Soheil Kolouri is an Assistant Professor in the Department of Computer Science at Vanderbilt University. He leads the Machine Intelligence and Neural Technologies (MINT) Lab, focusing on cutting-edge research in artificial intelligence and computational systems. Education includes a PhD in Computer Science from Carnegie Mellon University (2015), an MSc from Colorado State University (2012), and a BSc from Sharif University of Technology (2010). Research interests center on Machine Learning , Computer Vision , and Deep Learning , with specialized applications in optimal transport theory, medical imaging, continual learning systems, and neural network efficiency. Recent work demonstrates strong cross-disciplinary collaboration with medical and engineering domains. Publications (2024-2025) reveal three dominant themes: Advancements in optimal transport methodologies (Wasserstein distances, partial transport) Innovations in continual/lifelong learning systems Medical AI applications including surgical gesture recognition and cancer detection The MINT Lab explores neural network architectures, reinforcement learning, and efficient model compression techniques for real-world deployment.
David L. Brown, MD is a Clinical Professor of Medicine in the Division of Cardiovascular Medicine at the Keck School of Medicine of USC. A general cardiologist specializing in interventional cardiology, he has extensive experience in critical care cardiology, consultative cardiology, and outpatient cardiology. His career spans over 40 years with over 300 publications, 8,500 citations (h-index 37), and four edited textbooks, including the leading Cardiac Intensive Care . He holds editorial board positions at JAMA Internal Medicine and mentors ~50 trainees annually. Education : Bachelor’s degree from University of Texas MD from Baylor College of Medicine Internal Medicine residency and chief residency at Baylor Cardiology fellowship at UCSF Interventional cardiology fellowship at Cleveland Clinic Research Interests : Outcomes research in cardiovascular disease, particularly addressing knowledge gaps arising from clinical practice. Focus areas include coronary artery disease management, revascularization strategies, and the clinical utility of diagnostic tools like coronary calcium scoring. His work emphasizes evidence-based practices and patient-centered outcomes. Articles Trends : Recent work analyzes long-term outcomes of transcatheter vs. surgical aortic valve replacement, critiques trial design biases in chronic coronary syndrome management, and explores obesity’s impact on coronary physiology. His meta-analyses highlight controversies in revascularization efficacy and surrogate endpoint reliability. Awards : Not explicitly listed, but recognized for textbook contributions and mentorship. Advising/Grants : Mentor to 50+ trainees (medical students, residents, fellows). Active in grant-funded research on cardiovascular outcomes and diagnostic techniques. Labs/Teams : Engaged in collaborative research networks analyzing cardiovascular interventions and outcomes, including contributions to the STICH trial follow-up and meta-analyses in interventional cardiology.
Qian Wang is an Associate Professor in the Department of Civil & Environmental Engineering at Manhattan University. He holds a Ph.D. in Structures, Mechanics and Materials from the University of Iowa. As a Licensed Professional Engineer (P.E.) and LEED AP, his expertise spans structural engineering, resilience engineering, and educational psychology. His research focuses on crashworthiness analysis, optimization algorithms, and sustainable infrastructure systems. Dr. Wang has published extensively in journals like Engineering Structures , Structural and Multidisciplinary Optimization , and International Journal for Numerical and Analytical Methods in Geomechanics . His work bridges structural reliability, computational methods, and real-world engineering challenges such as blast-resistant design and geotechnical risk assessment. He collaborates on UTRC grants addressing aging infrastructure resilience and fire behavior in steel bridges. Active in professional organizations including ASEE and ASCE, he teaches courses ranging from introductory civil engineering to advanced structural design. His pedagogical research explores personality traits and cognitive factors influencing engineering student performance. Notable contributions include developing novel meta-modeling techniques for reliability analysis and advancing crash simulation methodologies for transportation barriers. His peer-review activity spans top journals in structural and computational engineering.
