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Romain LopezView profile

Assistant Professor

• Single-cell omics
• Spatial transcriptomics
• Deep generative models
• +5 more

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.

Jimeng SunView profile

Professor

• Artificial Intelligence
• Healthcare Analytics
• Drug Discovery
• +5 more

Dr. Jimeng Sun is a Health Innovation Professor at the Siebel School of Computing and Data Science and Carle Illinois College of Medicine at the University of Illinois Urbana-Champaign. Co-founder of Keiji AI , he leads groundbreaking research at the intersection of artificial intelligence and healthcare, actively deploying clinical AI systems and developing frameworks like PyHealth and Therapeutics Data Commons . His research spans four major areas: Clinical AI Systems : Developing interpretable models (e.g., RETAIN) for patient similarity, temporal event prediction, medication recommendation, and clinical outcome forecasting Drug Discovery : Creating molecular optimization frameworks, drug-target interaction models, and AI-driven platforms Clinical Trials : Pioneering patient-trial matching, outcome prediction, and optimization frameworks using deep learning and graph neural networks Biosignal Analysis : Advancing sleep staging, seizure classification, and automated EEG/Cardiac monitoring systems With over 500 top-tier publications (including in Nature , NEJM AI , and leading AI conferences) and an h-index of 99, his work has been recognized with the Top 100 AI Leaders in Drug Discovery and Advanced Healthcare award. He maintains active collaborations with institutions like Massachusetts General Hospital , Medidata Solutions , and OSF Healthcare . His recent publications reveal a strong focus on: Reinforcement learning applications in medical data analysis Large language model adaptation for clinical tasks Knowledge graph integration with AI systems Synthetic data generation for healthcare Multi-modal learning in clinical contexts Explainable AI for medical applications Dr. Sun's lab ( Sunlab ) emphasizes practical impact over theoretical work, actively collaborating with hospitals and healthtech companies. He welcomes contributions from clinicians, researchers, and industry partners through initiatives like his AI for Health webinar series .

Hang LuView profile

Professor

• Microfluidics
• Systems Biology
• Neuroscience
• +6 more

Hang Lu is a Professor and holds the Cecil J. "Pete" Silas Chair of Chemical & Biomolecular Engineering at the Georgia Institute of Technology. Dr. Lu also holds a Love Family Professorship and leads the Lµ Fluidics Group, which focuses on engineering microfluidic systems and machine learning tools to address complex questions in neuroscience, developmental biology, and cell biology that are difficult to address with conventional techniques. Dr. Lu's research lies at the intersection of engineering and biology, with primary interests including: Microfluidic systems for high-throughput screens and image-based genetics and genomics Systems biology: large-scale experimentation and data mining Microtechnologies for optical stimulation and optical recording Big data, machine vision, and automation Developmental neurobiology, behavioral neurobiology, and systems neuroscience Cancer biology, immunology, embryonic development, and stem cells Her laboratory engineers microfluidic devices and BioMEMS to study neuroscience, genetics, cancer biology, and biotechnology. These miniaturized Lab-on-a-chip tools operate at scales comparable to biological systems, leveraging unique micro and nano-scale phenomena to gather large-scale quantitative data about complex biological systems. Current projects include Microfluidics for Life Sciences, Optical Neuron Recordings and Manipulations, Machine Learning Tools for Neuroscience, Measuring and Modeling Behavior, and High-throughput, High-content Cell-based Assays. Analysis of Dr. Lu's recent publications (2024-2025) reveals a strong trend toward integrating microfluidics with advanced computational methods: Development of deep learning frameworks for biological image analysis Advanced neuron tracking and functional imaging techniques Non-invasive characterization of 3D organoid cultures Sophisticated neuromechanical modeling of locomotion Microfluidic temperature control systems for in vivo studies Label-free imaging pipelines for neural development Dr. Lu's significant professional honors include: Cecil J. "Pete" Silas Chair of Chemical & Biomolecular Engineering Love Family Professorship The Lµ Fluidics Group actively mentors students and postdocs, currently accepting new postdoctoral researchers. The lab receives substantial funding for interdisciplinary projects at the engineering-biology interface, with research implications spanning fundamental biological understanding to therapeutic development. The group operates within Georgia Tech's School of Chemical & Biomolecular Engineering, with specialized facilities for microfluidic device fabrication, biological experimentation, and advanced imaging, maintaining strong collaborative ties across engineering, neuroscience, and biological disciplines.

