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James ZouView profile

Associate Professor

• Machine Learning
• Artificial Intelligence in Healthcare
• Biomedical Data Science
• +3 more

James Zou is an Associate Professor of Biomedical Data Science at Stanford University, with courtesy appointments in Computer Science and Electrical Engineering. His research focuses on advancing machine learning methodologies for healthcare applications, emphasizing reliability, fairness, and statistical rigor. He holds a Ph.D. from Harvard University and has held positions at Microsoft Research, Cambridge University (as a Gates Scholar), and UC Berkeley (Simons Fellow). Zou leads the Stanford Data4Health hub and is a Chan-Zuckerberg Investigator. His work spans AI-driven diagnostics, spatial transcriptomics, and ethical AI frameworks. Key achievements include the EchoNet AI system for echocardiography and foundational contributions to data valuation (e.g., Data Shapley). Awards include the Sloan Fellowship, NSF CAREER Award, and Google/Tencent AI awards. Education: Ph.D., Harvard University (2014); Postdoctoral roles at Microsoft Research, Cambridge, and Berkeley. Research Interests: Machine learning for healthcare, algorithmic fairness, interpretable AI, spatial omics, and translational bioinformatics. His lab develops tools like TextGrad (PyTorch for text agents) and frameworks for evaluating medical AI systems. Recent work addresses LLMs in peer review and clinical decision-making. Grants/Grants: Supported by NSF, Sloan Foundation, Chan-Zuckerberg Initiative, and industry partnerships (Google, Amazon, Adobe). Advises on over 20 doctoral students, many contributing to high-impact papers in Nature , Science , and top conferences (NeurIPS, ICML). Leads collaborations in cardiology, oncology, and veterinary medicine. Labs/Teams: Stanford AI Lab, Stanford Data4Health, and interdisciplinary groups in precision medicine. Active in open-source projects like FrugalML and MetaViz.

Ye ZhishengView profile

Associate Professor

• Inventory Control
• Reliability Engineering
• Emergency Response Systems
• +4 more

Ye Zhisheng is the Dean’s Chair and Associate Professor in the Department of Industrial Systems Engineering & Management at the National University of Singapore (NUS). His research focuses on reliability engineering, inventory control, emergency response systems, and statistical modeling. He holds a PhD in Industrial and Systems Engineering from NUS, along with a BEng in Material Science and Engineering and a BEco in Economics from Tsinghua University. His work emphasizes practical applications in mission-critical systems, predictive maintenance, and data-driven decision-making. Current research initiatives include optimal maintenance policies for manufacturing systems, degradation analysis of bearings, and federated learning approaches for battery lifecycle prediction. He has pioneered methods for integrating physics-informed neural networks into prognostics and health management (PHM) systems. Key technical contributions span advanced statistical methodologies like sieve estimation for survival data, phase-type distributions modeling, and condition-based maintenance optimization. His interdisciplinary approach bridges operations research, mechanical engineering, and computer science to address complex reliability challenges. Recent projects include resilient consensus-based power grid management and contamination source identification frameworks. Notable collaborations involve developing intelligent cross-domain fault diagnosis systems using transformer networks and advancing the Internet of Federated Things (IoFT) for distributed data analytics. His work has been applied in aerospace, telecommunication infrastructure, and medical emergency response systems.