Dr. Seung-Kyum Choi is an Associate Professor at the School of Mechanical Engineering, Georgia Institute of Technology. He joined Georgia Tech in 2006 and currently directs the Center for Additive Manufacturing Systems (CAMS), focusing on advancing additive manufacturing technologies and educational programs. His research emphasizes robust design optimization, uncertainty quantification, and probabilistic mechanics applied to engineered systems, including additive manufacturing, metamaterials, and aerospace components. Education: Ph.D., Mechanical Engineering, Wright State University (2006) M.S., Mechanical Engineering, Ajou University, South Korea (2001) B.S., Mechanical Engineering, Ajou University, South Korea (1996) Research interests span additive manufacturing, structural reliability, and multidisciplinary design optimization. He develops simulation tools for uncertainty management in complex systems, such as 3D-printed lattice structures and aerospace components. His work integrates machine learning, meta-learning, and surrogate modeling to enhance decision-making in engineering design. Notable awards include the 2009 Lockheed Martin Aeronautics Dean’s Award for Teaching Excellence. He contributes to journals like International Journal of Materials and Product Technology and Structure & Infrastructure Engineering , and actively mentors students through CAMS and his research labs.
Roland Löwe is an Associate Professor and Head of the Water Systems Section at the Department of Environmental and Resource Engineering, Technical University of Denmark (DTU). He specializes in applying machine learning and statistical methods to water resource management, flood risk assessment, and urban development modeling. His work contributes to UN Sustainable Development Goals related to climate action and sustainable cities. Education includes a PhD in Probabilistic Forecasting for Urban Drainage Systems (DTU Compute, 2014) and a Diploma in Hydrology from Dresden Technical University (2008). Prior roles include Postdoc at DTU, Project Engineer at Krüger A/S, and Hydrologist at the Institute for Technical and Scientific Hydrology (itwh). Key research areas focus on surrogate models for water systems, flood risk mitigation under climate change, and participatory urban planning. Notable projects include CLACOS (scientific machine learning for urban drainage), Jammerbugt Municipality flood prediction using remote sensing, and the Water Smart Cities initiative for resilient urban infrastructure. He supervises PhD students in topics like coastal protection under sea-level rise, real-time sewer control systems, and non-tangible benefits of nature-based solutions. His work has been featured in media discussions on Danish coastal protection policies and climate adaptation strategies.
Corrine F Elliott is a Statistical Scientist at the University of California, Berkeley, where she earned her Ph.D. in Statistics in 2024. Her research bridges statistical methodology and interdisciplinary applications, including biostatistics, energy storage technologies, and materials science. She specializes in developing data-driven frameworks for scientific problem-solving, such as MERITS-Driven Simulation, and has contributed to clinical trial design, battery technology innovation, and policy analysis. Her work spans topics like PET imaging in oncology, redox shuttle chemistry for overcharge protection in batteries, and statistical methodologies for model validation. Education highlights include a B.S. in Mathematics from the University of Kentucky (2017) and an M.S. in Statistics (2018). Her dissertation, 'MERITS-Driven Simulation: A Framework and Case Studies for Data Science Intervention in Scientific Problem-Solving,' was advised by Prof. Bin Yu. She has published extensively on redox flow batteries, phenothiazine derivatives, and statistical computing tools like the rFSA R package. Her research interests emphasize interdisciplinary collaboration, with notable contributions to electrochemistry, medical research, and public policy. Though currently no awards are listed, her work reflects significant engagement in both technical and applied domains.
Prof. Dr. Eline Slagboom is a Full Professor of Molecular Epidemiology at the Leiden University Medical Center and a Max Planck Fellow at the Max Planck Institute for Biology of Ageing. Her research bridges clinical studies, biobanking, and molecular biology to elucidate mechanisms underlying human aging and disease susceptibility. PhD in Biology (Leiden University, 1993) with thesis on Genomic Instability and Ageing MSc in Biology/Biochemistry (Leiden University, 1985) Dr. Slagboom’s work spans: Molecular epidemiology of aging and longevity Integrating genetic and multi-omics data from biobanks Developing metabolomic biomarkers for healthspan and disease Studying interventional impacts on aging through lifestyle and therapeutics Key collaborations include the Leiden Longevity Study (3500+ participants since 2002), BBMRI-NL , and FP7-Health EU projects . Her team utilizes cutting-edge omics technologies on biological samples like fibroblasts and stem cells to decode aging pathways. She has received the Max Planck Fellow Programme award and has contributed to landmark studies on: Genetic determinants of longevity Mendelian randomization of aging biomarkers Metabolomic profiling for disease risk prediction Thyroid hormone impacts on aging populations Her authored tools like MiMIR (for metabolomic data analysis) demonstrate computational innovation in the field.
