جست‌وجوی استاد و پژوهشگر

• Hydrology
• Climate Change Impact Assessment
• Drought Monitoring
• +۲ مورد دیگر

Ehsan Modiri is a researcher at the Department of Hydrosystem Modelling , Helmholtz Centre for Environmental Research (UFZ), Germany. His work focuses on climate change impacts on hydrological systems, drought monitoring, and environmental modeling using advanced computational frameworks. Affiliation: UFZ - Helmholtz Centre for Environmental Research Department: Hydrosystem Modelling Research Themes: Climate Change, Droughts, Hydrological Forecasting, Water Resource Management Research Interests: Modiri specializes in understanding hydrological responses to climate change, particularly in drought dynamics and soil moisture variability. His work bridges observational data with sophisticated modeling frameworks to improve predictability of water balance components under warming scenarios. Scientific Contributions: Recent publications highlight his role in developing high-resolution drought simulations, evaluating hydrological model performance, and analyzing groundwater responses to global warming. He participates in large-scale European hydrological projects and collaborates on climate-hydrology integration initiatives.

Jes Frellsenمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Generative AI
• Deep Learning
• +۵ مورد دیگر

Jes Frellsen is an Associate Professor at the Department of Applied Mathematics and Computer Science (DTU) since 2016. Previously, he held academic positions at the IT University of Copenhagen (2016-2019), postdoctoral roles at University of Cambridge (2013-2016) and University of Copenhagen (2011-2013). Education: PhD in Bioinformatics (2011), University of Copenhagen MSc in Bioinformatics (2007), University of Copenhagen BSc in Mathematics and Computer Science (2005), University of Copenhagen EAP Exchange at University of California, Santa Cruz (2004-2005) Research Focus Jes Frellsen specializes in statistical machine learning , particularly generative AI and deep generative models with applications in bioinformatics . His work integrates Bayesian inference , directional statistics , and Markov chain Monte Carlo methods to address challenges in macromolecular structure prediction and missing data imputation . Recent efforts explore uncertainty quantification in image segmentation and generative modeling for materials science. Advising & Collaborations He actively supervises PhD students and postdoctoral researchers in projects spanning news recommendation systems , medical imaging , and 3D structure generation . Collaborations include work with Zoubin Ghahramani (Cambridge) and Thomas Hamelryck (Copenhagen), with contributions to protein structure prediction and statistical methods in structural bioinformatics .

Oliver Johnsonمشاهده پروفایل

استاد

• Information Theory
• Probability
• Statistics
• +۳ مورد دیگر

Professor Oliver Johnson is a faculty member at the School of Mathematics, University of Bristol, UK, where he serves as Head of School and holds the Professor of Information Theory position. His research bridges information theory, probability, and statistics, focusing on entropy convergence, group testing, and fundamental limits in data analysis. Current PhD students: Kieran Morris, Conor Crilly Ex-PhD students: Matt Aldridge, Leonardo Baldassini, Dan Cowley, Vaia Kalokidou, Tom Kealy, Jennifer Chakravarty, Zichen Gui, Chrys Paschou Ex-postdoc: Erwan Hillion His work includes ORCiD profile and collaborations across information theory, cybersecurity, and ecological modeling.

Mike Rubensteinمشاهده پروفایل

استادیار

• Swarm Robotics
• Multi-Robot Systems
• Decentralized Control
• +۳ مورد دیگر

Mike Rubenstein is an Assistant Professor with joint appointments in the Department of Computer Science and Department of Mechanical Engineering at Northwestern University. He holds the Lisa Wissner-Slivka and Benjamin Slivka Professorship in Computer Science and is affiliated with the Center for Robotics and Biosystems. His educational background includes a Ph.D. in Computer Science from the University of Southern California, an M.S. in Electrical Engineering from USC, and a B.S. in Electrical Engineering from Purdue University. Prior to joining Northwestern, he completed a postdoctoral fellowship at Harvard University's Self-Organizing Systems Research Group. Rubenstein's research focuses on advancing multi-robot systems to enable capabilities beyond traditional single robots, emphasizing parallelism, adaptability, and fault tolerance at scale (hundreds to millions of robots). His work spans swarm shape control, modular self-reconfigurable robotics, bio-inspired satellite constellations, and novel sensing for air vehicle swarms. Key themes include algorithmic control for large-scale systems and hardware innovations to overcome current limitations in swarm robotics. His advising has produced notable student achievements, including Petras Swissler's Best Student Paper Award at DARS 2021 and Drew Curtis's NDSEG Fellowship. Research trends across his publications reveal a consistent emphasis on scalability, real-world applicability, and bridging hardware constraints with algorithmic innovation in swarm systems. Rubenstein actively mentors graduate students and leads projects involving swarm robotics platforms like FireAnt and PCBot. His lab focuses on developing systems where simplicity in individual robots enables emergent complexity at the swarm level, with applications ranging from space exploration to medical imaging.

