جست‌وجوی استاد و پژوهشگر

Kevin F. Kellyمشاهده پروفایل

دانشیار

• Optics and Photonics
• Nanotechnology
• Imaging and Spectroscopy
• +۷ مورد دیگر

Kevin F. Kelly is an Associate Professor in the Department of Electrical and Computer Engineering at Rice University. He was formerly the Chair of the Applied Physics Program and is affiliated with the Smalley-Curl Institute. Additionally, he has been a member of the Penn State Center for Nanoscale Science and the Mid-Infrared Technologies for Health and the Environment (MIRTHE) Center at Princeton University. Dr. Kelly co-founded Inview Technology Corporation as its Chief Scientist, focusing on commercializing compressive imaging technologies. He has also consulted for the Baker Institute for Public Policy regarding photovoltaics and taught courses in anthropology and history at Rice. Dr. Kelly holds a B.S. in Engineering Physics from the Colorado School of Mines (1993), followed by an M.S. (1996) and Ph.D. (1999) in Applied Physics from Rice University. His postdoctoral work included fellowships at the Institute for Materials Research in Sendai, Japan, and the Chemistry Department at Penn State University. His research interests span Optics and Photonics, Imaging and Spectroscopy at the nanoscale, and the role of mathematics in image acquisition. He develops Scanning Probe Microscopy techniques and studies Electronic Materials such as graphene and topological insulators. A major focus is Compressive Hyperspectral Imaging systems, including single-pixel camera innovations and advanced microscopy methods. He also pioneers molecular machines like the Nanocar and investigates charge transport in polymer photovoltaics. Over recent years, his work emphasizes interdisciplinary applications, such as integrating compressive sensing with neural networks for machine vision and exploring technological disaster analysis through history courses. His contributions have been recognized with awards like the IEEE Fellow (2022) and Technology Review’s Top 10 Emerging Technologies (2007). In addition to academic roles, Dr. Kelly has co-founded Inview Technology and contributed to grants and collaborations through his involvement in the MIRTHE Center and other institutes. While no formal advisees are listed, his teaching includes courses on nanotechnology since 2009 and he actively engages in policy consultations for photovoltaic commercialization. His lab at Rice and collaborations with the Smalley-Curl Institute drive advancements in nanotechnology and imaging, with a particular emphasis on practical applications of compressive sensing and molecular-scale devices.

Steven O. Kimbroughمشاهده پروفایل

استاد

• Artificial Intelligence
• Computational Rationality
• Decision Support Systems
• +۶ مورد دیگر

Steven O. Kimbrough is a Professor of Operations, Information and Decisions at the Wharton School, University of Pennsylvania. His research spans artificial intelligence, computational rationality, and strategic optimization with applications to political science, economics, and service innovation. He teaches courses like Agents, Games, and Evolution and Thinking With Models , focusing on experimental approaches to bounded rationality and uncertainty in decision-making. Primary Email: kimbrough@wharton.upenn.edu Office: 3730 Walnut Street, 565 Jon M. Huntsman Hall, Philadelphia, PA 19104 His research interests include: Artificial intelligence and metaheuristics for constrained optimization Evolutionary computation in electoral redistricting Agent-based modeling of market dynamics Logic modeling for normative reasoning Text mining applications in event analysis Publications demonstrate expertise in computational economics, political modeling, and service analytics. Recent work focuses on: Empirical validation of electoral compactness Strategic learning in oligopolies Multi-objective matching algorithms Feasible-infeasible solution spaces Service network optimization Teaching emphasizes: Game-theoretic approaches to strategic behavior Modeling life-cycle for energy sustainability Computational experiments in social science

Mine Doganمشاهده پروفایل

استادیار

• Environmental Geophysics
• Groundwater Modeling
• Stochastic Hydrology
• +۳ مورد دیگر