Rafael Messias Martins is a Researcher at Linnaeus University, affiliated with the Department of Computer Science and Media Technology within the Faculty of Technology. He holds an MSc in Computer Science from the University of São Paulo and a PhD in Computer Science from the University of Groningen. His primary research focuses on Information Visualization and Visual Analytics, particularly emphasizing Multidimensional Data and Networks. He is a core member of the Information and Software Visualization (ISOVIS) research group and leads multiple ongoing and completed research projects, including InfraVis (a national research infrastructure for data visualization) and initiatives addressing medication risks and carbon mitigation in forestry. His work bridges theoretical advancements in visualization with practical applications in education, healthcare, and environmental science. Education MSc in Computer Science, University of São Paulo, Brazil PhD in Computer Science, University of Groningen, Netherlands Research Interests His research explores the intersection of visualization techniques with complex data analysis, emphasizing: Interactive visual analytics for high-dimensional data Machine learning interpretability through visualization Educational data analytics for K-12 institutions Applications in healthcare (e.g., medication risk prediction) and environmental science (e.g., carbon footprint reduction) Development of national visualization infrastructures (InfraVis) Recent Trends in Articles His recent work emphasizes: Enhancing trust in machine learning models through visual explanations Optimizing visualization tools for educational stakeholders Algorithmic fairness in urban planning simulations Scalable dimensionality reduction techniques for streaming data Grants & Collaborations He has coordinated projects such as IDEAL (interaction design curriculum development), TimberVis (3D timber structure visualization), and seed projects addressing carbon mitigation and medication risks. Collaborations span academic, industrial, and governmental partners in Sweden and internationally. Labs & Teams He leads the ISOVIS group, which develops open-source tools like SBGTool (student grouping analytics) and FeatureEnVi (feature engineering visualization). The group also contributes to InfraVis, a national platform for visualization resources.
Dr. Joshua Wallach is an Associate Professor in the Department of Epidemiology at Emory University's Rollins School of Public Health. He specializes in regulatory science, health policy, and evidence synthesis, focusing on improving the evaluation of medical product safety and efficacy through advanced methodologies. His core affiliations include the Collaboration for Regulatory Rigor, Integrity, and Transparency (CRRIT) and the Yale Open Data Access (YODA) Project. Dr. Wallach holds a PhD and MS from Stanford University. His research spans regulatory science (FDA processes), systematic reviews, meta-research, and real-world data applications. Notable projects include evaluating surrogate endpoints, postmarket surveillance methods, and bias analysis techniques. He serves as an Associate Editor for the Journal of the American College of Cardiology. Recent work emphasizes transparency in clinical trial data, with studies on preprint impact, FDA approval pathways, and trial emulation using real-world data. Key collaborations involve the Yale-Mayo Clinical and Translational Science Award (CERSI) and NIH-funded projects on alcohol use disorder treatment subgroups. Labs/Teams: Core faculty at CRRIT and YODA Project, contributing to initiatives like FDA regulatory rigor and data access advocacy. Current projects focus on enhancing evidence-based policy through innovative methodological frameworks.
Willem H Collier is a Clinical Assistant Professor in the Department of Population and Public Health Sciences at the University of Southern California. His work focuses on the design, conduct, and analysis of pediatric cancer clinical trials through the Children’s Oncology Group, with an emphasis on supportive care, survivorship, and cellular therapy. Additionally, he specializes in statistical methods for surrogate endpoint evaluation, particularly Bayesian hierarchical modeling for meta-regression, in collaboration with the Chronic Kidney Disease Epidemiology Collaboration. Department: Population and Public Health Sciences Specialties: Biostatistics, Pediatric Oncology, Surrogate Endpoint Analysis Affiliations: University of Southern California, Children’s Oncology Group, Chronic Kidney Disease Epidemiology Collaboration Research Interests include developing advanced biostatistical techniques for surrogate endpoints in heterogeneous clinical trials. His work bridges methodological innovation with real-world applications in pediatric oncology and chronic kidney disease, focusing on improving trial efficiency and patient outcomes. He also investigates healthcare utilization patterns in nonelderly adults and postsurgical complications in plastic surgery contexts. Scientific Awards and honors are not explicitly mentioned in the provided text. Advising details are unavailable, but his collaborations with major clinical research groups indicate significant mentorship and team-based contributions to trial design and analysis.
