Joseph A. November is an Associate Professor in the Department of History at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. His research focuses on the history of biomedical computing, distributed computing, and the intersection of technology and medicine. He holds a Ph.D. from Princeton University (2006), an M.A. from the University of Chicago (2002), and a B.A. from Hamilton College (1997). His work includes the award-winning book Biomedical Computing: Digitizing Life in the United States (2012), which explores the co-development of biomedicine and computing technologies. Current projects include Revolutions@home , examining distributed computing in protein folding research, and a biography of computing pioneer Robert S. Ledley. He has received grants from the NSF, NIH, and the Charles Babbage Institute. Teaching interests span the history of science and technology, including courses on the history of medicine, digital humanities, and the role of games in historical education. He actively contributes to professional organizations like SHOT and the History of Science Society. Awards include the Computer History Museum Prize (2013) and the National Institutes of Health DeWitt Stetten Fellowship (2007-2008). His research bridges historical analysis with contemporary issues in technology and biomedical ethics.
Byron Wallace is the Sy and Laurie Sternberg Interdisciplinary Associate Professor at Northeastern University's Khoury College of Computer Sciences, where he also serves as Associate Dean for Research and Director of the BS in Data Science Program. His research focuses on Natural Language Processing and Machine Learning applications in healthcare. Education Details of formal education are not explicitly provided in the available text, though he holds a PhD from Tufts University (mentioned in thesis award context). Research Interests His work centers on developing NLP and ML models for health applications, with particular emphasis on: Biomedical evidence synthesis automation Electronic Health Record processing Model interpretability and trustworthiness Human-in-the-loop systems Learning with limited supervision Research Trends Recent publications demonstrate strong focus on large language model applications in healthcare, including factuality evaluation for medical summarization, evidence extraction from clinical trials, and interpretable risk prediction models. Notable contributions include work on GPT-3 applications in medical evidence synthesis and neural methods for EHR analysis. Scientific Awards ACL Outstanding Paper Award (2022) ICLR Spotlight (top 5% acceptance) (2024) Best Student-led Paper Award at AMIA 2021 NSF CAREER Award (2018-2023) Advising and Grants Currently advises 4 PhD students and has mentored numerous others. Major funding includes: NSF CAREER Award ($500K+) NIH R01 grant for EHR summarization NSF Medium grant for healthcare summarization Support from Army Research Office, Amazon, and Seton Hospital Labs and Teams Leads the Evidence Inference project team working on automated biomedical evidence synthesis. Collaborates with Brigham and Women's Hospital, Mass General Hospital, and Reboot Rx for clinical translation of research.
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.
Zeyad Ibrahim is a Doctor of Philosophy student at the University of Western Australia, engaged in Casual Teaching and acting as a Multi-Discipline Contractor/Visitor. Affiliated with the School of Allied Health, UWA Medical School, and the UWA Centre for Medical Research (affiliated with the Harry Perkins Institute of Medical Research), he focuses on pharmacogenomics in pediatric cancer treatment. Education: Doctor of Philosophy, School of Allied Health, University of Western Australia. Research interests include methotrexate pharmacogenomics, CRISPR/Cas9 genomic models for drug efficacy assessment, and systematic reviews addressing gaps in pediatric cancer therapies. His work aligns with UN Sustainable Development Goals related to health and well-being. Publications include two peer-reviewed abstracts in the Asia-Pacific Journal of Clinical Oncology (2019), exploring methotrexate pharmacogenomics in pediatric cancers and a CRISPR-based genomic model. He received the Churchill Fellowship 2022 for pharmacogenomics research. Grants: Co-investigator on two major projects funded by the Western Australian Department of Health, focusing on personalized cancer treatment using pharmacogenomics to improve outcomes and reduce costs for seriously ill children (MiSSK initiative). Labs/Teams: Involved with the Centre for Optimisation of Medicines and collaborates with the Harry Perkins Institute of Medical Research.
