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Samuel ChengView profile

Associate Professor

University of OklahomaUnited States

Dr. Samuel Cheng is an Associate Professor at the Gallogly College of Engineering , University of Oklahoma , specializing in Electrical and Computer Engineering . He holds a Ph.D. in Electrical Engineering from Texas A&M University (2004), preceded by M.S. and M.Phil. degrees from the University of Hawaii and Hong Kong University of Science and Technology. Education: B.S. (University of Hong Kong, 1995), M.Phil. (HKUST, 1997), M.S. (University of Hawaii, 2000), Ph.D. (Texas A&M, 2004) Professional Experience: Senior Research Engineer at Advanced Digital Imaging Research (2004-2005), prior internships at Microsoft Asia and Panasonic Technologies His research focuses on Information Theory , Signal and Image Processing , and Pattern Recognition , with applications in remote sensing, urbanization analysis, and disaster monitoring. His publications span topics including urban impervious surface mapping , nighttime light analysis , and machine learning for environmental data . His work often integrates multi-source datasets (e.g., Landsat, LiDAR, social media) for spatiotemporal modeling. Technical Expertise: Spectral unmixing, machine learning, thermal remote sensing, GIS integration Key Applications: Power outage detection, vegetation-crime correlation, PM2.5 estimation, smart meter data fusion Dr. Cheng holds three US patents in digital watermarking and is affiliated with IEEE, Sigma Xi, and AAAS. His recent articles demonstrate a trend toward leveraging AI for remote sensing challenges and analyzing urbanization impacts on ecosystems.

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Marc G. BermanView profile

Professor

University of ChicagoUnited States

Marc G. Berman is a Professor and Chair of the Department of Psychology at the University of Chicago. His research focuses on understanding how environmental factors interact with human cognition, emotion, and behavior, particularly through the lens of environmental neuroscience. He leads the Environmental Neuroscience Lab (ENL), investigating how natural and urban environments influence brain function, memory, attention, and mental health. Dr. Berman holds a B.S.E. in Industrial and Operations Engineering from the University of Michigan and a Ph.D. in Psychology and Engineering from the same institution. His postdoctoral training was at the Rotman Research Institute in Toronto. Prior to Chicago, he was an Assistant Professor at the University of South Carolina. His research interests include the cognitive and affective benefits of natural environments, brain network efficiency, and the neurobiological underpinnings of self-control and emotion regulation. Recent work explores how urban design elements (e.g., greenspace, street activity) relate to crime rates and mental health outcomes, leveraging big data from social media and geospatial tools. Key contributions include demonstrating that natural environments improve memory and attention by ~20%, and that city characteristics like population diversity and segregation correlate with implicit racial biases. His lab employs fMRI, neuroimaging, computational modeling, and ecological data to quantify brain-environment interactions. Dr. Berman collaborates across disciplines, integrating neuroscience, psychology, urban planning, and data science to inform evidence-based environmental design for public health. Current projects include analyzing social media data to map gang networks and investigating how heat and greenspace influence emotional states in urban populations.

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Sharon RozovskyView profile

Professor

University of DelawareUnited States

Sharon Rozovsky is a Professor in the Department of Chemistry and Biochemistry at the University of Delaware's College of Arts & Sciences, where she leads research on oxidative stress response mechanisms and protein quality control pathways. Her work bridges biochemistry, chemical biology, and structural biology with direct implications for understanding neurodegenerative diseases and viral pathogenesis. Her academic foundation includes a B.S. from Tel Aviv University (1994) and a Ph.D. from Columbia University (2000), establishing her expertise in protein dynamics and redox biochemistry. These credentials underpin her innovative approaches to studying cellular stress responses. Rozovsky's research program centers on selenoproteins—proteins containing the rare amino acid selenocysteine—and their critical roles in endoplasmic reticulum (ER) stress resolution. She investigates how membrane-bound selenoproteins like Selenoprotein S and K regulate the ER-associated degradation (ERAD) pathway, with recent work revealing their surprising autoproteolytic activity and involvement in SARS-CoV-2 replication. Her lab pioneers chemical tools including expressed protein ligation and advanced 77Se NMR spectroscopy to characterize these systems at molecular resolution. Analysis of her 2021-2025 publications shows dominant themes in selenoprotein structure-function relationships, ER stress mechanisms, and viral interactions, alongside methodological innovations in cryo-EM grid technology and NMR. This body of work demonstrates consistent focus on redox biochemistry with expanding applications in virology and structural biology. No major scientific awards or fellowships were explicitly documented in the available materials, though her research impact is evident through high-impact publications and methodological contributions. She directs the active Rozovsky Research Group, mentoring graduate students and postdoctoral researchers in biochemical and biophysical techniques. Her laboratory operations are supported by competitive funding including an NSF CAREER award (2011) focused on selenoprotein reactivity, reflecting sustained recognition of her innovative research program.