Dr. Mine Dogan serves as Assistant Professor of Environmental Geophysics in the Department of Geological and Environmental Sciences at Western Michigan University, with her office located in 1121 Rood Hall (Kalamazoo, MI). She holds a Ph.D. from Michigan State University (2013) and previously held research positions at Clemson University's Department of Environmental Engineering and Earth Sciences. Education: Ph.D., Michigan State University, 2013 Her research integrates geophysics, hydrology, and environmental engineering to investigate subsurface processes using advanced methodologies including drone-based electromagnetic surveys, time-lapse monitoring, and 4D X-ray computed tomography. Key focus areas include tree root hydrology, contaminant transport in groundwater, permafrost characterization, and macropore flow dynamics in heterogeneous soils. Analysis of her recent publications (2018-2024) reveals strong emphasis on unmanned aerial systems for geophysical data acquisition, visualization of fluid transport mechanisms in porous media, and forensic/environmental applications of electromagnetic methods. Recurring themes include the role of biological structures in hydrological processes and innovative approaches to subsurface imaging. Scientific Awards: No awards documented in source material While her advising activities and grant funding remain unspecified in available information, her publication record demonstrates active collaboration across geophysics, hydrology, and environmental engineering disciplines. Laboratory facilities and research team structures are not detailed in the provided texts.

Romain Raveauxمشاهده پروفایل

دانشیار

• Image Analysis
• Machine Learning
• Structural Pattern Recognition
• +۵ مورد دیگر

Romain Raveaux is an Associate Professor at the LIFAT Computer Science Laboratory, University of Tours, affiliated with Polytech Tours. His research focuses on Image Analysis, Machine Learning, Structural Pattern Recognition, Graph Matching, Graph Neural Networks, Discrete Optimization, Reinforcement Learning, and Transfer Learning . Email: romain.raveaux@gmail.com , romain.raveaux@laposte.net Address: 64 av. Jean Portalis, Tours, France, 37200 Phone: +33 (0)2 47 36 14 27 Research Interests Graph Matching and Neural Networks Discrete Optimization for Pattern Recognition Transfer Learning in Graph-Based Models Historical Document Analysis Scientific Trends His recent work bridges Graph Neural Networks with Mixed-Integer Programming , focusing on Image Semantic Segmentation and Graph Cycle Detection . Earlier studies emphasize Genetic Algorithms for graph classification and Graph Edit Distance optimization in pattern recognition.

• Robotics Perception
• Alternative Sensing
• Inertial Fusion
• +۶ مورد دیگر

Teresa Vidal-Calleja is an Associate Professor and Research Director at the Robotics Institute, University of Technology Sydney (UTS). She holds a Deputy Head of School (Research) role in the School of Mechanical and Mechatronic Engineering. Her research focuses on robotics perception, alternative sensing, inertial fusion, SLAM, and applications in manufacturing, agriculture, mining, and construction. Education: B.Sc. Mechanical Engineering (UNAM, 2000), M.Sc. Electrical Engineering (CINVESTAV-IPN, 2002), Ph.D. Automatic Control (Polytechnic University of Catalonia, 2007). Postdoctoral Experience: LAAS-CNRS (France), Australian Centre for Field Robotics (Sydney). Research Interests: Teresa’s work spans robotics perception, sensor fusion, autonomous navigation, and collaborative robotics. She leads projects on next-gen infrastructure robotics (ARC Discovery Grant) and is co-lead of the Australian Cobot Centre. Her innovations target hazardous environments, infrastructure maintenance, and human-robot collaboration. Funded Research: Includes grants for robotic sensors, satellite docking systems, and multi-robot collaboration. Recent projects involve Navantia, the NSW Space Research Network, and industry partnerships. Professional Roles: IEEE Senior Member, Treasurer of the Australian Robotics and Automation Association, and Associate Editor for IEEE Transactions on Robotics and other journals. She chairs conferences like the Australasian Conference on Robotics and Automation. Labs/Teams: UTS Robotics Institute, collaborating with global institutions like ETH Zürich and DLR. Her work integrates robotics with industries such as agriculture and construction, emphasizing sustainability and safety.

