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Theodore CohenView profile

Professor

• Tuberculosis
• Infectious Disease Epidemiology
• Mathematical Modeling
• +6 more

Dr. Theodore Cohen is a Professor of Epidemiology (Microbial Diseases) at the Yale School of Public Health. His primary research focus is tuberculosis, with particular interest in understanding how TB drug-resistance and medical comorbidities such as HIV frustrate current efforts to control epidemics. His work includes mathematical modeling, fieldwork, and analysis of programmatic data. Education: DPH, Harvard School of Public Health (2006) MPH, University of North Carolina, Chapel Hill (2001) MD, Duke University (2001) BA in Neuroscience, Oberlin College (1996) Dr. Cohen's research interests span tuberculosis epidemiology, drug resistance, HIV-TB co-infections, mathematical modeling of infectious diseases, and health disparities in TB outcomes. His work focuses on developing more effective approaches to limit TB morbidity through targeted interventions and improved understanding of transmission dynamics. He examines racial and ethnic disparities in TB incidence and outcomes, particularly among US-born populations. Analysis of Dr. Cohen's recent publications (2022-2025) reveals a strong focus on tuberculosis epidemiology, with particular attention to health disparities, cost-effectiveness of interventions, and the impact of comorbidities like HIV and diabetes on TB outcomes. His work frequently employs mathematical modeling and analysis of large datasets to understand TB transmission dynamics and evaluate intervention strategies across diverse populations in the United States. Scientific Awards: Elected member, Connecticut Academy of Science and Engineering (2022) NIH New Innovator Award (2009) Merck Manual Scholar, Duke University School of Medicine (2001) Alpha Omega Alpha, Duke University School of Medicine (2000) Senior Scholarship Recipient, Duke University School of Medicine (2000) Dr. Cohen's research program is currently funded by NIH and US CDC Awards. His work includes multiple NIH-funded projects, including a R01 grant focused on evaluating health and economic effects of targeted strategies in TB/HIV epidemics (2014-2018), participation in the MIDAS Center for Communicable Disease Dynamics (2014-2019), and projects with the Bill and Melinda Gates Foundation on TB modeling and drug dosing optimization. Dr. Cohen leads the Ted Cohen Group (T Cohen Lab) at Yale, which focuses on infectious disease epidemiology, particularly tuberculosis. His lab conducts research on TB transmission dynamics, mathematical modeling of epidemics, and analysis of programmatic data to inform public health interventions. The lab is part of the Yale Institute for Global Health and the Yale-UPR Integrated HIV Basic and Clinical Sciences Initiative.

• Astronomy
• Astrophysics
• Machine Learning
• +5 more

Robert Brunner serves as Professor of Astronomy at the University of Illinois at Urbana-Champaign, where he bridges astrophysical research with computational innovation. His work focuses on extracting knowledge from massive astronomical datasets through advanced statistical and machine learning techniques, while also extending methodologies to finance and agricultural applications. Research interests center on developing machine learning algorithms (random forests, deep neural networks, Bayesian estimation) for astronomical data analysis, cosmological parameter constraints via n-point clustering measurements, and hardware acceleration using GPUs/cloud systems. His interdisciplinary approach spans source classification, transient phenomena detection in surveys like SDSS and DES, and applications in financial time-series analysis and agricultural remote sensing. Recent publications (2019-2025) reveal strong cross-domain expertise: astronomical catalogs for Rubin Observatory and Spitzer surveys coexist with financial market analysis using community detection methods and agricultural computer vision systems. Key methodological threads include spatio-temporal forecasting, multimodal learning for earnings calls, and anomaly detection via extended isolation forests, demonstrating consistent innovation in handling petascale datasets across scientific boundaries.

Martin HebartView profile

Professor

• Visual Perception
• Computational Cognitive Neuroscience
• Quantitative Psychiatry
• +5 more

