grants & Collaborations: Works with interdisciplinary teams across computer science and biology, supported by grants enabling projects in bacterial genomics and computational tool development. Labs & Teams: Leads the Bioinformatics Lab at MUN, fostering collaborations with researchers in microbiology, computer science, and healthcare.
Paul Lu is a Professor in the Department of Computing Science at the University of Alberta, Faculty of Science. His research focuses on high-performance computing, parallel and distributed systems, cloud computing, and bioinformatics. He holds a B.Sc. (1991), M.Sc. (1993) in Computing Science from the University of Alberta, and a Ph.D. in Computer Science from the University of Toronto (2000). His research explores software systems, including operating systems, virtual machines, and parallel programming. Recent work emphasizes high-performance data transfers and IaaS cloud computing. He teaches courses such as MINT 706: Internet Application and Programming, covering internet protocols and client-server programming. Publications highlight contributions to network optimization, machine learning-driven protocol selection, and distributed systems. His work bridges theoretical advancements with practical applications in cloud infrastructure and wide-area networks.
Christopher M. Overall is a Full Professor at the University of British Columbia in the Faculty of Dentistry, Department of Oral Biological and Medical Sciences . He is also a Principal Scientist at the Centre for Blood Research and holds associate memberships in UBC's Biochemistry & Molecular Biology , Obstetrics and Gynecology , and Bioinformatics Graduate Program departments. As a Canada Research Chair Laureate , he pioneered the field of degradomics to study proteases in vivo. B.D.S., University of Adelaide Ph.D., University of Toronto Postdoctoral Fellowship, UBC (with Nobel Laureate Michael Smith) Dr. Overall’s research focuses on protease proteomics and systems biology , particularly degradomics to analyze protease substrates in diseases like COVID-19 and immunodeficiency . His work on matrix metalloproteinases has revealed new therapeutic strategies for inflammatory diseases and cancer . His 15 most recent articles (2015–2008) demonstrate expertise in TAILS proteomics , protein terminomics , and protease network analysis with applications in arthritis , antiviral immunity , and precision medicine . Scientific Awards 2022 Helmut Holzer Award 2018 Royal Society of Canada Fellow 2014 Tony Pawson Canadian Proteomics Award 2013 IADR Distinguished Scientist Award Dr. Overall has mentored 61 trainees , including 9 full professors with department chairs, and received the UBC John McNeill Mentorship Award (2023). He leads the HUPO Chromosome-centric Human Proteome Project and consults for Genentech and Novartis .
Raphael Hauser is an Associate Professor in Numerical Mathematics at the University of Oxford's Mathematical Institute, Director of Graduate Studies - Teaching, and Tanaka Fellow in Applied Mathematics at Pembroke College. His affiliations include membership in the Data Science, Numerical Analysis, and Mathematical and Computational Finance research groups, as well as a fellowship at the Alan Turing Institute. Education: PhD in Operations Research, Cornell University, Ithaca, USA Dipl. Math. ETH, Swiss Federal Institute of Technology (ETH Zurich), Switzerland Research interests span data science, numerical optimisation, medical imaging, distributed computing, and applied probability/statistics. His work integrates mathematical rigor with practical applications, particularly in optimization algorithms, machine learning theory, and medical imaging technology. Publications focus on optimization theory, stochastic processes, medical imaging systems, and computational finance, with recurring themes in non-convex optimization guarantees, PCA variants, and X-ray tomography innovations. Awards: Oxford University Teaching Award (2007) SIAM Optimization Prize (2005) SIAM Student Paper Prize (2000) Advising includes 15+ DPhil students and 40+ MSc students, with projects in optimization, finance, imaging, and machine learning. Current postdocs and students are affiliated with the Alan Turing Institute and industrial partners like Siemens and Macquarie Group. He leads teams in the Mathematical Institute's research groups and collaborates with the Alan Turing Institute on large-scale data science initiatives.
