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Sophia TsokaView profile

Associate Professor

• Computational genome analysis
• Genome data mining
• Network analysis and reconstruction
• +7 more

Sophia Tsoka is a Reader in Bioinformatics at King's College London specializing in computational genome analysis, network reconstruction, and machine learning applications in cancer immunology and microbiome research. She leads the 'Algorithms for Antibodies' project funded by the Royal Society and serves as Co-Investigator on multiple research projects including 'Understanding the significance of patient B cells and expressed antibodies in melanoma' supported by the British Skin Foundation. Dr. Tsoka's research focuses on computational genome analysis, genome data mining, network analysis and reconstruction, metabolic networks, protein interaction networks, and the evolution of genome properties and dynamics. Her work bridges bioinformatics, machine learning, and immunology, with particular emphasis on applying computational approaches to understand antibody mechanisms, tumor microenvironments, and microbiome dynamics. She has developed innovative algorithms for network analysis, classification, and multi-omics data integration that have advanced our understanding of complex biological systems. Her recent publications demonstrate a strong trajectory in applying computational methods to cancer immunology, with multiple high-impact papers in 2025 spanning IgE antibody therapeutics, tumor microenvironment analysis, and machine learning approaches for biomedical data. These works reveal a consistent focus on developing interpretable computational models that can translate complex biological data into clinically relevant insights. Best paper award (2022) Best Paper Award (2020) Dr. Tsoka supervises numerous research projects and has secured significant grant funding from prestigious organizations including the Royal Society and British Skin Foundation. Her collaborative work spans multiple disciplines, connecting computational scientists with immunologists and clinicians to advance cancer therapeutics. She has established herself as a key contributor to the field of computational immunology with over 4,800 citations to her work. Her laboratory focuses on developing and applying advanced computational methods for analyzing complex biological networks, with particular emphasis on cancer immunology applications. The team combines expertise in algorithm development, machine learning, and biological data analysis to address challenging problems in antibody engineering and tumor microenvironment characterization.

• Human Factors Engineering
• Medical Image Perception
• Perceptual Errors
• +5 more

William F. Auffermann is a Professor at the Department of Radiology and Imaging Sciences, University of Utah, with a dual board certification in Diagnostic Radiology and Clinical Informatics. He serves as Vice Chair for IT and Informatics and Section Chief for Cardiothoracic Imaging, focusing on improving informatics systems and medical education through simulation-based tools. Education: MD and PhD from University of Minnesota Medical School, Fellowship in Cardiothoracic Imaging at Duke University. Board Certifications: American Board of Radiology (Diagnostic Radiology), American Board of Preventive Medicine (Clinical Informatics). His research centers on human factors engineering , integrating medical image perception studies with computer simulation-based education to reduce diagnostic errors. Key areas include biomedical informatics , machine learning , structured reporting , and clinical decision support . Collaborative work spans chest and cardiac imaging with applications in occupational lung disease (NIOSH B-Reader certification). Recent publications emphasize AI integration in radiology , perceptual training , and gaze-display feedback for medical education. He also contributed to ACR Appropriateness Criteria for thoracic imaging and led studies on virtual reality education and gamification in radiology training .

Robert SzalaiView profile

Associate Professor

• Applied Mathematics
• Dynamical Systems
• Nonlinear Dynamics
• +3 more

Dr. Robert Szalai is an Associate Professor of Applied Mathematics at the School of Engineering, University of Bristol. He is affiliated with the Engineering Mathematics Research Group and the Intelligent Systems Laboratory, focusing on nonlinear dynamics, piecewise smooth systems, and invariant manifold theory. Research Interests: Nonlinear dynamics, dynamical systems, mathematical modeling of mechanical and biological systems, bifurcation analysis, and reduced order modeling. Key Collaborations: Active collaborations include researchers like Paul Glendinning, Stephen J. Hogan, and Alan R. Champneys. His recent work explores piecewise smooth systems, contact mechanics, and data-driven model reduction using invariant foliations and machine learning. Research spans applications from mechanical vibrations to bio-mathematical systems. Dr. Szalai’s publications cover diverse topics including Filippov-type singularities, nonlinear cochlear models, and traffic jam dynamics. His methodological focus includes invariant manifolds and bifurcation analysis. Contact: R.Szalai@bristol.ac.uk | ORCID 0000-0002-3602-2397