Professor Heiko Spallek is Head of School and Dean at the University of Sydney’s Sydney Dental School, leading the school’s integration with the Faculty of Medicine and Health. He also serves as Academic Lead for Digital Health and Health Service Informatics at the faculty level. His research focuses on advancing dental informatics, teledentistry, and evidence-based practice, while advocating for improved oral health policy and public health initiatives. He holds roles including director at Community Connections Australia and membership in the Charles Perkins Centre. Education: DMD, Dr. med. dent., MSBA(CIS), FACD, FAIDH Leadership: Oversees Sydney Dental School’s academic and clinical programs, emphasizing interprofessional education and digital health innovation. Research interests include leveraging big data and machine learning in dentistry, improving access to oral healthcare through teledentistry, and addressing systemic issues in dental education and policy. He has led projects on laser dentistry applications, dental caries prevention, and healthcare workforce regulation. Notable contributions include the OpenWide conference series, the BigMouth dental data repository, and advocacy for equitable dental funding in Australia. Grants include initiatives on oral hygiene in aged care and analysis of healthcare advertising perceptions. Media engagements highlight his role in public discourse on dental access and policy, including commentary on Australia’s dental crisis and aged care reforms. He actively promotes interdisciplinary collaboration through platforms like the Dental Informatics Online Community. Labs/Teams: Directs the Sydney Dental School’s research programs in informatics and public health, collaborating with national and international networks such as the National Dental PBRN.
Yan Ma serves as Professor and Chair of Biostatistics at the University of Pittsburgh, with additional appointments in Orthopaedic Surgery and Clinical and Translational Science. Previously, he was Professor and Vice Chair at George Washington University Milken Institute of Public Health (2014-2022) and Assistant Professor at Hospital for Special Surgery/Weill Cornell Medical College (2008-2014). His educational background includes: PhD in Statistics, University of Rochester (2008) MA in Statistics, University of Rochester (2004) MS in Mathematics, Syracuse University (2003) BS in Statistics, Beijing Normal University (2001) Ma's research centers on advanced statistical methodologies including missing data imputation, machine learning, meta-analysis, causal inference, and longitudinal methods, applied across orthopedics, anesthesiology, health disparities, and emergency medicine through team science and translational research frameworks. His publication trajectory demonstrates sustained innovation from methodological foundations (2008-2012) to contemporary applications in health disparities and machine learning (2016-2022), consistently addressing complex biomedical challenges through high-impact journals like JAMA and Health Services Research. His scientific recognition includes: ASA's Statistics in Epidemiology Young Investigator Award (2010) Interorganizational Team Science Award (2012) ORISE FDA Research Fellowship (2017) APHA Achievement in Academia Award Ma has secured R01 funding from NIH/AHRQ for missing data methods in health disparities research while serving as Associate Editor for ASA journals and reviewer for NIH/PCORI/VA panels, demonstrating leadership in statistical methodology development and interdisciplinary collaboration. His team-science approach bridges statistical innovation with clinical implementation across orthopedics and anesthesiology, driving evidence-based practice through methodological rigor and cross-disciplinary partnerships.