• Ecohydrology
• Water Quality Management
• Environmental Impact Assessment
• +۶ مورد دیگر

Dr. Pouyan Nejadhashemi is an MSU Research Foundation Professor in the Departments of Plant, Soil and Microbial Sciences and Biosystems & Agricultural Engineering at Michigan State University. He serves as Director of the MSU Institute of Water Research and the Center for Intelligent Water Resources Engineering. His expertise spans ecohydrology, climate change adaptation, environmental impact assessment, and decision support systems for ecosystem sustainability. He holds a Ph.D. in Biological Resources Engineering from the University of Maryland. Dr. Nejadhashemi's research focuses on water quality modeling, non-point source pollution prevention, and integrating machine learning with hydrological systems. Key areas include irrigation optimization, PFAS contamination monitoring, and watershed management. He teaches courses on water resources systems analysis and ecohydrology. Awards: MSU Research Foundation Professor Title Grants: FAA grant for PFAS remediation, grants for developing agricultural innovations in Senegal and Bangladesh Labs/Teams: MSU Institute of Water Research, Center for Intelligent Water Resources Engineering His work bridges computational methods with environmental challenges, addressing global water security, food systems, and sustainable resource management. Recent projects include modeling water quality in the Chesapeake Bay and developing AI tools for agricultural extension platforms.

Brian Fisherمشاهده پروفایل

استاد

• Cognitive Science
• Visual Analytics
• Human-Computer Interaction
• +۲ مورد دیگر

Brian Fisher is a Professor at Simon Fraser University's School of Interactive Arts and Technology, where he leads the Interaction Science Research Group. He holds affiliate positions at the University of British Columbia and serves as Associate Director for the Media And Graphics Interdisciplinary Centre (MAGIC). His research investigates human reasoning in responsive environments through cognitive science approaches, developing visualization systems that support individual and distributed cognition. Fisher's work spans personalized health, aircraft safety analysis, space object classification, and emergency management. His teaching integrates perceptual, cognitive, and social science knowledge into design education. He actively participates in science policy through organizations including NSERC, Defence R&D Canada, EUREKA Eurostars, and IEEE committees. Fisher has been recognized as a Fellow of the Psychonomics Society and chaired major conferences including IEEE VIS 2019. His research is characterized by strong interdisciplinary collaboration and real-world applications in high-stakes decision domains.

Nikos Giatrakosمشاهده پروفایل

استادیار

• Big Data Management
• Internet of Things (IoT)
• Distributed Systems
• +۳ مورد دیگر

Nikos Giatrakos is an Assistant Professor at the School of Electronic & Computer Engineering, Technical University of Crete, and a core member of the Software Technology and Network Applications Lab (SoftNet) . His work bridges Big Data systems, IoT, and advanced analytics, with a focus on real-time processing and scalable architectures. Previously, he served as a postdoctoral researcher at the same laboratory. Education PhD in Computer Science, University of Piraeus (2012) Postgraduate Diploma in Information Systems, Athens University of Economics and Business (2008) BSc in Computer Science, University of Piraeus (2006) Research Focus : Nikos specializes in software architectures for Big Data streaming, including Distributed Big Data Processing , Federated Machine Learning , Cloud-to-Edge Data Management , and Approximate Query Processing . His work has also advanced Complex Event Processing and Outlier Detection in decentralized environments. Scientific Contributions : His research has led to the DAG* workflow optimizer for IoT, the SuBiTO framework for real-time neural learning, and the INFORE approach for cross-platform analytics. He received the Best System Demonstration Award at ACM CIKM 2020 for INforE. Academic Leadership : Nikos teaches Object-Oriented Programming, Data Science, and Distributed Systems. He has supervised numerous European and national grants as Principal Investigator and served on program committees for top-tier conferences like SIGMOD, VLDB, and DEBS.