Carlos Fernandez-GrandaView profile

Associate Professor

• Machine Learning
• Medical AI
• Climate Science
• +7 more

Carlos Fernandez-Granda is an Associate Professor of Mathematics and Data Science at New York University, holding joint appointments at the Courant Institute of Mathematical Science and the Center for Data Science. He currently serves as the Interim Director of the Center for Data Science. His academic career spans over a decade at NYU, where he has taught probability and statistics to data-science students. Dr. Fernandez-Granda's research focuses on designing and analyzing data-science methodology, with current emphasis on machine learning applications in medicine, climate science, and scientific imaging. His work bridges theoretical foundations with practical applications across multiple domains. His notable contributions include: Development of the COBRA (COnfidence-Based chaRacterization of Anomalies) score for automatic assessment of impairment and disease severity Applications of machine learning to improve climate projections through the M2LInES project Research on magnetic resonance fingerprinting for quantitative tissue parameter estimation Development of AI systems for medical diagnostics including Alzheimer's detection and breast cancer diagnosis Dr. Fernandez-Granda is the author of the book "Probability and Statistics for Data Science," published by Cambridge University Press. The book serves as a comprehensive guide to the two pillars of data science, featuring real-world datasets and addressing fundamental challenges like overfitting, the curse of dimensionality, and causal inference. His research has been supported by grants from the National Science Foundation (Division of Mathematical Sciences, grants 1616340 and 2009752) and the Alzheimer's Association (grant AARG-NTF-21-848627). Dr. Fernandez-Granda is actively involved in several collaborative projects: M2LInES project: An international collaboration focused on improving climate projections using machine learning to capture unaccounted physical processes at the air-sea-ice interface Math and Data group: Exploring the intersection of mathematical theory and data science applications

Igor JurisicaView profile

Professor

• Data Science and Computational Biology
• Machine Learning
• Artificial Intelligence
• +6 more

Igor Jurisica is a Professor at the University of Toronto and a Senior Scientist at the Krembil Research Institute’s Data Science Discovery Centre for Chronic Diseases. He also serves as Visiting Scientist at IBM CAS, Scientific Director of the World Community Grid, and Chief Scientist at the Creative Destruction Lab (Rotman School of Management). His research focuses on integrative computational biology, data mining, and AI-driven models for cancer mechanisms, drug discovery, and chronic disease management. Key affiliations include the Osteoarthritis Research Program, Schroeder Arthritis Institute, and leadership roles in open science initiatives like the World Community Grid, a global distributed computing platform with 810,000+ volunteers. Jurisica’s work bridges computational tools (e.g., NAViGaTOR visualization platform, MirDIP databases) and clinical applications, emphasizing explainable AI in healthcare. Research interests span proteomics, microRNA regulation, systems vaccinology, and multi-omics integration for disease stratification. Notable contributions include identifying prognostic signatures in cancer and osteoarthritis, machine learning models for drug repurposing, and sportomics analyses of athletic biomarkers. He has been recognized as a Thomson Reuters Highly Cited Researcher (2014-2016) and ranked among the Top 100 AI Leaders in Oncology (2023). His labs develop open-access tools like PathDIP, OsteoDIP, and miRAnno to advance translational research.

• Oral Surgery
• Maxillofacial Surgery
• Head and Neck Cancer
• +12 more

Martin Gosau is a Professor at the Clinic and Polyclinic for Oral and Maxillofacial Surgery within the Medical Faculty of the University Medical Center Hamburg-Eppendorf (UKE). His research focuses on Oral Surgery , Maxillofacial Surgery , and Regenerative Medicine , with a strong emphasis on Dental Implants , Head and Neck Cancer , and Oral Pathology . University: University Medical Center Hamburg-Eppendorf School: Medical Faculty Department: Oral and Maxillofacial Surgery Academic Rank: Professor His recent work explores: Oral Health in Genetic Disorders (e.g., hypophosphatasia) Advanced Surgical Techniques (e.g., nanosecond lasers, fluorescence angiography) Biomaterials and Tissue Engineering (e.g., silk fibroin membranes, extracellular vesicles) Cancer Prognostics (e.g., DCBLD1 overexpression in HNSCC) Key trends in his 15 most recent articles include applications of machine learning in oral diagnostics, stem cell research for bone regeneration, and biomaterials in reconstructive surgery. He frequently collaborates with Ralf Smeets and Thomas Vollkommer , with publications spanning Frontiers in Immunology , Oral Surgery , and Scientific Reports .

• Intensive Care Medicine
• Critical Care
• Sepsis
• +9 more

Stefan Kluge is a Professor at the Department of Intensive Care within the University Medical Center Hamburg-Eppendorf . With over 150 publications in 2025-2024 alone, his work focuses on critical care medicine, sepsis, ARDS, and cardiogenic shock. His recent research explores ARDS impact on liver cirrhosis outcomes Hyperoxia effects in traumatic brain injury Biomarkers for cardiogenic shock prognosis Novel retrograde limb perfusion techniques during ECPR Dual-energy CT for body composition analysis He contributes extensively to German national guidelines for nosocomial pneumonia and participates in multi-center critical care trials. Stefan Kluge's collaborations span neuro-intensive care , cardiovascular research , and infection/immunity domains. His work integrates machine learning applications and medical informatics to improve ICU decision-making.

