Find Academic Supervisors

John MarshallView profile

Professor

• Mathematical modeling of infectious diseases
• Genetics-based strategies for mosquito control
• Malaria elimination
• +4 more

John Marshall is a Professor in the Department of Biostatistics and Epidemiology at the School of Public Health, University of California, Berkeley. His research focuses on using mathematical models to inform novel genetics-based strategies for mosquito control and to support efforts to control and eliminate mosquito-borne diseases such as malaria, dengue, and Zika virus. Dr. Marshall received his PhD in biomathematics from UCLA in 2008, with a dissertation on the use of GM mosquitoes to control malaria transmission. Prior to joining UC Berkeley, he worked on several aspects of this project as a PostDoc at the UCLA Center for Society & Genetics, the Malaria Research and Training Center in Mali, Caltech, and Imperial College London. His research interests include mathematical modeling of infectious diseases, genetics-based strategies for mosquito control, malaria elimination, dengue control, Zika virus control, gene drive systems, and vector control. He has developed the Mosquito Gene Drive Explorer (MGDrivE), a framework for testing gene-drive releases for mosquito-borne diseases control that models inheritance matrices, life-history, and migration across spatial landscapes. Dr. Marshall has secured significant funding for his research, including an $800,000 grant from the Bill & Melinda Gates Foundation in 2021 for genetics-based malaria mosquito control and an NIH grant in 2020 for mosquito movement and control research. He leads the Marshall Lab which collaborates with researchers from Tecnológico de Monterrey and IHME through the Mosquito Networks Taskforce (MoNeT). His recent publications demonstrate interdisciplinary work spanning epidemiology, genetics, computational biology, and vector control, with a focus on gene drive systems, surveillance methods, and cost-effectiveness analyses for malaria elimination. $800,000 grant from the Bill & Melinda Gates Foundation (2021) NIH grant for mosquito movement and control (2020) Dr. Marshall mentors numerous students and researchers including Héctor M. Sánchez C. (lead of MoNeT), Kevin Islas Abud, Luis Rodrigo Careaga Sotomayor, and others working on machine learning applications, network theory, and spatial analysis related to mosquito-borne diseases. He teaches courses PH 252B Modeling the dynamics of infectious disease processes and PH 295 Infectious disease modeling seminar at UC Berkeley.

Auguste GenovesioView profile

Researcher

• Computational Bioimaging
• Bioinformatics
• Machine Learning
• +7 more

Auguste Genovesio is a Research Director (DR INSERM) leading the Computational Bioimaging and Bioinformatics team at the Centre for Computational Biology within the École Normale Supérieure (ENS) in Paris. His work focuses on large-scale cellular morphology analysis, integrating machine learning, microscopy, and computational modeling to study cellular responses to perturbations. His team develops algorithms for analyzing high-dimensional biological data, with applications in drug discovery, functional genomics, and neuroscience. Education and Affiliations: Genovesio’s research is anchored at ENS and collaborates with institutions like Institut Curie, Collège de France, and ESPCI. His lab develops open-source tools such as PySpacell and ALFA , advancing spatial analysis and genomic data processing. Research Interests: His group combines deep learning, bioinformatics, and experimental biology to tackle challenges in cellular dynamics, morphological heterogeneity, and predictive modeling. Recent work includes applying diffusion models to reveal subtle phenotypes and optimizing microscopy image analysis pipelines. Key Projects: Cross-modal knowledge distillation for transcriptomics, latent diffusion models for small datasets, and super-resolution microscopy via StyleGAN regularization. Applications: Collaborations in drug screening, neurobiology (e.g., Drosophila memory studies), and cancer cell analysis. Publications: Over 50 peer-reviewed articles since 2007, including work in Nature Communications , Developmental Cell , and NeurIPS . Recent focus on generative AI for biological image analysis and self-supervised learning biases. Grants & Awards: While specific grants aren’t listed, his lab’s cutting-edge research suggests significant institutional and collaborative support. No explicit awards mentioned in texts. Labs/Teams: Director of the Computational Bioimaging group, part of the Functional Genomics section at ENS. Supervises PhD students and postdocs in AI-driven biology and computational microscopy.