Rodney Stephen Taylor is Professor of Population Health Research at the University of Glasgow's School of Health and Wellbeing, with joint affiliations to the MRC/CSO Social and Public Health Sciences Unit and Robertson Centre for Biostatistics/Glasgow Clinical Trials Unit. He also holds adjunct professorships at the National Institute of Public Health (Copenhagen) and Odense University of South Denmark. His career includes former roles at the London School of Hygiene & Tropical Medicine, Universities of Birmingham and Exeter, and as inaugural Director of Technology Appraisals at NICE (1999-2000). Taylor holds a PhD in Clinical Physiology (Glasgow), MSc in Medical Statistics (London), and Postgraduate Diploma in Health Economics (Aberdeen). Research Focus : Taylor leads pioneering work in cardiac rehabilitation, clinical trial methodology, and secondary prevention of heart disease. His research emphasizes: Development/evaluation of heart disease rehabilitation strategies Health technology assessment and trial design for complex interventions Economic evaluations of cardiovascular therapies He currently directs the Cardiac Rehabilitation Cochrane Review Centre and co-leads the Glasgow Centre for Clinical Trials Collaboration. Publication Trends : Taylor's recent articles (2023-2025) focus on methodological rigor in clinical trials, cost-effectiveness of cardiac interventions, and innovative rehabilitation models. Key themes include SPIRIT/CONSORT guideline updates, surrogate endpoint validation, and home-based rehabilitation for heart failure. His work demonstrates consistent emphasis on translational research, global health equity, and patient-centered outcomes. Awards & Honors : Honorary Doctorate, University of Southern Denmark (2023) BMJ Stroke and Cardiovascular Team of the Year (2020) NIHR Senior Investigator (2015-2020) University of Exeter Research Impact Award (2015) Multiple NHS and postgraduate scholarships Grants & Leadership : Taylor is Chief Investigator for major NIHR/MRC-funded trials including REACH-HFpEF, PERFORM, and DK:REACH. He has secured competitive grants from NIHR, MRC, Wellcome Trust, and CSO. He directs the Glasgow Heart-Review Centre and serves on panels for NIHR, ESC, and BHF. Labs & Teams : He co-directs the Glasgow Centre for Clinical Trials Collaboration, leading multidisciplinary teams in cardiovascular trials. His work involves collaborations across 15+ countries, focusing on scalable rehabilitation models and methodological innovation.
Steven G. Johnson is an Associate Professor of Applied Mathematics and Physics at the Massachusetts Institute of Technology (MIT), affiliated with the Research Laboratory of Electronics (RLE). He holds a B.S. in physics, mathematics, and computer science (1995) and a Ph.D. in physics (2001), both from MIT. His research spans the influence of complex geometries on wave phenomena, high-performance computation (including FFTW library development), and nanoscale electromagnetism in structured media. Johnson's work focuses on two primary areas: photonic crystals and high-performance computing. He co-developed the FFTW fast-Fourier transform library, earning the 1999 J.H. Wilkinson Prize. His research group explores nanostructure effects on partial differential equations, numerical electromagnetism, and device design. He teaches courses such as Mathematical Methods in Nanophotonics and Numerical Methods , reflecting his expertise in computational physics. Key contributions include advancing surface-enhanced Raman scattering substrates, inverse design of meta-optics, and quasi-normal mode theory. Collaborations span experimental and theoretical groups, including the Ab Initio Physics Group at MIT. His team's work bridges fundamental theory with applied photonics, yielding innovations in imaging, lasers, and computational tools. Recent projects highlight end-to-end metasurface design for imaging, physics-enhanced machine learning models, and tunable terahertz lasers. Johnson's interdisciplinary approach integrates nanophotonics, optimization, and AI, driving advancements in light-matter interaction and computational methods.