Steven Y. Liangمشاهده پروفایل

استاد

• precision manufacturing
• additive manufacturing
• materials science
• +۳ مورد دیگر

Steven Y. Liang , Regents' Professor at the Georgia Institute of Technology 's Woodruff School of Mechanical Engineering, focuses on precision manufacturing , additive manufacturing , and materials-driven process optimization . His research program bridges materials science and computational mechanics to develop predictive models for advanced manufacturing systems. Ph.D., University of California, Berkeley (1987) M.S., Michigan State University (1984) B.S., National Cheng-Kung University, Taiwan (1980) Dr. Liang's work emphasizes physics-based modeling of thermal-mechanical interactions in machining and additive manufacturing, particularly for Ti6Al4V and Inconel 718 alloys. Recent publications highlight tool wear prediction , laser-assisted micro-milling , and residual stress modeling using machine learning and analytical mechanics. His research has been recognized with the ASME Milton C. Shaw Manufacturing Research Medal (2016) , SME Gold Medal (2021) , and Outstanding Lifetime Service Award of NAMRI/SME (2021) , among others. Funded by federal agencies and aerospace/automotive industries, his work provides scientific foundations for process planning and optimization.

Jennifer Shangمشاهده پروفایل

استاد

• Healthcare Analytics
• Operations Management
• E-Commerce
• +۴ مورد دیگر

Jennifer Shang is a Professor of Business Administration and Area Director for Business Analytics and Operations at the Katz Graduate School of Business , University of Pittsburgh. With a focus on healthcare analytics, operations management, and e-commerce, she applies data-driven methodologies to enhance patient care, hospital efficiency, and business productivity. Education: PhD in Operations Management, University of Texas at Austin MBA, University of Iowa Bachelor of International Business, National Taiwan University Her research integrates multi-criteria decision-making techniques (e.g., AHP/ANP, DEA) and combines human judgment with quantitative data to improve organizational outcomes. She has published over 140 papers, with recent trends emphasizing healthcare analytics, big data applications, and supply chain optimization in service industries. Scientific Awards: Distinguished Professor for EMBA class 32 (2004-2005) Excellence in Teaching Award for MBA program (2002-2004, 2009-2010) Excellence in Research Awards (2013-2015, 2017-2020, 2021-2022) Best Paper Award (2010) in Information Systems Research Professor Shang teaches courses in operations management, supply chain, statistical analysis, and multivariate data analysis at undergraduate, MBA, EMBA, DBA, and PhD levels. She serves on editorial boards for journals including International Journal of Revenue Management and International Journal of Productivity and Quality Management .

Mihran Tuceryanمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Pattern Recognition
• +۳ مورد دیگر

Professor Mihran Tuceryan is a Professor of Computer Science at Purdue University Indianapolis, affiliated with the Department of Computer Science within the College of Science. He holds a PhD from the University of Illinois at Urbana-Champaign (1986) and a BS from MIT (1978). His expertise spans Computer Vision, Image Processing, Pattern Recognition, and Augmented Reality. Recent research focuses on crime prediction via video analysis, forensic imaging, and distributed tracking systems. He is a Senior Member of IEEE and ACM. Key research interests include augmented reality integration for industrial training, real-time illumination modeling, and monocular SLAM algorithms. His work addresses challenges in photorealistic AR, dynamic object labeling, and medical imaging applications such as hepatic fibrosis detection. He has contributed to projects like the e-DOTS indoor tracking system and forensic 3D impression acquisition. His publications span over three decades, emphasizing real-world applications in security, healthcare, and robotics. Education: Bachelor of Science in Computer Science and Engineering, MIT, 1978 PhD in Computer Science, University of Illinois at Urbana-Champaign, 1986 Awards: Senior Member, IEEE Senior Member, ACM Labs/Teams: Focus on AR, SLAM, and medical imaging applications Collaborative frameworks for distributed visual SLAM