Jonathan E. Martinمشاهده پروفایل

استاد

• Synoptic Meteorology
• Atmospheric Dynamics
• Jet Stream Variability
• +۳ مورد دیگر

Jonathan E. Martin is a Professor in the Department of Atmospheric and Oceanic Sciences at the University of Wisconsin–Madison since 1994. He received his Ph.D. in Atmospheric Sciences from the University of Washington in 1992. Research Focus: Mid-latitude cyclones, jet stream dynamics, and weather-climate interactions Key Methods: Multi-scale observational analysis, high-resolution mesoscale modeling, potential vorticity diagnostics Recent Projects: Jet stream waviness trends, subtropical-midlatitude cyclone transformation, snow cover-cyclone feedback His 2025-2024 publications examine extratropical cyclone precipitation distribution, Pacific basin rapid cyclogenesis, and Earth system model validation for cyclone occlusion. Earlier work (2023-2019) addresses: Jet superposition climatology Self-organizing map analysis of jet variability Cyclone-induced Southern Hemisphere waviness Nonlinear jet retraction mechanisms Tornado outbreak jet influences Historical cold pool contraction trends Awarded the 2021 Atmospheric Sciences Librarians International Choice Runner Up for his historical meteorology work, he also authored the textbook Mid-Latitude Atmospheric Dynamics: A First Course (2013). His research spans cyclogenesis, frontogenesis, and climate change impacts on jet streams, with applications in extreme weather forecasting and climate modeling validation.

Grant Van Hornمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Robotics
• +۲ مورد دیگر

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.

Mitra Baratchiمشاهده پروفایل

دانشیار

• Spatio-Temporal Data Analysis
• Machine Learning
• Mobility Data Modeling
• +۵ مورد دیگر

Mitra Baratchi is an Associate Professor at the Leiden Institute of Advanced Computer Science (LIACS) , Leiden University. She leads the Spatio-temporal data Analysis and Reasoning (STAR) research group, co-leads the Automated Design of Algorithms (ADA) group, and founded the Special Interest Group on Spatio-Temporal Data Mining (SIG-SDTM) . PhD from University of Twente (Mobility Data) Master’s/Bachelor’s in Computer Engineering, Iran Research Interests focus on automated pattern extraction from spatio-temporal data across urban, environmental, and industrial domains. Key applications include: Automated Machine Learning (AutoML) for Earth Observations Time-Series Forecasting for public health (e.g., pandemic modeling) Urban Mobility Optimization with ESA, Honda, and municipalities Reliable Vehicular Communication Systems Smart Garments for Health Risk Detection Geocast Protocols for Internet-wide Communication Grant Highlights include €120K NWO-Aspasia, €2.9M Marie Skłodowska-Curie, €350K NWO-KLEIN, and €135K Center for BOLD Cities funding. She has supervised 12 PhD students and 4 current Master’s students since 2011, with notable best paper award at WWIC'16. Teaching includes Machine Learning (2020-present) and Urban Computing (2018-present) at Leiden, plus past courses in Data Visualization, Software Engineering, and Research Methods.

• Computational Sociology
• Social Movements
• Gender and Sexuality Studies
• +۵ مورد دیگر

Laura Nelson is an Associate Professor in the Department of Sociology at the University of British Columbia and Director of the Centre for Computational Social Science. She specializes in computational sociology, social movements, gender studies, and intersectionality, employing text analysis, machine learning, and network methods to study organizational inequality, STEM equity, and historical activism. Education: PhD (2014) - University of California, Berkeley MA (2009) - University of California, Berkeley BA (2006) - University of Wisconsin-Madison (Phi Beta Kappa) Her research spans feminist movements, environmental activism, and computational methods, with a focus on combining quantitative analysis with qualitative interpretation. Current projects include studying gender-equity idea diffusion in higher education networks (funded by an NSF grant), analyzing social movement media coverage, and developing frameworks for computational grounded theory. Key publication trends reveal interdisciplinary work across gender studies , computational social science , historical sociology , and organizational equity . Articles frequently utilize machine learning , text mining , and network analysis to examine intersectionality , activism cycles , and institutional change . Scientific Awards: Best Meta-Reviewer, 12th International Conference on Social Informatics (2020) Outstanding Faculty of the Year at Northeastern University (2020) As a methodological innovator, she has advised graduate students like Jinyang Yu and developed courses on Python/R for social scientists, computational text analysis, and sociological theory. Her work appears in journals such as American Sociological Review , Signs , and Social Science Quarterly , with replication repositories available on GitHub and Zenodo.

