Tim Althoff is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington, specializing in Artificial Intelligence and Human-Centered Computing. His research focuses on behavioral data science, combining Data Science Natural Language Processing Social Computing Human-Centered AI Ethics & Fairness to extract insights about health and well-being. Recent publications highlight advancements in: Mental health support through AI Wearable sensor health monitoring Online community analysis Reproducibility in data science Public health interventions with notable papers in ACL, Nature Machine Intelligence, and NeurIPS. Scientific recognition includes: ACL 2023 Outstanding Paper Award WWW 2021 Best Paper Award Double ICWSM 2021 Best Paper Awards SIGKDD Dissertation Award 2019 Fulbright Scholarship German National Merit Foundation Actively mentoring postdoctoral researchers and seeking PhD students in areas like neural representation learning, NLP applications to psychology, and mobile health technologies through his Behavioral Data Science Group .
Rolf Halden is a Professor and Center Director at Arizona State University, holding appointments in the School of Sustainable Engineering and the Built Environment, the Biodesign Center for Environmental Health Engineering, and the Global Futures Scientists and Scholars program. He is a leading expert in environmental health engineering, wastewater-based epidemiology, and bioremediation. Education: Ph.D. in Civil (Environmental) Engineering, University of Minnesota (1997) M.S. in Civil (Environmental) Engineering, University of Minnesota (1994) M.S. in Biology, Technical University of Braunschweig, Germany (1992) Postdoctoral Fellow, Environmental Science, Lawrence Livermore National Laboratory (1998) Research Focus: Halden’s research integrates environmental chemistry, public health, and sustainability to understand how anthropogenic chemicals affect ecological and human health. His lab uses advanced mass spectrometry (LC-MS/MS, MALDI-TOF) to track pollutants such as PPCPs, microplastics, pesticides, and dioxins across air, water, soil, and biological systems. He pioneered the use of wastewater treatment plants as public health observatories, enabling real-time tracking of disease, drug use, and environmental exposures. Scientific Awards & Recognition: Leadership Award, Arizona State University (2018) Rocky Mountain Emmy Award (2018) Leroy E. Burney Lecturer, Johns Hopkins School of Public Health (2011) ACS Expert Program Member (2014–present) NIEHS Superfund R01 Working Group National Leader (2012–2014) Labs & Teams: Halden is the Founding Director of the Biodesign Center for Environmental Health Engineering at ASU, where he leads a multidisciplinary team focused on environmental proteomics, bioremediation, and diagnostic tool development. He also co-leads the Human Health Observatory , a national wastewater monitoring network. Grants & Funding: Halden has secured major funding from NSF, NIH, EPA, DoD, and private industry. Notable projects include the development of in-situ groundwater monitoring devices, national wastewater surveillance of SARS-CoV-2, and studies on microplastics and neurodegenerative disease.
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 .
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
Associate Professor Adam Irwin is an NHMRC Emerging Leadership Fellow and Associate Professor in Infectious Diseases at the University of Queensland (UQ) and Queensland Children’s Hospital. He holds positions within the UQ Centre for Clinical Research, Faculty of Health, Medicine and Behavioural Sciences. His work focuses on early sepsis recognition, antimicrobial stewardship, and pediatric infectious diseases. Dr. Irwin completed his medical degree at the University of Birmingham and his PhD in 2016 from the University of Liverpool’s Institute of Infection and Global Health. He has led initiatives like the Queensland Paediatric Sepsis Program, recognized with a Global Sepsis Alliance Award and Queensland Health Excellence Awards. His research spans sepsis pathway implementation, antimicrobial resistance trends, and rapid diagnostic tools. Notable grants include NHMRC funding for studies on multi-drug resistant infections and Children’s Hospital Foundation support for genomics and antimicrobial stewardship projects. He supervises PhD candidates in areas like bloodstream infections and molecular diagnostics. Dr. Irwin collaborates across institutions, contributing to projects such as the RAPIDS Study Group and the PAEDS surveillance network. Awards include the NHMRC Emerging Leadership Fellowship and recognition for sepsis program excellence. His work emphasizes translating research into clinical practice, improving outcomes for critically ill children through evidence-based protocols and innovative diagnostics.
