جست‌وجوی استاد و پژوهشگر

Pascal Van Hentenryckمشاهده پروفایل

استاد مدعو

• Optimization
• Artificial Intelligence
• Power Systems
• +۴ مورد دیگر

Pascal Van Hentenryck is an Adjunct Professor at the School of Computational Science and Engineering, Georgia Institute of Technology. His research focuses on integrating optimization techniques with artificial intelligence, particularly in power systems, transportation networks, and constraint programming. He leads projects on trustworthy optimization learning, grid reliability, and AI-driven solutions for public transit systems like MARTA REACH in Atlanta. Key areas include optimal power flow, machine learning proxies for large-scale optimization, and stochastic decision-making in renewable energy systems. Van Hentenryck's work spans academic and industrial collaborations, including the ARPA-E Grid Optimization Competition and the design of on-demand multimodal transit systems. He emphasizes practical applications of optimization algorithms in real-world scenarios such as supply chain management, rider behavior modeling, and fair policy design. His contributions to constraint programming education and MOOC development highlight his commitment to advancing computational methods in academia. Recent projects include developing frameworks for privacy-preserving convex optimization and analyzing the impact of equity considerations on adversarial vulnerability in AI systems. He explores topics like neural network verification, battery storage integration in grids, and scalable solutions for large-scale scheduling problems using column generation and reinforcement learning.

• Inverse Problems
• Dynamic X-ray Tomography
• Regularization Methods
• +۳ مورد دیگر

Tommi Heikkilä is a Visiting Professor in the Department of Mathematics and Statistics at the University of Helsinki. His research focuses on inverse problems, dynamic X-ray tomography, and spatio-temporal regularization methods. He is a core member of the Centre of Excellence in Inverse Modelling and Imaging (2018–2025), contributing to advancements in optimization algorithms and imaging techniques. Key projects include developing the STEMPO dynamic X-ray tomography phantom and advancing regularization methods with optimal space-time priors. His work bridges applied mathematics and computational imaging, with applications in medical and engineering domains. He has contributed datasets for dynamic tomography experiments, including gel phantom studies and cone-beam imaging. His doctoral thesis (2024) explores spatio-temporal regularization in dynamic tomography, reflecting his expertise in both theoretical and applied aspects of inverse problems. Collaborations span international teams in optimization, machine learning, and biomedical imaging.

• Hydrology
• Environmental Science
• Civil Engineering
• +۳ مورد دیگر

Dr. Sevvandi Kandanaarachchi is an Associate Professor in the School of Science at RMIT University's City Campus, Australia. Her research focuses on environmental data analytics, hydrology, and civil engineering applications. Key areas include water quality monitoring, sediment transport modeling, and algorithm development for environmental systems. Her work integrates machine learning with environmental science, addressing challenges in early event detection, anomaly identification, and predictive modeling for water resources. Notable contributions include frameworks for high-frequency sensor data analysis and meta-modeling approaches for stream turbidity prediction. Dr. Kandanaarachchi collaborates on interdisciplinary projects involving civil engineering, atmospheric sciences, and computational methods. She holds a PhD and has published extensively in peer-reviewed journals such as Hydrological Processes and PLOS One . No specific grants, awards, or student advisees are listed in the provided information.

Monica Wachowiczمشاهده پروفایل

استاد

• Data Science
• Geospatial Science
• Machine Learning
• +۵ مورد دیگر

Professor Monica Wachowicz holds the rank of Professor of Data Science at RMIT University, within the School of Science. Her academic roles include Associate Dean of Geospatial Science, Research Leader of Cyber and Data Analytics at the Space Industry Hub, and Deputy Director of the Sir Lawrence Wackett Defence & Aerospace Centre. She leads interdisciplinary research in streaming analytics, IoT integration, and smart city technologies. Wachowicz has pioneered innovations in edge computing and geospatial intelligence, collaborating with global firms like Cisco, Siemens, and IBM. She is a founding member of the IEEE Technical Sub-Committee on Big Data and serves on nine editorial boards. Her research focuses on leveraging machine learning for IoT data streams to address sustainability challenges in smart cities, including mobility analytics and energy systems. Over 52 students under her supervision have contributed to industries like tech, finance, and academia. Wachowicz is the first woman engineer in Geomatics awarded an NSERC Industrial Research Chair in Canada and holds an Honorary Visiting Scholar title at Flinders University. Her work emphasizes cross-sector collaboration, advancing green economies and data-driven decision-making. Awards include NSERC Industrial Research Chair (2010s) and Honorary Visiting Scholar (Flinders University, ongoing). Key projects include EVStationSIM (EV charging infrastructure analysis), Smart Campus Integration Lab, and IoT-GIS platforms for real-time analytics. She supervises projects on AI for urban sensing, smart mobility, and space situational awareness.