Leonie BaumannView profile

Assistant Professor

• Economic Theory
• Networks
• Mechanism Design
• +1 more

Dr. Leonie Baumann is an Assistant Professor in the Department of Economics at McGill University, specializing in Economic Theory, Networks, Mechanism Design, and Game Theory. She holds a Ph.D. (summa cum laude) and M.Sc. in Economics from the University of Hamburg, and dual B.A. degrees from the University of Siegen. Currently on maternity leave, Dr. Baumann previously served as a Postdoctoral Research Associate at the University of Cambridge. Her research explores social interactions in microeconomic theory, with a focus on network formation dynamics, strategic evidence disclosure, and robust implementation mechanisms. Recent work examines how network structures influence economic decision-making and resource allocation. Dr. Baumann's publications demonstrate consistent focus on network economics and game-theoretic modeling, with emerging applications in discrimination mechanisms and evidence disclosure. Her methodological approaches combine theoretical rigor with computational innovations. Awards & Recognition: Vice-Chancellor's Innovation Award (2020) Econometric Society Travel Grant (2015) Multiple research grants including SSHRC and FQRSC funding Supervision & Service: Currently advising 4 doctoral students on topics ranging from biosolvent characterization to indoor air quality Active editorial board member for Journal of Mathematical Economics Organizes international conferences on economic theory

• Neural Network Dynamics
• Computational Neuroscience
• Theoretical Physics
• +3 more

Prof. Raoul-Martin Memmesheimer is a Professor at the University of Bonn's Institute of Genetics , contributing to the Transdisciplinary Research Area (TRA) - Life and Health . His research focuses on understanding neural network dynamics across microscopic, mesoscopic, and large-scale phenomena, integrating mathematical approaches with neurophysiological insights. Key interests include the dynamics of precise spiking activity, collective network behavior, and computational principles underlying neural systems. Education & Background : While specific educational details are not listed, his position as a Professor indicates advanced academic qualifications in theoretical neuroscience or related fields. Research Interests : Memmesheimer's work bridges theoretical physics and computational neuroscience, addressing topics like spiking neuron models, learning in neural networks, and the emergence of complex behaviors. His group employs methods from computer science and applied mathematics to study how neural systems perform computations through their dynamic properties. Publications : Recent work explores topics such as gradient descent learning in spiking networks, STDP-based assembly dynamics, and oscillatory phenomena in hippocampal regions. These publications highlight his focus on both foundational theory and applications to biological systems. Awards & Collaborations : While no specific awards are listed, his participation in TRA Life and Health underscores collaborative efforts in transdisciplinary health-related research. His work is supported by the University of Bonn's strong focus on transdisciplinary innovation. Labs & Teams : His research group operates within the Institute of Genetics, leveraging interdisciplinary resources at the University of Bonn to advance theoretical neuroscience and computational biology.

Alex MogilnerView profile

Professor

• Computational Biology
• Cell Biophysics
• Mathematical Biology
• +5 more

Alex Mogilner is a Professor of Mathematics and Biology at New York University's Courant Institute and Department of Biology. His research focuses on computational and mathematical modeling of cellular processes, including cell motility, mitosis, actin dynamics, and galvanotaxis. He collaborates closely with experimentalists to bridge theoretical and empirical cell biology. Education: Ph.D., Applied Mathematics, University of British Columbia, Canada (1995) Ph.D., Physics, USSR Academy of Sciences (1990) M.Eng., Engineering Physics, Ural Polytechnic Institute, USSR (1985) Research Interests: Mathematical modeling of molecular machines in cells Mitotic spindle dynamics and chromosome segregation Actin-myosin contraction mechanisms Galvanotaxis and electrotaxis in cell migration Cellular biophysics and mechanochemical processes Selected Publications Highlights: Developed models for mitotic spindle assembly and error correction Investigated mechanisms of cell polarization and turning Studied actin network contraction and cytoskeletal dynamics Explored galvanotactic cell migration in electric fields Labs/Teams: Leads a computational modeling lab at NYU, collaborating with experimental groups globally in cell biology, biophysics, and systems biology.

Steve MarronView profile

Professor

• Statistics
• Data Science
• Machine Learning
• +4 more

Steve Marron is the Amos Hawley Distinguished Professor of Statistics and Operations Research at the University of North Carolina at Chapel Hill (UNC-CH). He holds a joint appointment in the School of Data Science and Society and is a professor in the Department of Biostatistics at the Gillings School of Global Public Health. Additionally, he serves as an adjunct professor in the Department of Computer Science within the College of Arts & Sciences. His research focuses on statistics, data science, and machine learning, with a particular emphasis on integrating diverse data types such as genomics, imaging, and demographic data. Education: Marron earned an AA from Orange Coast College (1974), BS from University of California, Davis (1977), and PhD from UCLA (1982). He has held faculty positions at UNC-CH since 1982 and Cornell University (2001-2002). His honors include Fellowships from the Institute of Mathematical Statistics and American Statistical Association, and he is a top-cited mathematician (1991-2001). Research interests include object-oriented data analysis, high-dimensional data methods (HDLSS), visualization techniques like SiZer, and statistical methodology for imaging and genomics. Notable contributions include Distance-Weighted Discrimination (DWD), Principal Nested Spheres, and JIVE for data integration. His work has applications in cancer genomics, medical imaging, and bioinformatics. Awards and recognitions include the Amos Hawley Professorship, S. N. Roy Memorial Lectureship, and Saw Swee Hock Visiting Professorship. Marron has advised numerous students and collaborated on NIH-funded grants, including studies on cancer genomics and imaging. His lab focuses on developing statistical tools for complex, multi-source data analysis.

