Find Academic Supervisors

• Animal Communication
• Social Cognition
• Anthropogenic Impacts on Animal Behavior
• +3 more

Çağlar Akçay is a Senior Lecturer in the School of Life Sciences at Anglia Ruskin University, part of the Faculty of Science and Engineering. He holds a PhD in Animal Behavior from the University of Washington, alongside an MA in Cognition and Perception and a BS in Biology and Psychology from Middle East Technical University. His research focuses on behavioral ecology, particularly the evolution of animal communication and social cognition in songbirds. He investigates how animals adapt to anthropogenic environments, including effects of urbanization on signaling and aggression. Key research areas include animal communication systems, social interactions mediated by vocal signals, and the impact of environmental changes on behavior. He leads the Bird Lab, studying the interplay between communication and social behavior in birds, with projects on Darwin’s finches and song sparrow territorial dynamics. His work emphasizes field studies combined with experimental approaches, such as acoustic manipulations and observational tracking. Member of the Behavioral Ecology Research Group Recipient of Turkish Science Academy Early Career Fellowship (2019-2021) Teaching responsibilities include BSc programs in Animal Behaviour and Zoology, and an MSc in Animal Behaviour Applications for Conservation Recent publications highlight topics like survival processing effects in memory, avian behavioral plasticity in urban settings, and the role of acoustic signals in territorial disputes. His interdisciplinary work bridges ecology, cognition, and conservation, with a focus on translating findings into wildlife management strategies.

Klaus MoeltnerView profile

Professor

• Environmental Economics
• Natural Resource Valuation
• Applied Econometrics
• +3 more

Klaus Moeltner is a Professor in the Department of Agricultural and Applied Economics at Virginia Tech since 2015. He previously held positions at the University of Nevada, Reno, including Associate and Assistant Professor roles. His expertise lies in environmental economics, natural resource valuation, and applied econometrics. He has secured over $6 million in grants as PI or Co-PI and contributes to interdisciplinary research through the Global Change Center and Remote Sensing Program at Virginia Tech. Education: Ph.D., Economics, University of Washington, 2000 M.A., Economics, University of Washington, 1998 M.A., International Policy Studies, Monterey Institute of International Studies, 1994 M.S., Environmental Planning & Engineering, University of Agriculture and Forestry, Vienna, Austria, 1990 Research Interests: Dr. Moeltner’s work focuses on environmental and natural resource economics, including water quality valuation, wildfire health impacts, urban water use, and coastal flooding risks. His research employs advanced econometric methods, such as Bayesian modeling and meta-regression, to inform policy decisions. Current projects include analyzing the economic benefits of stream restoration, harmful algal bloom forecasts, and offshore wind farm co-location opportunities. Awards: 2023: Distinguished Graduate Teaching Award (AAEA) 2014: Best Paper in Environmental and Resource Economics (EAERE) 2004: CABNR Outstanding Instructor Award Advising & Grants: Advised 6 Ph.D. students and secured over $6 million in grants. Research collaborations include USDA-NIFA, EPA, and the European Union. Labs & Teams: Active in interdisciplinary groups like the Global Change Center and Remote Sensing Program at Virginia Tech.

Eric RoyView profile

Associate Professor

• Nutrient Biogeochemistry
• Circular Bioeconomy
• Ecological Engineering
• +3 more

Eric Roy is an Associate Professor at the Rubenstein School of Environment and Natural Resources, University of Vermont, and Director of the Casella Center for Circular Economy and Sustainability. He is also a Fellow at the Gund Institute for Environment. His research focuses on nutrient cycling, biogeochemistry, and ecological engineering to design sustainable systems for nutrient management in food, waste, and water systems. Education: Ph.D. in Oceanography & Coastal Sciences from Louisiana State University (2013), M.S. in Food, Agricultural & Biological Engineering from Ohio State University (2008), and B.S. in Mechanical Engineering from Old Dominion University (2006). Research interests include nutrient stewardship, circular bioeconomy, and nature-based solutions. His work integrates lab and field studies with modeling to explore nutrient dynamics in engineered, urban, and agricultural ecosystems. Key themes include improving nutrient use efficiency in food systems, resource recovery, and green infrastructure design. Teaching emphasizes ecological design in water quality, waste management, and food systems. His recent publications highlight advancements in phosphorus retention modeling, floodplain function analysis, and composting system optimization. He leads the Nutrient Cycling and Ecological Design Lab, advancing interdisciplinary solutions for environmental sustainability.

