جستجوی استاد راهنمای دانشگاهی

Romain Lopez is an Assistant Professor of Computer Science and Biology at New York University, with a joint appointment in the Courant Institute of Mathematical Sciences and the Department of Biology. He will be joining NYU in September 2025, bringing expertise at the intersection of machine learning and computational biology. Prior to joining NYU, he was a Postdoctoral Fellow at Genentech and Stanford Medicine from 2021 to 2025, working with Jonathan Pritchard and Aviv Regev. Dr. Lopez received his educational training at prestigious institutions: PhD in Computer Science (2021) from the University of California, Berkeley, advised by Mike Jordan and Nir Yosef M.S. in Applied Mathematics (2016) from École polytechnique, Palaiseau, France Dr. Lopez's research focuses on developing machine learning methods to understand biological systems at the cellular level. His work bridges computational techniques with biological applications, particularly in single-cell and spatial omics analysis. He pioneered probabilistic approaches for single-cell analysis with scVI and co-developed scvi-tools, now widely adopted tools in the field. His research spans deep generative models, causal inference, perturbation modeling, and representation learning for biological data. His publication record demonstrates a consistent trajectory of innovation in computational biology, with recent work focusing on spatial biology, disentangled representations of cellular perturbations, and causal modeling of cellular responses. He has made significant contributions to the field of single-cell analysis, developing methods that help scientists interpret complex cellular data and predict how cells respond to various perturbations. Dr. Lopez has received numerous honors and awards for his research: Best Paper Award from the ICML Workshop on AI for Science (2024) Best Paper Award Honorable Mention from the AAAI Conference on Artificial Intelligence (2021) Best Student Poster Award from the ICML Workshop on Computational Biology (2019) UC Berkeley EECS Departmental Graduate Fellowship (2016) Carnot Foundation Fellowship (2016) Monahan Foundation Fellowship (2016) French National Defence Medal, Bronze Echelon (2014) At NYU, Dr. Lopez will lead the Biological Machine Learning group, which develops probabilistic machine learning methods to uncover biological mechanisms governing cellular behavior and disease. His lab focuses on creating tools that transform complex cellular data into biological insights, with applications in understanding cancer, immune responses, and fundamental cellular processes. His work has significant implications for precision medicine and drug discovery.

• Single-cell omics
• Spatial transcriptomics
• Deep generative models
• +۵ مورد دیگر

Jonas Fischer is the head of the Explainable Machine Learning group at the Max Planck Institute for Informatics, Department of Computer Vision and Machine Learning. His research focuses on interpreting complex machine learning models, particularly in genomics and healthcare, aiming to enhance robustness and alignment with human decision-making. Prior to his role at MPI, he was a postdoctoral fellow at Harvard University's Department of Biostatistics, where he worked on interpretable models for gene regulatory systems in cancer. Education: PhD in Computer Science from Saarland University (2022), with a thesis titled More than the sum of its parts , exploring the intersection of pattern mining and deep learning. He has contributed to advancing methods in neural network pruning, federated learning, and low-dimensional embeddings (e.g., dtSNE, Mercat). His work bridges computational biology, data mining, and machine learning, with applications in DNA methylation analysis, graph-based differential networks, and biomedical informatics. Key research areas include: (1) Explainable AI and neural network interpretability, (2) Biomedical applications of machine learning (e.g., gene regulatory networks, cancer genomics), (3) Low-dimensional embeddings and visualization techniques, (4) Federated learning for privacy-preserving collaborative models, and (5) Pattern mining for error analysis in NLP and classification tasks. Publications span top venues like NeurIPS, ICLR, Bioinformatics, and Genome Biology. His group develops tools such as BONOBO for omics data integration and node2vec2rank for scalable graph analysis. He actively collaborates with biomedical researchers to address challenges in data-driven healthcare and precision medicine.