• Machine Learning
• Climate Modeling
• Optimization Algorithms
• +۷ مورد دیگر

Jorge Pérez Aracil is an academic researcher at the Department of Signal Theory and Communications at the University of Alcalá. His work focuses on applying artificial intelligence and optimization algorithms to solve complex problems in climate science, renewable energy systems, structural engineering, and extreme event prediction. He leads the GHEODE research group, specializing in modern heuristic optimization techniques for network design and environmental modeling. His research interests span multiple disciplines including machine learning for weather prediction, metaheuristic algorithms for engineering design, vibration isolation systems, and the integration of AI with numerical models for climate analysis. Notable contributions include hybrid deep learning frameworks for energy forecasting, spatio-temporal analysis of droughts, and AI-driven models for understanding heatwaves and their socio-economic impacts. Recent work emphasizes extreme event modeling through projects like Spain on fire (wildfire risk assessment via satellite data) and Autoencoder-based flow-analogue methods for reconstructing heatwaves. He also explores pedagogical innovations, such as hybrid flipped/project-based learning models for AI-era education. Key technical contributions include the CRO-SL optimization algorithm, used in applications ranging from smart grid management to structural design optimization. His publications often bridge theory and practice, with a focus on explainability in AI systems and real-world implementation challenges.

Kevin Leahyمشاهده پروفایل

استادیار

• Autonomous Systems
• Formal Methods
• Multi-Agent Systems
• +۳ مورد دیگر

Kevin Leahy is an Assistant Professor in the Robotics Engineering Department at Worcester Polytechnic Institute (WPI) and Director of the Automata Lab. He holds a PhD (2017) and MS (2016) in Mechanical Engineering from Boston University, along with a BA in Economics (2009). Prior to WPI, he served as a Technical Staff member at MIT Lincoln Laboratory (2017–2023), focusing on multi-agent systems, collision avoidance, and formal methods. He currently chairs the IEEE Technical Committee for Verification of Autonomous Systems. His research integrates formal methods, AI, and control theory to address challenges in autonomous systems. Key areas include decentralized control for multi-agent teams, safe reinforcement learning, and privacy-preserving control systems. His work emphasizes practical applications in robotics, such as vision-based localization and persistent surveillance for UAVs. Leahy has led projects on scalable coordination algorithms (ScRATCHeS) and developed tools for temporal logic-based motion planning. His lab, Automata, explores the intersection of formal verification, machine learning, and robotics. He has published extensively in top journals like International Journal of Robotics Research and conferences such as ICRA and CDC. Before academia, he worked in health economics consulting, analyzing treatment cost-effectiveness. He maintains active roles in the robotics community, including editorial work for Nonlinear Analysis: Hybrid Systems and service on the IEEE Verification committee.

Oluyomi Simpsonمشاهده پروفایل

مدرس

• 5G/6G Wireless Communication Systems
• Physical Layer Security
• Machine Learning in Communications
• +۶ مورد دیگر

Dr. Oluyomi Simpson is a Principal Lecturer and academic lead of the Communications Lab at the University of Hertfordshire's School of Physics, Engineering & Computer Science. He specializes in wireless communication systems, with a focus on 5G/6G, physical layer security, and machine learning integration. His research also encompasses cognitive radio networks, RFID technologies, and RF energy harvesting. Simpson has over 50 publications and serves as an Associate Editor for IEEE Communications Letters. Education: PhD in Communications and Electronics Engineering from the University of Hertfordshire (2011). Research Interests: His work spans interdisciplinary applications in wireless communication systems, including next-generation multiple access, near-field sensing, and STARS (Simultaneously Transmitting and Reflecting Surfaces). He emphasizes practical industry applications in telecommunications, manufacturing, and healthcare. Projects: Advanced Telematics and RFID Systems for Asset Monitoring in Emergency Medical Services (2025–2027) RFID System for Sample Management (2021–Present) Asset Monitoring via Coaxial Cable Strain Sensing (2025) Grants & Funding: Supported by ERDF, Innovate UK, and the Niger Delta Development Commission (NDDC). Labs & Teams: Leads the Wireless and Mobile Communication Lab, focusing on 6G research and collaborative projects with industry partners.