Bill HoweView profile

Associate Professor

• Data Science
• Algorithmic Fairness
• Urban Computing
• +7 more

Bill Howe is an Associate Professor at the University of Washington's Information School, with adjunct appointments in Computer Science & Engineering and Electrical Engineering. He serves as Founding Program Director and Faculty Chair of the UW Data Science Masters Degree, Founding Associate Director and Senior Data Science Fellow at the UW eScience Institute, Director of the Urbanalytics Lab, and Co-Founding Director of the Center for Responsible AI Systems and Experiences. He also co-founded Urban@UW and created the first Data Science MOOC through Coursera. His research focuses on making data science accessible in public sector applications with emphasis on equity, privacy, and compliance. Current interests include: Algorithmic fairness in urban and social contexts Privacy-preserving synthetic data generation Machine learning for heterogeneous data Database systems and high-performance computing Human-computer interaction for data systems Responsible AI development and deployment Publication analysis reveals strong focus on responsible data science, with recent work emphasizing differential privacy, COVID-19 data equity, urban mobility fairness, and relational data systems. Earlier foundational work established contributions to scientific workflow systems and data pricing models. Awards and Honors: Runner-up Best Paper Award (VLDB 2023) Best Paper Award (SIGMOD 2019) Best Paper Award (VIs 2019) Best Paper Award (InfoVis 2018) He leads the Urbanalytics Lab and advises multiple students including An Yan (fairness in urban mobility), Sean Yang (machine learning embeddings), and Dominik Moritz (visualization systems). His projects span EZLearn for automatic claim validation, privacy-preserving synthetic data, and Myria middleware for polystores.

Santiago OntañónView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Game AI
• +10 more

Santiago Ontañón is an Associate Professor in the Department of Computer Science at Drexel University's College of Computing and Informatics. He is also a Senior Research Scientist at Google DeepMind, reflecting a strong dual affiliation in both academic and industrial AI research. His work bridges theoretical AI with practical applications in gaming and machine learning. PhD in Computer Science (Artificial Intelligence), cum laude, Autonomous University of Barcelona Postdoctoral Researcher, Georgia Institute of Technology Researcher, Artificial Intelligence Research Institute (IIIA), Barcelona, Spain Dr. Ontañón's research focuses on artificial intelligence, machine learning, and robotics, with a particular emphasis on game AI. His interests span case-based reasoning, reinforcement learning, Monte Carlo tree search, player modeling, and procedural content generation. He has made significant contributions to AI in real-time strategy games and explainable AI systems. His recent publications reflect a consistent trend in AI for games, hierarchical planning, and learning from demonstration. The articles span topics such as reproducible deep reinforcement learning, adaptive player modeling, and integrating domain knowledge into search algorithms, indicating a mature and impactful research trajectory in AI and game technologies. Senior Research Scientist, Google DeepMind Organizer, microRTS AI Competition Advising multiple PhD students in AI and game-related topics He has advised numerous PhD students, many of whom have completed their theses on advanced AI topics in games and reasoning. His research is supported by access to substantial computational resources and collaborative networks in both academia and industry. He actively promotes open science by releasing software, data, and teaching materials. He leads research efforts in AI for games and maintains an active lab focused on game AI, with projects like microRTS, FTL, and Darmok. His team develops systems for reinforcement learning, planning, and natural language understanding in game environments.