Professor Yiming Ying is a faculty member in the Faculty of Science at the University of Sydney, where he joined in December 2023. Previously, he held tenured positions at SUNY Albany (Departments of Mathematics & Statistics and Computer Science) and was a Lecturer at the University of Exeter. He completed his PhD in Mathematics at Zhejiang University (2002) and postdoctoral training at CityU Hong Kong, UCL, and University of Bristol. Research Focus His research spans statistical learning theory, optimization algorithms, trustworthy AI, and data science mathematics. Key applications include cancer informatics for early detection. His work aligns with Faculty research strengths in Data and Decisions and Decision-Making for a Sustainable Future. Recent Research Trends Analysis of recent publications shows strong focus on theoretical foundations of machine learning: differential privacy, fairness algorithms, optimization methods for AUC maximization, generalization guarantees, and robust learning techniques for adversarial settings and biological data. Awards and Honors SUNY Chancellor’s Award for Excellence (2023) University at Albany Presidential Research Award (2022) University of Exeter Merit Award (2012) Grants and Advising Significant funding includes current ARC DP250101359 (2025-2028) and multiple past NSF grants. He founded the UALBANY Machine Learning Group and currently advises PhD student Peilin LIU on operator learning.
Zhe Ji is an Assistant Professor in the Department of Biomedical Engineering at McCormick School of Engineering and the Department of Pharmacology at Feinberg School of Medicine, Northwestern University. His research integrates computational and experimental genomics to study gene transcription and RNA translation in cell fate commitment and oncogenic processes, aiming to develop precision medicine strategies. **Education**: Postdoctoral Fellow in Cancer Systems Biology, Harvard Medical School Postdoctoral Fellow in Computational Biology, Broad Institute of MIT and Harvard Ph.D. in Computational Genomics, Rutgers University B.S. in Biotechnology, Nanjing University, China **Research Focus**: Keywords include Data Science, Computational Biology, Functional Genomics, RNA, Cancer, Inflammation, and Machine Learning. The lab explores regulatory mechanisms underlying disease, with a focus on translational control, cancer metastasis, and inflammatory networks. **Grants & Advising**: No specific grants or student advisees listed. The lab emphasizes collaborative projects and computational-experimental approaches. **Lab Affiliations**: Zhe Ji’s lab is part of Northwestern’s interdisciplinary environment, bridging engineering and medicine to advance genomic technologies and therapeutic strategies.
Benjamin Machta is an Assistant Professor of Physics at Yale University, affiliated with the Department of Physics and the QBio Institute. He holds a BS from Brown University and a PhD from Cornell University, followed by a postdoctoral fellowship at Princeton University. His research focuses on applying theoretical physics to understand biological systems, particularly leveraging statistical physics and information theory to study biological membranes near critical points and the energetic constraints of biological signaling. Education: BS in Physics (Brown University), PhD in Physics (Cornell University), Postdoc at Princeton University (Lewis-Sigler Theory Fellow). Research Interests include: membrane criticality, phase transitions in biological systems, information-theoretic limits in organism function, and energy dissipation in biological processes. His work often bridges theoretical models with experimental data, such as collaborations with Sarah Veatch’s lab on membrane phase behavior. Publications highlight themes like membrane criticality, protein phase separation, and energy constraints in signaling. His group’s current projects explore cochlear mechanics, thermodynamic control in biological systems, and the role of criticality in sensory systems. Awards: 2019 Simons Investigator Award. Lab Affiliations: QBio Institute and Department of Physics at Yale, located in YSB-C164. Group members include postdocs Isabella Graf and Michael Abbott, and graduate students Asheesh Momi, Mason Rouches, and others.
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.
Karoline Faust is an Associate Professor at KU Leuven, affiliated with the Laboratory of Molecular Bacteriology (Rega Institute) and the Faculty of Medicine . She contributes to the iSi Health and Leuven One Health institutes, and serves on senior academic councils. Her research spans microbial systems biology, focusing on community dynamics and network analysis. Education: PhD in bioinformatics (2010, KU Leuven) Affiliations: KU Leuven, ISME Journal editorial board, Belgian Society for Microbiology Her research investigates microbial community dynamics , systems biology approaches to microbiomes, and bioinformatics tool development . She specializes in modeling human gut microbiota , synthetic microbial communities , and environmental microbiomes (e.g., microplastic impacts on Daphnia microbiomes). Her work integrates metabolic modeling , network analysis , and experimental systems to understand microbial interactions. Recent publications highlight her contributions to microbial network inference , 16S rRNA sequencing protocols , microfluidics , and ecological modeling of microbiomes. She develops tools like manta , miaSim , and CoNet to analyze community structures. Teaching: Karoline co-teaches courses in microbiology, bioinformatics, and network analysis at KU Leuven, and has contributed to international workshops on microbial network inference. Scientific Engagement: She serves as Senior Editor at ISME Journal and Secretary of the Belgian Society for Microbiology .
