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Sreyasee Das BhattacharjeeView profile

Assistant Professor

• Multimodal Data Analytics
• Affective Computing
• Generative AI
• +10 more

Sreyasee Das Bhattacharjee is an Assistant Professor of Teaching in the Department of Computer Science and Engineering at the University at Buffalo (SUNY Buffalo), within the School of Engineering and Applied Sciences. She leads research in multimodal data analytics, affective computing, and generative AI, with extensive collaborations across institutions like NASA, JPL, Roswell Park, and UCSF. She is actively involved in academic leadership as the MS-AI Program Director at the Institute of AI and Data Science UB and serves on the Faculty Senate. Education: Ph.D., Indian Institute of Technology, Madras M.Tech., Indian Institute of Technology, Delhi M.Sc. in Mathematics, Chennai Mathematical Institute, India Her research focuses on developing AI systems for human-centered applications, particularly in emotion recognition, educational data mining, and immersive technologies. She investigates multimodal generative models, human-machine collaboration, and AI-powered interactive agents, often leveraging wearable sensors and large-scale datasets. Her work bridges computer vision, machine learning, and real-world healthcare and educational challenges. The most recent publications highlight a consistent trajectory in multimodal AI, with emphasis on robust emotion recognition under incomplete data, generative modeling for affective states, and applications in healthcare and education. Key themes include diffusion models, knowledge distillation, explainable AI, and personalized interactive systems, reflecting a strong interdisciplinary approach combining computer science with psychology and medicine. Scientific Awards and Honors: Best Paper Award, IEEE Big Data 2019 and 2017 IBM PhD Fellowship Award 2008 Australian Endeavour Research Fellowship 2007 Fellow, New Faculty Academy, UB 2021 NSF Travel Award 2025 National Scholarship, Govt of India 2000 Dr. Das Bhattacharjee has secured significant external funding, including a $1M NSF grant for a STEM program for refugee and underprivileged students, and leads projects on AI for elder care and plant health monitoring. She mentors a diverse group of graduate and undergraduate students, many of whom have gone on to top institutions and companies like Amazon and CMU. She also serves as Associate Editor for JMIR mHealth and Frontiers in AI, and has held leadership roles in major conferences as Area Chair and Meta Reviewer. She leads the Visual Computing Lab at UB, where her team develops cutting-edge AI systems for multimodal understanding, affective computing, and educational technology. The lab fosters interdisciplinary collaboration and innovation, with ongoing projects in BCI-AI integration, immersive agents in VR, and biosignal-based emotion modeling.

Zbynek MalenovskyView profile

Professor

• Remote Sensing
• Optical Remote Sensing
• Imaging Spectroscopy
• +9 more

Prof. Dr. Zbynek Malenovsky is a full Professor at the Institute of Geography, University of Bonn , where he leads the Remote Sensing Research Group (AG Malenovsky) . His work integrates advanced optical remote sensing, imaging spectroscopy, and 3D radiative transfer modeling to study plant eco-physiology and ecosystem responses to climate change. His research interests include: Quantitative remote sensing of vegetation biochemical and structural traits Solar-induced chlorophyll fluorescence (SIF) detection 3D radiative transfer modeling (e.g., DART model development) Climate change impacts on terrestrial ecosystems Machine learning applications in hyperspectral data analysis Land surface modeling and data assimilation His recent publications span high-impact journals in remote sensing and environmental science, with a strong focus on vegetation stress detection, canopy-level photosynthesis modeling, and deep learning-based biodiversity estimation using multisensor satellite data (Sentinel-1/2, Landsat, hyperspectral UAS). His work often involves collaborations with institutions like CESBIO (France) and the Global Change Research Institute (Czech Republic). Scientific awards and honors are not mentioned in the provided text. He actively supervises graduate students, including PhD candidates Thang Quang Nguyen and Lucas Casuccio , and offers thesis topics in areas such as bark beetle infestation mapping, pollinator habitat monitoring, and urban imperviousness analysis. He teaches advanced courses in physical geography, remote sensing methods, and land surface modeling, emphasizing hands-on use of software like ENVI, QGIS, SNAP, and Google Earth Engine. His research group is embedded in key initiatives such as PhenoRob (DFG Cluster of Excellence) and the Center for Remote Sensing of Land Surfaces (ZFL) , highlighting strong institutional support and interdisciplinary collaboration.