Aaron ShugarView profile

Professor

• Art Conservation
• Archaeological Materials
• X-ray Fluorescence Spectroscopy
• +6 more

Aaron Shugar is a Professor and current Bader Chair in Art Conservation at Queen’s University. With a background in archaeometallurgy and conservation science, he specializes in non-destructive analysis techniques for cultural heritage, including X-ray fluorescence (XRF), Raman spectroscopy, and hyperspectral imaging. His work bridges art history, material degradation, and technological innovation. Honours H.B.A. in Anthropology and Law & Society from York University M.S. in Archaeological Materials from the University of Sheffield Ph.D. in Archaeometallurgy from University College London His research focuses on historic artist’s pigments , ancient metallurgy , and technical history of artifacts , with particular interest in degradation pathways and manufacturing processes. Recent publications highlight trends in AI integration with XRF analysis and preservation of modern materials in art conservation. Bader Chair in Art Conservation Mellon Foundation Professor in Conservation Science Aaron co-directed the Archaeometallurgy Laboratory at Lehigh University, served as a guest scientist at NIST, and remains a research associate at the Smithsonian Institution. He actively contributes to TEFAF’s Scientific Vetting Committee and acts as a forensic materials expert for the Court of Arbitration for Art.

Kushal DeyView profile

Assistant Professor

• Computational Biology
• Genomics
• Machine Learning
• +5 more

Kushal Dey, PhD, is an Assistant Professor in the Computational and Systems Biology Program at Memorial Sloan Kettering Cancer Center (MSKCC). His research develops machine learning models that integrate genetic, genomic, and epigenomic data (e.g., RNA-seq, ChIP-seq, Perturb-seq, spatial transcriptomics) to decode the causal functional architecture of heritable complex diseases, including immune-related disorders like Alzheimer’s and inflammatory bowel disease, as well as heritable cancers such as breast and prostate cancer.

Steven GreybushView profile

Associate Professor

• Atmospheric Science
• Meteorology
• Climate Modeling
• +9 more

Steven Greybush is an Associate Professor in the Department of Meteorology and Atmospheric Science at Pennsylvania State University, College of Earth and Mineral Sciences. He is based in University Park, PA, and his research bridges atmospheric science, climate modeling, and interdisciplinary applications. He leads and contributes to major research initiatives involving AI-enhanced weather forecasting, planetary meteorology, and climate impacts on water and health systems. His research interests include Atmospheric Science , Climate Modeling , Data Assimilation , Planetary Meteorology (especially Mars) , Lake-Effect Snowbands , Tropical Cyclones , and Climate-Health Interactions . His work applies advanced techniques such as the Ensemble Kalman Filter (EnKF), Local Ensemble Transform Kalman Filter (LETKF), and AI-driven models to improve predictions of weather and climate phenomena. His recent publications (2021–2025) reveal a strong trend in integrating satellite and radar data into numerical models, enhancing forecasts of convection, hurricanes, and snowstorms. He also explores Martian atmospheric dynamics and the impact of climate variability on public health in Africa. His work is supported by major grants from NASA and NSF, including a $1.23 million NASA grant to improve AI satellite weather forecasting and an NSF grant for AI-powered weather pattern understanding. $1.23 million NASA grant for AI satellite weather forecasting NSF grant for AI-powered weather pattern understanding Penn State part of $6.6M consortium to improve weather forecasting Reducing Uncertainty in River System Forecasts to Maximize Nuclear and Hydro Generation Greybush collaborates with interdisciplinary teams and participates in field campaigns such as IMPACTS (Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms). He advises or co-advises graduate students and researchers, though specific advisees are not listed. His work is published in top journals including Journal of Geophysical Research , Monthly Weather Review , JAMA Network Open , and PNAS .

• Hydrology
• Climate Change Impact Assessment
• Drought Monitoring
• +2 more

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.

Volker DellwoView profile

Associate Professor

• Phonetics
• Speech Recognition
• Computational Linguistics
• +4 more

Prof. Dr. Volker Dellwo is an Associate Professor of Phonetics and head of the Department of Computational Linguistics. His research focuses on phonetics, speech recognition, computational linguistics, and dialectology, with applications in forensic analysis, voice biometrics, and multimodal emotion recognition. Academic Rank: Associate Professor Department: Computational Linguistics His work explores phonetic convergence , speaker discrimination, and the role of prosodic features in voice recognition. Recent studies analyze whispered speech processing, cross-dialect accommodation, and neural mechanisms of speaker identity encoding. Key article trends include self-supervised learning for speech recognition , multimodal emotion detection , and forensic voice analysis . Subfields span acoustic variability, temporal envelope dynamics, and voice quality metrics. Publications emphasize computational phonetics , cross-linguistic studies , and neural network applications in speaker identification. Research also addresses challenges in forensic audio analysis and synthetic speech dataset generation.