Martin Hebart is a Professor for Computational Cognitive Neuroscience and Quantitative Psychiatry at Justus Liebig University Giessen and an Independent Max Planck Research Group Leader at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany. His work bridges cognitive neuroscience, computer science, and psychology to explore visual perception, object recognition, and computational models of brain function. PhD in Psychology from Bernstein Center for Computational Neuroscience Berlin (2014) M.Sc. and B.Sc. in Neuro-cognitive Psychology from Ludwig Maximilian University Munich His research integrates psychophysics , neuroimaging (fMRI, MEG), and machine learning to decode how visual input transforms into stable object representations and how these insights inform psychiatric conditions like hallucinations. Articles highlight his focus on computational models , neural network alignment , and large-scale behavioral-neuroimaging datasets (e.g., THINGS-data). His group’s work spans from basic visual cognition to translational applications in psychiatry. Scientific awards include postdoctoral fellowships from the National Institute of Mental Health (2016) and Alexander von Humboldt Foundation (Feodor Lynen, 2016), alongside doctoral and study scholarships. He leads a multidisciplinary team at the intersection of JLU Giessen’s Medical Department and MPI, mentoring students in visual neuroscience , AI-driven modeling , and clinical applications .

Lisa HultmanView profile

Professor

• Peace and Conflict Research
• International Relations
• Civil War Studies
• +5 more

Lisa Hultman serves as a Professor at Uppsala University's Department of Peace and Conflict Research, where she leads groundbreaking research on peacekeeping operations, civilian protection, and conflict dynamics. Her work bridges academic rigor with practical policy implications for international peace and security institutions. Professor Hultman's research focuses on the empirical analysis of UN peacekeeping effectiveness, particularly examining how different mandate configurations impact violence against civilians and local conflict trajectories. Her work demonstrates sophisticated methodological approaches, frequently utilizing geocoded data and large-N quantitative analyses to assess peacekeeping outcomes at subnational levels. Key contributions include developing innovative datasets like the Geocoded Peacekeeping Operations (Geo-PKO) and examining the economic dimensions of peacekeeping deployments on local development. Her publication record reveals consistent engagement with critical questions in peace and conflict studies, with recent work exploring mandate complexity in UN operations, the relationship between peacekeeping and civilian protection norms, and forecasting models for political violence. Her research often involves extensive collaboration with leading scholars in the field, resulting in publications in top political science journals including the American Political Science Review , American Journal of Political Science , and Journal of Peace Research . Professor Hultman's influential 2019 book "Peacekeeping in the Midst of War" (co-authored with Jacob Kathman and Megan Shannon) synthesized years of research on peacekeeping effectiveness. Her work has been widely cited and referenced in both academic literature and policy discussions, demonstrating significant real-world impact on understanding how international interventions can effectively protect civilians and reduce violence in conflict zones.

Aleda M. LeisView profile

Academic

• Epidemiology
• Public Health
• Chronic Diseases
• +8 more

Aleda (Allie) M. Leis, PhD, is a Research Assistant Professor in the Department of Epidemiology at the University of Michigan School of Public Health. Her work bridges chronic and infectious disease epidemiology, with a focus on cardiometabolic outcomes following viral infections. Education: PhD in Epidemiological Science, University of Michigan (2022) MS in Biostatistics, Drexel University (2015) BS in Mathematics, St. Bonaventure University (2013) Research Interests: Dr. Leis investigates how viral infections like influenza and COVID-19 interact with metabolic and cardiovascular diseases across the lifespan. Key areas include aging, women's health, bone health, and precision health initiatives using large datasets such as the Study of Women’s Health Across the Nation (SWAN) and CDC respiratory surveillance programs. Projects: She leads the Surveillance of Respiratory Infections’ Sequelae (SunRISE) program, contributes to the AI & Digital Health Innovation network, and co-investigates vaccine effectiveness in the Michigan-Ford Initiative (MFIVE). Her work utilizes electronic health records and longitudinal cohorts to explore post-viral health impacts. Contact: Email aledat@umich.edu for collaboration or media inquiries.

• Experimental High-Energy Particle Physics
• CP Violation Studies
• B-Physics and Strange Quark Physics
• +4 more

Roger William Lewis Jones is a Professor and Head of the Department of Physics at Lancaster University . His career spans experimental high-energy particle physics, QCD research, and global computing infrastructure development for large-scale scientific projects like ATLAS and the Rubin Observatory. ATLAS experiment (LHC) for CP violation studies in B-physics NA62 experiment for strange quark physics LEGEND collaboration for neutrino research Leadership in GridPP and Worldwide LHC Computing Grid Research Focus : Experimental particle physics with emphasis on CP violation, QCD dynamics, and data-intensive computing systems. His work bridges hardware development (e.g., tracking detectors) and software innovation (e.g., Grid computing farms across UK institutions). Recent Publications highlight precision measurements in jet physics, Higgs decays, and searches for new particles like heavy neutral leptons. Articles span 2025 with collaborations on ATLAS and NA62, focusing on detector calibration, cross-section analysis, and beyond-Standard-Model signatures. Leadership Roles : Chair of NorthGrid Management Board Former Chair of STFC Particle Physics Advisory Panel (2019-2022) Chair of Institute of Physics Head of Department Forum Steering Committee (2019-2024) Key contributor to GridPP and Worldwide LHC Computing Grid

