جست‌وجوی استاد و پژوهشگر

David Condonمشاهده پروفایل

استادیار

• Social-Personality Psychology
• Personality and Individual Differences
• Data Science
• +۷ مورد دیگر

David Condon is a Courtesy Assistant Professor in the Department of Psychology at the University of Oregon , affiliated with the College of Arts and Sciences . His research focuses on the structure and measurement of psychological individual differences , including temperament, personality, cognitive abilities, interests, values, and motivation. Specializes in large-scale datasets via the SAPA-Project , collecting data from over 250,000 participants annually. Develops measurement tools for clinical settings and advocates for open science practices , including open data, reproducible methods, and freely accessible publications. Current research explores empirically-informed prediction tools and taxonomic models linking individual differences across the lifespan. His scholarly work spans personality science , creativity research , and natural language processing , with publications analyzing personality structures at multiple levels of abstraction. While no longer accepting new graduate students, he continues to lead the PIE Lab , fostering interdisciplinary collaboration.

Richard Durbinمشاهده پروفایل

استاد

• Evolutionary Genomics
• Computational Biology
• Human Genetics
• +۳ مورد دیگر

Professor Richard Durbin (FRS) is a computational biologist at the Department of Genetics , University of Cambridge, and Associate Faculty member at the Wellcome Trust Sanger Institute . His work spans computational methods development, large-scale genomics projects, and evolutionary studies. Academic Affiliation: Professor of Genetics (University of Cambridge) Research Institute: Associate Faculty (Wellcome Sanger Institute) Key Projects: 1000 Genomes Project, UK10K Project, Gorilla Genome Sequencing Research Interests Durbin's group focuses on: Evolutionary Genomics: Human population history through modern and ancient DNA, Malawi cichlid fish speciation with adaptive introgression Computational Methods: Burrows-Wheeler transform algorithms (BWA), variant call format (VCF), variation graph mapping (vg package) Genome Assembly: Long-read sequencing techniques for high-contiguity reference genomes across vertebrates Scientific Contributions Co-author of Biological Sequence Analysis (HMM methods for gene finding) Co-developer of ACeDB software and founding contributor to WormBase, Pfam, TreeFam, Ensembl Scientific Awards Fellow of the Royal Society (FRS) - Recognized for outstanding contributions to computational biology

Luo Maiمشاهده پروفایل

استادیار

• Computer Systems
• Machine Learning Systems
• Data Management
• +۳ مورد دیگر

Luo Mai is an Assistant Professor at the University of Edinburgh's School of Informatics , with an upcoming promotion to Associate Professor (UK Reader) in August 2025. He leads the Large-Scale Machine Learning Systems Group and co-leads the UK EPSRC Centre for Doctoral Training in Machine Learning Systems and an ARIA Project on Scaling AI Compute by 1000X . PhD in Computer Science (Imperial College London, 2018) MRes in Advanced Computing (Imperial College London, 2012) His research focuses on the intersection of computer systems , machine learning , and data management . Key contributions include award-winning systems like WaferLLM (wafer-scale LLM inference), Tenplex (elastic ML), and ServerlessLLM (serverless LLM serving), published at top venues (OSDI, SOSP, ICML, NeurIPS, JMLR). Recent publications demonstrate trends in GPU-based distributed systems , LLM optimization , and adaptive machine learning . His team has developed groundbreaking open-source projects including TensorLayer , TorchOpt , and ServerlessLLM . Awarded Microsoft Research StarTrack Scholar (2024) , secured ARIA grant (2024) with Imperial College & Cambridge University, and received Google Fellowship during PhD (2012-2016). As an educator, he designed Edinburgh's popular Machine Learning Systems course (150+ students). His group supervises multiple PhD students including Yao Fu (recognized as 2024 Rising Star in ML & Systems) and Leyang Xue .