• Transportation Engineering
• Location Optimization
• Geospatial Information Science
• +4 more

Tae J. Kwon is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Alberta . His research spans Winter Transportation Engineering , Intelligent Transportation Systems (ITS) , Traffic Optimization , and Geospatial Information Science . Developed LoRWIS , a web-based decision support system for road weather sensor optimization Recipient of multiple awards including ITS Canada Excellence in Research and Development Award (2024) , KOFST Scientist of the Year (2024) , and Early Career Research Award (University of Alberta, 3 times) Active in professional committees like the TRB Surface Transportation Weather Research Group (2016–present) Research Highlights : Winter Road Maintenance : Advanced geostatistical and deep learning techniques for road condition monitoring Location Optimization : Pioneered models for RWIS sensor placement using hybrid geostatistical methods Intelligent Transportation Systems : Focused on big data applications for traffic safety and connected vehicles Climate Resilience : Integrated ecological vulnerability into fire hazard modeling Scientific Awards : ITS Canada Excellence in Research (2024) KOFST Scientist of the Year (2024) Donald Stanley Best Paper Award (2023) AKCSE Early Achievement Award (2021) University of Alberta Early Career Research Award (2020) Great Supervisor Award (2019) Teaching & Leadership : Teaches courses like CIV E 419 (Transportation Engineering: Highway Planning and Design) and CIV E 612 (Transportation Planning: Methodology and Techniques) Leads the Geospatial Transportation System & Sensing Solutions Lab (GeoTrans) , mentoring PhD students Juan Cuellar and Jiehao Bi Collaborates with agencies including Alberta Transportation , NSERC , and Iowa DOT

• Geospatial Technology
• Machine Learning
• Natural Hazard Prediction
• +7 more

Dr. Husam Al-Najjar is a Lecturer at the School of Computer Science within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). He serves as the Course Director for the Bachelor of Information Systems (BIS) program. With expertise in geospatial technology and machine learning, Dr. Al-Najjar focuses on predicting and mitigating natural hazards and environmental issues to contribute to a sustainable digital earth. Dr. Al-Najjar earned his PhD from the University of Technology Sydney. Before joining academia, he worked in project management and has received numerous prestigious awards, scholarships, and grants throughout his career. Dr. Al-Najjar's research primarily centers on the application of machine learning techniques to address complex environmental challenges. His work spans geospatial AI, natural hazard prediction (particularly landslides and bushfires), and sustainable development. He has developed innovative approaches that integrate physical models with machine learning algorithms to improve prediction accuracy in data-scarce environments. His research also extends to remote sensing applications, urban planning, and smart city technologies, with a strong emphasis on practical solutions for real-world problems. Analysis of Dr. Al-Najjar's recent publications reveals a strong focus on applying explainable AI techniques to natural hazard prediction, particularly landslides. His work consistently bridges the gap between theoretical machine learning approaches and practical geospatial applications. He has made significant contributions to integrating physical models with AI, developing methods for handling imbalanced data through generative adversarial networks, and improving feature selection for remote sensing applications in environmental monitoring. Best Paper Award at the ISPRS Geospatial Week in Enschede, the Netherlands As an educator, Dr. Al-Najjar is actively involved in mentoring and teaching. He serves as Course Director for the Bachelor of Information Systems program and teaches courses in GIS, Information Systems, IS development methodologies, Design & Innovation, and Project Management. He welcomes prospective PhD candidates interested in his research areas and emphasizes the importance of detailed research proposals that demonstrate novelty and significance. His teaching philosophy focuses on fostering an engaging and inclusive learning environment that promotes student success and well-being. Dr. Al-Najjar is affiliated with 'The Trustworthy Digital Society' concentration at UTS and serves as a referee and holds editorial roles in respected journals. His work contributes to the development of geospatial AI frameworks that support decision-making in environmental management and disaster preparedness.