Grant Van HornView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Robotics
• +2 more

Grant Van Horn is an Assistant Professor at the University of Massachusetts Amherst, affiliated with the College of Information and Computer Sciences. He specializes in computer vision and machine learning, focusing on applications in biodiversity and conservation. His work underpins popular tools like iNaturalist, Seek, and Merlin Bird ID. Prior to UMass, he held roles at AWS and the Cornell Lab of Ornithology. Education: PhD in Computer Science, California Institute of Technology (2019) MS in Computer Science, University of California San Diego (2014) BS in Computer Science, University of California San Diego (2012) Research Interests: Grant’s research bridges computer vision and machine learning to create systems that integrate human expertise and large datasets for environmental conservation. Key areas include wildlife species identification, acoustic monitoring, and ecological modeling. His work emphasizes leveraging technology for public engagement and scientific impact. Articles & Trends: Recent publications focus on audio geolocation, species range estimation, and satellite imagery analysis, reflecting his commitment to advancing tools for biodiversity conservation. His work often combines citizen science data with machine learning innovations. Awards: Computing Research Association Outstanding Undergraduate Researcher Honorable Mention (2012) Ben P.C. Chou Doctoral Prize (2019) Fast Company’s 2021 Recognition Advising & Labs: Grant advises students in computer science and conservation technology through the Computer Vision Research Laboratory. He has collaborated on projects like the iWildCam dataset and systems for salmonid counting in sonar data.

Peter LambertView profile

Associate Professor

• multimedia signal processing
• visual communication
• computer graphics
• +2 more

Peter Lambert is a full-time Associate Professor at Ghent University – imec (Belgium), affiliated with the Internet Technology and Data Science Lab (IDLab) where he coordinates the MEDIA research team since 2013. His academic background includes a Master's degree in Science (Mathematics) and Applied Informatics from Ghent University, followed by a Ph.D. in Computer Science from the same institution in 2007. Prior to his current role, he served as a Technology Developer at Ghent University (2010-2013). Lambert's research focuses on: Multimedia signal processing and visual communication systems Computer graphics and computational geometry Augmented and virtual reality (AR/VR) technologies Video compression and perceptual quality assessment Multimedia security and digital watermarking His recent publications (2024-2025) demonstrate strong emphasis on real-time multimedia systems, VR/AR applications, perceptual quality metrics, and multimedia security. Research trends include deep learning-based video forensics, light field rendering optimizations, perceptual hashing techniques, and adaptive video streaming solutions. As leader of the IDLab-MEDIA team, Lambert oversees research on emerging visual media formats with applications in immersive experiences. The team develops technologies like OpenDIBR (real-time light field renderer) and maintains datasets such as SILVR (Synthetic Immersive Large-Volume Plenoptic Dataset).

Kristopher MicinskiView profile

Assistant Professor

• Programming Languages
• Static Analysis
• Formal Methods
• +4 more

Kristopher Micinski is an Assistant Professor in the Electrical Engineering and Computer Science Department at Syracuse University. His research focuses on scalable program analysis, static analysis, and formal methods applied to computer security and privacy. He holds a PhD in Computer Science from the University of Maryland and a BS in Computer Engineering from Michigan State University. Education: PhD, Computer Science, University of Maryland at College Park BS, Computer Engineering, Michigan State University Research Interests: His work bridges theory and application of program analyses, emphasizing scalable static analysis frameworks, Datalog optimization for distributed systems, and security applications. Recent efforts include GPU-accelerated Datalog engines and large-scale malware analysis pipelines like Assemblage. Grants & Projects: NSF PPoSS Large: $1M grant for declarative analytics DARPA V-SPELLS: $400K for legacy software optimization Assemblage: $350K DoD grant for malware classification Teaching: He teaches undergraduate and graduate courses on programming languages (CIS352) and formal methods (CIS700), with materials publicly available on YouTube.