Sriram SubramaniamView profile

Professor

• cryo-electron microscopy
• structural biology
• drug-design
• +4 more

Sriram Subramaniam is a Professor in the Department of Biochemistry and Molecular Biology at the University of British Columbia (UBC) and holds the Gobind Khorana Canada Excellence Research Chair in Precision Cancer Drug Design. His research leverages cryo-electron microscopy (cryo-EM) to advance structural biology and drug design, focusing on protein dynamics and therapeutic target identification. Education: PhD in Physical Chemistry (1987) from Stanford University; MSc in Chemistry (1981) from Indian Institute of Technology, Kanpur. Subramaniam's interdisciplinary work combines cryo-EM with computational tools and molecular biology to study protein structures at atomic resolution. His lab has pioneered cryo-EM applications in precision medicine, including mapping small molecule drugs on patient-specific cancer mutants. Recent publications (2024-2022) highlight his contributions to understanding SARS-CoV-2 immune evasion, structural mechanisms of ATPases, and AI integration in structural biology. His research spans viral entry mechanisms, CRISPR systems, and neurodegenerative disease pathways. Scientific Awards: Gobind Khorana Canada Excellence Research Chair NIH Director’s Award for Scientific Excellence Fellow of the Biophysical Society Breakthrough Prize nomination Based at the Djavad Mowafaghian Center for Brain Health, Subramaniam leads the Program in Cryo-EM Guided Drug Design, contributing to over 177 peer-reviewed publications with a career h-index of 58 and citations exceeding 12,340.

Philip S. YuView profile

Professor

• Data Mining
• Big Data
• Privacy-Preserving Data Publishing
• +9 more

Philip S. Yu is a Distinguished Professor in the Department of Computer Science at the University of Illinois at Chicago and holds the Wexler Chair in Information Technology. Previously, he led the Software Tools and Techniques department at IBM Thomas J. Watson Research Center. Education: B.S. in Electrical Engineering, National Taiwan University M.S. and Ph.D. in Electrical Engineering, Stanford University M.B.A., New York University His research spans data mining , big data , social networks , privacy-preserving data publishing , graph/network mining , recommender systems , and deep learning . He has authored over 970 papers with 74,500+ citations and an H-index of 127. Recent work focuses on heterogeneous graph representation, quantum walks in network analysis, and federated unlearning. Scientific Honors: ACM SIGKDD 2016 Innovation Award IEEE Computer Society 2013 Technical Achievement Award IEEE ICDM 2003 Research Contributions Award IEEE Region 1 Award (1999) UIC Research of the Year (2013) IBM Master Inventor with 300+ patents AI 2000 Most Influential Scholar Honorable Mentions (2024-2025) He served as Editor-in-Chief for ACM Transactions on Knowledge Discovery from Data and IEEE Transactions on Knowledge and Data Engineering , and on steering committees for ACM KDD and IEEE Data Mining. His work bridges theoretical advances in graph neural networks , deep learning , and privacy-preserving systems with applications in healthcare, social media, and enterprise analytics.

• Uncertainty Quantification
• Scientific Machine Learning
• Stochastic Optimization
• +3 more

Hadi Meidani is a Clinical Associate Professor at the Carle Illinois College of Medicine , specifically within the Department of Biomedical and Translational Sciences at the University of Illinois at Urbana-Champaign . He teaches courses in Civil and Environmental Engineering, including topics like Systems Engineering & Economics , Machine Learning in CEE , and Uncertainty Quantification . Ph.D., Civil Engineering, University of Southern California (2012) M.S., Electrical Engineering, University of Southern California (2012) M.S., Structural Engineering, Sharif University of Technology (2005) B.S., Civil Engineering, K.N. Toosi University of Technology (2002) Dr. Meidani's research focuses on uncertainty quantification , scientific machine learning , and optimization under uncertainty for engineering systems. His work spans stochastic multiscale analysis , physics-informed machine learning , and model reduction techniques. His recent publications emphasize machine learning for infrastructure systems , graph neural networks , physics-informed models , and traffic assignment . Key trends include deep learning , multi-fidelity modeling , and neural operator transformers applied to metamaterial design , seismic reliability , and autonomous freight delivery .