• Agricultural Sustainability
• Environmental Impact Assessment
• Animal Behavior and Welfare
• +3 more

Maurizio Ramanzin is a Full Professor at the University of Padova , affiliated with the School of Animal Science and Department of Agronomy, Animals, Food and Natural Resources (DAFNAE) . His research focuses on Agricultural Sustainability , Environmental Impact Assessment , and Precision Livestock Farming . Academic Field : AGR/19 Email : maurizio.ramanzin@unipd.it Address : Agripolis - Viale dell'università, 16 - Legnaro (Padova) – ITALY His work explores the interactions between livestock systems and ecosystem services in mountainous regions, with emphasis on: Grazing Management and biodiversity conservation Life Cycle Assessment (LCA) of dairy and beef systems Climate Change Adaptation in Alpine ungulates Animal Welfare in small-scale farms Technological Tools (GPS, NIRS) for monitoring grazing behavior Key trends in his recent publications include: Quantifying environmental drivers of wolf predation on livestock Developing low-cost biologging systems for dairy cows Analyzing social-ecological trade-offs in mountain agriculture Assessing microbial dynamics in alpine soils

Taein KwonView profile

Research Fellow

• Egocentric Vision
• Action Recognition
• Contextual AI
• +6 more

Taein Kwon is a postdoctoral research fellow at the Visual Geometry Group (VGG) within the Department of Engineering Science at the University of Oxford, working under Prof. Andrew Zisserman. Previously, he completed his PhD at ETH Zurich under Prof. Marc Pollefeys and earned master's and bachelor's degrees from UCLA and Yonsei University, respectively. His educational background includes: Bachelor's in Electrical Engineering from Yonsei University, Seoul, Korea Master's degree from UCLA PhD from ETH Zurich (defended July 2024) His research spans Egocentric Vision, Action Recognition, Hand-object Interaction, Video Understanding, AR/VR, and Multi-modal Learning, with emphasis on first-person perspective analysis for AI assistants and human-computer interaction. His work integrates 3D reconstruction, pose estimation, and multimodal signals to model complex human activities and physical interactions. Analysis of his 2021-2025 publications reveals a consistent focus on egocentric vision datasets (H2O, HoloAssist, EgoPressure) and novel frameworks for hand-object interaction, action recognition, and gesture understanding. His research demonstrates strong interdisciplinary connections between computer vision, robotics, and human-centered AI, with increasing emphasis on pressure sensing, co-speech gestures, and cross-modal alignment. His scientific recognition includes: CVPR Egovis 2022/2023 Distinguished Paper Award for HoloAssist (July 2024) SNSF Postdoc.Mobility fellowship (May 2024) He actively mentors students on egocentric vision projects, supervising master's theses, semester projects, and collaboration initiatives leading to publications at top conferences. His research is supported by the SNSF fellowship and industry collaborations with Meta Reality Labs and Microsoft Research. As part of Oxford's Visual Geometry Group, he contributes to cutting-edge computer vision research while maintaining strong ties with ETH Zurich's computer vision community through ongoing collaborations and dataset development efforts.

Christian Pascal HirschView profile

Associate Professor

• Topological data analysis
• Large deviations theory
• Spatial random networks
• +3 more

Christian Pascal Hirsch is an Associate Professor for Data Science and Statistics at the Department of Mathematics, Aarhus University. His research focuses on random networks inspired by biology and health sciences, utilizing techniques from topological data analysis and stochastic geometry. He is affiliated with the Stochastics group, AU DIGIT Centre, and AU Quantum Campus. Research Interests: Topological data analysis, large deviations theory, spatial random networks, and stochastic geometry. His work includes studies on percolation theory, Gibbs measures, and applications to neural networks and geometric functionals. Publications span journals such as the Journal of Applied and Computational Topology, Journal of Statistical Physics, and Stochastic Processes and Their Applications, covering topics from network topology to Poisson approximation.