Philip Dixonمشاهده پروفایل

استاد

• Ecological Statistics
• Environmental Statistics
• Biostatistics
• +۳ مورد دیگر

Philip Dixon is a University Professor of Statistics at Iowa State University's Department of Statistics. His research focuses on developing statistical methods for ecological and environmental challenges, including spatial data analysis, equivalence testing, and computer-intensive techniques like bootstrapping. Collaborative projects include tracking monarch butterflies and analyzing climate impacts on crop yields. He teaches advanced statistical courses and consults on experimental design, environmental data analysis, and statistical computing. Dixon is actively involved in software development for R and SAS, emphasizing reproducible research practices. His work spans ecological, agricultural, and public health applications, with a strong emphasis on methodological innovation and practical solutions.

Sankar Arumugamمشاهده پروفایل

استاد

• Hydroclimatology
• Water Management
• Climate Modeling
• +۷ مورد دیگر

Sankar Arumugam is a Professor and University Faculty Scholar in the Department of Civil, Construction and Environmental Engineering at North Carolina State University (NC State), where he leads the Climate, Hydrology and Water Resources Modeling and Synthesis Group. His research focuses on integrating geospatial data and probabilistic climate information to improve decision-making in water and energy sectors, with expertise in hydroclimatology, spatio-temporal modeling, and large-scale nexus issues like food-water-energy systems. Education: Ph.D. in Water Resources Engineering (Tufts University), M.Sc. in Civil Engineering (IIT Madras), B.E. in Agricultural Engineering (Tamil Nadu Agricultural University). Research interests include: Multi-timescale streamflow forecasting Climate elasticity and hydrological modeling Remote sensing applications for flood prediction Reservoir operations optimization Urban heat island effects Publications highlight advancements in probabilistic hydrological forecasting, climate-water-energy nexus analysis, and reservoir storage optimization. He has pioneered frameworks like GRAPS and COREGS for multi-reservoir systems and seasonal water-power system co-optimization. His work bridges academia and practice through collaborations with institutions like the International Research Institute for Climate and Society (Columbia University) and the World Bank.

Vaclav Petrasمشاهده پروفایل

پژوهشگر

• Geospatial Software Engineering
• Open Source GIS Development
• Environmental Modeling
• +۳ مورد دیگر

Vaclav Petras is a Senior Geospatial Software Engineer and Researcher at North Carolina State University. He holds a Master's in Geoinformatics and a Ph.D. in Geospatial Analytics. His primary role involves advancing spatio-temporal models and tools for geospatial research through open-source development, particularly as a core member of the GRASS GIS project since 2012, where he also serves on the Project Steering Committee. His research focuses on improving geospatial software usability, open science practices, and participatory modeling. Key areas include integrating Jupyter Notebooks with GRASS GIS for education, redesigning user interfaces collaboratively, and developing cloud-based platforms for geospatial analysis. He actively contributes to projects like Tangible Landscape and has published widely on topics ranging from lidar data analysis to invasive species forecasting. Professional contributions span over 100 peer-reviewed articles, with recent work emphasizing cloud-based geospatial tools, risk-based inspection simulations, and ecological modeling. Petras is a strong advocate for open-source software, demonstrated through leadership roles in GRASS GIS and contributions to the Geoforall Lab at NCSU.