Prof. Dr. Helma Wennemers serves as a Full Professor at ETH Zurich's Department of Chemistry and Applied Biosciences, leading the Laboratory for Organic Chemistry. Her research group operates from HCI H 313 at Vladimir Prelog Way 1-5/10 in Zurich, Switzerland, with active teaching responsibilities including Organic Chemistry I and Chemical Biology - Peptides for the Fall 2025 semester. Her research program centers on the intersection of organic chemistry and chemical biology , with particular emphasis on collagen triple helix engineering , peptide-catalyzed asymmetric synthesis , and development of chemical tools for tissue remodeling diagnostics . Key focus areas include designing hyperstable collagen heterotrimers for fibrosis monitoring, creating fluorophore-based probes for collagen cross-linking visualization, and pioneering organocatalytic methodologies for complex heterocycle synthesis. Her group actively explores how hydrophobic modifications and proline derivatives influence collagen stability and cellular uptake mechanisms. Analysis of her 15 most recent publications (2024-2025) reveals three dominant research trajectories: (1) collagen structural engineering for biomedical applications, (2) innovative peptide/organocatalysis enabling stereoselective transformations, and (3) chemical probe development targeting tissue remodeling processes. These works consistently integrate synthetic chemistry with biological validation, demonstrating translational potential in fibrosis diagnostics and regenerative medicine. While specific grant details aren't provided in available sources, her research program clearly supports advanced laboratory infrastructure including peptide synthesis facilities and photochemical reaction systems like the ETHos photoreactor. Her group maintains strong industry and clinical collaborations evident in applications targeting liver cancer cells and prostate cancer diagnostics. The Laboratory for Organic Chemistry functions as an interdisciplinary hub where synthetic organic chemists collaborate with biologists to develop collagen-based diagnostic platforms and catalytic systems. Current projects focus on lysyl oxidase-responsive probes for real-time tissue monitoring and engineered peptide catalysts for sustainable chemical synthesis under environmentally relevant conditions.
Dr. Changyou Chen is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York. His research focuses on Multi-Modal Learning Foundation Models Deep Generative Models Large-scale Bayesian Sampling with applications in document understanding, music-AI integration, and molecular representation learning. Research Trends revealed through his recent publications include Optimizing Multimodal Large Language Models Developing Novel Retrieval-Augmented Generation Frameworks Creating Benchmark Datasets for Visual Text Understanding Advancing Diffusion Models with Domain-Specific Constraints across domains from music sheets to biomedical documents. Scientific Contributions : UB Young Investigator Award (2020) Architect of LoCAL Framework for Long Document Understanding Co-developer of MusiXQA Benchmark Pioneering Work in Probability Contrastive Learning Academic Leadership includes mentoring 10+ graduate students and serving as Area Chair for major AI conferences (ICML, NeurIPS, AAAI, IJCAI). His Labs develop scalable solutions for multimodal reasoning, with recent work demonstrating practical GPU memory optimization through LoRA adapter sharing.
Byron Crape, PhD, MSPH, is an Associate Professor of Practice at the Nazarbayev University School of Medicine, Department of Biomedical Sciences. He has previously served as Assistant Professor of Practice at the same institution and has held academic positions at the American University of Armenia College of Health Sciences, Anton de Kom University School of Medicine in Suriname, and King Saud University College of Medicine. His extensive career spans global health roles with WHO, Johns Hopkins University, and the Maryland State Department of Health. Education: PhD in Infectious Disease Epidemiology, Johns Hopkins University (2006) MSPH in Biostatistics, Loma Linda University Health (1988) BS in Physics, University of Washington (1980) Research Interests: Dr. Crape's research focuses on Evidence-Based Medicine , Infectious Diseases , Chronic Diseases , Mental Health , and Environmental Health . He employs Population-Based Research Methodologies and Clinical Research approaches to address global health challenges, particularly in resource-limited settings. His work on Disaster Surveillance systems has been critical in managing public health emergencies. His recent publications demonstrate a strong focus on COVID-19 , neurological disorders , and environmental health impacts in Central Asia, especially Kazakhstan. The research spans from assessing the burden of Parkinson's disease to evaluating the neurotoxic effects of indoor air pollution from cooking. Scientific Awards: While specific awards are not detailed in the provided text, his significant contributions are evidenced by a substantial publication record and active research funding. Projects and Funding: Prerequisites for end-of-life care provision in Kazakhstan (2021-2025, PI) Effectiveness of preloaded combination nicotine replacement therapy on smoking cessation in Kazakhstan (2022-2024, Co-PI) Exposure to Cooking Ultrafine Particles and Neurodegenerative Diseases (2022-2024, Co-PI) Clinico-epidemiological assessment of COVID-19 infection in Kazakhstan (2021-2022, Co-Investigator) Longitudinal Tracking of COVID-19 seroprevalence in Kazakhstan (2021-2024, Co-Investigator) Laboratory and Team: Dr. Crape collaborates extensively with multidisciplinary teams across institutions, mentoring students and junior researchers. His projects involve partnerships with WHO, national ministries of health, and academic institutions worldwide, fostering a collaborative environment for global health research.