David Garlanمشاهده پروفایل

استاد

• Software Engineering
• Software Architecture
• Self-Adaptive Systems
• +۴ مورد دیگر

David Garlan is a Professor at the Software and Societal Systems Department within the School of Computer Science at Carnegie Mellon University , where he also serves as Associate Dean for Master’s Programs . He received his Ph.D. from Carnegie Mellon in 1987 after working in industry as a software architect. His research focuses on controlling complexity in large software systems through formalized architectural design, self-adaptive systems, and cyber-physical systems. He developed AcmeStudio , a widely used architecture design environment, and pioneered formal representation and analysis of software architecture. Education : Ph.D. in Computer Science (Carnegie Mellon, 1987) Research Interests include: Software Architecture: Formal methods for architectural design, end-user composition, and architectural styles Self-Adaptive Systems: Stochastic planning, model checking, security adaptation, and uncertainty reduction Cyber-Physical Systems: Multi-view design methods, consistency checking, and automotive systems Recent Article Trends address microservice resiliency, hybrid planning (combining formal methods and ML), simulation-augmented robotics, and sustainable machine translation. Themes include stochastic modeling , probabilistic verification , and adaptive decision-making . Scientific Awards : Stevens Award Citation (2005) ACM SIGSOFT Outstanding Research Award (2011) Allen Newell Award for Research Excellence (2016) IEEE TCSE Distinguished Education Award (2017) Nancy Mead Award (2017) Fellow of IEEE and ACM Advising and Grants : He has advised 25+ graduate students and collaborated on projects with Toyota and the Software Engineering Institute. His work includes model-based adaptation, automated planning, and formal verification of adaptive systems. Labs & Teams : Affiliated with the Institute for Software Research and works on tools like AcmeStudio, Rainbow, and IPL for architectural modeling and self-adaptation.

• Smart Cities
• Geovisual Analysis
• Urban Dynamics
• +۵ مورد دیگر

Dr. Gavin McArdle is an Associate Professor at the University College Dublin (UCD) School of Computer Science, specializing in spatial data analysis and smart cities. He holds academic affiliations with the National Centre for Geocomputation (Maynooth University) and CeADAR (Data Analytics Centre). His research focuses on urban dynamics, geovisual analytics, smart transportation, and remote sensing applications. He has received a College of Science Teaching Excellence Award for his contributions to education. McArdle earned his BSc, PhD, and a Prof Dip in University Teaching & Learning from UCD. His work bridges academia and industry through collaborative grants, including those from Science Foundation Ireland and EU funding. Notable projects include the Dublin Dashboard (urban analytics platform) and DubSim (traffic simulation using digital footprints). His research outputs span over 147 publications, with recent work addressing Airbnb's impact on urban gentrification, sustainable mobility, and environmental monitoring via satellite data. He actively contributes to professional committees, including roles in the UCD Data Protection Impact Assessment Committee and international conferences like Web and Wireless GIS. McArdle coordinates courses such as Research Practicum and Computer Programming II, emphasizing practical research and technical skills. His interdisciplinary approach integrates machine learning, spatial statistics, and urban informatics to address real-world challenges in smart cities and environmental sustainability.

Joshua Marshallمشاهده پروفایل

استاد

• Field Robotics
• Autonomous Vehicles
• Control Systems Engineering
• +۷ مورد دیگر