Laura CowenView profile

Professor

• Ecological Statistics
• Capture-Recapture Methods
• Tag Loss Models
• +5 more

Laura Cowen is a Professor in the Department of Mathematics and Statistics at the University of Victoria, specializing in ecological statistics with a focus on capture-recapture methods. Her work addresses real-world challenges in wildlife, fisheries, and public health, including estimating population parameters for species like seabirds and fishery populations, and modeling hidden human populations such as injection drug users and homeless individuals. She leads the Cowen Lab, committed to interdisciplinary collaboration and equity, diversity, and inclusion (EDI) initiatives. Her research integrates statistical methods with ecological and public health applications, including tag loss modeling, hidden Markov models, and large-scale data analysis. She collaborates with diverse fields such as microbiology, sociology, and anthropology. Key projects include estimating salmon exploitation rates, California sea cucumber movement, and lemur population viability in fragmented habitats. Cowen received her PhD from Simon Fraser University and has held roles in initiatives like the VADA (Visualization and Data Analytics) team and the WHALE program at SFU. She was awarded the 2025 Bob Riffenburgh Award for bridging statistical ecology and public health applications, including pandemic modeling and serotype occupancy analysis. Her lab emphasizes fostering an inclusive environment and supports Indigenous student initiatives through AISES chapter formation and travel awards.

Pavlos TsouvaltzisView profile

Assistant Professor

• Vegetable Crop Science
• Horticultural Physiology
• Postharvest Technology
• +3 more

Dr. Pavlos Tsouvaltzis serves as an Assistant Professor of Vegetable Horticulture at the Southwest Florida Research and Education Center (SWFREC), part of the University of Florida's Institute of Food and Agricultural Sciences (IFAS). Previously, he held the position of Associate Professor at the Aristotle University of Thessaloniki in Greece, focusing on vegetable crop horticulture. His research emphasizes non-destructive quality assessment , postharvest physiology , and climate change impacts on vegetable crops. Key methodologies include spectral imaging (VIS/NIR/SWIR), chemometrics, and machine learning applications in agriculture. Dr. Tsouvaltzis' work spans diverse crops including tomatoes, melons, broccoli, peppers, and aromatic herbs. He investigates shelf-life prediction , nutritional composition , and abiotic stress tolerance under changing environmental conditions. Recent studies address challenges like salinity stress in Mediterranean plants and boron toxicity in pepper cultivars. His research bridges fundamental plant physiology with practical agricultural solutions. Publications demonstrate a strong focus on precision agriculture through integration of sensor technologies and data analytics. While no formal awards are listed, his active publication record reflects significant contributions to horticultural science. Ongoing projects likely involve optimizing crop resilience and adopting innovative technologies for sustainable vegetable production.

• Remote Sensing
• Earth Observation
• Synthetic Aperture Radar (SAR)
• +10 more

Camilla Brekke is a Professor at the Department of Physics and Technology , UiT The Arctic University of Norway, with a focus on satellite remote sensing and Earth observation using Synthetic Aperture Radar (SAR) and optical ocean color monitoring. Former Vice-Rector for Research and Development (2021-2023) and Deputy Rector at UiT, she previously served as Pro-Dean for Research at the Faculty of Science and Technology (2018-2021) and Deputy Director at CIRFA (2019-2021). Academic degrees from the University of Oslo (MSc in 2001, PhD in 2008) Former roles at the European Space Agency (ESA) and the Norwegian Defence Research Establishment (FFI) Her research spans algorithm development for multidimensional data analysis, integrated remote sensing and modeling for Arctic applications, and marine pollution/oil spill detection. Recent work includes machine learning for satellite data harmonization in the Barents Sea and SAR-based sea ice discrimination near oil platforms. Current affiliations include the LIE-STØRMER CENTER and the Norwegian Polar Institute (2023-), where she leads national Arctic Earth observation initiatives. She collaborates on projects involving ship/fly-based data collection, laboratory experiments, and oil drift simulations for environmental monitoring.