• Bayesian statistics
• Stochastic simulation
• Markov chain Monte Carlo
• +5 more

Professor Chris Holmes is a Professor of Biostatistics at the University of Oxford, where he moved from Imperial College London in February 2004. He is a Fellow at St Anne's College and works in the Department of Statistics. His research focuses on applications and statistical methods development in genomic sciences and genetic epidemiology, holding a prestigious Programme Leaders Grant in Statistical Genomics from the Medical Research Council. Prior to his position at Oxford, Professor Holmes completed his doctorate in Bayesian statistics at Imperial College London, investigating novel nonlinear pattern recognition methods. This was followed by a post-doctoral position and then a lectureship at Imperial. Before his academic career, he worked in industry for several years in scientific computing, developing techniques for real-time pattern recognition models in defense and SCADA systems. Professor Holmes has a broad interest in the theory, methods and applications of statistics and statistical modeling, with a particular foundation in Bayesian statistics which he views as providing a unified framework for stochastic modeling and information processing. His specific research interests include: Bayesian statistics and stochastic simulation Markov chain Monte Carlo methods Pattern recognition and nonlinear, nonparametric methods Spatial statistics Statistical genetics and genomics Genetic epidemiology His recent publications (2023-2025) demonstrate a strong focus on the intersection of biostatistics, artificial intelligence, and healthcare applications. His work spans multiple domains including AI-driven disease classification in neurology, genomic data analysis for health equity, machine learning tools for healthcare prediction, and addressing bias in medical AI systems. A notable trend across his research is the application of advanced statistical methods to solve pressing problems in genomics, epidemiology, and medical diagnostics, with an increasing emphasis on health equity and the ethical implications of AI in healthcare. Professor Holmes currently supervises PhD students Oscar Clivio, Sahra Ghalebikesabi, and Natalia Garcia Martin. His research is supported by multiple grants, including the MRC Programme Leaders Grant in Statistical Genomics which funds his work in statistical genomics. He is actively involved in three research groups at Oxford that reflect the breadth of his scholarly interests: Computational Statistics and Machine Learning Statistical Genetics and Epidemiology Statistical Theory and Methodology

Silvestro MiceraView profile

Professor

• Neural Engineering
• Neuroprosthetics
• Robotics
• +3 more

Silvestro Micera is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) and holds the Bertarelli Foundation Chair in Translational Neuroengineering. He directs the Translational Neural Engineering Laboratory and teaches courses including Neural signals and signal processing and Translational neuroengineering . His research bridges neural interfaces, robotics, and neuroprosthetics to restore motor functions in spinal cord injuries, stroke, and amputations. Micera's research integrates implantable neural interfaces, robotic rehabilitation, and hybrid neuro-prosthetic systems. Key focus areas include: Robotic neurorehabilitation for mobility restoration Neural control mechanisms in movement CNS/PNS neural interface development Bioelectronic modulation for sensory feedback His recent publications emphasize machine learning-driven motor recovery prediction, closed-loop sensory feedback systems, and minimally invasive neuroprosthetics. Trends include AI-optimized stimulation protocols, multimodal data fusion for rehabilitation, and clinical translation of neural bypass technologies. Awards: IEEE EMBS Early Career Achievement Award (2009) IEEE EMBS Technical Achievement Award (2021) Micera leads EU-funded projects such as TIME, CLONS, and NeuWalk, focusing on neural prostheses. He advises 8 current and 18 former PhD students in neuroengineering. His lab collaborates with MIT, Harvard, and industry partners (e.g., Plexon) to advance translational neurotechnologies.

• Gynecologic Oncology
• Ovarian Cancer
• Breast Cancer
• +5 more

Federica Tomao is an Associate Professor at the Department of Maternal, Child and Urological Sciences within Sapienza University of Rome. She actively teaches and supervises courses in Medicine and Surgery , Nursing , and Midwifery programs across multiple institutions. Current faculty member with academic rank Teaching roles in 6th, 3rd, and 2nd year courses Specialized in gynecologic oncology Research Focus : Her work spans ovarian cancer, breast cancer, and endometrial cancer, emphasizing chemotherapy optimization, precision medicine, and radiomics. Notably, she contributes to understanding PARP inhibitors, immune checkpoint therapies, and fertility preservation techniques in cancer survivors. Recent Publications highlight advancements in sarcopenia analysis, BRCA testing, and radiogenomic nomograms. These studies demonstrate her commitment to bridging imaging and molecular data for improved cancer management. Email : federica.tomao@uniroma1.it