Benjamin Domingueمشاهده پروفایل

دانشیار

• Psychometrics
• Quantitative Methods
• Assessment and Testing
• +۵ مورد دیگر

Ben Domingue is an Associate Professor at Stanford University's Graduate School of Education and, by courtesy, in the Department of Sociology. His research bridges psychometrics, quantitative methods, and interdisciplinary applications in education, psychology, and social sciences. He leads the development of the Item Response Warehouse, a data resource for psychometrics research, and explores how statistical tools can better measure complex educational and psychological outcomes like reading ability and treatment effects. PhD in Education from the University of Colorado at Boulder (2012). MA and BS in Mathematics from the University of Texas at Austin (2006, 2001). His work focuses on advancing psychometric methodologies, including response time analysis, item-level treatment effects, and predictive accuracy metrics (e.g., InterModel Vigorish). He investigates how genetic and environmental factors interact with educational outcomes and social mobility, using large-scale datasets like the Health and Retirement Study and Add Health. Recent articles emphasize AI-driven psychometric tools, cross-cultural validation of medical assessments, and equity in educational testing. 2024–2025: Associate Professor, Stanford GSE. 2015–2022: Assistant Professor, Stanford GSE. Affiliated: Stanford Center for Longevity, Bio-X, Population Health Sciences. Scientific awards include the Jacobs Foundation Research Fellowship (2022–2024) and AERA Open Outstanding Reviewer (2018, 2019). His advising roles span doctoral and master’s students, with a focus on psychometrics and social-genomic research.

Matias E. Margulisمشاهده پروفایل

دانشیار

• Global Governance of Food Security
• Agricultural Trade Liberalization
• Large-Scale Land Acquisitions
• +۴ مورد دیگر

Matias E. Margulis is an Associate Professor at the University of British Columbia, jointly appointed between the Faculty of Land and Food Systems and the School of Public Policy and Global Affairs. His research explores the intersection of global governance, food security, and international trade, with a focus on regime complexity and power dynamics in multilateral institutions. Ph.D. in International Relations, McMaster University (2012) M.A. in Public Policy and Politics, University of Toronto (2003) B.A. (Honours) in Economics and Political Science, University of Toronto (2000) Postdoctoral Fellow, Max Planck Institute (2012-2013) His academic work investigates how food insecurity stems from human-made social, economic, and political systems, analyzing the role of international organizations like the WTO, UN, and EU in shaping trade and food policy. Recent publications examine regime entanglements, human rights in trade negotiations, and climate crisis impacts on food governance. Matias has received multiple accolades, including the Distinguished Academic Award (Early in Career, 2014) and the Cadieux-Léger Fellowship (2010-2011). He previously held academic positions at the University of Edinburgh, University of Stirling, and Max Planck Institute, and served as a Canadian representative to the WTO, OECD, and FAO. His expertise extends to advising the UN Special Rapporteur on the Right to Food and consulting for NGOs and the Brookings Institution.

Michael J. Cafarellaمشاهده پروفایل

دانشیار

• Databases
• Information Extraction
• Data Integration
• +۶ مورد دیگر

Michael J. Cafarella is an Associate Professor in the Computer Science and Engineering department at the University of Michigan . His research focuses on databases, information extraction, data integration, and data mining, with applications in economics, social media analysis, and combating human trafficking. He leads the Software Systems Lab and Michigan Database Group . Scientific Awards NSF CAREER award Sloan Research Fellowship (2016) 2018 VLDB Ten-Year Best Paper award Research Impact : Cafarella co-founded the Hadoop open-source project and Lattice Data (acquired by Apple). His work on DeepDive and DARPA MEMEX was featured on 60 Minutes and in Scientific American . Funding from The Census Bureau, DARPA, Google, NSF, Yahoo!, General Electric, and Dow.

Carolyn Conner Seepersadمشاهده پروفایل

استاد

• Design for Additive Manufacturing
• Engineering Design
• Additive Manufacturing
• +۴ مورد دیگر