Yuan WuView profile

Associate Professor

• Survival Analysis
• Sequential Clinical Trial Design
• Machine Learning
• +7 more

Yuan Wu is an Associate Professor in the Department of Biostatistics & Bioinformatics at Duke University and a member of the Duke Cancer Institute. His research focuses on survival analysis, sequential clinical trial design, machine learning, causal inference, and statistical computing. Ph.D., University of Iowa (2010) His work spans clinical trial methodology , biostatistical modeling , and cancer genomics , with recent papers on ordinal outcome measures and RNA splicing mechanisms. Articles trends include machine learning in medical imaging , immune biomarker discovery , and epigenetic therapies . NIH R01 funding for AYA STEPS (2024-2029) DoD Clinical Consortium grants (2021-2025) NIH grants for biofluidic diagnostics (2020-2025) Dr. Wu mentors students through courses like BIOSTAT 903 and BIOSTAT 713, emphasizing advanced survival analysis techniques.

Kingshuk Roy ChoudhuryView profile

Adjunct Associate Professor

• Statistical Modelling
• Medical Imaging Analysis
• Tumor Growth Modelling
• +3 more

Kingshuk Roy Choudhury is an Adjunct Associate Professor at Duke University's Department of Biostatistics & Bioinformatics, specializing in statistical modeling and medical imaging analysis. His work spans tumor growth dynamics, neurodegenerative disease modeling, and computational imaging biomarker development. Education: Ph.D. in Biostatistics from the University of Washington (1998). Research Interests focus on advanced statistical methodologies for medical imaging data, including multi-observer studies, tumor microcirculation analysis, and spatial atrophy quantification in Alzheimer's disease. He has pioneered techniques for virtual nodule insertion and adaptive CT dose estimation, addressing critical challenges in radiology and clinical trial design. Publications highlight his expertise in liver cancer diagnostics (LI-RADS, MRI follow-up), radiation therapy outcomes (erectile dysfunction, tumor response prediction), and high-dimensional Bayesian modeling of brain connectomes. Key projects include NIH-funded studies on pulmonary ventilation MRI and radiation-induced tissue injury mitigation. Grants from the National Institutes of Health (2012–2019) support his work in healthcare systems research, tumor microcirculation, and lung imaging biomarkers. His collaborations span radiology, oncology, and neuroscience, integrating imaging with computational statistics to improve diagnostic accuracy and therapeutic monitoring.

Rui MiaoView profile

Assistant Professor

• Reinforcement Learning
• Causal Inference
• Health AI
• +3 more

Rui Miao serves as an Assistant Professor in the Department of Mathematical Sciences within the School of Natural Sciences and Mathematics at The University of Texas at Dallas. Previously, he worked as a Mathematical Statistician at the Office of Biostatistics Research at NIH/NHLBI and completed postdoctoral training under supervision of Dr. Annie Qu and Dr. Babak Shahbaba. His academic foundation includes a PhD in Statistics from The George Washington University under Dr. Xiaoke Zhang's mentorship. Education: PhD in Statistics, The George Washington University Dr. Miao's research program bridges advanced statistical methodology with critical healthcare applications. His work focuses on Reinforcement Learning for personalized treatment policies, Causal Inference methods addressing unmeasured confounding, Health AI applications, Functional Data Analysis techniques, and computational approaches to Few-shot Learning . His methodological innovations particularly address challenges in heterogeneous medical data and complex decision frameworks. Analyzing his publication trajectory reveals a strong emphasis on developing statistical frameworks for personalized medicine, with increasing interdisciplinary collaboration in cardiology, immunology, and neuroscience. His work spans theoretical statistics in journals like Annals of Statistics and Journal of the American Statistical Association to applied medical research in Science Advances and Journal of the American College of Cardiology . Scientific Recognition: 2021 ICSA Student Paper Award for work on wavelet-based independence testing Dr. Miao actively contributes to the academic community through invited talks at institutions including National Cancer Institute, Duke University, and NIH, presenting on reinforcement learning under heterogeneity and functional data analysis methods. His teaching portfolio at UT Dallas includes STAT 5304 Introduction to Human Health Research, building on previous teaching experience at The George Washington University covering statistical theory and applied courses.