Jodi Schneider is an Associate Professor at the University of Illinois Urbana-Champaign , with affiliate appointments at the Beckman Institute , Health Care Engineering Systems Center , European Union Center , and Center for Health Informatics . She directs the Information Quality Lab and focuses on the science of science through argumentation and evidence analysis. PhD in Informatics (National University of Ireland, Galway) M.S. in Library and Information Science (UIUC) M.A. in Mathematics (UT-Austin) B.A. in Liberal Arts (St. John's College) Her research examines how scientific controversies persist through citation patterns, the role of knowledge brokers in public policy, and information quality in biomedical contexts. She has developed semantic frameworks for micropublications and knowledge maintenance in digital libraries. Recent publications include citation integrity studies in Scientometrics , retraction indexing in STI Conference , and argumentation mining in Human Language Technologies . Collaborative projects span institutions like Harvard Radcliffe Institute and RWTH Aachen . NSF CAREER Award IMLS Early Career Award Senior Member, Association of Computing Machinery Marie Curie Fellow She advises graduate students in information quality and knowledge representation , with funding from the Alfred P. Sloan Foundation , NIH , and European Commission . Her lab develops tools to combat scientific misinformation and improve public health informatics .
Charless Fowlkes is a Professor in the Department of Computer Science at the University of California, Irvine (UCI). His research focuses on computational vision, spanning human visual system understanding, machine vision systems, and applications in biomedical informatics and forensic science. He holds a Ph.D. from UC Berkeley (2005). His work integrates techniques from computer vision, AI, and applied mathematics to address challenges in automated biological data analysis, morphology, and spatial gene expression. Key research areas include forensic science (e.g., shoeprint matching via 3D reconstruction), biomedical applications (e.g., heart function mapping and pollen classification), and AI-driven systems for scene understanding. Recent projects include a $20M forensic science center funded by the National Institute of Justice. His publications emphasize geometric reasoning, 3D reconstruction, and adaptive learning algorithms. Notable contributions include developing algorithms for 3D human pose estimation with scene constraints, automated pollen identification via CNNs, and frameworks for cross-domain forensic analysis. His work bridges theoretical computer vision with real-world applications in forensics, healthcare, and environmental science.
Karin Jacobs is a Professor in the Department of Physics at Saarland University, where she leads the research group for soft matter physics within the Faculty of Natural Sciences and Technology. Her work bridges experimental physics and applied materials science, focusing on interfacial phenomena, thin films, and functional materials. Research Interests: Her group investigates the stability of coatings, properties of simple and complex fluids, and the adhesion of biomolecules on surfaces. Using advanced experimental techniques such as atomic force microscopy (AFM), ellipsometry, surface plasmon resonance spectroscopy, optical microscopy, and ultra-high vacuum (UHV) methods like photoelectron spectroscopy, her team probes nanoscale and microscale interactions at solid-liquid and solid-gas interfaces. The research spans fundamental and applied domains, including the synthesis and characterization of graphene and boronitrene, production of water-in-water vesicles using hydrophobins, and bacterial adhesion studies. These investigations are often linked to industrial applications in the paint, semiconductor, and biomedical sectors. Publication Trends: Over the past 15 years, her publications reflect a consistent focus on surface physics and soft matter. Key themes include graphene synthesis via liquid precursor deposition (including unconventional sources like fingerprints), interfacial rheology, biopolymer adsorption, and quantitative imaging analysis. The interdisciplinary nature of her work is evident in the combination of physics, chemistry, and biological interfaces. Scientific Awards: No specific awards are mentioned in the provided text. Advising and Grants: As head of an active research group, Prof. Jacobs supervises graduate students and postdoctoral researchers, though specific names are not listed. Her collaborations with theoretical groups and external institutions (e.g., University of Augsburg) suggest participation in joint grants and funded projects, particularly in nanomaterials and surface science. The applied orientation of her research indicates engagement with industry partners in coatings and semiconductor technologies. Labs and Teams: The Jacobs Group operates a well-equipped experimental laboratory at Campus E2 9, Saarland University, specializing in surface analysis and soft matter characterization. The team includes researchers working on biofilms, microfluidics, and functional materials, supported by technical and administrative staff.