Joshua Marshall is a Professor of Electrical & Computer Engineering at Queen’s University, Canada, and Director of the Offroad Robotics research group. He holds a PhD from the University of Toronto and has cross-appointments in Mechanical & Materials Engineering and the Robert M. Buchan Department of Mining. His expertise spans field robotics, autonomous systems, control engineering, and harsh-environment applications in mining, space, and marine domains. He led the Ingenuity Labs Research Institute (2018–2024) and served as a Visiting Professor at Örebro University (2016–17). Dr. Marshall’s work focuses on autonomous vehicle navigation, robotic excavation, and spatiotemporal mapping. He has received the 2025 OPEA Engineering Medal and has commercialized technologies through partnerships with companies like Epiroc and RockMass Technologies. Education: PhD, Electrical & Computer Engineering, University of Toronto (2005) MSc(Eng), Mechanical Engineering, Queen’s University (2001) BSc (Hons), Engineering, (details not specified) Research Interests: Autonomous robotics in mining, space, and marine environments Data-driven control systems and model predictive control Proprioceptive sensing and terrain classification Multi-robot coordination and task planning Underground navigation and SLAM Professional Activities: Senior Member, IEEE Editorial roles: International Journal of Robotics Research , IEEE Transactions on Mechatronics Co-founded the NSERC Canadian Robotics Network (NCRN) Contributions to the IEEE Medal for Environmental & Safety Technologies Committee Labs/Teams: Offroad Robotics Group (Queen’s University) Ingenuity Labs Research Institute (founding Director) Advisor to Queen’s AutoDrive Challenge II Team and aQuatonomous ASV Design Club

Lynn Kistlerمشاهده پروفایل

استاد

• Plasma Physics
• Space Weather
• Magnetospheric Dynamics
• +۳ مورد دیگر

Lynn Kistler is a Professor in the Department of Physics & Astronomy at the University of New Hampshire (UNH), part of the College of Engineering and Physical Sciences. Her research focuses on plasma physics, space weather, and magnetospheric dynamics, particularly investigating the interactions between the solar wind and Earth's magnetosphere-ionosphere system. She holds a Ph.D. in Physics from the University of Maryland, along with a B.S. from Harvey Mudd College. Dr. Kistler's work emphasizes understanding plasma processes such as ion outflow from the ionosphere, magnetic reconnection, and storm-time magnetospheric evolution. She has led studies using data from missions like the Van Allen Probes, Solar Orbiter, and Cluster, contributing to advancements in instrumentation (e.g., the SWA suite) and computational modeling. Her research bridges observational analysis, theoretical frameworks, and machine learning to address challenges in space weather prediction and plasma dynamics. Key areas of her research include the role of ionospheric ions (O⁺, H⁺) in plasma sheet dynamics, the effects of geomagnetic storms on ring current formation, and the behavior of heavy ions in near-Earth space. She has authored or co-authored over 260 publications, spanning journals like Nature Communications , Geophysical Research Letters , and Journal of Geophysical Research . Dr. Kistler has secured grants and collaborations through initiatives like the NASA Interstellar Mapping and Acceleration Probe (IMAP) and has served as a co-investigator on multiple missions. Her work emphasizes interdisciplinary approaches, combining spacecraft observations with ground-based data and numerical simulations to unravel the complexities of Earth's space environment.

Chong Liuمشاهده پروفایل

استادیار

• Machine Learning
• Bayesian Optimization
• Bandit Algorithms
• +۳ مورد دیگر

Dr. Chong Liu is an Assistant Professor of Computer Science at the State University of New York at Albany (SUNY Albany) in the College of Nanotechnology, Science, and Engineering. He received his PhD in Computer Science from UC Santa Barbara in 2023 and completed a postdoctoral fellowship at the University of Chicago's Data Science Institute (2023-2024). His research focuses on Machine Learning and AI for Science, particularly Bayesian optimization, bandit algorithms, generative models, and AI applications in drug discovery. He has received the SUNY IITG/OER Impact Grant and serves as Associate Editor for IEEE-TNNLS, Area Chair for ICML/AISTATS, and editorial board reviewer for JMLR. PhD: UC Santa Barbara (2023), advised by Yu-Xiang Wang Postdoc: University of Chicago Data Science Institute (2023) Research Interests : Broad: Machine Learning, Optimization, AI for Science Specific: Bayesian optimization, Bandit algorithms, Active learning, Experimental design, Generative models, AI for drug discovery Applications: Binding affinity prediction, Drug screening, Policy optimization Recent Article Trends : His 2024-2025 publications focus on extending Bayesian optimization theory under practical constraints, quantum-accelerated bandit methods, and multi-objective optimization for drug discovery. Earlier works include private learning frameworks and human-in-the-loop systems. Scientific Awards : 2025: SUNY IITG/OER Impact Grant Professional Activities : Organized NeurIPS workshops on AI for Drug Discovery (2023, 2025), co-organizing INFORMS sessions, and serving on program committees for ICML, NeurIPS, ICLR, and AAAI. He has given invited talks at institutions including University of Chicago, UC Santa Barbara, and Genentech. Teaching : Teaching courses like Numerical Methods (CSI 401) and Machine Learning (CSI 436/536) with syllabi spanning 2024-2025 semesters.