• Real-time computing
• Cyber-Physical Systems
• Internet of Things
• +7 more

Tarek Abdelzaher is the Sohaib and Sara Abbasi Professor and Willett Faculty Scholar in the Department of Computer Science at the University of Illinois at Urbana-Champaign. He leads research in multiple cutting-edge areas including cyber-physical systems, Internet of Things, social sensing, and edge AI. His work has significant applications in smart cities, defense systems, and social media analysis. Abdelzaher received his Ph.D. in Computer Science and Engineering from the University of Michigan, Ann Arbor in 1999. His educational background laid the foundation for his extensive research career spanning real-time systems, distributed computing, and intelligent sensing. His primary research interests focus on understanding and influencing performance and temporal properties of networked embedded, social, and software systems in the face of increasing complexity, distribution, and degree of interaction with external physical environments. Key areas include: Real-time computing and cyber-physical systems Internet of Things (IoT) and sensor networks Machine learning for IoT/CPS applications Social sensing and social media analysis Edge AI and deep learning for resource-constrained environments Battlefield IoT for defense applications Abdelzaher's recent publications demonstrate a strong trend toward integrating machine learning with cyber-physical systems, particularly focusing on edge computing applications. His work bridges theoretical foundations with practical implementations in smart cities, transportation systems, and defense applications. The research shows increasing emphasis on social sensing, where social media data is analyzed to understand real-world events and human behavior patterns. His scientific achievements have been recognized with numerous prestigious awards: IEEE Outstanding Technical Achievement and Leadership Award in Real-time Systems (2012) Xerox Award for Faculty Research (2011) Willett Faculty Scholar (2010) Multiple best paper awards at top conferences including IEEE/ACM International Conference on Cyber-physical Systems (2017), International Conference on Mobile Ad hoc and Sensor Systems (2016), and International Conference on Autonomic Computing (2015) Fellow of both IEEE and ACM NSF CAREER Award (2001) Abdelzaher has advised numerous graduate students who have gone on to make significant contributions in academia and industry. His research has been generously supported by major funding agencies including the National Science Foundation (NSF), Defense Advanced Research Projects Agency (DARPA), Army Research Lab (ARL), and others. He leads several major research projects including the $25M Internet of Battlefield Things (IoBT) collaboration between the Army Research Lab, academia, and industry. His research group operates several specialized labs and projects including: GreenGPS: A participatory sensing system for fuel-efficient navigation Apollo: A real-time social media data analysis toolkit for situation understanding Edge AI project: Developing deep learning capabilities for embedded IoT devices Social Mathematics and Information Dynamics project: Establishing laws of information dynamics in social spaces Internet of Battlefield Things (IoBT): Connecting soldiers with smart technology for enhanced defense capabilities

• Remote Sensing
• Geoinformatics
• Landscape Planning
• +5 more

Professor Damian Bargiel serves as Head of Institute and holds the Professorship for Applied Geoinformatics in Landscape Planning within the Department of Landscape Planning & Nature Conservation at Geisenheim University. His work bridges advanced geospatial technologies with practical applications in agricultural monitoring and landscape planning. Professor Bargiel's research focuses on applied geoinformatics with particular expertise in remote sensing technologies , especially Synthetic Aperture Radar (SAR) satellite data. His work spans crop classification, agricultural land monitoring, habitat structure detection, and the integration of time series analysis with crop phenology information. He has developed innovative classification methods that significantly improve accuracy over conventional approaches by leveraging the temporal dimension of radar imagery. His publication record demonstrates consistent contributions to the field since 2011, with recent work expanding into satellite-based disease detection (2025) and small satellite Earth observation systems (2024). The research shows a clear progression from foundational work on TerraSAR-X applications to more integrated approaches combining multiple data sources and advanced machine learning techniques. Professor Bargiel is currently leading significant research initiatives including the AGRECO4CAST project (2025-2028), which aims to develop a transdisciplinary assessment framework for agroecological transitions in European agroforestry landscapes. This EU and German government-funded project reflects his expanding research scope into sustainable agricultural systems and policy-relevant applications of geospatial technologies. His work provides valuable methodological advances for agricultural monitoring systems and contributes to practical applications in precision agriculture, landscape planning, and environmental conservation. The integration of technical remote sensing expertise with real-world agricultural and landscape planning challenges represents the distinctive contribution of Professor Bargiel's research program.

Allard MoskView profile

Professor

• Nanophotonics
• Condensed Matter Physics
• Quantum Materials
• +4 more

Prof. Allard Mosk is a Professor of Nanophotonics at the Debye Institute for Nanomaterials Science, Utrecht University. His research focuses on nanophotonics, condensed matter physics, quantum materials, and sustainable energy transition. He leads the Physics of Light in Complex Systems group, advancing imaging techniques through scattering media and wavefront shaping. His work bridges fundamental optics with applications in security, energy, and biophotonics. Research interests include: Coherent light scattering and imaging in complex media Optical energy density control Ptychography and advanced imaging algorithms Photonic crystal nanocavities and nonlinear dynamics Quantum-secure authentication systems Recent publications emphasize EUV imaging, deep generative models in ptychography, and sub-nanometer precision measurements. He contributes to SciPost as an Editorial Fellow and advises initiatives like the ARCNL institute. Teaching includes courses on quantum mechanics and photon physics for the Nanomaterials Science program.