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Alastair KeyView profile

Research Fellow

University of CambridgeUnited Kingdom

Dr. Alastair Key serves as Director of Studies in Archaeology and Official Fellow in Archaeology at Queens' College, University of Cambridge. His research bridges Paleolithic archaeology, stone tool technology, and hominin behavioral evolution through experimental and computational approaches. Director of Studies and Official Fellow at Queens' College, Cambridge Specializes in Paleolithic stone tool analysis, Acheulean technology, and hominin adaptation Conducts experimental archaeology and computational modelling to assess tool functionality Key's research focuses on Acheulean handaxe production , lithic microwear patterns , and ergonomic constraints in prehistoric tool use . He has extensively published on topics including glacial-stage hominin occupations , Oldowan toolmakers , and machine learning applications to archaeological analysis . His recent publications (2025-2023) span diverse subfields: Acheulean chronology , hominin tool use biomechanics , experimental projectile testing , and computational morphometric methods . The work often integrates multidisciplinary datasets and open-source analytical tools to address fundamental questions about human technological evolution. Current research directions include stone tool sharpness quantification , handaxe social signaling potential , and cross-species tool use comparisons through primate studies.

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Wojciech JaroszView profile

Associate Professor

Dartmouth CollegeUnited States

Wojciech Jarosz is an Associate Professor of Computer Science at Dartmouth College, affiliated with the College of Engineering and Computer Science. His research focuses on computer graphics, particularly light transport simulation, rendering algorithms, and digital fabrication. He co-founded the Visual Computing Lab and previously led the rendering group at Disney Research Zürich. Jarosz holds a Ph.D. and M.S. from UC San Diego and a B.S. from the University of Illinois Urbana-Champaign. His educational background includes studies in computer science and engineering, with a strong emphasis on graphics and rendering. Research interests span light transport simulation, Monte Carlo methods, appearance capture, and fabrication. Notable achievements include the Eurographics Young Researcher Award (2013) and the NSF CAREER Award (2019). Jarosz's work integrates theoretical rigor with practical applications, such as real-time rendering techniques and volumetric light transport. His lab develops tools for artistic authoring, including intuitive metaphors for volumetric lighting in animated films. Recent projects explore wave-optics BSDF models, optical heterodyne rendering, and unifying radiative transfer models. Key awards include the SIGGRAPH 2024 Best Paper Award and Neukom Institute prizes. His teaching includes courses on rendering algorithms, computer graphics, and computational photography. Jarosz collaborates with industry (e.g., Disney, NVIDIA) and advocates for diversity in computer graphics research.

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Richard A. DavisView profile

Professor

Columbia UniversityUnited States

Richard A. Davis is the Howard Levene Professor of Statistics at Columbia University's Faculty of Arts and Sciences. He is affiliated with the Data Science Institute (DSI) and the Financial and Business Analytics Center. His research focuses on applied probability, time series analysis, stochastic processes, and extreme value theory, with applications to financial data and spatial modeling. He co-founded the Space-Time Aquatic Resources Modeling and Analysis Program (STARMAP), supported by an EPA-STAR grant. Education details are not explicitly provided in the text, but his academic roles indicate advanced qualification in statistics. His work combines theoretical advancements with practical applications, such as analyzing financial time series models (e.g., GARCH) and spatial environmental data. Recent research emphasizes high-dimensional extremes, sparsity, and privacy-preserving methods. His articles explore cutting-edge topics like kernel PCA for multivariate extremes, quantile treatment effects, and goodness-of-fit testing for time series. He has also contributed to applications in healthcare imaging and disaster economics. His collaborative projects aim to bridge statistical theory with environmental and societal challenges. Key contributions include the STARMAP initiative and grants focused on extreme value analysis. His work often integrates advanced statistical techniques with real-world data challenges, reflecting a commitment to both methodological innovation and interdisciplinary impact.