Joel Goh is Associate Professor at the Department of Analytics and Operations, NUS Business School, National University of Singapore. He serves as Director of the J.Y. Pillay Comparative Asia Research Centre (under NUS Global Asia Institute) and PhD Program Director at the Institute of Operations Research and Analytics (IORA). Previously, he was Assistant Professor at Harvard Business School (2014-2017) and Visiting Scholar (2017-2022). BSc, MSc, PhD in Operations, Information, and Technology from Stanford University His research focuses on healthcare analytics (preventing health conditions, hospital operations, frailty assessment), supply chain analytics (digital business models, platform leakage), and service platform operations (hospital-at-home programs, incentive design). He co-created the Robust Optimization Made Easy (ROME) software. Recent publications analyze workplace psychological safety (2024), hospital-at-home models (2024), and platform leakage dynamics (2023). His work spans 18+ journals with 740+ citations for burnout cost studies (2022) and 606+ citations for physician well-being research (2017). Teaching Honors : 2023: Best MBA Teaching & Skinner Innovation Award 2021: NUS Annual Teaching Excellence Award 2020: Early Career Research Excellence Award & 40 Under 40 Best MBA Professors Advising & Grants : Served as PhD Program Director. Received NUS Start-Up Grant R-314-000-110-133 (2021) and Humanities & Social Sciences Fellowship (2021). Editorial roles include Associate Editor at Management Science , Manufacturing & Service Operations Management , and Senior Editor at Production and Operations Management .
Mark Lewis is the Kennedy Chair in Mathematical Biology at the University of Victoria, holding joint appointments in the Departments of Mathematics and Statistics and Biology. His research focuses on spatial ecology and mathematical modeling, addressing ecological challenges such as animal movement, invasive species, and disease dynamics. Lewis earned his D.Phil. in Mathematical Biology from the University of Oxford and has been elected a Fellow of the Royal Society UK. His work integrates mathematical analysis, field studies, and interdisciplinary approaches to solve ecological problems. Current projects include modeling polar bear populations, cyanobacteria dynamics, and the impact of climate change on wildlife. Lewis supervises students across both UVic and his former University of Alberta lab. Education: D.Phil. in Mathematics (Mathematical Biology), University of Oxford Awards: Royal Society Fellowship, CRM-Fields-PIMS Prize, and Okubo Prize Key Research Areas: Animal movement modeling, aquatic ecology, wildlife disease, and invasive species management Publications highlight his contributions to understanding disease spread, parasite dynamics, and ecological responses to environmental changes. Lewis collaborates widely, applying mathematical tools to real-world conservation and health challenges.
Wengong Jin is an Assistant Professor at the Khoury College of Computer Sciences, Northeastern University, and a visiting research scientist at the Eric and Wendy Schmidt Center at the Broad Institute. He holds a PhD from MIT CSAIL, advised by Prof. Regina Barzilay and Prof. Tommi Jaakkola. Research Interests: His work focuses on geometric and generative AI models for drug discovery, biology, and chemical engineering. Key areas include equivariant neural networks (e.g., FAFormer), diffusion models for binding energy prediction, antibody/enzyme design (RefineGNN, SurfPro), and molecular design through graph neural networks (Junction Tree VAE). He also explores domain generalization and systems for autonomous molecular discovery. Publications: His research has been published in top venues like NeurIPS, ICLR, ICML, Nature, Science, and Cell. Recent breakthroughs include discovering novel antibiotics using explainable AI and designing synergistic drug combinations for cancer treatment. Awards: He has received the BroadIgnite Award, Dimitris N. Chorafas Prize, and MIT EECS Outstanding Thesis Award for his contributions to computational biology and AI-driven drug discovery. Teaching: Currently teaches a PhD seminar on AI for Science, focusing on integrating machine learning into scientific discovery processes.
Hyun (Michel) Koo is a Professor at the University of Pennsylvania School of Dental Medicine , with affiliations in the Department of Orthodontics , Division of Community Oral Health , and Division of Pediatric Dentistry . As Co-Founder and Co-Director of the Center for Innovation & Precision Dentistry (CiPD) , he leads interdisciplinary efforts merging bioengineering, nanotechnology, and oral health research. Education : DDS and PhD Research Focus : Biofilms, bacterial-fungal interactions, and nanotechnology for oral disease prevention Leadership : Co-Director of CiPD; key roles in training programs like NIDCR-sponsored R90 and T90/R90 Dr. Koo’s research explores biofilm mechanisms in oral infectious diseases, particularly childhood caries, through engineering methods and microrobotics . His team developed micron-scale robots for automated biofilm eradication and FDA-approved nanoparticles for caries prevention. Collaborations with Penn Engineering, including Dr. Daeyeon Lee and Dr. Kacy Cullen, emphasize translational approaches. The 15 most recent publications highlight his work in nanorobotics , interkingdom biofilms , and precision diagnostics . Articles span 2025–2024 and address topics like adaptive micromotors , biofilm matrix degradation , and single-cell microbial interactions . These emphasize his focus on targeted therapies and biofilm microenvironment engineering . Key Awards : Elected Fellow, American Association for the Advancement of Science (AAAS) IADR Distinguished Scientist Award for innovative dental research Dr. Koo trains next-generation researchers through the CiPD NIDCR T90/R90 Postdoctoral Training Program , mentoring fellows like Smruti Nair (ACE2 Chewing Gum development) and Zhi Ren (K99 awardee). His work intersects with Penn Health-Tech, CT3N , and Penn Institute for Biomedical Informatics , fostering transdisciplinary innovation.