Moo ChungView profile

Professor

• Computational Neuroimaging
• Topological Data Analysis
• Brain Networks
• +5 more

Moo Chung is a Professor of Biostatistics and Medical Informatics at the University of Wisconsin-Madison, affiliated with the School of Medicine and Public Health. He holds additional appointments in the Department of Statistics and the College of Letters and Science. Chung earned his Ph.D. in Mathematics and Statistics from McGill University under Professors Keith J. Worsley and James O. Ramsay. His research focuses on computational neuroimaging, leveraging MRI, fMRI, and DTI to study brain dynamics through topological and geometric methods. Key areas include persistent homology, brain network analysis, and statistical modeling of high-dimensional imaging data. He has pioneered methods like hyper-network construction and exact topological inference for paired brain networks, addressing computational challenges in analyzing large-scale neuroimaging datasets. Chung’s work integrates advanced mathematical techniques such as Hodge Laplacian, spectral graph theory, and Wasserstein distances to analyze brain connectivity. He has secured NIH funding for projects like the Brain Initiative (2017-2020) and current grant MH133614 (2023), focusing on geometric data analysis and topological dynamics. His contributions include three books on brain imaging and network analysis, with ongoing research on topological data analysis applications. Awards: Vilas Associate Award (2013-2014), NIH Brain Initiative Award, Editor's Award (2011) Labs/Teams: Leads brain imaging workshops globally, including Seoul National University (2024) and POSTECH (2024), and organizes conferences like ISBI and MICCAI special sessions. Grants: NIH EB022856 (Brain Initiative), MH133614 (2023), and collaborations with institutions like Vanderbilt University and University of Chicago. Chung actively mentors students and postdocs in biomedical data science, offering fellowships through the CIBM program. His group maintains a Google mailing list for brain image analysis discussions and hosts regular seminars on methodological advancements.

• Natural Language Processing
• Text Generation
• Machine Learning
• +1 more

Zihao Fu is a Researcher at the Oxford Internet Institute (OII), University of Oxford, where he served as a Postdoctoral Researcher from March 2024 to April 2025, focusing on the Trustworthiness Auditing for AI project. Previously, he was a Research Associate (PostDoc) at the University of Cambridge's Language Technology Lab under Prof. Nigel Collier. His research emphasizes Natural Language Processing, Text Generation, Machine Learning, and Biomedical Applications. Education includes a Ph.D. from The Chinese University of Hong Kong (supervised by Prof. Wai Lam) and a visiting student period at Tsinghua University's NLP Lab. He has substantial experience with large-scale distributed algorithms in Alibaba Cloud's PAI platform. Research interests span NLP, biomedical applications, and AI ethics, with notable contributions to datasets like BAND and frameworks like OxonFair. His work addresses challenges in text generation repetition, parameter-efficient fine-tuning, and algorithmic fairness.

Paul GoodingView profile

Professor

• Digital Humanities
• Library and Information Science
• Digital Libraries
• +3 more

Paul Gooding is a Professor of Library Studies and Digital Scholarship at the University of Glasgow. His research focuses on digital library collections' impact, reuse in Digital Humanities, and user behavior analysis. He holds a BA in English Literature (University of East Anglia), MA in Library and Information Studies (UCL), and a PhD in Digital Humanities (UCL). Research interests include digital collections evaluation, legal deposit frameworks, and AI applications in cultural heritage. Notable publications include Historic Newspapers in the Digital Age (2016) and co-editing Electronic Legal Deposit (2020). He leads projects like Digital Library Futures (AHRC-funded) and the Global Dataset of Digitised Texts Network. Recent articles explore topics such as 'Slow AI' principles, HTR in heritage contexts, and inclusive AI requirements for Indigenous knowledges. His work bridges library studies, digital humanities, and ethical technology. Fellowships include the Higher Education Academy and Royal Historical Society. He serves as Associate Editor for Digital Scholarship in the Humanities and AHRC Peer Review College member.