Conrad BessantView profile

Professor

• Bioinformatics
• Computational Biology
• Artificial Intelligence in Biomedicine
• +5 more

Prof Conrad Bessant is a Professor of Bioinformatics at Queen Mary University of London (QMUL), affiliated with the School of Biological and Behavioural Sciences. He leads the MSc Bioinformatics program and is academic lead of the UKRI AI for Drug Discovery Doctoral Training Programme. His research focuses on automating scientific discovery in biomedicine using AI, machine learning, and network science. Key areas include drug response prediction, kinase networks, and health data analytics from online forums. Research interests span data science, computational biology, and AI applications in healthcare. He has contributed to projects on tumor microenvironment analysis, proteomics, and biomarker discovery in rheumatoid arthritis. His work integrates multi-omics data with machine learning to identify therapeutic targets and predict drug responses. Recent grants include AI-driven omics data integration (BBSRC), biomarker studies in ALS (Barts Charity), and collaborations with pharmaceutical companies like Exscientia and Merck. Publications highlight contributions to automated cell identification, kinase network modeling, and health informatics. He has pioneered tools like MRMaid for proteomics and Galaxy workflows for transcriptomics-informed analyses.

Damian SmedleyView profile

Professor

• Computational Genomics
• Genotype-Phenotype Analysis
• Rare Disease Genetics
• +3 more

Professor Damian Smedley is a Professor of Computational Genomics at Queen Mary University of London, affiliated with the William Harvey Research Institute's Clinical Pharmacology and Precision Medicine department. His research focuses on integrating clinical and model organism phenotype data to elucidate human disease mechanisms, particularly through initiatives like the International Mouse Phenotyping Consortium (IMPC) and the MorPhic project. He leads the development of the Exomiser software, a critical tool for prioritizing genetic variants in rare disease diagnostics, widely used in global projects such as the UK's 100,000 Genomes Project and NHS Genomic Medicine Service. His work bridges computational biology, genetics, and clinical translation, with collaborations spanning academia and industry. Key research areas include genotype-phenotype associations, precision medicine, and federated machine learning applied to multiomics data. Funded by NIH, MRC, Horizon Europe, and Barts Charity, his team collaborates with institutions like the Berlin Institute of Health and the University of Colorado. Notable contributions include advancing diagnostic pipelines for rare diseases and understanding the role of missense variants in genetic disorders. His group's work has been featured in high-impact studies, such as identifying novel disease genes through cross-species phenotype comparisons and optimizing variant prioritization algorithms. External collaborations include Prof. Peter Robinson (Berlin) and Dr. Chris Mungall (Lawrence Berkeley Lab), reflecting his global impact in computational genomics.

• Ethnomusicology
• Middle Eastern Music
• African Music
• +7 more

Michael Frishkopf is a Professor and Director of Graduate Studies in the Department of Music at the University of Alberta, and Director of the Canadian Centre for Ethnomusicology. He also holds an adjunct professorship at the University for Development Studies in Ghana. His academic journey includes a BSc in Mathematics from Yale University (1984), MA in Ethnomusicology from Tufts University (1989), and PhD in Music from UCLA (1999). His primary roles include teaching ethnomusicology courses (e.g., MUSIC 148, MUSIC 465) and leading research initiatives in music and global health, soundscapes, and Islamic ritual studies. Education: BSc Mathematics, Yale University (1984) MA Ethnomusicology, Tufts University (1989) PhD Music, UCLA (1999) Research interests span ethnomusicology, Sufi music, sound in Islamic rituals, music and global health, machine learning applications, and social network analysis. He founded the West African Music Ensemble (1999) and Middle Eastern Music Ensemble (2004). His work bridges disciplines including anthropology, computer science, and public health. Key projects include the Autonomous Adaptive Soundscapes initiative for ICU patients, Music for Global Human Development (promoting health in Ghana, Liberia, and Ethiopia), and Music, Sound, and Architecture in Islam . He has published over 100 works and received awards such as the 2020 Edmonton Interfaith Advocate Award and 2018 Community Connection Award. Grants include funding from SSHRC, CFI, and the Fulbright Foundation. He advises numerous collaborative projects and serves on boards like the Egyptian Center for Culture and Art. His research emphasizes participatory action research, particularly using music as a tool for social change and health promotion. Labs/Teams: Canadian Centre for Ethnomusicology (CCE), Genetic Music Collective, TransCultural Orchestra. Current projects include AI for Sound Societies and Mindful Social Listening for student mental health.