David Mouldمشاهده پروفایل

استاد

• Computer Graphics
• Computer Games
• Non-Photorealistic Rendering
• +۴ مورد دیگر

David Mould is a Professor in the School of Computer Science at Carleton University. His research focuses on computer graphics, procedural modeling of natural phenomena, non-photorealistic rendering, and computer games. He holds a PhD from the University of Toronto (2002), MSc from the University of Saskatchewan (1996), and BSc from the University of British Columbia (1994). PhD: University of Toronto (2002) MSc: University of Saskatchewan (1996) BSc: University of British Columbia (1994) Research interests include procedural modeling of trees, lightning, and terrain; image stylization techniques such as stained glass transformation and wax crayon simulation; and nonlinear storytelling in games. His work emphasizes algorithmic innovation and perceptual quality in graphics. Recent publications span topics like texture synthesis, real-time video stylization, and fluid animation techniques. He leads the Graphics, Imaging, and Games (GIGL) research group at Carleton. Teaching responsibilities include courses in game development (COMP 1501–4501), technical writing (COMP 3301), and graduate courses on game design and image processing.

Lucy Bastinمشاهده پروفایل

استاد

• Biodiversity Informatics
• Remote Sensing
• GIS (Geographic Information Systems)
• +۷ مورد دیگر

Lucy Bastin is a Professor in the School of Computer Science and Digital Technologies at Aston University, part of the College of Engineering and Physical Sciences. She holds academic roles since 2003, including leadership in the Digital Observatory for Protected Areas (DOPA) project at the European Commission. Her research focuses on biodiversity informatics, remote sensing, and citizen science, with applications in conservation planning and sustainable development. She advises PhD students on topics like GIS, remote sensing, and citizen observatories. Education: BSc Zoology (University of Nottingham), MSc GIS (University of Leicester), PhD in Spatial Population Ecology (University of Birmingham). She also holds a Postgraduate Certificate in Teaching and Learning in Higher Education. Research Interests: Essential Biodiversity Variables, metadata standards for citizen science, uncertainty in conservation models, disease mapping (e.g., MRSA), and environmental policy support. She developed the DOPA toolkit for protected area monitoring and co-authored the Bari Manifesto for biodiversity variables. Key Projects: DOPA Explorer 2.0, FLIERS EU project, EuroGEOSS initiatives Recent Awards: Midlands Women in Tech Finalist (2021), Best Paper Award (2018) Teaching: Software Engineering, Professional Ethics in Computing, GIS modules Labs/Teams: Part of the Computer Science Research Group and Aston Centre for Artificial Intelligence Research and Application. Collaborates with global partners on initiatives like BIOPAMA and the Green Deal Data Space.

Shiva Jahangiriمشاهده پروفایل

استادیار

• Big Data Management Systems
• Databases for AI/ML
• Knowledge Graphs
• +۶ مورد دیگر

Shiva Jahangiri is an Assistant Professor in the Department of Computer Science and Engineering at Santa Clara University's School of Engineering. His research focuses on Big Data Management Systems, Databases for AI/ML, and Query Optimization. He leads the DBIS Lab, which explores database internals, vectorized data processing, and open-source projects like Apache AsterixDB. Education: Ph.D. in Computer Science from the University of California, Irvine; M.S. in Computer Science (Data Science) from the University of Southern California. Current courses taught include Advanced Programming, Advanced Database Systems, and Introduction to Database Systems. He advises Ph.D. and Master’s students on topics like Vector Databases, Query Scheduling, and Resource Management. Recent research trends involve optimizing group-by/aggregation operators, schema inference for semi-structured data, and memory management in complex join queries. His work bridges theoretical advancements with practical implementations in open-source systems. DBIS Lab activities include student participation in senior design projects, directed research, and volunteer roles. The lab emphasizes industry collaboration for hands-on experience in database systems development.