Peter Ojiamboمشاهده پروفایل

استاد

• Botanical Epidemiology
• Integrated Disease Management
• Plant Pathogen Ecology
• +۳ مورد دیگر

Peter Ojiambo is a Professor and Director of International Programs in the Department of Entomology and Plant Pathology at North Carolina State University. His research focuses on epidemiology and integrated management of plant diseases, with an emphasis on spatio-temporal modeling for disease prediction and risk assessment. He holds a B.S. and M.S. from the University of Nairobi and a Ph.D. in Plant Pathology from the University of Georgia (2004). Education: B.S. Agriculture, University of Nairobi (1994) M.S. Plant Pathology, University of Nairobi (1997) Ph.D. Plant Pathology, University of Georgia (2004) Research Interests: His work bridges botanical epidemiology and population genetics, with projects on: Spatio-temporal models for disease dynamics Decision support systems for disease management Pathogen dispersal mechanisms and landscape-level risk Integration of ecological and genetic data for improved disease control Key Publications (2024-2021): Focus on disinfestant efficacy meta-analyses, cucurbit downy mildew epidemiology, and R-based statistical tools for plant science research. Themes include fungal/non-fungal pathogen control, spatial risk modeling, and transcriptomic studies of pathogen development. Honors and Awards: American Phytopathological Society Hewitt Award (2012) Advising & Grants: Current advisees include Vinicius Garnica (PhD) and Aleksander Tako (PhD). Active grants total over $4M, addressing: Wheat/barley disease resistance phenotyping Decision support tools for fungicide optimization Genetic bases of quantitative disease resistance in maize Ecological spillovers in pathogen spread Solar cold storage solutions in Kenya Labs & Teams: Leads a research group working with postdoctoral researchers like Dr. Fangfang Guo. Teaches advanced courses in epidemiology and evolutionary ecology.

Krishna Pacificiمشاهده پروفایل

دانشیار

• Quantitative Ecology
• Wildlife Ecology
• Spatio-Temporal Modeling
• +۷ مورد دیگر

Krishna Pacifici is an Associate Professor in the Department of Forestry and Environmental Resources at North Carolina State University (NC State), affiliated with the College of Natural Resources and the Center for Geospatial Analytics. His work focuses on applying quantitative methods to understand ecological responses to environmental stressors and inform conservation decisions. He holds a Ph.D. in Forest Resources (2011) from the University of Georgia, M.S. in Statistics (2012) and Wildlife Ecology (2007), and a B.S. in Fisheries and Wildlife Sciences (2003), all from NC State. Research interests include spatiotemporal modeling, structured decision making, and adaptive management. He leads projects on North American mammal phylogeography, endangered species recovery (e.g., Neuse River Waterdog), and red snapper abundance estimation. His grants total over $6 million from NSF, USGS, NOAA, and state agencies, supporting collaborative efforts like the Southeast Climate Science Center and wild turkey ecology studies. Key grants: NSF-funded continental mammal SDMs ($374K), Neuse River Waterdog studies ($180K), and red snapper abundance modeling ($713K). Teaches FW 453/553: Principles of Wildlife Science. Publications emphasize ecological modeling, conservation applications, and methodological advancements in occupancy analysis and camera trap data.

Nikoleta Anicicمشاهده پروفایل

پژوهشگر

• Entomology
• Vector Ecology
• Invasive Species Monitoring
• +۴ مورد دیگر

Nikoleta Anicic is a Scientific Collaborator in Vector Ecology at the Department of Environment Constructions and Design (DACD) at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI), where she focuses on monitoring and researching invasive mosquito species and their ecological impacts. Education: PhD in Evolutionary Ecology, University of Namur (Belgium), 2017 Master of Science in Biology with specialization in environmental microbiology and ecology, University of Zurich, 2017 Bachelor of Science in Biology, University of Neuchâtel, 2014 Anicic's research centers on vector ecology with particular expertise in mosquito monitoring and control. Her work combines molecular biology, entomology (specializing in mosquitoes), data management, ArcGIS, and programming in R to address public health challenges posed by invasive species. She has developed specialized skills in high-resolution optical identification of mosquito eggs and spatio-temporal modeling of invasive species dynamics. Her research has direct applications for arboviral disease surveillance and prevention, particularly for diseases like Dengue, Chikungunya, and Zika transmitted by invasive Aedes species. Analysis of her publication record reveals a strong interdisciplinary approach spanning entomology, environmental microbiology, and computational ecology. Her research trajectory shows progression from fundamental ecological studies on subterranean biodiversity and freshwater zooplankton to increasingly applied work on invasive mosquito surveillance. Recent publications demonstrate sophisticated integration of machine learning techniques with traditional ecological monitoring to predict seasonal mosquito population dynamics across multiple European countries. Nikoleta Anicic leads and participates in numerous research projects focused on invasive mosquito surveillance across Swiss cantons (including Zurich, Uri, Glarus, Schwyz, and the Romandy region) and Liechtenstein. These projects involve close collaboration with cantonal environmental offices and national authorities, with SUPSI serving as the national coordination center for invasive species monitoring designated by the Swiss Federal Office for the Environment. Her work directly informs Swiss public health strategies for controlling invasive mosquito species including Ae. albopictus, Ae. japonicus, and Ae. koreicus. At SUPSI, Anicic is a core member of the Vector Ecology Sector (SECOVETT) within the Institute of Microbiology. She contributes significantly to the Swiss mosquito network, providing scientific expertise for developing monitoring protocols, analyzing field samples, and interpreting spatial patterns of mosquito invasion. Her recent presentation at the 26th International Conference on Subterranean Biology demonstrates her continued engagement with broader ecological questions beyond her primary mosquito research focus.