Pan Xu is a tenure-track assistant professor with joint appointments in the Department of Biostatistics & Bioinformatics, Department of Computer Science, and Department of Electrical & Computer Engineering at Duke University. Previously, Xu was a Postdoctoral Scholar Research Associate at Caltech's Department of Computing and Mathematical Science and earned a Ph.D. in Computer Science from UCLA. Xu's research focuses on developing computationally- and data-efficient machine learning algorithms with strong empirical performance and theoretical guarantees. Xu's research interests center around Machine Learning with broad applications in Artificial Intelligence, Data Science, Optimization, Reinforcement Learning, and High Dimensional Statistics. The research specifically targets real-world problems in Bioinformatics and Healthcare, with recent work emphasizing distributionally robust decision making, efficient exploration strategies, and multi-agent systems. Xu has developed novel algorithms that address the challenges of exploration in sequential decision making and robustness to distributional shifts between training and deployment environments. Xu's recent publications demonstrate a strong trend toward developing theoretically grounded yet practical algorithms for reinforcement learning and bandit problems, with particular emphasis on distributionally robust methods, efficient exploration techniques, and applications to healthcare. The work spans both theoretical analysis (providing minimax optimal regret bounds) and practical implementations (validated on benchmarks like Atari games and real healthcare datasets). Whitehead Scholar award from Duke University School of Medicine (2023) Best Paper Award at ACM FAccT 2023 for Queer In AI paper PIMCO Postdoctoral Fellowship in Data Science (2022) TMLR Featured Certification (2023) NSF award on approximate sampling based exploration (2023) Xu actively mentors multiple Ph.D. students across Duke's Biostatistics & Bioinformatics, Computer Science, and Electrical & Computer Engineering programs, with several alumni now pursuing doctoral studies at top institutions. The research group has secured competitive funding including an NSF award for approximate sampling based exploration for sequential decision making. Xu serves as an action editor for TMLR and as an area chair for major conferences including ICML, NeurIPS, AAAI, ICLR, and AISTATS. Xu leads a dynamic research group focused on sequential decision making, with projects spanning theoretical algorithm development, implementation of practical systems, and applications to healthcare and bioinformatics. The group maintains active collaborations across Duke's medical and engineering schools, with recent work applying machine learning to epidemic forecasting during the pandemic.
Prof. Dr. Deniz Tasdemir is a Full Professor (W3) of Marine Natural Products Chemistry at GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel and serves as Director of the GEOMAR-Biotech center and Head of the Marine Natural Product Chemistry Research Unit. Her career spans institutions including the National University of Ireland Galway and UCL School of Pharmacy. PhD in Pharmacy, ETH Zurich (1997) Post-doctoral work, University of Utah (2001) Dr. Helmut Legerlotz Fellowship, University of Zurich (2002-2025) Her research focuses on marine chemical ecology , metabolomics , and bioprospecting for bioactive compounds from sponges, algae, and marine microbiomes. Recent work explores seagrass pathogen reduction, microbiome interactions, and aquafeed applications. Scientific awards include: Waters Award for Natural Products Innovation (2016) Egon Stahl Silver Medal (2005) Pierre Fabre Prize (2004) ETH Zurich Medal (1997) She leads collaborative projects on ocean sustainability and marine drug discovery, with editorial roles in Marine Drugs , Planta Medica , and Phytochemistry Letters .
Heping Zhang is the Susan Dwight Bliss Professor of Biostatistics at the Yale School of Public Health , with secondary appointments in the Child Study Center , Department of Statistics and Data Science , and Department of Obstetrics, Gynecology, and Reproductive Sciences . He directs the Collaborative Center for Statistics in Science (C²S²) and leads the Reproductive Medicine Network data coordinating center. Education: PhD in Statistics, Stanford University (1991) Postdoctoral Fellow, Mathematical Science Research Institute (1991) Research Focus : Zhang specializes in biostatistical methodology for genomic data analysis , clinical trials , and reproductive medicine . His work bridges genetics , mental health , and maternal-child health through innovative statistical approaches. Awards : 2023 Web of Science Highly Cited Researcher 2023 International Chinese Statistical Association Distinguished Achievement Award 2022 Institute of Mathematical Statistics Neyman Award and Lecture 2011 Royan Institute International Research Award 2011 Institute of Mathematical Statistics Medallion Award 2008 Harvard School of Public Health Myrto Lefokopoulou Distinguished Lecturer Professional Roles : He served as President of the International Chinese Statistical Association (2019) and Former Editor of the Journal of the American Statistical Association - Applications and Case Studies . His lab develops open-source software tools like ABESS , STREE , and modSaRa for genomic and clinical data analysis.