Melissa CoyleView profile

Senior Lecturer

• Sport Psychology
• Exercise Science
• Mental Health
• +3 more

Melissa Coyle is a Senior Lecturer at the School of Sport, Exercise and Rehabilitation, Marjon University. She specializes in sport and exercise psychology, focusing on applications in sport science, coaching, physical education, outdoor education, and health and well-being. Her qualifications include HCPC registration as a sport and exercise psychologist, BASES chartered scientist, and BASES-accredited sport scientist (Psychology), alongside a PGCert, MSc, and BSc. Her research explores psychological factors in high-stakes environments, including military training, cancer patient exercise programs, and aging populations. Notable studies include a qualitative evaluation of an 8-week exercise referral program for cancer patients and an investigation into psychological factors in Royal Artillery Army Commando training. She has also examined motivations and barriers to indoor bowls participation among over-65s and mental health transitions in university students. Dr. Coyle has been awarded grants for studies like 'Psychological factors in Army Commando training' and 'Exercise adherence for cancer recovery.' Her publications span journals like Psychology of Sport and Exercise and conferences such as the Association of Applied Sport Psychology. She actively contributes to professional bodies like HCPC and BASES. Her teaching emphasizes practical applications of psychology in sports and health, complemented by research-driven insights. Beyond academia, she is an avid golfer and hill walker.

Alexis BattleView profile

Associate Professor

• Genomics
• Machine Learning
• Computational Biology
• +3 more

Alexis Battle is an Associate Professor at Johns Hopkins University with appointments in Biomedical Engineering , Computer Science , and Genetic Medicine (secondary). She directs the Malone Center for Engineering in Healthcare and serves as Deputy Director of the Data Science and AI Institute . Educated at Stanford University (PhD in Computer Science, 2013), Battle transitioned to academia after leadership roles at Google. Research Focus: Battle’s work bridges genomics and machine learning , emphasizing the impact of genetic variation on human health. Her lab develops tools like Watershed to predict functional effects of rare variants, aiming to enhance rare disease diagnosis. Key themes include non-coding DNA analysis , personalized genomics , and systems biology , with applications in cardiovascular disease and neurodegenerative disorders . Publications & Awards: Over 60 peer-reviewed articles in journals like Nature , Science , and Genome Biology , with recent emphasis on single-cell transcriptomics , multiomics integration , and telomere biology . Recipient of the President’s Frontier Award (2022), Microsoft Investigator Fellowship (2019), and Searle Scholar (2016). Scientific Awards: 2022 President’s Frontier Award 2019 Microsoft Investigator Fellowship 2019 Johns Hopkins Discovery Award 2017 Johns Hopkins Catalyst Award 2016 Searle Scholar Advising & Funding: Mentors 11 PhD students, 3 undergraduates, and postdoctoral fellows. Her research is funded by NIH, Searle Scholars, and institutional grants. The Battle Lab collaborates on projects like the GTEx Consortium , focusing on gene regulation and clinical genomics .

Martin T. WellsView profile

Professor

• Applied Statistics
• Theoretical Statistics
• Bayesian Statistics
• +7 more

Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.

Aleksandar JeremicView profile

Associate Professor

• Biomedical Signal Processing
• Statistical Signal Processing
• Biometrics
• +7 more

Aleksandar Jeremic is an Associate Professor in both the Electrical & Computer Engineering department and the McMaster School of Biomedical Engineering at McMaster University. His research focuses on biomedical signal processing, statistical signal processing, and biometrics, with applications in healthcare and biomedical systems. He holds a Dipl. Ing. from the University of Belgrade, and an M.S. and Ph.D. from the University of Illinois at Chicago. His expertise spans physiological signal analysis (e.g., ECG/EEG), medical imaging, and machine learning for biomedical applications. He has authored over 50 technical articles and two book chapters, and has been recognized with a teaching award from the McMaster Electrical and Computer Engineering Society (2018). He supervises graduate students in both theoretical and applied research areas. Dr. Jeremic teaches advanced courses such as Biomedical Signal Modeling and Processing and Advanced Probability and Random Processes , emphasizing practical applications of signal processing in healthcare. His work includes clinical implementations like neonatal seizure monitoring software and microwave imaging for breast cancer detection.