• Microchannel Engineering
• Proton Exchange Membrane Fuel Cell
• Two-phase Flow
• +7 more

Eon Soo Lee is an Associate Professor in the Department of Mechanical and Industrial Engineering at the New Jersey Institute of Technology (NJIT). His primary research focuses on advanced materials engineering, biomedical microfluidics, and assistive technologies for individuals with disabilities. He has led federally funded projects including 'I-Corps: Multiplex Diagnostic Assay Using Interdigitated Nano-Sensing Technology' (NSF, 2023-2025) and 'Innovative Nano Catalysts for Automobile and Fuel Cell Applications' (NSF, 2018). Research Interests: Lee's work spans interdisciplinary areas including: Development of N-doped graphene/MOF composites for energy applications Microfluidic systems for blood plasma separation and antigen detection Design of accessible technologies for visually impaired users, including VR audio descriptions and remote sighted assistance systems Grants and Projects (select): National Science Foundation (2023): $500K for multiplex diagnostic assays National Science Foundation (2018): $300K for nano-catalysts in fuel cells Multiyear collaborations with industry partners on biosensor integration Innovation Highlights: Developed AIGuide: AR hand-guidance system for visual impairments Pioneered omnidirectional audio descriptions for VR music performances Published extensively in Carbon , Biomicrofluidics , and ACM/IEEE accessibility venues

Kevin StangeView profile

Professor

• Labor Economics
• Public Economics
• Education Economics
• +4 more

Kevin Stange is a Professor of Public Policy and Education (by courtesy) at the University of Michigan's Ford School of Public Policy, where he serves as co-director of the Education Policy Initiative and director of the PhD Program. He is also a Research Associate at the National Bureau of Economic Research and a faculty affiliate of the Center for the Study of Higher and Postsecondary Education at the Marsal Family School of Education. His educational background includes: Bachelor of Science in Mechanical Engineering and Economics, Massachusetts Institute of Technology (1999) Ph.D. in Economics, University of California, Berkeley (2008) Stange's research focuses on empirical labor and public economics within education contexts. He investigates how higher education shapes labor market trajectories, geographic mobility, and responses to skill demand shifts. Current projects examine Michigan's financial aid programs and college major impacts on career outcomes, utilizing large-scale datasets like College and Beyond II to analyze postsecondary pathways and institutional effectiveness. His recent publications (2023-2025) emphasize college major effects on earnings inequality, financial aid efficacy, community college transfer pathways, and graduate mobility patterns. These works leverage administrative data to address policy-relevant questions about educational equity and labor market alignment. Key recognition includes: Robert Wood Johnson Scholar in Health Policy Research Stange has secured major funding from the U.S. Department of Education, National Science Foundation, Spencer Foundation, W.T. Grant Foundation, Robert Wood Johnson Foundation, and Russell Sage Foundation. During 2022-2023, he served as Senior Advisor to the Under Secretary at the U.S. Department of Education, applying his research expertise to federal policy. He co-directs the Education Policy Initiative, which produces evidence-based research for education practitioners, and spearheaded the College and Beyond II dataset containing 50 million course records from 19 universities. This resource enables comprehensive analysis of student pathways and institutional effectiveness across diverse higher education contexts.

Malcolm MistryView profile

Adjunct Professor

• Climate Change
• Environmental Epidemiology
• Energy Policy
• +5 more

Malcolm Mistry is an Adjunct Professor at the Department of Economics, Ca' Foscari University of Venice. He specializes in climate change impacts, environmental epidemiology, and energy policy, focusing on sectors such as agriculture, health, and energy. His research integrates econometric methods, climate modeling, and large dataset analysis to assess climate-related risks and adaptation strategies. Education: PhD in Science and Management of Climate Change (Ca' Foscari University of Venice, 2017) MSc in Weather and Climate Modelling (University of Reading, UK, 2006) Bachelor of Information Technology (Indira Gandhi National Open University, India, 2004) Research Interests: Climate extremes and human health impacts Energy demand for adaptation (e.g., air conditioning) Climate datasets and modeling tools (e.g., R Climpact package) Policy-relevant assessments of climate change vulnerabilities Teaching: He teaches advanced courses on climate change economics, statistical computing (R/Python), and environmental epidemiology in PhD/MRes programs at Ca' Foscari and the London School of Hygiene & Tropical Medicine (LSHTM). Awards: His work on climate spillovers in the EU received the Best Paper Award in 2021. He leads or co-leads multiple research projects funded by the EU, UK Medical Research Council, and Belmont Forum. Grants & Collaborations: Principal Investigator: Marie Skłodowska-Curie Fellowship (MORDIC Project, 2022–2024) Co-Investigator: EXHAUSTION (EU H2020) and GA MR/V034162/1 (MRC) Consultant: Red Cross Red Crescent Climate Centre Labs & Teams: Member of the EHM Lab (LSHTM) and developer of the R Climpact package for climate extreme indices. Active in global health collaborations addressing heat-related mortality and environmental exposures.