• Breast Cancer
• Tumor Biomarkers
• Liquid Biopsy
• +4 more

Daniel F. Hayes is the Stuart B. Padnos Professor of Breast Cancer Research at the University of Michigan Rogel Cancer Center, where he holds a professorship in the Department of Internal Medicine - Hematology/Oncology. He has served as President of the American Society of Clinical Oncology (ASCO) from 2016-2018 and has held leadership positions in multiple cancer centers including Dana-Farber Cancer Institute, Georgetown University's Lombardi Cancer Center, and the University of Michigan. Dr. Hayes received his undergraduate, master's and medical degrees from Indiana University, completed his residency in internal medicine at Parkland Memorial Hospital, and pursued fellowship training in medical oncology at Harvard's Dana Farber Cancer Institute. His career spans over three decades with significant contributions to breast cancer research and clinical practice. Dr. Hayes' research focuses on experimental therapeutics and cancer biomarkers, particularly in breast cancer. His work has centered on the development and validation of cancer biomarker tests including HER-2, CA15-3, circulating tumor cells, and pharmacogenomic markers. He has been instrumental in establishing international guidelines for the use of tumor biomarker tests and criteria for their clinical utility. His recent publications demonstrate continued leadership in areas such as liquid biopsy, circulating tumor cell analysis, breast cancer staging systems, and personalized treatment approaches based on genomic and molecular profiling. Dr. Hayes has received numerous prestigious awards including the ASCO Gianni Bonadonna Award, the Allen Lichter Visionary Leadership Award (2021), the Susan G. Komen Brinker Award for Scientific Distinction in Clinical Research (2023), and the Distinguished Alumni Award from the Indiana University Medical School (2024). Throughout his career, Dr. Hayes has secured substantial research funding from the National Institutes of Health, the Breast Cancer Research Foundation, and other major organizations. His current research portfolio includes projects on liquid biopsy methodologies, genomic profiling of metastatic breast cancer, and personalized treatment strategies for breast cancer patients. He has mentored numerous researchers and clinicians in the field of breast cancer and continues to contribute significantly to advancing cancer care through his research and leadership.

Prateek PrasanaView profile

Assistant Professor

• Biomedical Imaging
• Computer Vision
• Biomedical Informatics
• +4 more

Prateek Prasana is an Assistant Professor at Stony Brook University affiliated with the AI Innovation Institute, specializing in bridging human and machine interpretation of biomedical imaging data through computational methods. His academic credentials include: Ph.D. in Biomedical Engineering from Case Western Reserve University M.S. in Electrical and Computer Engineering from Rutgers University B.Tech. in Electrical and Electronics Engineering from National Institute of Technology, Calicut, India Dr. Prasanna's research focuses on developing quantitative tools for lesion growth prediction, disease progression markers, and therapy response signatures in thoracic, neuro, and breast imaging. His work integrates radiomics, pathomics, deep learning, and multi-modal data fusion to create companion diagnostic tools for clinical applications. His 2025 publications reveal dominant trends in diffusion models for medical image synthesis, attention-guided disease classification, and eco-evolutionary pathomic analysis. Key advancements include generative foundation models for histopathology, spatial radiomic graphs for outcome prediction, and gaze-guided frameworks that mimic radiologist visual attention patterns across diverse imaging modalities.

• Geo-AI (Geospatial Artificial Intelligence)
• XR (Extended Reality)
• Machine Learning
• +6 more

Dr. Abolghasem Sadeghi-Niaraki is an Associate Professor in the Department of Computer Science and Engineering at Sejong University's College of Engineering, where he has served since 2017. He leads the eXtended Reality (XR) Research Center and holds a Research Professor position at the Super-Realistic XR Technology Research Center (2022-2030). Previously, he was an Assistant Professor at INHA University's Department of Geo-Informatics Engineering (2009-2017). Recently appointed as a Fellow at Harvard University's Spatial Data Lab (SDL), he conducts collaborative research in spatiotemporal data analytics and geo-AI. Dr. Sadeghi-Niaraki's research spans Geo-AI, XR technologies, Machine Learning, Metaverse applications, and IoT. His work focuses on applying AI to geospatial problems, developing immersive technologies for education and cultural heritage, and creating predictive models for natural hazards. He has secured significant funding for XR Metaverse research from the Ministry of Science and ICT, focusing on Real-Virtual integration. His interdisciplinary approach combines computer science with environmental science, urban planning, healthcare, and education. His research portfolio demonstrates strong trends in geospatial AI applications for environmental monitoring, particularly in natural hazard prediction (floods, wildfires, earthquakes), as well as innovative educational applications of VR/AR. Recent work shows increasing integration of large language models with immersive technologies and a growing focus on healthcare applications within virtual environments. Dr. Sadeghi-Niaraki has received significant recognition including: Top 2% Scientist Worldwide (Stanford-Elsevier 2024) Fellow at Harvard SDL, Center for Geographic Analysis Australian Endeavour Fellowship Recipient Member of IEEE & AAG (American Association of Geographers) He has led over $9.3 million in research funding, supervised 40+ Master's and 6+ Ph.D. students, and collaborated with 14 universities across 8 countries in immersive technology development. His laboratory work centers on the eXtended Reality Research Center at Sejong University, where his team develops patented methods for context-aware AR/VR systems applied to tourism, cultural heritage, and smart city planning.