James C. Gee is a Professor of Radiologic Science in Radiology at the University of Pennsylvania's Perelman School of Medicine. He serves as Director of the Penn Image Computing and Science Laboratory and Co-Director of the Translational Biomedical Imaging Center , with affiliations in Bioengineering and Applied Mathematics graduate groups. His research focuses on biomedical image analysis, specialization in segmentation, registration, and morphometry applied to neurodegenerative diseases and multi-organ systems. Education : B.S. in Computer Science/Electrical Engineering (University of Washington, 1987), Ph.D. in Computer and Information Science (University of Pennsylvania, 1996) Research : Quantitative medical imaging methods, brain connectomics, neurodegeneration mapping, and translational imaging technologies Publications : 15+ recent works on AI-driven image analysis for Alzheimer's disease, cardiac amyloidosis, and radiomics applications Leadership : Directs MSE-DS Online Degree Program, co-chairs Radiology DCOAP Committee, and founded RISE (Radiology Initiative to Support Inclusive Excellence) His laboratory develops advanced computational tools like ITK-SNAP for biomedical imaging, with applications in both in vivo clinical imaging and ex vivo histology . The work spans cross-disciplinary collaborations in computer science, neuroscience, and clinical medicine.
Ray Bai is an Assistant Professor in the Department of Statistics at the University of South Carolina (USC), part of the McCausland College of Arts and Sciences. Effective August 2025, he will join the George Mason University (GMU) Department of Statistics as a faculty member. His research focuses on Bayesian statistics, deep learning, and causal inference, with applications to biomedical and public health challenges such as genomic studies, drug repositioning, and electronic health records analysis. Bai holds a PhD in Statistics from the University of Florida (2018), an MS in Applied Mathematics from the University of Massachusetts Amherst, and a BA from Cornell University. His work has been supported by the National Science Foundation (NSF). Education: PhD in Statistics, University of Florida (2018) MS in Applied Mathematics, University of Massachusetts Amherst BA, Cornell University Research interests include scalable algorithms for high-dimensional data, nonconvex optimization, and distributed inference methodologies. His work bridges statistical theory with practical applications in healthcare, emphasizing robustness and computational efficiency. Recent contributions address challenges in single-index models for skewed data, generative quantile regression, and Bayesian varying-coefficient models. Advising includes supervising PhD students Zile Zhao and Shijie Wang, who have contributed to survival analysis and deep learning frameworks. Future openings for students at GMU focus on Bayesian methodology and machine learning. Labs/Teams: Collaborates on projects involving interdisciplinary teams in biostatistics and computational biology.
Dr. Ruth F. Lucas, PhD, RNC, CLS, is an Associate Professor at the University of Connecticut School of Nursing. Her research focuses on breastfeeding equity through biopsychosocial and molecular mechanisms, infant breastfeeding biomechanics, and pain self-management. Education: PhD in Nursing from University of Illinois at Chicago (2011) Dr. Lucas leads projects translating the PROMPT study for WIC-eligible populations and testing biomedical lactation devices for real-time intraoral pressure measurement. Her work integrates genetic factors (e.g., COMT and OXTR variants) with social determinants of health, particularly in African American communities. Her recent research trends span genomics education for nurses, maternity care deserts in the U.S., breastfeeding self-efficacy metrics, and global lactation disparities. Current efforts include competency frameworks for genomics nurse educators and meta-ethnographies on diverse breastfeeding experiences. Contact: ruth.lucas@uconn.edu