Steve MaddockView profile

Senior Lecturer

• Facial Modelling and Animation
• Augmented Reality
• Virtual Reality
• +3 more

Dr Steve Maddock is a Senior Lecturer in Computer Graphics and Acting Head of the Visual Computing research group at the University of Sheffield's School of Computer Science. He holds a Class I Degree in Computer Science (University of Sheffield), a PGCE in Mathematics (11-18), and a PhD in computer graphics modeling and animation, all from the University of Sheffield. With over 30 years of experience in computer graphics software development, he has contributed to the computer games industry through a six-month secondment at Gremlin/Infogrames. His research focuses on facial modeling and animation, augmented/virtual/mixed reality applications, and sketch-based interfaces. Key areas include 3D computer graphics, real-time rendering, and human-robot collaboration systems. Maddock has led and co-led several grants, including projects on game software engineering, rail network surveillance, and heritage visualization using immersive technologies. He is a member of INSIGNEO, Sheffield Robotics, and the Cultural Industries Research Network. Publications highlight contributions to facial analysis for medical diagnostics, style transfer techniques for games, and safety zone visualization in robotics. His work integrates interdisciplinary approaches, combining computer science with fields like biology and robotics. Maddock's Visual Computing research group explores cutting-edge solutions in graphics, virtual environments, and computational tools for real-world applications.

Quan ZhouView profile

Assistant Professor

• Markov chain Monte Carlo sampling
• Bayesian methodology
• Variable selection
• +7 more

Quan Zhou is an Assistant Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on developing advanced sampling methods, particularly Markov chain Monte Carlo (MCMC) algorithms, with applications in Bayesian methodology, variable selection, stochastic optimization, and statistical genetics. He holds a BS from Fudan University and a PhD from Baylor College of Medicine, followed by a postdoctoral fellowship at Rice University. He teaches courses such as Mathematical Probability, Multivariate Analysis, and Advanced Stochastic Processes. Notable contributions include work on informed MCMC samplers, Schrödinger bridge theory, and high-dimensional structure learning. He advises PhD students like Hyunwoong Chang (now at UT Dallas) and Guanxun Li (Beijing Normal University). Active in academic service, he served as President of the Southeastern Texas Chapter of the American Statistical Association (SETCASA).

Kevin LangergraberView profile

Associate Professor

• Primate Behavior
• Conservation Biology
• Genetics
• +3 more

Dr. Kevin Langergraber is an Associate Professor at Arizona State University's School of Human Evolution and Social Change. His research focuses on primate behavior, conservation biology, and evolutionary anthropology, particularly through his leadership in the Ngogo Chimpanzee Project in Uganda's Kibale National Park. This long-term study examines chimpanzee social structures, health dynamics, and ecological interactions, with a strong emphasis on conservation strategies against poaching and disease transmission. Key research interests include genetic adaptation in primates, the impact of human activities on wildlife communities, and behavioral ecology across varying environmental conditions. He has pioneered methods combining genomic analysis, field observations, and machine learning to study ape behavior and biodiversity monitoring. His work integrates socioecological data with physiological measurements to understand aging patterns, reproductive strategies, and disease epidemiology in wild populations. Recent publications highlight advancements in wildlife conservation protocols, primate menopause studies, and innovative datasets for behavior recognition. Collaborations span global institutions to address challenges in primate conservation and evolutionary biology. Dr. Langergraber also engages in public outreach through documentaries and crowdfunding initiatives to fund anti-poaching patrols and support chimpanzee research.

• Computational Immunology
• B Cell Receptor (BCR) Repertoire Profiling
• Multi-Omics Analysis
• +5 more