• Machine Learning
• Explainable AI
• Neural Networks
• +۳ مورد دیگر

Florentina Bunea is a Professor in the Department of Statistics and Data Science at Cornell University’s Bowers College of Computing and Information Science, and an active member of the Graduate Fields of Statistics, Applied Mathematics, and Computer Science. She also serves on the Diversity and Inclusion Council of her college, championing workforce diversity in data-science disciplines. Education & Institutional Roles Professor, Department of Statistics and Data Science, Cornell University Member, Graduate Fields of Statistics, Applied Mathematics, Computer Science Member, Diversity and Inclusion Council, Bowers College of Computing and Information Science Research Interests Professor Bunea’s research lies at the intersection of statistical machine-learning theory and high-dimensional inference. She develops rigorous methodology supported by sharp theoretical guarantees to tackle core problems in modern data science. Recent themes include: Soft-max mixtures for understanding large-language-model/AI algorithms Optimal transport for high-dimensional mixture distributions Wasserstein-distance inference for sparse mixing measures in topic models Latent-space clustering and cluster-based inference in high dimensions Network modeling and hidden-structure inference Applications spanning genetics, systems immunology, neuroscience, sociology, and economics Research Funding & Awards Her work is supported by grants from the National Science Foundation (NSF-DMS). She is a Fellow of the Institute of Mathematical Statistics and a recipient of the IMS Medallion Award. Editorial & Service Contributions Associate Editor: Annals of Statistics, Bernoulli, JASA, JRSS-B, EJS, Annals of Applied Statistics Co-Editor: Chapman & Hall/CRC Statistics and Applied Probability Monograph Series Advising & Collaboration Professor Bunea has mentored numerous doctoral and post-doctoral researchers, including Xin Bing, Shuyu Liu, Seth Strimas-Mackey, and Yang Ning, among others. Collaborative projects extend across Cornell and external institutions, producing widely-used software packages and high-impact publications. Contact Office: 1184 Comstock Hall, Cornell University Email: fb238@cornell.edu Phone: (607) 255-8449

• Statistical machine learning theory
• High-dimensional statistical inference
• Optimal transport
• +۶ مورد دیگر

Pierre Vandergheynst is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Department of Electrical Engineering, with a courtesy appointment in Computer and Communication Sciences. He serves as EPFL’s Vice-Provost for Education since 2015 and leads the Signal Processing Laboratory 2 (LTS2). His research spans harmonic analysis, sparse approximations, mathematical data processing, and applications in signal/image processing, computer vision, machine learning, and graph-based data analysis. PhD in Mathematical Physics (1998), Université catholique de Louvain Postdoctoral Researcher at EPFL (1998-2001) Assistant Professor at EPFL (2002-2007) His research explores geometry/symmetry in high-dimensional data, redundant dictionaries for dimensionality reduction, and computational harmonic analysis on manifolds. Recent work focuses on protein structure modeling, geometric deep learning, and graph-based signal processing. Key article trends include graph neural networks for protein analysis, geometric deep learning in neuroscience, and structured knowledge priors in neural models. His 2023-2025 publications emphasize interpretable AI, long-range dependencies in graphs, and molecular representation learning. Scientific Awards: IEEE Signal Processing Magazine Best Paper Award (2023) Signal Processing Society Best Paper Award (2022) Apple ARTS Award (2007) De Boelpaepe Prize, Royal Academy of Sciences of Belgium (2009-2010) He has supervised over 30 PhD theses and contributed to foundational work in graph signal processing, compressive sensing, and geometric deep learning. His lab develops tools for data science on non-Euclidean structures, with applications in medicine, astronomy, and wireless systems.

• Data Science
• Machine Learning
• Computational Harmonic Analysis
• +۶ مورد دیگر

Qianwen Wang is a tenure-track Assistant Professor in the Computer Science department at the University of Minnesota, Twin Cities. Her research combines interactive visualization with interpretable machine learning to foster intuitive, efficient, and reliable Human-AI collaboration. She actively seeks motivated students, research assistants, and interns to join her dynamic research team at UMN CS. Dr. Wang's research focuses on three primary themes: Human-AI Collaboration, where she designs tools to facilitate Human-AI interaction; Automatic & Intelligent Visualization, where she develops techniques to make visualizations accurately interpreted and easily used; and VIS+(X)AI in Biomed/Healthcare, where she studies how visualization and explainable AI can promote scientific discoveries in biomedicine and healthcare. Her work has made significant contributions to visualization, human-computer interaction, and bioinformatics, with particular applications in biomedical knowledge graphs and single-cell omics analysis. Her recent publications demonstrate a strong trend toward integrating visualization with large language models and graph neural networks for biomedical applications. She has published extensively at top venues including IEEE VIS, ACM CHI, and Nature Medicine, with a focus on making AI systems more interpretable and useful for domain experts, particularly in healthcare contexts. Her work often bridges theoretical advances in visualization with practical applications in genomics and healthcare. Two IEEE VIS Honorable Mention Awards (2022, 2024) Best Paper Award from IMLH@ICML 2021 Two Best Abstract Awards from BioVis ISMB (2021, 2022) HDSI Postdoctoral Research Fund Award Research covered by MIT News and Nature Technology Features Dr. Wang actively contributes to the academic community through service roles including General Chair for ISMB BioVis, VisNotes and Poster Chair for IEEE PacificVis, and Program Committee member for IEEE VIS, ACM CHI, and ACM IUI. Her research has been supported by various grants that enable her team to develop innovative visualization techniques and explore their practical applications in biomedical domains. Her research group maintains an active presence in the visualization and HCI communities, with members participating in major conferences and workshops. The lab focuses on creating tools that bridge the gap between complex AI models and human understanding, with particular emphasis on making these technologies accessible and useful for domain experts in biomedical research.