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Stacy FarinaView profile

Associate Professor

Howard UniversityUnited States

Stacy Farina is an Associate Professor in the Department of Biology at Howard University, affiliated with the College of Arts & Sciences. Her research focuses on functional morphology, biomechanics, and evolutionary biology, particularly in fish systems. She holds a B.S. in Marine and Freshwater Biology from the University of New Hampshire (2010) and a Ph.D. in Evolutionary Biology from Cornell University (2015). Her work examines skeletal morphology, respiratory systems, and functional adaptations in diverse fish species, including flatfishes, snailfishes, and elasmobranchs. Recent studies investigate remote research methodologies during the pandemic, deep-sea adaptation, and biomechanical integration in feeding systems. She directs a research lab in Just Hall, Room 410, and maintains a Google Scholar profile. Publications span topics like ventilation mechanics, acoustic signaling evolution, and skeletal density patterns in deep-sea environments. Her research emphasizes interdisciplinary approaches, combining anatomical analysis, computational modeling, and experimental biomechanics. No scientific awards or grants are explicitly listed in the provided texts. She advises no listed students, though her research involves undergraduate participation. Her lab focuses on fish morphology, ecology, and evolutionary processes.

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LIN ZhenhuaView profile

Associate Professor

National University of SingaporeSingapore

Associate Professor LIN Zhenhua serves as a Presidential Young Professor in the Department of Statistics and Data Science at the National University of Singapore (NUS), with additional affiliation at the Institute of Data Science since 2021. His research develops cutting-edge statistical methodologies for complex data structures across multiple domains. Dr. LIN completed his Ph.D. at the University of Toronto in 2017 under Fang Yao's supervision, following M.Sc. degrees from Simon Fraser University (2013, 2010) and a B.Sc. from Fudan University (2008). Ph.D., University of Toronto, 2017 (Advisor: Fang Yao) M.Sc., Simon Fraser University, 2013, 2010 B.Sc., Fudan University, 2008 His research program spans functional data analysis (developing techniques for curves and surfaces), non-Euclidean data analysis (statistical methods on manifolds), high-dimensional statistics (p > n problems), and constrained statistical modeling. LIN's work bridges theoretical statistics with practical applications through rigorous mathematical frameworks and computational implementations. Recent publications reveal strong emphasis on bootstrap methods for high-dimensional inference, Riemannian geometry approaches for manifold-valued data, and innovative functional data techniques. His research shows consistent output with multiple 2025 publications in top journals including Biometrika, Bernoulli, and Journal of the American Statistical Association. Professional Recognition Presidential Young Professor, NUS (2019-present) Associate Editor, Bernoulli (2022-2024) Associate Editor, Statistics (2023-present) Young Researchers Committee, Bernoulli Society (2020-2024) Professor LIN actively mentors graduate students as evidenced by numerous collaborative publications with trainees. He teaches advanced courses including ST5215 Advanced Statistical Theory, DSA4211 High-dimensional Statistical Analysis, and ST5223 Statistical Models across multiple academic years. His research group develops specialized software packages including hdanova, matrix-manifold, synfd, mcfda, and iRFDA, making advanced statistical methods accessible to practitioners.