• Pulmonary Pathology
• Molecular Pathology
• Lung Cancer Research
• +3 more

Sanja Dacic is a Professor of Pathology and Vice Chair/Director of Anatomic Pathology at the Yale School of Medicine . Her work focuses on diagnostic pulmonary pathology , molecular pathology , and prognostic biomarker development for lung cancer and pleural mesotheliomas . MD and PhD from University of Zagreb School of Medicine Residency and Postdoctoral Training at University of Pittsburgh Medical Center Her research drives translational projects resulting in hundreds of peer-reviewed articles, practice guidelines, and book chapters. She co-authored the College of American Pathologists/IASLC/Association for Molecular Pathology guidelines for lung cancer molecular testing and served on the 5th WHO Thoracic Tumors Classification editorial board. Recent publications highlight advances in immunotherapy biomarkers , MET exon 14 skipping in NSCLC , mesothelioma in situ detection , and thoracic specimen handling standards . Scientific awards include IASLC Translational Research Award (2020) Fred R. Hirsch Lectureship (2020) Young Investigator Award (2011) Daljit and Elaine Sarkaria Lecture (2010) Research Day Award (2004) Best Web Case Report (2000) Dr. Dacic serves as Deputy Editor-in-Chief of Archives of Pathology & Laboratory Medicine and holds leadership roles in the Pulmonary Pathology Society and IASLC Pathology Committee . She is a key contributor to interdisciplinary research at Yale, including the Tumor Profiling Lab and Office of Collaborative Excellence .

• Data Science
• Artificial Intelligence
• Machine Learning
• +5 more

Ye Xu, Ph.D., is a senior data science manager and business analyst at the Yale School of Medicine's Biomedical Informatics & Data Science department. With over 15 years of experience in healthcare technology innovation, Xu specializes in applying artificial intelligence, machine learning, and image analysis to medical diagnostics and education. Ph.D. in Computer Science (2007) and MS in Computer Science (2001) from the University of Iowa Executive certifications in Data Science (Johns Hopkins 2024) and AI Business Strategy (MIT 2024) Current role in Health Sciences IT focuses on digital technology integration in medical education Xu's research bridges computational methods with clinical applications, particularly in: Medical imaging analysis for lung and breast diagnostics Environmental health impact modeling AI-driven curriculum development Text mining for clinical insights Public health informatics Digital innovation in education Recent publications demonstrate Xu's expertise in 3D texture classification algorithms for pulmonary pathologies and pollution impact analysis. The work shows consistent application of computer vision to medical diagnostics, with a transition toward educational technology innovation. Scientific recognition includes: 2023 Digital Transformation Recognition Award 2014 Individual R&D Award 2013 Team Excellence Award for breast cancer diagnostics 2007 Gerard P. Weeg Scholarship Xu mentors graduate students across multiple institutions including MIT's Business Analytics program and Columbia University's Data Science Institute, while maintaining active collaborations in clinical informatics projects at Yale.

• Lung Cancer
• Respiratory Diseases
• Infection and Immunity
• +4 more

Clinical Professor David Barnes is affiliated with the University of Sydney , specifically the Central Clinical School . His research focuses on lung cancer , respiratory diseases , infection and immunity , and sleep disorders . He also specializes in precision medicine and chronic obstructive pulmonary disease (COPD) management. His work emphasizes improving lung cancer care through multidisciplinary teams and diagnostic pathways, particularly for culturally diverse populations. He has contributed to the development of standardized reporting templates for general practitioners and the use of non-invasive ventilation in respiratory therapy. His publications span clinical trials on respiratory support, cancer staging, and diagnostic innovations. Recent articles examine the lung microbiome's role in cancer treatment and the impact of non-invasive ventilation on COPD patients. Earlier studies focus on sleep disorders and pneumonia management. Scientific Awards & Grants: 2016: LEAD Grant (Cancer Australia) for lung cancer diagnostic pathways in CALD populations 2013: CHEST Australia Trial Grant (NHMRC) to reduce lung cancer consultation delays He has collaborated extensively with researchers in respiratory medicine, oncology, and sleep disorders, contributing to clinical practice guidelines and healthcare delivery improvements.