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.
John P.A. Ioannidis is the C.F. Rehnborg Professor in Disease Prevention and Professor of Medicine, Health Research and Policy, Biomedical Data Science, and Statistics at Stanford University. He is Co-Director of the Meta-Research Innovation Center at Stanford (METRICS) and an Einstein BIH Visiting Fellow at Charité - Universitätsmedizin Berlin. His academic appointments span multiple departments and institutes at Stanford, including the Stanford Prevention Research Center, Biomedical Data Science, and Statistics. He is internationally recognized for his work in meta-research, evidence-based medicine, and research reproducibility. Ioannidis holds an MD and DSc in Biopathology from the National University of Athens, with training in internal medicine and infectious diseases from Harvard and Tufts. He previously chaired the Department of Hygiene and Epidemiology at the University of Ioannina Medical School and held adjunct positions at Harvard, Tufts, and Imperial College. He joined Stanford in 2010, where he launched the PhD program in Epidemiology & Clinical Research, the MS in Community Health & Prevention Research, and METRICS in 2014. His research focuses on improving research methods, appraising biases, enhancing reproducibility, and integrating evidence across scientific disciplines. He is a pioneer in meta-research, with seminal contributions on the reliability of published findings, statistical practices, and research synthesis. His influential 2005 paper, "Why Most Published Research Findings Are False," is the most-accessed article in PLoS history. His recent work examines peer review, data sharing, AI in medicine, and pandemic research impact, consistently advocating for transparency and methodological rigor. His publications span epidemiology, statistics, genomics, clinical trials, and meta-analysis, with a strong emphasis on bias detection, replication, and open science. Trends in his recent articles highlight concerns about research integrity, citation practices, peer review reform, and the scientific response to global health crises. Founders' Medal for Lifetime Contributions to Meta-science (2024) Honorary doctorates from McMaster, Thessaloniki, Edinburgh, Tilburg, Athens, and Rotterdam Elected member, US National Academy of Medicine (2018) Elected member, European Academy of Sciences and Arts (2015) President, Association of American Physicians (2023–2024) President, Society for Research Synthesis Methodology Gordon Award, NIH (2019) Chanchlani Global Health Award (2017) Highly Cited Researcher (Clarivate) in Clinical Medicine, Social Sciences, and Psychiatry Ioannidis has advised numerous students and mentored early-career researchers. He has served as Senior Advisor for Knowledge Integration at the National Cancer Institute (2012–2016) and Editor-in-Chief of the European Journal of Clinical Investigation (2010–2019). He has received over 700 invited lectures and is deeply involved in shaping research policy and scientific infrastructure. He leads METRICS, a hub for meta-research innovation, and is affiliated with multiple Stanford institutes, including Bio-X, the Cardiovascular Institute, and the Stanford Cancer Institute.
Ashli Owen-Smith is a behavioral scientist affiliated with the School of Public Health at Georgia State University , where her research focuses on mental health disparities, suicide prevention, and integrative/complementary approaches for complex mental-physical health conditions. She works with underserved populations including refugees/immigrants, incarcerated individuals, and LGBTQ+ communities through community-based participatory research and mixed-methods frameworks. Her current projects are funded by CDC , DBHDD , and DPH . Education: Ph.D. in Behavioral Sciences (Emory, 2009), S.M. in Public Health (Harvard, 2005), B.A. in Psychology (Smith College, 2001) Her research spans mental health , trauma , suicide prevention , and mindfulness-based interventions . Recent work examines telehealth adaptations for chronic pain and mental health, vaccine hesitancy in refugee communities, and social determinants of suicide . She leads studies on gender-affirming care and health disparities in LGBTQ+ populations. Key article trends include epidemiological analysis of suicide risk factors, COVID-19 impacts on mental health, ICD-10 coding for autism, and complementary medicine in trauma recovery. Subfields span telehealth , health equity , mental-physical comorbidity , and community engagement . Students she has mentored include C.A. Scarlett , T. Griner , and M.M. Sesay . Her work integrates public health policy , clinical research , and health systems analysis .