Carolyn Conner Seepersad serves as the J. Mike Walker Professor of Mechanical Engineering at the University of Texas at Austin and directs the Center for Additive Manufacturing and Design Innovation. She holds membership in the U.T. System Academy of Distinguished Teachers and maintains active leadership in the additive manufacturing community through roles such as co-organizer of the Solid Freeform Fabrication Symposium and ASME Design Engineering Division Executive Committee membership. Her academic credentials include: PhD in Mechanical Engineering from Georgia Tech (2004) MA/BA in Philosophy, Politics and Economics from Oxford University (1998, Rhodes Scholar) BS in Mechanical Engineering from West Virginia University (1996) Dr. Seepersad's research centers on computational design methodologies and additive manufacturing innovation , with particular expertise in simulation-based design of complex systems, environmentally conscious product development, and materials engineering. Her work bridges theoretical design frameworks with practical manufacturing applications, emphasizing sustainability and performance optimization across aerospace, automotive, and energy systems. Current projects explore reactive extrusion additive manufacturing, negative stiffness materials, and machine learning integration for process-aware design. Analysis of her 15 most recent publications reveals a dominant focus on process innovation in additive manufacturing (70%), particularly stereolithography and selective laser sintering, with growing emphasis on data-driven design approaches (20%) and sustainable engineering applications (10%). Her work demonstrates consistent progression from fundamental material design toward integrated system optimization and industrial scalability. Her scientific recognition includes: International Outstanding Young Researcher Award in Freeform and Additive Manufacturing (2009) UT System Regents’ Teaching Award (2010) ASME Design Automation Committee Outstanding Young Investigator Award (2010) ASEE Outstanding New Mechanical Engineering Educator Award (2013) Multiple ASME and ASEE best paper awards U.T. System Academy of Distinguished Teachers membership Dr. Seepersad maintains an extensive advising portfolio with 48 graduate students (16 PhD, 24 MS, and 8 current) plus 2 postdoctoral researchers, reflecting sustained research productivity and educational impact. Her Product, Process, and Materials Design Lab fosters interdisciplinary collaboration between mechanical engineering, materials science, and computational design teams.

Tiffany A. Radcliffمشاهده پروفایل

استاد

• Health Economics
• Health Policy
• Health Services Research
• +۵ مورد دیگر

Tiffany A. Radcliff, PhD , is Professor of Health Policy & Management in the Texas A&M University School of Public Health and concurrently serves as the Senior Associate Dean for Research . In this dual role she guides the school’s strategic research agenda while maintaining an active, federally funded research portfolio. Education PhD, Health Services Research, Policy & Administration – University of Minnesota BS (Magna Cum Laude), Economics; Minor, Political Science – Texas A&M University Research Interests Radcliff’s work lies at the intersection of health economics , health policy , and health-services research . Her overarching goal is to improve the quality, accessibility, and affordability of care—particularly for rural and underserved populations . Current emphases include: Economic evaluation of behavioral interventions for obesity and diabetes in rural settings Post-acute care transitions and long-term care quality Industrial organization of healthcare markets and provider responses to policy Disaster preparedness and health-system resilience Publication Trends Across 200+ peer-reviewed articles spanning 1997–2025, Radcliff’s scholarship shows a clear evolution from foundational work on rural hospital markets to large-scale comparative-effectiveness and implementation-science studies. Recent clusters (2020–2025) focus on COVID-19 policy impacts, telehealth cost-effectiveness, and VA transitional-care programs, reflecting a sustained commitment to policy-relevant, methodologically rigorous research. Scientific Awards & Honors (No specific awards listed in the provided text; extensive federal funding and invited lectureships implied by CV and publication record.) Grants & Advising Principal or Co-Investigator on multiple NIH, AHRQ, and VA Health Services Research & Development grants Faculty mentor for PhD, MPH, and post-doctoral trainees in health policy, economics, and implementation science Serves on dissertation and thesis committees for students across Texas A&M and collaborating institutions Labs & Teams Radcliff leads an active research group of graduate research assistants, post-docs, and biostatisticians housed within the Center for Health Organization Transformation (CHOT) and collaborates closely with the VA Health Services Research Center of Innovation and the Southwest Rural Health Research Center at Texas A&M.