• Interactive Visualization
• Interpretable Machine Learning
• Human-AI Collaboration
• +۵ مورد دیگر

Karen Piper Hanley is Professor of Molecular Medicine and Head of Division in the Division of Diabetes, Endocrinology & Gastroenterology at the University of Manchester. She serves as the fibrosis lead in the Wellcome Trust Centre for Cell-Matrix Research and previously served as Director of Core Research Technologies (2019-2022). She is also an editorial board member for Scientific Reports. Her research focuses on fibrosis (scarring) mechanisms across multiple organs, particularly liver, kidney, and lung fibrosis. Her lab investigates the cellular and molecular basis of fibrotic disease, with emphasis on how pathological scar components are produced and how they signal to surrounding cells. Key areas include the role of SOX9 transcription factor, integrin beta-1 signaling, Group 1 PAKs, and YAP1 in fibrosis progression. Her recent publications reveal trends toward increased clinical translation of fibrosis research, with studies on health inequalities in liver cancer care, post-COVID diabetes incidence, and innovative approaches to liver disease detection. Her work increasingly integrates spatial transcriptomics and advanced omics technologies to understand cellular transitions in liver disease. Career-track postdoctoral fellowship from the University of Southampton (2006) As senior postgraduate tutor, she has mentored numerous PhD students who have presented at key conferences and authored high-quality manuscripts. All her PhD students have submitted and been awarded their degrees within 4 years. She has successfully guided previous Academic Clinical Fellows to win MRC clinical training fellowships. Her teaching extends to undergraduate Personal & Professional Development and supervision of MRes and BSc students, many of whom have achieved first-class degrees and prestigious awards. Her lab maintains active collaborations with Professor Neil Hanley's group on human development, stem cells, and regenerative medicine, bridging developmental biology with fibrosis research to understand why regeneration is impaired in chronic diseases.

• Fibrosis Research
• Liver Fibrosis
• Kidney Fibrosis
• +۹ مورد دیگر

Laura Elo serves as Professor of Computational Medicine and Head of the Medical Bioinformatics Centre at the University of Turku, Finland. She concurrently holds the position of Research Director at Turku Bioscience Centre and acts as InFLAMES Flagship Contact, driving interdisciplinary biomedical research initiatives. Her academic foundation includes a PhD in Applied Mathematics (2007) and Adjunct Professorship in Biomathematics (2011), establishing her quantitative expertise before transitioning into biomedical applications. Her research program focuses on transforming molecular and clinical datasets through statistical modeling and advanced machine learning . Key thrusts include robust computational tools for proteome/epigenome analysis, AI-driven digital health diagnostics, and computational systems immunology for immune-mediated diseases. This work directly addresses challenges in reproducibility and scalability of high-throughput biotechnology data. Analysis of her recent publications reveals dominant themes in type 1 diabetes biomarker discovery , multi-omics integration , and immune system modeling , with strong emphasis on clinical translation through collaborations with experimental and medical teams. Her scientific recognition includes: JDRF Career Development Award Professor Elo actively trains MSc/PhD students and postdoctoral fellows while leading major research initiatives including ERC grants. Her teaching portfolio spans Bioinformatics Journal Club, AI in Diagnostics, and Systems Biology courses. The Elo Lab (https://elolab.utu.fi) operates as a hub for computational biomedicine, developing open-source tools like CellRomeR while maintaining close ties with Turku Bioscience Centre's experimental facilities for validating computational predictions in immunology and metabolic disease contexts.

• Computational Biomedicine
• Medical Bioinformatics
• Data Science
• +۷ مورد دیگر

Ryo Morimoto is a Research Fellow and Group Leader at the Department of Molecular Biology, Umeå University, affiliated with Molecular Infection Medicine Sweden (MIMS). He leads studies on the evolutionary foundations of adaptive immunity and host genome editing mechanisms. MD (2011) and PhD (2017) from University of Tokyo Postdoctoral work at Max Planck Institute for Immunobiology & Epigenetics (2017-2024) Project Leader (2022) and Group Leader (2024) at MIMS His research explores antigen receptor diversification in jawless vertebrates, evolutionary emergence of lymphopoietic systems, and self-tolerance mechanisms. Using non-conventional models, his lab integrates in vitro biochemistry, single-cell omics, and computational genomics to address genome integrity challenges in adaptive immunity. The lab's recent publications highlight evolutionary insights into immune system development and its implications for human immunity. Research focuses on conserved enzyme families mediating genome editing and their biotechnological applications. Ryo Morimoto's team emphasizes multicultural collaboration, interdisciplinary training, and skill development for early-career scientists. The lab maintains partnerships with Umeå Centre for Microbial Research (UCMR) and employs in vivo techniques alongside molecular and computational approaches.