Manuel R. Amieva is a Professor at Stanford University School of Medicine , holding joint appointments in Pediatrics - Infectious Diseases and Microbiology & Immunology . He is also a member of the Maternal & Child Health Research Institute (MCHRI) . His clinical practice at Stanford Medicine Children's Health focuses on pediatric infectious diseases. Education: Medical Education: Stanford University School of Medicine (1997) Fellowship: Stanford University Pediatric Infectious Disease Fellowship (2004) Internship & Residency: Stanford Health Care at Lucile Packard Children's Hospital (1998-1999) Dr. Amieva's research investigates host-pathogen interactions at epithelial barriers, with specific expertise in Helicobacter pylori , Listeria monocytogenes , Salmonella enterica , and Staphylococcus aureus . His lab develops innovative organoid culture systems with controlled polarity to study microbial colonization and oncogenic mechanisms. Key discoveries include: H. pylori's manipulation of epithelial junctions via the CagA protein Listeria's exploitation of cell extrusion sites for invasion Staphylococcus toxin interactions with adherens junctions Gastric stem cell activation by pathogens Recent publication trends show continued leadership in infectious disease mechanisms (2020-2025), with a focus on: Pathogen-specific epithelial breach strategies Organoid modeling of viral/bacterial interactions Redox-dependent host factor regulation Single-cell spatial transcriptomic analyses Multi-institutional educational frameworks His scientific collaborations span disciplines including: Gastric cancer genomics initiatives COVID-19 lung infection models Stem cell-microbe interactions Medical education reform projects Dr. Amieva maintains active clinical research while mentoring students in both the Microbiology & Immunology and Pediatrics programs. His lab at Stanford employs advanced 3D confocal microscopy and organ-on-a-chip technologies to visualize epithelial colonization dynamics.
Pinar Keskinocak serves as the H. Milton and Carolyn J. Stewart School Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology. She co-founded and directs the Center for Health and Humanitarian Systems, demonstrating leadership in both academic administration and research innovation. Her career includes prior roles as College of Engineering ADVANCE Professor and interim associate dean for faculty development, alongside industry experience at IBM T.J. Watson Research Center. Her educational foundation includes a Ph.D. in Operations Research from Carnegie Mellon University and M.S. and B.S. degrees in Industrial Engineering from Bilkent University. This rigorous training underpins her interdisciplinary approach to complex societal challenges. Dr. Keskinocak's research pioneers operations research applications for societal impact , with dual emphases on health systems and humanitarian logistics . She develops advanced models for infectious disease dynamics (including COVID-19, malaria, and Guinea worm), vaccination strategies, disaster response, and supply chain optimization. Her work uniquely bridges engineering analytics with real-world implementation through partnerships with the CDC, American Red Cross, Carter Center, and Children's Healthcare of Atlanta. Analysis of her 2023-2025 publications reveals intensifying focus on eradication program modeling for polio and Guinea worm, with increasing integration of machine learning for predictive accuracy in vaccine distribution. A pronounced trend addresses health equity through targeted interventions for under-immunized populations in Sub-Saharan Africa, emphasizing resource allocation under constraints and cross-border coordination challenges. Her scientific recognition includes: INFORMS Fellow (2015) NSF CAREER Award (2001) Outstanding Professional Education Award, Georgia Tech (2018) Denning Award for Global Engagement, Georgia Tech (2016) Women in Engineering Excellence Teaching Award (2005) Moving Spirit Award, INFORMS (2005) Dr. Keskinocak has secured extensive project funding through collaborations with federal agencies and NGOs, directing research on pandemic response systems, vaccine logistics, and disaster debris management. While specific advisees aren't publicly cataloged, her leadership in the Center for Health and Humanitarian Systems mentors numerous graduate researchers through interdisciplinary projects. She significantly influences national policy via National Academies committees addressing supply chain resilience and vaccine distribution infrastructure. The Center for Health and Humanitarian Systems operates as a dynamic hub where engineering rigor meets humanitarian action, coordinating multi-institutional teams to develop deployable solutions for global health crises and disaster response systems.
Zhiling Gu is a Research Fellow at Yale School of Public Health, having earned her Ph.D. in Statistics at Iowa State University. Her work integrates statistical theory with applications in public health and medicine. Her research spans Functional Data Analysis Network Analysis Spatiotemporal Modeling Statistical AI Foundations Nonparametric Learning applied to neuroimaging, electronic health records, and environmental health studies. Recent publications focus on Adaptive spatiotemporal models Neuroimaging data processing Pandemic forecasting frameworks Environmental exposure modeling with methodological rigor and practical implementation. Scientific achievements include Runner-up in SMI 2023 Student Paper Competition She has taught STAT 305: Engineering Statistics (ISU) STAT 226: Business Statistics Statistical Computing Statistical Learning and actively engages in academic presentations at conferences like SMI 2024 and CMStatistics 2022.