Le-Nam Tranمشاهده پروفایل

پژوهشگر

• Wireless Communications
• 5G/6G Network Optimization
• Signal Processing
• +۴ مورد دیگر

Dr. Le-Nam Tran is a researcher at the UCD School of Electrical & Electronic Engineering , University College Dublin. His work focuses on optimizing the last hop of 5G/6G wireless networks through mathematical programming, with emphasis on energy efficiency, interference management, and security against eavesdropping. Develops low-cost, low-complexity transmission techniques Projects supported by Science Foundation Ireland Career Development Award Author of over 80 peer-reviewed publications Research Keywords: Wireless Communications Network Security Signal Processing Energy-Efficient Systems Beamforming Optimization Interference Mitigation

Kenneth M. Andersonمشاهده پروفایل

استاد

• Software Engineering
• Crisis Informatics
• Human-Computer Interaction
• +۳ مورد دیگر

Professor Kenneth M. Anderson is Chair of the Department of Computer Science and holds the Palmer Endowed Chair at the University of Colorado Boulder's College of Engineering & Applied Science. He co-directs Project EPIC, a $4M NSF-funded initiative on social media use during mass emergencies, and serves as Co-Director of the Center for Software and Society. Ph.D. in Computer Science, University of California, Irvine (1997) Joined CU Boulder in 1998; tenured in 2005 Former Associate Dean for Education (2016-2019) Former Associate Chair (2010-2013) His research bridges software engineering, crisis informatics, and human-computer interaction. Current projects focus on large-scale social media analytics, crisis response systems, and software architecture for data-intensive environments. Recent publications explore multilingual social media analysis (ML-EPIC), bug fix patterns in software development (FIXR), and collaborative big data platforms. Themes include disaster risk communication, data modeling challenges, and asynchronous analysis tools. ATLAS Fellow (2010) As department chair, he spearheaded initiatives for inclusivity, including creating a Bachelor of Arts in Computer Science and establishing the NSF Broadening Participation in Computing plan. He oversees major academic reforms and departmental operations.

Florian Börgelمشاهده پروفایل

پژوهشگر

• Climate Science
• Oceanography
• Computational Modeling
• +۳ مورد دیگر

Dr. Florian Börgel is a Senior Research Scientist and leader of the Junior Research Group Large-Scale Circulation and North European Climate Variability at the Leibniz Institute for Baltic Sea Research (IOW) in Warnemünde. He holds a Dr. rer. nat. in Physical Oceanography (2020) from IOW, an M.Sc. in Engineering Physics from the University of Oldenburg (2017), and a B.Eng. from the University of Applied Sciences Münster (2014). His research focuses on large-scale ocean-atmosphere interactions, regional climate dynamics in Northern Europe and the Baltic Sea, and the application of machine learning to improve climate models and river runoff forecasting. Key areas include Atlantic Multidecadal Variability (AMV), teleconnections between the North Atlantic and regional climates, and the impacts of climate drivers on marine environments. Notable achievements include the Outstanding Early Career Scientist Award (EGU 2023) and membership in expert groups such as the UN’s World Ocean Assessment III and the CMIP7 Data Analysis Working Group. He co-leads the Baltic Earth Teleconnections Working Group, focusing on North Atlantic-Baltic Sea linkages. Awards: EGU Early Career Award (2023), Fulbright Scholarship (2015-16) Grants/Positions: IOW appointee to Deutsches Klima-Konsortium (DKK), Parental Leave (2019-2020) Labs/Teams: Leads the Large-Scale Circulation group; contributes to the Baltic Sea Model Intercomparison Project (BMIP) and Validator tool development

Guoquan Huangمشاهده پروفایل

استادیار

• Robotics
• Computer Vision
• Robot Learning
• +۵ مورد دیگر

Guoquan Huang is an Assistant Professor in the Department of Mechanical Engineering at the University of Delaware. He holds a B.Eng. in Automation from the University of Science and Technology, Beijing (2002), and M.Sc. and Ph.D. degrees in Robotics from the University of Minnesota (2009 and 2012). Prior to his current role, he was a Postdoctoral Associate at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on robotics, computer vision, and autonomous systems, emphasizing probabilistic perception, estimation, and control for ground, aerial, and underwater vehicles. He leads the development of the OpenVINS platform for visual-inertial estimation and has contributed to advancements in SLAM (Simultaneous Localization and Mapping), sensor fusion, and multi-robot coordination. Education: B.Eng in Automation (Electrical Engineering), University of Science and Technology, Beijing, 2002 M.Sc. in Robotics, University of Minnesota, Twin Cities, 2009 Ph.D. in Robotics, University of Minnesota, Twin Cities, 2012 His research interests span robotics, computer vision, and autonomous systems , with a focus on: Visual-inertial navigation and SLAM Sensor fusion (LiDAR, IMU, camera) Autonomous vehicle control and safety Multi-robot cooperative localization His recent publications (2023–2025) emphasize robust algorithms for navigation in GPS-denied environments, real-time sensor calibration, and dataset development for aerial visual localization. He has pioneered techniques like decoupled error-state estimation and consistent parallel frameworks for SLAM. Labs/Teams: Leads the development of the OpenVINS research platform, focusing on visual-inertial state estimation. Collaborates on projects involving human-swarm interactions and resilient ground vehicle navigation.