• Radiotherapy
• Non-melanoma Skin Cancer
• Merkel Cell Carcinoma
• +3 more

Clinical Professor Michael Veness is a full-time Radiation Oncology specialist at Westmead Hospital, Sydney, with appointments at the Sydney Medical School, University of Sydney since 1998. His clinical research focuses on radiotherapy for non-melanoma skin cancer , particularly Merkel cell carcinoma and high-risk cutaneous squamous cell carcinomas . Veness maintains a large prospective database for advanced skin cancers and has published over 160 peer-reviewed works. Education: Master of Clinical Epidemiology, MD from University of NSW and University of Sydney Research Interests include: Clinical benefits of radiotherapy in skin cancer Definitive and adjuvant radiation protocols Merkel cell carcinoma epidemiology High-risk cutaneous malignancies Prognostic staging systems Article Trends show sustained focus on radiotherapy applications for non-melanoma skin cancers , with emphasis on Merkel cell carcinoma and cutaneous squamous cell carcinomas . Recent works analyze metastatic patterns, immunotherapy integration, and staging system improvements. Clinical Approaches combine radiation therapy with surgical interventions, including sentinel lymph node biopsy protocols. His collaborations span multiple institutions, with significant contributions to head and neck cancer clinical guidelines.

Eric B. DurbinView profile

Associate Professor

• Cancer Informatics
• Natural Language Processing
• Cancer Epidemiology
• +6 more

Eric B. Durbin holds the position of Associate Professor in Internal Medicine at the University of Kentucky. He serves in multiple leadership roles, including Director of Cancer Research Informatics and Director of the Kentucky Cancer Registry at the Markey Cancer Center and Cancer Research Priority Initiative . His work intersects biomedical informatics and cancer epidemiology , with a focus on data-driven solutions for public health. Education: Doctor of Public Health (2012) and Master of Science (1989) from the University of Kentucky Dr. Durbin's research centers on electronic cancer surveillance , natural language processing (NLP) of clinical texts , and population health informatics . His projects analyze cancer trends in Kentucky and Appalachia , addressing disparities in incidence, treatment, and outcomes for breast cancer , lung cancer , and pediatric brain tumors . He develops advanced machine learning systems for pathology report classification and clinical data mining. Recent publications highlight his methods in graph convolutional networks , uncertainty quantification for NLP, and privacy-preserving deep learning for sensitive health data. Funded by the National Cancer Institute and KY Cabinet for Health and Family Services , his grants focus on air pollution's impact on pediatric brain tumors , spatial transcriptomics , and health equity in fertility care for cancer survivors. Collaborations span institutions like MD Anderson and involve multi-site data integration for precision oncology.

Edward MiaoView profile

Professor

• Immunology
• Microbiology
• Cell Death Mechanisms
• +5 more

Edward Miao, MD, PhD is a Professor at Duke University with dual appointments in the Department of Immunology and Department of Molecular Genetics and Microbiology . His research program centers on gastrointestinal innate immunity against infections in intestines and liver, utilizing both in vivo models and organoid cultures. Primary research areas: Inflammasome biology, Cell death mechanisms, Host-pathogen interactions Key model pathogens: Salmonella , Chromobacterium violaceum , Staphylococcus aureus Major discoveries: Caspase-7's role in intestinal epithelial extrusion, NLRC4 inflammasome's translocation sensing, Hepatocyte niche for bacterial infection Recent publication trends (2023-2025) focus on: Expanding inflammasome functions beyond canonical roles Comparative studies of gasdermin activation across species Novel insights into epithelial repair mechanisms Immune response dynamics in liver infections Reactive oxygen species in antibiotic tolerance Evolution of regulated cell death nomenclature Dr. Miao's laboratory has made significant contributions to understanding: How pathogens manipulate caspase activity Non-traditional roles of cell death proteins Organoid culture applications in infection biology Granuloma formation efficiency in liver infections