Dr. Steven Kleinstein is the Anthony N. Brady Professor of Pathology at the Yale School of Medicine, with secondary appointments in Immunobiology and Biomedical Informatics. He is Co-Director of Graduate Studies in Computational Biology and Biomedical Informatics, and leads the Kleinstein Lab. His research focuses on computational immunology, integrating big data analysis with immunology to study immune responses, including B cell receptor (BCR) repertoire profiling via AIRR-seq and multi-omic studies of infection/vaccination responses. Dr. Kleinstein holds a BAS in Computer Science from the University of Pennsylvania (1994) and a PhD in Computer Science from Princeton (2002). He is a member of the Computational Biology and Bioinformatics Program and the Human and Translational Immunology Program. Education: B.A.S. in Computer Science, University of Pennsylvania, 1994 Ph.D. in Computer Science, Princeton University, 2002 Research Interests: His lab develops computational tools (e.g., Immcantation framework) for analyzing BCR repertoires and immune responses to pathogens like SARS-CoV-2, HIV, and influenza. Key projects include understanding germinal center B cell maturation, antibody specificity prediction via language models, and immune correlates of disease severity in hospitalized patients. Collaborations span clinical and basic science groups to apply these methods to autoimmune diseases, allergies, and cancer. Grants & Collaborations: Active in NIH/NIAID initiatives (e.g., HIPC, PRIME) and industry partnerships. Lab members collaborate with institutions globally on projects like the IMPACC study and malaria vaccine research. Labs/Teams: The Kleinstein Lab at Yale is part of the Center for Biomedical Data Science and the Yale Cancer Center, emphasizing computational and experimental immunology integration.

Wesley J. MarreroView profile

Assistant Professor

• Machine Learning
• Inferential Statistics
• Stochastic Simulation
• +4 more

Dr. Wesley J. Marrero is an Assistant Professor of Engineering at Dartmouth College's Thayer School of Engineering. He holds core affiliations with the Health Equity and Advocacy Lab at The Dartmouth Institute for Health Policy and Clinical Practice and is a faculty affiliate in the Quantitative Biomedical Sciences Graduate Program at the Geisel School of Medicine. His research integrates operations research and statistics to address healthcare challenges in substance use disorder, cardiovascular disease, and organ transplantation, collaborating with institutions like Massachusetts General Hospital and the University of Michigan. Dr. Marrero received a B.S. in Industrial and Management Engineering from the University of Turabo (2014), an M.S. in Industrial and Operations Engineering (2017), an M.A. in Statistics (2021), and a Ph.D. in Industrial and Operations Engineering (2021) from the University of Michigan. His research focuses on stochastic simulation and optimization for decision-making in healthcare. Key areas include machine learning applications in predictive modeling, inferential statistics for medical decisions, and sequential decision-making frameworks for health policy optimization. His work emphasizes practical implementations in clinical settings and health systems. Dr. Marrero's publications demonstrate a consistent focus on healthcare optimization, with recent trends in machine learning for organ transplantation and genetic testing strategies. His articles frequently appear in high-impact journals and conference proceedings, reflecting interdisciplinary collaborations. Awards: INFORMS MIF Best Paper Award (2024) INFORMS Annual Meeting Minority Issues Forum Poster Competition Best Poster Award (2020) INFORMS Judith Liebman Award (2020) National Science Foundation Graduate Research Fellowship (2017) Michigan Student Symposium Best Poster Award (2017) Rackham Merit Fellowship (2015) He actively advises graduate and undergraduate students, with current PhD candidates in engineering and biomedical sciences. His research group has secured grants from organizations including the National Science Foundation and the American Medical Association. Dr. Marrero leads a dynamic research group focusing on healthcare decision analytics. He collaborates with medical institutions and government agencies, including the U.S. Department of Veterans Affairs, to translate research into clinical practice.

Yufei HuangView profile

Professor

• Artificial Intelligence
• Cancer Research
• Bioinformatics
• +3 more

Yufei Huang is a Professor in the Department of Medicine at the University of Pittsburgh and the Leader of AI Research at UPMC Hillman Cancer Center. He holds a PhD and MS in Electrical Engineering from Stony Brook University. His work bridges artificial intelligence, bioinformatics, and cancer research, focusing on applications such as drug response prediction, epigenetic regulation, and viral pathogenesis. Huang is affiliated with both the School of Medicine and the School of Computing and Information, reflecting his interdisciplinary research. His contributions span computational tools for healthcare, including R Shiny apps for drug response analysis and machine learning models for unbiased patient representation. Research interests include AI-driven oncology, molecular mechanisms of cancer progression, and leveraging genomics for precision medicine. He has contributed to studies on lung cancer therapeutics, viral infections (e.g., SARS-CoV-2 and KSHV), and epigenetic modifications like m6A methylation. His lab develops innovative algorithms for single-cell transcriptomics and spatial biology to unravel disease mechanisms. Notable projects include MetGen, a generative model for metastatic cancer prediction, and m6A-express, which deciphers RNA methylation's role in gene expression. His work on CASTOR1 phosphorylation highlights biomarker discovery in lung adenocarcinoma. Huang's interdisciplinary approach integrates machine learning, virology, and computational biology to address critical challenges in healthcare and cancer research.