• Evolutionary Biology
• Immunology
• Genome Editing
• +۲ مورد دیگر

Hyunghoon Cho is an Assistant Professor at Yale School of Medicine in the Department of Biomedical Informatics & Data Science, with a secondary appointment in the Department of Computer Science. He received his PhD in Electrical Engineering and Computer Science from MIT (2019) and MS/BS in Computer Science from Stanford University (2013). His research focuses on computational challenges in biomedical data privacy, single-cell genomics, and network biology. Assistant Professor (Primary): Biomedical Informatics & Data Science Assistant Professor (Secondary): Computer Science Appointments: Yale School of Medicine | Broad Institute (Schmidt Fellow) Research Themes: Privacy-Enhancing Technologies for genomic and health data Scalable AI/ML tools for omics data analysis Structured biological modeling for system-level discovery His work includes secure GWAS, transcriptomic privacy assessment, and sfkit - a federated genomic analysis toolkit. He received the NIH Director's Early Independence Award and leads NSF-funded projects on confidential genome analytics. Awards: NIH Director's Early Independence Award Lab Members: Haris Smajlović (Postdoc), Vincent Angelo (CBB MS), Denis Loginov (Senior Software Engineer), Lucy Zheng (CBB PhD)

• Biomedical Data Privacy
• Genomics
• Machine Learning
• +۳ مورد دیگر

Dr. Vakil Takhaveev is a Lecturer at ETH Zurich's Department of Health Sciences and Technology, within the Institute of Food, Nutrition and Health. His research focuses on DNA damage mechanisms, aging, cancer, and neurodegeneration, with particular emphasis on developing novel DNA-damage-sequencing methods like click-code-seq and TRABI-Seq . He investigates anticancer drug action (e.g., trabectedin), aging clocks using DNA oxidation profiling, and stress-induced carcinogenesis. His work integrates multi-omics approaches and advanced sequencing techniques. Research Directions: Novel DNA-Damage-Sequencing Methods: Developed click-code-seq and TRABI-Seq for genomic mapping of DNA lesions and repair dynamics. Anticancer Drug Action: Explored mechanisms of trabectedin and other chemotherapeutics, linking DNA repair vulnerabilities to therapy resistance. Aging Clocks: Created DNA oxidation-based biomarkers for biological aging using genome-wide profiling in human and mouse models. Stress-Induced Pathologies: Studies metabolic and DNA damage links to early tumorigenesis and neurodegeneration. Awards & Recognition: 2025 Public Award Winner in PIs of Tomorrow competition 2024 ETH Zurich Career Seed Award Best presentation awards (Swiss Chemical Society, American Chemical Society) Grants & Collaborations: Impetus grants for aging clock development Swiss Chemical Society and American Chemical Society fellowships Labs & Teams: Leads research on DNA damage and aging mechanisms at ETH Zurich, collaborating with international groups in oncology and toxicology.

• DNA Damage and Repair
• Aging Mechanisms
• Cancer Biology
• +۶ مورد دیگر

Vadim Cherezov, the Ester Dornsife Chair in Biological Sciences and Professor at the University of Southern California (USC), leads groundbreaking research in membrane protein structure and function. Affiliated with the Bridge Institute, Department of Chemistry, and Michelson Center for Convergent Bioscience, his work focuses on GPCRs, ion channels, and transporters—critical targets for drug discovery. His team leverages advanced techniques like Lipidic Cubic Phase (LCP) and Serial Femtosecond Crystallography (SFX) at XFEL facilities to solve high-resolution structures under physiological conditions. Institutional Affiliations: Bridge Institute, USC Michelson Center, Department of Chemistry, Department of Pharmacology and Pharmaceutical Sciences. Key Collaborations: Katritch Lab, Kuhn Lab, NIH, European XFEL. His research explores the role of lipids in modulating GPCR function, addressing diseases like Alzheimer’s, diabetes, and cancer. By solving the structure of the A 2A adenosine receptor via sulfur SAD phasing at XFEL, Cherezov’s lab demonstrated de novo phasing without heavy atoms. This breakthrough enables structural studies of previously intractable membrane proteins. Scientific Awards & Grants: NIH R01 GM108635, U54 GM094618, U54 GM094599, R01 GM095583 Science Signaling Breakthroughs of the Year (2014) Cherezov mentors a dynamic team, including postdocs (e.g., Dong-Gyun Kim), graduate students (e.g., Behnaz Davoudinasab), and alumni (e.g., Benjamin Stauch at Eli Lilly, Nairie Michaelian at Genentech). His lab’s publications span Nature , Science , and Cell , with recent work on Science Advances (2025) addressing ABEL-FRET for GPCR dynamics.