• Cancer Genomics
• Liquid Biopsies
• Machine Learning in Oncology
• +7 more

Ash A. Alizadeh is the Moghadam Family Professor of Medicine, Oncology, and Hematology (by courtesy) at Stanford University, where he serves as leader of the Cancer Genomics Program at Stanford Cancer Institute. He holds multiple academic appointments including Professor in Medicine - Oncology, and membership in Bio-X, the Institute for Stem Cell Biology and Regenerative Medicine, and the Maternal & Child Health Research Institute (MCHRI). Dr. Alizadeh received his BS in Biochemistry from UCLA (1994), MD from Stanford Medical School, and PhD in Biophysics from Stanford. He completed additional training at the National Cancer Institute (NCI), the National Institutes of Health (NIH), and the Howard Hughes Medical Institute (HHMI). His primary research focuses on developing and applying genome technologies and computing (machine learning & data science) to problems in human disease, with special emphasis on cancer detection, classification, monitoring, and tumor immunology. His laboratory pioneers noninvasive cancer genomic techniques including CAPP-Seq, PhasED-Seq, and EPIC-Seq for "liquid biopsies" that analyze circulating nucleic acids for early cancer detection and monitoring therapeutic response. Using machine learning approaches, his group studies how cellular compositional variation impacts cancer behavior and therapeutic response, including anti-tumor immunity. His work spans molecular, cellular, organism and population levels of tumor behavior analysis. Dr. Alizadeh has received numerous prestigious awards including the Scholar Award from the American Society of Hematology (ASH), the Leukemia & Lymphoma Society (LLS), the V-Foundation, as well as awards from the American Red Cross, Damon Runyon Cancer Research Foundation, and Doris Duke Charitable Research Foundation. He is an elected member of the American Society for Clinical Investigation (ASCI) and serves on the Scientific Advisory Board of the Lymphoma Research Foundation (LRF). As an educator and mentor, Dr. Alizadeh advises numerous doctoral students, postdoctoral fellows, and medical scholars. He teaches in the Department of Medicine and Immunology and serves on various admissions panels at Stanford. His laboratory, the Alizadeh Lab, is a hub for interdisciplinary cancer genomics research that combines computational biology, molecular genetics, and clinical oncology to develop novel cancer diagnostics and therapeutics.

Franz Lennard RicklefsView profile

Professor

• Neurosurgery
• Neuro-oncology
• Molecular Neurobiology
• +5 more

Prof. Dr. med. Franz Lennard Ricklefs is a Senior Physician and Head of the Working Group at the Department of Neurosurgery, University of Hamburg Faculty of Medicine. He is a Medical Specialist in Neurosurgery with cross-disciplinary expertise in neuro-oncology, molecular pathology, and extracellular vesicle research. Affiliations: University Medical Center Hamburg-Eppendorf (UKE), European Liquid Biopsy Society (ELBS), International Consortium on Meningiomas (ICOM) Research Interests: His work focuses on neurosurgical oncology, particularly glioblastoma and meningioma pathobiology. He investigates DNA methylation patterns, extracellular vesicle biomarkers, and liquid biopsy implementation in clinical neuro-oncology. Additional interests include surgical outcomes for epilepsy and aneurysm management. Article Trends: Over the last decade, Dr. Ricklefs has published extensively on: Extracellular vesicle applications as liquid biopsy markers DNA methylation subclasses for glioblastoma and meningioma Multicenter surgical outcome benchmarking Immune evasion mechanisms in neuro-oncology Technological innovations in neurosurgical visualization Molecular characterization of rare CNS tumors Professional Contributions: He co-authored the MISEV2023 guidelines for extracellular vesicle studies and participates in international consensus reviews for meningioma classification. His collaborations span institutions across Europe and North America.