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Jeff S AbramsonView profile

Professor

University of California, Los AngelesUnited States

Jeff S Abramson is a Professor of Physiology in the David Geffen School of Medicine at the University of California Los Angeles (UCLA). His research focuses on the structural and functional characterization of membrane transport proteins, particularly sugar transporters and mitochondrial channels. He maintains an active laboratory investigating the molecular mechanisms of cellular transport processes. Dr. Abramson's primary research interests center on membrane transport proteins, with particular emphasis on sugar symporters and voltage-dependent anion channels (VDACs). His work combines structural biology, biophysics, and biochemistry to understand the molecular mechanisms of transport, including conformational changes during transport cycles, substrate recognition, and regulation by membrane potential. His research has significant implications for understanding metabolic disorders, mitochondrial function, and potential therapeutic targets. Analysis of Dr. Abramson's publication record reveals a consistent focus on membrane protein structure-function relationships over the past two decades. His work demonstrates expertise in X-ray crystallography, cryo-electron microscopy, and functional assays to characterize transport proteins. Recent publications show increasing emphasis on mitochondrial biology, particularly VDAC structure and function, while maintaining his longstanding interest in sugar transport mechanisms. His research bridges fundamental biophysical principles with potential biomedical applications in metabolic diseases. Dr. Abramson has been awarded multiple NIH grants supporting his research, including the R35GM135175 grant titled 'Deciphering molecular details of cellular sugar transport and their roles in disease' (2020-2024), R01GM124783 'Functional and structural studies of unique pathogenic transporters involved in glycobiology' (2017-2021), and R01GM078844 'Structural and functional characterization of sugar transporters in health and disease' (2006-2020). As Principal Investigator, Dr. Abramson has mentored numerous graduate students and postdoctoral researchers. His laboratory has made significant contributions to understanding the structure-function relationships of membrane transport proteins through collaborations with researchers across multiple disciplines. The lab utilizes advanced techniques including X-ray crystallography, cryo-EM, electrophysiology, and computational modeling to address fundamental questions about membrane protein mechanisms. Dr. Abramson's laboratory is part of UCLA's broader research ecosystem focused on structural biology and membrane protein research. His work intersects with several research centers at UCLA including those focused on metabolic diseases and structural biology. The lab maintains active collaborations with researchers specializing in biophysics, computational modeling, and disease mechanisms to translate basic findings into potential biomedical applications.

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Philip BooneView profile

Academic

Boston Children's HospitalUnited States

Philip Boone, MD, PhD, is an Attending Physician in the Division of Genetics and Genomics at Boston Children's Hospital and an Instructor of Pediatrics at Harvard Medical School. He specializes in medical genetics with particular expertise in rare disorders, medical mysteries, deletion and duplication syndromes, and Cornelia de Lange syndrome. Dr. Boone sees patients at Boston Children's Brookline location (2 Brookline Place, 7th Floor) and provides comprehensive genetic care including diagnostics, counseling, and individualized management. Stanford University (Undergraduate, 2006) Baylor College of Medicine (Graduate & Medical School, 2013-2014) Boston Combined Residency Program (Internship & Residency, 2016-2020) Harvard Medical School Genetics Training Program (Fellowship, 2020) Dr. Boone's research focuses on neurodevelopmental disorders, chromatin regulation, and genetic diagnostics. His work spans from fundamental genetic mechanisms to clinical applications, with particular emphasis on cohesinopathies including Cornelia de Lange syndrome. He has contributed significantly to understanding genetic variants associated with growth disorders, developmental features, and structural chromosomal abnormalities. His research combines advanced genomic technologies with clinical insights to improve diagnosis and management of rare genetic conditions. Analysis of Dr. Boone's publication record reveals a strong focus on medical genetics with emphasis on neurodevelopmental disorders, chromatin regulation, and genetic diagnostics. His work spans basic research on gene function and regulation to clinical applications in rare disease diagnosis. A notable trend is his investigation of cohesin complex disorders, particularly SMC3 variants and their relationship to Cornelia de Lange syndrome. His publications demonstrate expertise in both traditional genetic analysis and cutting-edge genomic technologies including long-read sequencing and telomere-to-telomere assembly. Dr. Boone actively contributes to medical education through publications on genetic diagnostics and distance learning resources for medical genetics. He has co-authored educational materials that help advance the field's knowledge base and training capabilities. As an attending physician in the Division of Genetics and Genomics at Boston Children's Hospital and a research fellow in the Center for Genomic Medicine at Massachusetts General Hospital, Dr. Boone works within one of the largest pediatric genetics practices in the country. The division includes over 30 board-certified clinical geneticists, genetic counselors, dieticians, and nursing staff who provide comprehensive care for patients with both common and extremely rare genetic conditions.