• Membrane Protein Structure and Function
• G Protein-Coupled Receptors (GPCRs)
• Lipid-Protein Interactions
• +۳ مورد دیگر

Calliope Dendrou is an Associate Professor in Clinical Pathology and Inflammation at the Kennedy Institute of Rheumatology (KIR), University of Oxford, leading the Immune Disease Multiomics Laboratory. She previously held a Wellcome & Royal Society Sir Henry Dale Fellowship at the University of Oxford’s Centre for Human Genetics before joining KIR in 2023. Her research focuses on immune disease mechanisms using multiomics approaches, including genomic profiling to identify therapeutic targets across tissues and immune-mediated diseases. She co-leads large-scale projects like the Oxford-J&J Cartography Consortium and the Chan Zuckerberg Initiative’s LEGACY Network, and teaches on the MSc in Genomic Medicine program. Educational Background: BSc (Biology, Imperial College London, 2005; Forbes Memorial Medal Winner); PhD in Infection & Immunity (University of Cambridge, 2010). Postdoctoral training at the Weatherall Institute of Molecular Medicine under Prof. Lars Fugger. Research interests include immunogenetics, cytokine signaling pathways, drug repositioning, and cross-disease pathophysiology. Her work integrates single-cell and spatial transcriptomics to dissect immune-cell interactions in diseases like rheumatoid arthritis, inflammatory bowel disease, and celiac disease. Recent articles highlight her contributions to understanding vaccine adjuvant responses, Th17 cell roles in spondyloarthritis, and immune-epithelial networks in celiac disease. Collaborations emphasize multi-omic data analysis (e.g., Panpipes pipeline) and translational studies toward precision medicine. Awards: Forbes Memorial Medal (BSc), Wellcome & Royal Society Sir Henry Dale Fellowship. Leadership roles include Equality, Diversity, and Inclusion Champion and 'Single-Cell & Spatial Omics for Precision Medicine' Module Lead. Lab & Teams: Immune Disease Multiomics Lab at KIR. Active in collaborative initiatives such as the LEGACY Network, focusing on large-scale immune profiling in ancestrally diverse populations.

• Immunology
• Genomics
• Multiomics Analysis
• +۳ مورد دیگر

Kathryn Roeder is the UPMC University Professor of Statistics and Life Sciences at Carnegie Mellon University (CMU), affiliated with the Dietrich College of Humanities and Social Sciences and the Departments of Statistics & Data Science and Computational Biology. Her research focuses on developing statistical methods for genetic and genomic data, particularly in identifying autism risk genes and analyzing single-cell multi-omic data. She earned her Ph.D. in Statistics from Penn State University and has been at CMU since 1994, previously serving as Vice Provost for Faculty (2015–2019). Education: Ph.D. in Statistics, Penn State University (1988) B.S. in Wildlife Resources, University of Idaho (1982) Research Interests: Her work integrates modern statistical techniques (high-dimensional statistics, machine learning, networks) to study complex diseases like autism and schizophrenia. Recent efforts include tools for analyzing single-cell RNA-seq and proteomic data, such as UNICORN, DAWN, and SCEPTRE. Key Awards: COPSS Distinguished Achievement Award (2020) National Academy of Sciences Member (2019) COPSS Presidents’ Award (1997) AAAS Fellow (2020) Advising & Grants: She has advised over 20 Ph.D. students, many contributing to landmark studies in autism genetics. Her grants include NIH funding for projects like the Autism Sequencing Consortium. Current research teams focus on computational biology and statistical genetics. Labs & Collaborations: Her lab develops software tools (e.g., TADA, MIND) and collaborates with the Autism Sequencing Consortium and iPSYCH-BROAD Consortium on large-scale genomic studies.

• Statistical Genetics
• Genomics
• Biostatistics
• +۵ مورد دیگر

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.

• Biomedical Computation and Modeling
• Drug Delivery and Therapeutics
• Systems Immunology
• +۵ مورد دیگر

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.

• Machine Learning
• Genomics
• Gene Regulatory Networks
• +۳ مورد دیگر