• Software Testing
• Regression Testing
• Flaky Tests
• +۳ مورد دیگر

Darko Marinov is a Professor at the Siebel School of Computing and Data Science and a member of the Information Trust Institute at the University of Illinois at Urbana-Champaign. His research focuses on software testing methodologies, particularly regression testing, flaky tests, and software reliability engineering. He has contributed to frameworks like Ekstazi for regression test selection and DeFlaker for identifying flaky tests. Marinov holds an NSF CAREER Award (2008) for his work in this domain. His research interests span testing techniques for modern software systems, including configuration testing, test prioritization, and fault localization. He has pioneered studies on the characteristics of flaky tests in large-scale projects and developed tools to improve software quality assurance processes. Marinov collaborates across academic and industrial settings, addressing challenges in continuous integration, reproducibility of computational experiments, and hardware-software co-design for resilience. His work bridges theory and practice, with applications in cloud computing, AI workloads, and cybersecurity. Awards: NSF CAREER Award (2008) Key Contributions: Ekstazi, DeFlaker, FastFlip, and Ctest4J frameworks Labs/Teams: Active member of the Information Trust Institute and leads software testing research groups at UIUC

Robert J Brunnerمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Statistical Analysis
• +۳ مورد دیگر

Robert J. Brunner is a Professor at the University of Illinois with primary appointments in the Gies College of Business (Department of Accountancy) and the School of Information Sciences. He holds affiliate roles across multiple departments including Astronomy, Computer Science, and Statistics, as well as research centers like the Beckman Institute and NCSA. His research focuses on applying statistical/machine learning to solve complex problems in astronomy, finance, and large-scale data science. Education: Ph.D. in Astrophysics from Johns Hopkins University (advisor: Alex Szalay). Postdoctoral work at Caltech on the Digital Sky project. Research Interests: Machine learning applications, computational techniques, data management/visualization, and observational cosmology. His work bridges astrophysical data analysis with modern data science methodologies. Recent work includes developing spatio-temporal neural networks for forecasting, evaluating AI-driven financial analysis tools, and planning for the Vera C. Rubin Observatory. He collaborates internationally on large-scale surveys like the Dark Energy Survey and SDSS. Labs/Teams: Leads data science initiatives at the University of Illinois Research Park. Active in interdisciplinary teams at NCSA and Beckman Institute focusing on algorithm optimization and data-intensive research.

Amritanshu Pandeyمشاهده پروفایل

استادیار

• Grid Simulation and Optimization
• Grid Cybersecurity
• Modeling of Future Energy Systems
• +۳ مورد دیگر

Amritanshu Pandey is an Assistant Professor in Electrical Engineering at the University of Vermont, with a part-time adjunct appointment in Electrical and Computer Engineering at Carnegie Mellon University. His research focuses on enhancing the efficiency, reliability, and security of electric grids through methods in circuit theory, optimization, and machine learning. He pioneered the SUGAR simulation engine for power systems and collaborates globally on grid challenges. Research Interests: Pandey's work spans renewable integration, grid cybersecurity, digital twins, and decarbonization. Key projects include developing algorithms for large-scale grid optimization, anomaly detection, electric vehicle infrastructure modeling, and cyber-resilient energy systems. His research addresses real-world challenges in rapidly evolving grids across Asia and Africa. Awards: Best Paper Award, IEEE PES General Meeting (2017, 2021) Best-of-the-Best Paper Award, IEEE PES General Meeting (2021) Best Student Paper Runner-up, ECML-PKDD (2018) Students & Labs: He advises 7 PhD students and has graduated 11 advisees (PhD/MS/BS). His lab focuses on power systems innovation, including the SUGAR simulation framework and projects on grid cybersecurity and sustainable electrification.