Thomas WalterView profile

Professor

• Biomedical Image Analysis
• Machine Learning
• Computational Pathology
• +4 more

Thomas Walter is a Professor at Mines ParisTech and Director of the Centre for Computational Biology (CBIO) , a research group affiliated with the Institut Curie and INSERM . His work focuses on applying Machine Learning and Computer Vision to biomedical image analysis, particularly in high-content screening and computational pathology . He also serves as Deputy Director of the Computational Oncology (U1331) unit and leads the Statistical Learning and Modeling of Biological Systems team. PhD in Medical Image Analysis (2003, Mines ParisTech) Postdoctoral work at EMBL (European Molecular Biology Laboratory) Director of CBIO since 2018 Holder of a PRAIRIE Chair (Paris Artificial Intelligence Research Institute) since 2019 Dr. Walter's research bridges biomedical imaging , machine learning , and cancer genomics . Key areas include: Statistical reconstruction of biological networks Prediction of tumor progression at genomic/transcriptomic levels Development of deep learning methods for cell cycle analysis Integration of multi-omics data for precision oncology Tools for spatial transcriptomics (e.g., autoFISH, RNA2seg) Recent publications highlight his work in spatial transcriptomics , immunotherapy outcome prediction , and deep learning for digital pathology . His team has developed open-source tools like FISH-quant and pyHiM for single-molecule RNA imaging analysis. Scientific Honors: PRAIRIE Chair (2019) for AI research in life sciences Dr. Walter actively contributes to teaching deep learning for image analysis in multiple graduate programs across France, including courses at Mines ParisTech , Université Paris-Saclay , and Institut Curie . His software tools (FISH-quant, pyHiM) and methodological frameworks (e.g., Cut-Detector, PointFISH) have become standard resources in bioimage informatics.

Simon CrouchView profile

Research Fellow

• Statistical Modeling
• Epidemiology
• Cancer Research
• +6 more

Simon Crouch is a Senior Research Fellow in Biostatistics at the University of York's Health Sciences department. With a strong mathematical background from Cambridge and Warwick, he leads the analytics team within the Epidemiology and Cancer Statistics Group and works closely with the Haematological Malignancy Research Network (HMRN) and Cardiovascular Health team. His work focuses on statistical modeling of complex epidemiological data related to hematological malignancies. University of Cambridge: MA, MMath in Mathematics University of Warwick: PhD in Mathematics University of Lancaster: MSc in Medical Statistics Dr. Crouch specializes in the statistical modeling of complex epidemiological data, with particular focus on hematological malignancies. His research encompasses predictive modeling, event history analysis, and machine learning applications in cancer epidemiology. He has made significant contributions to understanding myelodysplastic syndromes, lymphoma classification, and survival analysis in blood cancers through population-based studies. His work often involves collaboration with international registries including the European Myelodysplastic Syndromes Registry (EUMDS) and the Haematological Malignancy Research Network. Analysis of his recent publications reveals a strong focus on myelodysplastic syndromes (MDS), with particular attention to risk stratification, survival analysis, and treatment outcomes. His work increasingly incorporates genomic and molecular data to refine disease classification and prediction models. The trend shows progression from purely statistical methodology development toward integrated translational research that combines clinical, genomic, and epidemiological data to improve patient outcomes. Extensive publication record with 113 research outputs including 66 articles, 21 patents, and numerous meeting abstracts Active participation in major international research consortia including MDS-RIGHT and ImmunAID Significant contributions to the development of statistical methodologies for cancer epidemiology Dr. Crouch actively supervises PhD students in mathematical and statistical modeling applied to cancer epidemiology, with particular interest in time-to-event models, complex longitudinal models, and simulation techniques. His research has been supported through multiple projects, including the European Myelodysplastic Syndromes Registry and the MDS-RIGHT project focused on facilitating informed decision-making in hemato-oncology. He contributes to the Advanced Health and Social Statistics module for postgraduate students at the University of York.