Chao Huangمشاهده پروفایل

استادیار

• Large Language Models
• Graph Learning
• Recommender Systems
• +۳ مورد دیگر

Chao Huang is an Assistant Professor at the Department of Computer Science and Institute of Data Science at the University of Hong Kong (HKU). As the director of the Data Intelligence Lab@HKU, his research focuses on Large Language Models (LLMs), LLM Agents, Graph Learning, Recommender Systems, and AI for Smart Cities. He holds a PhD from the University of Notre Dame. Education: PhD in Computer Science from University of Notre Dame (USA). Research Interests: His work bridges machine learning with practical applications, including: Developing advanced LLM frameworks like GraphGPT and UrbanGPT Creating automated research tools (AutoAgent, AI-Researcher) Designing recommendation systems with LLM integration (RLMRec, LLMRec) Exploring spatio-temporal AI for urban challenges Notable Achievements: Over 11,000 Google Scholar citations (h-index 55), multiple top conference awards (WWW/SIGIR/KDD), and open-source projects with thousands of GitHub stars. His work has been recognized as most influential/pioneering in major AI conferences. Labs/Teams: Leads the Data Intelligence Lab, collaborating on projects like LightRAG, MiniRAG, and VideoRAG. The lab emphasizes open-source contributions with repositories on GitHub. Grants/Advising: Supervises PhD/MPhil students and offers research internships. Active in recruiting motivated researchers and students through HKU's programs.

Konstantinos Skianisمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Deep Learning
• +۱۴ مورد دیگر

Konstantinos Skianis is an Assistant Professor in the Department of Computer Science and Engineering at the University of Ioannina, Greece. His research lies at the intersection of Natural Language Processing, Machine Learning, Deep Learning, and Data Science, with a focus on representations, regularization, robustness, and reasoning (the 4R's). He has founded the Greek AI company BLUAI and co-founded PrimeHost AI, a startup automating guest conversations using AI. Ph.D. in Computer Science, École Polytechnique, France (2015–2019) M.Sc. in Math, Computer Vision, and Machine Learning, École Normale Supérieure de Cachan, France (2014–2015) B.Sc. in Computer Science, Athens University of Economics and Business, Greece (2013) His research interests span Natural Language Processing , Graph Neural Networks , Large Language Models , Representation Learning , and Medical AI . He investigates robustness in neural networks, reasoning with LLMs via pseudo-code, and multilingual applications in low-resource settings like Greek. His recent work includes digital twins for industrial optimization, climate adaptation in ports using ML, and mental health detection via LLMs. The 15 most recent publications reflect a strong trend in graph-based learning , LLM reasoning , representation robustness , and applied AI in healthcare and industry . His work combines theoretical rigor with practical applications, often involving real-world datasets from public health, industrial processes, and climate science. Best Paper Award, TextGraphs at NAACL 2018 OpenAI Researcher Access Program (2024) He has advised or collaborated on several student projects and competitions, including the Data Mining Cup and Kaggle challenges. He has secured research support through the OpenAI credits program. He has taught courses in Linear Algebra, Data Mining, and advanced labs on graph and text learning at ENS Paris-Saclay. He is actively involved in the research community as an Area Chair for ARR (2025) and reviewer for top-tier venues like NeurIPS, ICLR, ACL, and EMNLP. He leads or contributes to open-source tools such as GoWvis (text visualization), GraKeL (graph kernel library), and SpreadViz (influence spread visualization). His work on Greek NLP resources has helped advance language technology for low-resource languages.