• Database Systems
• Data Mining
• Social Network Analysis
• +۳ مورد دیگر

Eric Leclercq is a researcher at the University of Burgundy, affiliated with the LE2I Lab in Dijon, France. His work spans database systems, social network analysis, and biomedical data integration. He has contributed extensively to polystore systems, tensor decompositions, and category theory applications in data modeling. Fields of Interest : Database Systems, Data Mining, Social Network Analysis, Big Data Analytics, Semantic Web Leclercq's recent research focuses on formal frameworks for data lakes using category theory, multi-level tensor decomposition for social network stratification, and schema migration in multi-model systems. He has published in venues like CAiSE, IDEAS, and RCIS. His collaborations include Annabelle Gillet, Marinette Savonnet, and Nadine Cullot. Notable works include Lambda+ architecture for data processing, polarization analysis in social networks, and tools for tweet collection and biomedical data integration.

Sunil K. Prabhakarمشاهده پروفایل

استاد

• Database Systems
• Data Management
• Uncertain Data
• +۴ مورد دیگر

Sunil K. Prabhakar is a Professor of Computer Science at Purdue University, currently serving as the Vice Provost for Faculty Affairs and former Department Head. He holds a PhD from the University of California, Santa Barbara, and a BTech from the Indian Institute of Technology, Delhi. Education: PhD, Computer Science, University of California, Santa Barbara, 1998 MS, Computer Science, University of California, Santa Barbara, 1998 BTech, Electrical Engineering, Indian Institute of Technology Delhi, 1990 Research Interests: Focuses on database systems, uncertain data management, cloud database integrity, sensor databases, and digital rights management. His work emphasizes developing systems for probabilistic data, ensuring authenticity in outsourced databases, and scalable solutions for multimedia and sensor applications. Grants & Projects: NSF Grant IIS-1017990: Ensuring Integrity of Outsourced Databases NSF Grant IIS-09168724: Uncertain Data Management Students & Advising: Advised over 15 PhD and MS students, including Ryan Rossi (Palo Alto Research Center), Rohit Jain (Google), and Reynold Cheng (University of Hong Kong). Labs & Teams: Involved in projects like the ORION uncertain data management system, the C4E4 environmental cyberinfrastructure initiative, and collaborations with industry (e.g., Naval Surface Warfare Center).

Shixun Huangمشاهده پروفایل

مدرس

• Data Mining
• Machine Learning
• Algorithm Design
• +۳ مورد دیگر

Dr. Shixun Huang is a Lecturer in the School of Computing and Information Technology at the University of Wollongong, Australia. He holds a PhD from RMIT University and specializes in data mining, machine learning, and optimization algorithms for high-dimensional data problems. His research develops efficient algorithms for data discovery, similarity search, and network analysis. Current projects focus on optimized data acquisition strategies for machine learning, cost-effective labeling for graph neural networks, and cardinality estimation in high-dimensional databases. Methodologically, he combines combinatorial optimization with machine learning techniques. Dr. Huang supervises graduate research on diffusion models for medical imaging, graph prompt learning, and image captioning systems. His honors include multiple best paper awards at top database conferences and recognition for teaching excellence (College Top Course Award at RMIT). Recent publications address dataset distinctiveness maximization (WWW 2025), high-dimensional similarity search (VLDB 2025), and edge computing optimization (2024). Earlier foundational work established new approaches for influence maximization in social networks and temporal graph representation learning.