Rafael PereraView profile

Professor

• Medical Statistics
• Primary Care Health Sciences
• Monitoring of Long-Term Conditions
• +6 more

Rafael Perera is a Professor of Medical Statistics at the Nuffield Department of Primary Care Health Sciences (NDPCHS), University of Oxford, where he has been since 2002. He holds leadership roles as Director of the Statistics Group and Director of Graduate Studies , overseeing academic leadership and methodological research. He is also a Statistical Editor of BMJ and BMJ Medicine , and a fellow at St Hugh’s College Oxford . Education : DPhil, MSc, MA Rafael’s research focuses on monitoring for managing long-term conditions (e.g., Type 2 diabetes, hypertension) and complex multimorbidity phenotypes . His work includes impact of extreme temperatures on health , meta-analysis methods , and methodology for infectious diseases . He leads large methodological groups and has been a PI on NIHR-funded infrastructure programs (BRC, ARC, MIC). Recent publications highlight his expertise in clinical prediction models , digital health interventions , and diagnostic test evaluation . His grants span applied research , global health transformation , and ageing research . Scientific Awards : National and international recognition for methodology development in clinical trials (NIHR Progress Report 2008/09) Rafael supervises DPhil and MSc students and leads a fully accredited Clinical Trials Unit . His editorial and policy influence extends to healthcare policy panels and the Centre for Evidence-Based Medicine as Director of Research Methodologies.

• Virology
• Molecular Pathology
• Host-Pathogen Interactions
• +5 more

Dr. Richard Y. Zhao is a tenured Professor in the Department of Pathology and Microbiology-Immunology at the University of Maryland School of Medicine. His research combines molecular biology, fission yeast genetics, mammalian biology, and virology to study virus-host interactions, particularly for HIV and Zika virus. He previously held academic positions at Northwestern University and Columbia University and has contributed to over 120 peer-reviewed articles. B.S., China Oceanography University (1981) M.S., Oregon State University (1995) Ph.D., Oregon State University (1991) Postdoctoral Training, Columbia University (1991-1992) Dr. Zhao's research focuses on: Virus-host interactions and pathogenicity High-throughput drug screening for antivirals Role of viral proteins in neuroinflammation and cancer Translational genomics in precision medicine His recent publications highlight SARS-CoV-2 ORF3a, Zika envelope proteins, and HIV protease inhibitors, emphasizing host-pathogen mechanisms across species. He has served on NIH panels and editorial boards for journals like Cell Research and Retrovirology . Scientific awards include: Fellow, American Academy of Microbiology (2019) Bernard L Mirkin Endowed Chair (2001-2004) Honorary Director, Shandong Gallo Institute (2009) Distinguished Service from SCBA (2015) Outstanding Service from CBA-USA (2016) Dr. Zhao also contributes to clinical diagnostics and personalized medicine through molecular testing and pharmacogenetics programs.

James S. DuncanView profile

Professor

• Biomedical Image Processing
• Quantitative Image Analysis
• Cardiac Function Analysis
• +4 more

James S. Duncan is the Ebenezer K. Hunt Professor of Biomedical Engineering at Yale University, with additional appointments in Electrical & Computer Engineering and Radiology & Biomedical Imaging. His research focuses on biomedical image processing, quantitative image analysis using geometrical models, and applications in cardiac function and neuro-structure analysis. He has pioneered image-guided interventions and developed computational frameworks for medical imaging challenges. He holds a Ph.D. from the University of Southern California. His work integrates AI, deep learning, and statistical decision-making to advance medical imaging technologies. Notable contributions include advancements in 3D image segmentation, deformable models, and MRI-based tumor response assessment. Dr. Duncan has received prestigious awards, including IEEE Fellow (2001) and induction into the American Institute for Medical and Biological Engineering (2000). His recent research spans AI-driven hemodynamics modeling, trustworthy healthcare AI guidelines, and molecular MRI innovations in immunotherapy monitoring. He collaborates across disciplines to address challenges in cardiovascular, neuroimaging, and oncological applications.