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Mehmet KoyutürkView profile

Professor

Case Western Reserve UniversityUnited States

Mehmet Koyutürk serves as the Andrew R. Jennings Professor in the Department of Computer and Data Sciences at Case Western Reserve University's Case School of Engineering, with additional affiliation as a Member of the Cancer Genomics and Epigenomics Program at the Case Comprehensive Cancer Center. His computational research bridges algorithm development with biological applications, focusing on network-structured data analysis to address complex biomedical challenges. Dr. Koyutürk earned his Ph.D. in Computer Science from Purdue University following B.S. and M.S. degrees in Electrical Engineering and Computer Engineering from Bilkent University. His primary research domains include high-throughput biological data analysis, systems/network biology methodologies, data mining algorithms, and scientific computing optimization, with particular emphasis on phosphorylation networks, genomic interactions, and multi-omics integration. Recent publication trends reveal expanding applications of his network science expertise into Alzheimer's disease phosphoproteomics, bipolar disorder biomarker discovery, and intimate partner violence analysis, while maintaining core contributions to graph neural networks and biological link prediction. His group actively develops open-source analytical tools like RokaiXplorer for phospho-proteomic data accessibility. Scientific Recognition Andrew R. Jennings Professorship Dr. Koyutürk leads multiple NIH-funded initiatives including R01-LM012980 for phosphoproteomics analysis, U01-CA198941 (BD2K program) for big network integration, and R01-LM011247 for GWAS enhancement, complemented by NSF CAREER Award CCF-0953195. He serves on the steering committee for CWRU's Systems Biology and Bioinformatics graduate programs and as Associate Editor for IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), with extensive collaboration through Mark Chance's Center for Proteomics and Bioinformatics. His laboratory specializes in developing scalable algorithms for biological network analysis, currently advancing projects on kinase-substrate association prediction, co-phosphorylation network characterization in cancer, and network-based approaches to intimate partner violence data mining, with strong emphasis on translating computational methods into biomedical insights through open-source software dissemination.

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Jana KesenheimerView profile

Researcher

University of InnsbruckAustria

Jana Kesenheimer, PhD is a researcher at the Department of Social Psychology , University of Innsbruck . Her work bridges social psychology and environmental psychology , focusing on the interplay between personality traits, prosocial behavior, and psychological well-being. She employs intensive longitudinal methods (diary studies, experience sampling) to investigate how environmental actions, remote work, and ultra-endurance sports affect individuals differently based on their psychological profiles. Research Highlights Connecting dark personality traits (e.g., narcissism) with pro-environmental behavior fatigue Analyzing fluid retention and cardiac strain in ultra-distance cyclists Studying remote work's impact on mental health through basic psychological needs Scientific Focus : Her publications reveal a trajectory in behavioral ecology , health psychology , and personality-driven motivation . Key subfields include exercise psychology , pandemic-induced value shifts , and moral self-regulation in ecological contexts.

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Benjamin EysenbachView profile

Professor

Carnegie Mellon UniversityQatar

Benjamin Eysenbach leads the Princeton Reinforcement Learning Lab, where he designs algorithms that enable artificial intelligence systems to learn intelligent behaviors through trial-and-error, specializing in self-supervised methods that eliminate the need for human labels. He joined Princeton after completing his PhD in machine learning at Carnegie Mellon University under Ruslan Salakhutdinov and Sergey Levine, supported by the NSF Graduate Research Fellowship and Hertz Fellowship. His research bridges fundamental machine learning principles with practical applications in robotics and decision-making systems. Eysenbach's research focuses on developing self-supervised reinforcement learning algorithms that enable autonomous skill acquisition without external rewards. His investigations span contrastive learning methods, temporal abstraction techniques, and scalable architectures for goal-conditioned behaviors. These innovations aim to create more efficient and generalizable learning systems that can discover useful behaviors from unlabeled experience. Eysenbach's publications demonstrate consistent advancement in self-supervised RL methodologies, with recent work focusing increasingly on temporal abstraction and representation learning theory. His research shows progression from foundational contrastive RL frameworks toward more sophisticated analyses of generalization properties and uncertainty quantification. The 2025 works indicate expanding investigation into hierarchical control, probabilistic alignment, and hyper-deep network architectures. Eysenbach has been recognized with prestigious awards including the Hertz Fellowship and NSF Graduate Research Fellowship, supporting his doctoral research in self-supervised RL methodologies. His work has been presented at top machine learning conferences including NeurIPS, ICML, and ICLR. As director of the Princeton Reinforcement Learning Lab, Eysenbach oversees research initiatives in self-supervised RL, including projects on intention-conditioned modeling, horizon generalization, and contrastive learning frameworks. He has secured funding from the Princeton AI Lab to study neural correlates of temporal contrast in decision-making. Eysenbach teaches courses in reinforcement learning and has developed new benchmarks like JaxGCRL to accelerate research in goal-conditioned RL.