Christopher Hourigan is a Professor at the Fralin Biomedical Research Institute at Virginia Tech and holds a joint appointment in the Department of Internal Medicine at the Virginia Tech Carilion School of Medicine. He serves as Director of the Cancer Research Center – D.C., where he leads a translational research program focused on Acute Myeloid Leukemia (AML) and measurable residual disease (MRD). Previously, he was a Tenured Senior Investigator and Chief of the Laboratory of Myeloid Malignancies at the National Institutes of Health (NIH). His research is centered on precision oncology, aiming to improve survival in AML by developing high-sensitivity genomic tools to detect MRD and predict relapse. His work bridges basic science and clinical application, with a strong emphasis on personalizing cancer therapy. He leads the national 'MEASURE' clinical protocol to validate MRD testing across the U.S. Dr. Hourigan's recent publications highlight a consistent focus on MRD, genomics, AML treatment, and transplantation. His work integrates clinical data, molecular diagnostics, and translational research to refine risk prediction and therapeutic decision-making. Presidential Early Career Award for Scientists and Engineers (PECASE) American Society for Clinical Investigation (ASCI) Young Physician-Scientist Award National Heart, Lung, and Blood Institute Orloff Award Alpha Omega Alpha, Johns Hopkins Medical School Chapter Fellowship, Royal College of Physicians (London) National Cancer Institute Center for Cancer Research Group Special Act Award National Institute of Health Director’s Challenge Innovation Award Dr. Hourigan has led major clinical and translational research initiatives, including multi-institutional collaborations and national protocols. His lab at Virginia Tech fosters interdisciplinary research, partnering with governmental and private entities. He does not have a listed advisee roster, but leads a research team including postdoctoral associates and research scientists. The Hourigan Lab at the Virginia Tech Cancer Research Center in Washington, D.C., focuses on engineering cancer solutions through translational research. The lab brings together basic, computational, and clinical researchers to develop innovative approaches to prevent cancer relapse, particularly in AML.
Gopisankar Mohanannair Geethadevi is a Research Fellow at Monash University's Centre for Medicine Use and Safety (CEMUSS), affiliated with the Health Economics and Policy Evaluation Research Team (HEPER). Their work focuses on clinical prediction models, health economics, pharmacogenomics, and epilepsy research with contributions to global healthcare policy. Current research emphasizes dementia prevention, cardiovascular risk pathways, and mHealth interventions. University: Monash University Department: Centre for Medicine Use and Safety Research interests span dementia risk reduction through multidomain interventions, pharmacoepidemiology, and stress-related cardiovascular mechanisms. Recent studies include protocol development for the HAPPI MIND trial, reviews of cancer surrogate outcomes for policy decisions, and systematic analyses of airway disease treatments. Publications highlight contributions to Cochrane Reviews, BMJ Open, and the Journal of the American College of Cardiology. Their work aligns with UN Sustainable Development Goals related to good health and well-being.
Amos H. C. Ng is a Professor at the School of Engineering Science, University of Skövde, specializing in simulation-based optimization and Industry 4.0 technologies. His research bridges production engineering with human-robot collaboration, ergonomics evaluation, and cloud-based cyber-physical systems for manufacturing efficiency. Key Affiliations: University of Skövde (School of Engineering Science), Uppsala University (Industrial Engineering and Management) Research Themes: Multi-objective optimization, Digital Twin frameworks, Human-centric production systems, Reconfigurable manufacturing, Throughput bottleneck analysis Projects: ACCURATE 4.0 (Knowledge Foundation), VF-KDO (Virtual Factories with Knowledge-Driven Optimization), EWASS (Wire Harness Assembly Optimization) His recent publications demonstrate expertise in applying evolutionary algorithms, machine learning models, and digital human modeling tools to solve complex manufacturing problems ranging from crankshaft machining to wood supply chain robustness. Current work integrates motion capture technology with DHM tools for objective ergonomic assessments in assembly stations. Amos collaborates extensively with industrial partners like Volvo Penta and academic institutions, utilizing simulation-based approaches to enhance decision-making in production systems. His methodological focus includes non-dominated sorting genetic algorithms, surrogate modeling, and parallel computing architectures for optimization tasks.