• Lymphoma
• Hodgkin Lymphoma
• Non-Hodgkin Lymphoma
• +5 more

Jonathan W. Friedberg, M.D., M.M.Sc. is the Director of the Wilmot Cancer Institute and Professor in the Department of Medicine, Hematology/Oncology at the University of Rochester School of Medicine and Dentistry. He holds the Samuel E. Durand Chair in Medicine and leads one of the nation's premier lymphoma programs. Dr. Friedberg is internationally recognized for his expertise in lymphoma treatment, particularly in developing novel therapies and clinical trial approaches. Dr. Friedberg's research focuses on lymphoma, with particular expertise in Hodgkin lymphoma, non-Hodgkin lymphoma, Waldenstrom macroglobulinemia, and chronic lymphocytic leukemia. His work spans the entire treatment continuum from diagnosis through novel therapies including autologous stem cell transplantation and CAR-T cell interventions. He has built a comprehensive lymphoma program with expertise spanning hematopathology, radiation oncology, dermatology, and neurology, in addition to hematology and medical oncology. His research interests include developing risk-adapted treatment strategies, investigating novel therapeutic agents, and improving outcomes for patients with various lymphoma subtypes. Dr. Friedberg's extensive publication record demonstrates his leadership in lymphoma research, with recent work focusing on immunotherapy combinations, predictive modeling, risk stratification, and novel treatment approaches for various lymphoma subtypes. His research has significantly contributed to the understanding and treatment of lymphomas, particularly in developing more personalized and effective treatment strategies. Scientific Awards: Faculty Academic Mentoring Award (2012) Scholar in Clinical Research (2008) America's Top Doctors Selection (2008) Jacob Gitelman Award (2007) Lawrence A. Kohn Senior Teaching Fellow (2004-2006) Clinical Investigator Career Development Award (2003) Clinical Oncology Research Fellowship "Immunotherapy of Hodgkin's Disease" (2001-2003) Rising stars program for innovative research (2001-2003) As Director of the Wilmot Cancer Institute, Dr. Friedberg leads numerous clinical trials and research initiatives focused on advancing lymphoma treatment. He is actively involved in SWOG and has served in leadership roles for multiple clinical trials investigating novel therapies for lymphoma patients. His practice team includes Anna Morrison, R.N., and Kerri Hugelmaier, N.P., providing comprehensive care for lymphoma patients. Dr. Friedberg has established the Lymphoma Epidemiology of Outcomes (LEO) Consortium, a large observational cohort study supporting broad research on NHL prognosis and survivorship. His leadership extends to national organizations where he contributes to developing clinical practice guidelines and consensus recommendations for lymphoma treatment.

Alison LedgerwoodView profile

Professor

• Social Psychology
• Open Science
• Methodology
• +4 more

Alison Ledgerwood is a Professor in the Department of Psychology at the University of California, Davis, and Principal Investigator of the Attitudes and Group Identity Lab. Her research examines how social context shapes attitudes and preferences, with a focus on open and inclusive scientific practices. Ph.D., Social Psychology, New York University (2008) M.A., Psychology, New York University (2006) B.A., Psychology, Amherst College (2003) Her research explores psychological distance , framing effects , and system justification theory , while methodologically advancing preregistration , collaborative science , and equity in publishing . She also investigates group identity dynamics and implicit/explicit bias measurement . Recent publications highlight her work on racial bias methodology , open science reform , and contextual framing . Awards include the 2024 Distinguished Service to the Society award, 2021 UC Davis Advising and Mentoring Award, and 2017 SPSP Service to the Field award. 2024 Distinguished Service to the Society, SPSP 2021 UC Davis Graduate Advising and Mentoring Award 2017 Service to the Field, SPSP Hellman Fellowship (2010-2011), UC Davis UC Davis Chancellor's Fellow APS Fellow SESP Fellow Ledgerwood advises on scientific integrity through roles like Chair of the Transparency and Openness Promotion (TOP) II Guidelines Task Force and Anti Colorism and Eurocentrism in Methods and Practices (ACEMAP) Task Force. Her lab fosters contextual evaluation studies and psychological distance frameworks .