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Keith D. KoperView profile

Professor

University of UtahUnited States

Keith D. Koper is a Professor in the Department of Geology & Geophysics at the University of Utah and serves as Director of the University of Utah Seismograph Stations (UUSS). He is also the editor-in-chief of The Seismic Record . His work integrates academic research with operational seismic monitoring and public safety initiatives across Utah and the Intermountain West. Education: PhD in Geophysics, Washington University, 1998 BA in Math, Geology, and ISP, Northwestern University, 1993 Dr. Koper's research focuses on array seismology, forensic seismology, deep Earth structure (especially the inner core), earthquake rupture imaging, ambient seismic noise, and seismic hazards in the Intermountain West, including mining-induced and urban earthquakes. His work combines observational seismology with advanced signal processing and machine learning techniques to improve detection, discrimination, and imaging capabilities. He has led or contributed to major projects involving the Wasatch Front, Yellowstone, and regional seismic networks. His recent research emphasizes machine learning for earthquake detection, high-resolution relocation of aftershock sequences (e.g., Magna 2020, Bluffdale 2019), microseism generation in lakes, and fine-scale imaging of the Earth's inner core using seismic reflections. His studies often involve interdisciplinary collaboration, particularly with mining engineering and geodesy. Dr. Koper's research has been consistently funded by federal and state agencies, including the National Science Foundation (NSF), U.S. Geological Survey (USGS), Department of Energy (DOE), Air Force Research Laboratory (AFRL), and the Utah Department of Public Safety. His publications reflect a strong trend toward integrating computational methods with traditional seismological analysis to tackle complex problems in both natural and induced seismicity. Scientific Service and Leadership: Editor-in-Chief, The Seismic Record Director, University of Utah Seismograph Stations Secretary, U.S. Air Force Seismic Review Panel Former Chair and Vice-Chair, Utah Seismic Safety Commission Dr. Koper mentors graduate students in seismology and geophysics, including recent advisees Sean Hutchings and Alysha Armstrong. His research group actively engages in both fundamental and applied seismological research, with strong ties to national labs such as Sandia. The group is involved in deploying portable seismic arrays, analyzing large datasets, and developing new algorithms for event detection and classification. The University of Utah Seismograph Stations, under his leadership, plays a critical role in monitoring seismicity in Utah and Yellowstone, producing real-time earthquake information, ShakeMaps, and public outreach materials. The station also contributes to national and international efforts in nuclear test monitoring and volcanic hazard assessment.

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David GreenView profile

Professor

University of British ColumbiaCanada

David Green is a Professor at the Vancouver School of Economics , University of British Columbia, and an International Fellow at the Institute for Fiscal Studies in London. He holds a Ph.D. from Stanford University and has served as Chair of the BC Basic Income Panel . Research Focus : Labour economics, income inequality, wage distribution, and public policy. His work bridges macro-level employment trends with micro-level identification of causal effects. Key Contributions : Analysis of minimum wage impacts on job stability, immigrant economic integration, resource boom spillovers, and basic income policy evaluation. Article Trends include: Welfare reform and health outcomes (2022) Covid-19 occupational risks (2022) Resource boom wage spillovers (2019) Post-2000 skill demand reversal (2016) Minimum wage and job tenure dynamics (2013) Scientific Recognition : Harry Johnson Prize (2000, 1995) for best articles in the Canadian Journal of Economics. International Fellow, Institute for Fiscal Studies (London). Policy Engagement : Directed the 2017-2019 BC Basic Income Panel , producing evidence-based reports on poverty reduction and workforce participation. His empirical work frequently informs national policy debates on taxation, immigration, and social assistance.

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