Jacob YoungView profile

Assistant Professor

• Neuro-oncology
• Glioblastoma
• Brain Tumor Immunology
• +6 more

Jacob Young, MD, is an Assistant Professor in the Department of Neurological Surgery at the University of California, San Francisco (UCSF) School of Medicine and a Principal Investigator in the UCSF Brain Tumor Center. His clinical practice focuses on neurosurgical management of adult brain tumors including gliomas, metastatic tumors, and meningiomas, utilizing advanced brain mapping techniques to preserve critical motor, language, and sensory functions during resection. Dr. Young's educational background includes a BS in Neuroscience from Duke University (2012), an MD from the University of Chicago Pritzker School of Medicine where he was elected to Alpha Omega Alpha Honor Medical Society (2017), and a neurosurgery residency at UCSF (2017-2024). His research program integrates laboratory investigations with clinical trials to address fundamental challenges in brain tumor treatment. His primary research interests center on understanding glioblastoma immune microenvironment dynamics and developing innovative therapeutic strategies. Key focus areas include: First-in-human clinical trials of novel immunotherapies Focused ultrasound-mediated blood-brain barrier disruption to enhance drug delivery Longitudinal molecular profiling of tumor evolution during treatment AI-driven tools for patient care navigation and clinical trial assessment Prospective outcomes research through the RANO resect group and NeuroPoint Alliance His work bridges fundamental tumor biology with translational applications to overcome treatment resistance. Analysis of Dr. Young's 15 most recent publications (2023-2025) reveals a strong emphasis on surgical innovation, tumor immunology, and molecular characterization. Key trends include: development of prognostic classification systems for resection extent, investigation of glioma-neuronal circuit interactions driving immunosuppression, and optimization of drug delivery strategies. His collaborative work within the RANO consortium establishes evidence-based surgical guidelines while his lab's focus on microenvironmental factors informs next-generation immunotherapies. Dr. Young has received significant recognition including: Chan-Zuckerberg Physician Scientist Fellowship (2021-2022) ASCO Young Investigator Award (2022-2023) Andrew J. Lockhart Focused Ultrasound Fellowship (2023) Multiple Harold Rosegay Teaching Awards from UCSF Howard Naffziger Award for Clinical Excellence His research is supported by NIH, NCI, Focused Ultrasound Foundation, and AANS grants. As lab director, Dr. Young mentors a diverse team including PhD candidates like Edward Valenzuela (DSCB program) and specialists in immunology and neuro-oncology. His lab participates in the RANO resect group, ENCRAM research program, and NeuroPoint Alliance to advance clinical protocols. Current projects include developing intraoperative focused ultrasound prototypes, single-cell analysis of tumor evolution, and AI tools for patient navigation through care pathways. Future work focuses on translating microenvironment discoveries into combination therapies targeting treatment resistance mechanisms.

• Inverse Problems
• Nonlinear Acoustics
• Medical Imaging
• +5 more

Yang Zhang is a Visiting Assistant Professor in the Department of Mathematics at the University of California, Irvine (UCI), working under Prof. Katya Krupchyk. His research focuses on inverse problems in imaging sciences, nonlinear hyperbolic equations, and medical imaging applications. He previously held a postdoctoral position at the University of Washington, Seattle, under Prof. Gunther Uhlmann. His work integrates microlocal analysis and partial differential equations to address challenges in wave propagation, nonlinear acoustics, and elasticity. Education & Career: PhD from Purdue University (advisor: Prof. Plamen Stefanov) Postdoc: University of Washington, Seattle (2020–2024) Research Interests: Dr. Zhang's work spans inverse problems for nonlinear hyperbolic equations, acoustic imaging, and integral transforms in medical contexts. He develops novel methodologies using multi-fold linearization, wave interactions, and advanced calculus techniques. His studies on Rayleigh and Stoneley waves in elasticity further demonstrate his expertise in microlocal analysis. Key Contributions: His research bridges theoretical mathematics and applied imaging, with notable publications on inverse scattering, damping effects in wave equations, and Compton camera imaging. He is an active member of the Inverse Problems International Association (IPIA). Grants & Collaborations: Collaborations with prominent figures like Prof. Gunther Uhlmann and Prof. Katya Krupchyk highlight his network in inverse problems. His work often involves both analytical and computational approaches, with applications in medical diagnostics and geophysics.