Simone Maria StünziView profile

Research Fellow

• Permafrost Dynamics
• Boreal Ecosystems
• Numerical Modeling
• +3 more

Simone Maria Stünzi is a Research Fellow at Humboldt-Universität zu Berlin, specializing in Arctic permafrost-vegetation interactions. She leads the BorealFrost project investigating boreal forests' role in permafrost protection. Her research integrates numerical modeling, field observations, and remote sensing to study climate feedbacks in Siberia and Canada. Education: PhD in Geography (2018-2022), Humboldt-Universität & Alfred Wegener Institute MSc Environmental Science (2016-2018), University of Zurich BSc Geography (2013-2016), University of Zurich Research Focus: She examines how boreal forests modulate permafrost stability through thermal insulation mechanisms. Key areas include permafrost-vegetation-climate couplings, thermohydrological processes, and the impacts of forest disturbances. Her work emphasizes modeling tools like CryoGrid and integrates satellite data for landscape-scale analysis. Publications Trends: Recent work highlights boreal forests' delaying effect on thermokarst formation, permafrost thaw under climate change, and modeling advancements in Siberian and Arctic ecosystems. Cross-disciplinary collaborations enhance understanding of global carbon feedbacks. Awards: Helmholtz representative for Lindau Nobel Laureates Meeting 2024 Geo.X Young Academy Research Fellow Best Presentation Award at AWI Science Days 2020 Grants & Leadership: Principal Investigator: BorealFrost (DFG Walter-Benjamin Grant, 2024-2026) MOMENT Project (AWI, 2023-2024): Led field expeditions in Greenland PermaRisk Project (AWI, 2021-2023): Analyzed thermokarst dynamics Labs & Teams: Collaborates with the Alfred Wegener Institute, CryoGrid community model developers, and international field teams in Siberia, Canada, and Mongolia.

William HammondView profile

Researcher

• Plant Physiology
• Global Change Ecology
• Drought Tolerance
• +7 more

Dr. William Hammond is a Plant Ecophysiologist and Global Change Ecologist at the University of Florida, specializing in plant vascular systems' response to extreme environmental conditions. His research bridges basic and applied science, focusing on drought tolerance mechanisms, agricultural resilience, and remote sensing applications to monitor plant health and ecosystem dynamics. Hammond's work emphasizes understanding how hydraulic failure in plants leads to mortality and explores technological solutions like hyperspectral imaging and volatile organic compound (VOC) sensing for early stress detection. Research Interests: Plant hydraulics and climate change impacts Forest die-off mechanisms Urban tree ecophysiology through the UTEN network Agricultural adaptations to drought Remote sensing for invasive species and canopy cover mapping His recent studies highlight hotter-drought fingerprints driving global forest mortality, the need for improved aflatoxin detection in crops, and the role of cover crops in soil moisture retention. Hammond collaborates internationally through initiatives like the Urban Trees Ecophysiology Network (UTEN) to address urban forestry challenges under climate change.

Yaser Ajmal SheikhView profile

Associate Professor

• Computer Vision
• 3-D Vision and Recognition
• Human Behavior Analysis
• +8 more

Yaser Ajmal Sheikh is an Associate Professor at the Robotics Institute of Carnegie Mellon University (currently on leave) and serves as Director of Facebook Reality Lab in Pittsburgh. His work focuses on "metric telepresence": remote interactions in AR/VR that are indistinguishable from reality. He has made significant contributions to machine perception and rendering of social behavior, spanning computer vision, computer graphics, and machine learning. Dr. Sheikh's research encompasses analyzing dynamism in scenes from moving cameras, with particular focus on dynamic motion reconstruction, human behavior analysis, estimation of nonrigid motion, and modeling moving cameras in spacetime. His work has led to breakthroughs in social robotics, 3D vision and recognition, and multisensor data fusion. He founded and directs the Facebook Reality Lab in Pittsburgh, continuing his work on human-centered robotics and social robots. His publication record demonstrates consistent innovation in computer vision and graphics, with emphasis on human motion capture, facial animation, and social interaction modeling. His most recent work focuses on neural approaches to facial animation, full-body avatars, and capturing the complex dynamics of human interaction. The research shows a clear trajectory from foundational work in motion capture to sophisticated neural approaches for realistic human representation in virtual environments. Popular Science's Best of What's New Award Honda Initiation Award (2010) Best paper awards at WACV (2012), SAP (2012), SCA (2010), ICCV THEMIS (2009) First place in the MSCOCO Keypoint Challenge (2016) Hillman Fellowship for Excellence in Computer Science Research (2004) Dr. Sheikh has advised numerous doctoral and master's students who have gone on to prestigious positions in academia and industry. His research has been generously supported by government agencies including NSF and DARPA, as well as industrial partners such as Intel, Disney, Nissan, Honda, Toyota, and Samsung. He has served on senior committees at major conferences including SIGGRAPH, CVPR, ICRA, and ICCP, and was an Associate Editor of CVIU. He leads the Panoptic Studio project, a massively multiview system for social motion capture that has produced the CMU Panoptic Studio Dataset. His work on OpenPose has become a standard tool in the field for real-time 2D hand, body, and face keypoint detection. His current research at Facebook Reality Lab continues to push the boundaries of what's possible in virtual and augmented reality through advanced computer vision techniques.

Howard LiView profile

Professor

• Robotics
• Artificial Intelligence
• Control Systems
• +3 more

Howard Li is a Professor in the Department of Electrical and Computer Engineering at the University of New Brunswick's Faculty of Engineering. His research focuses on robotics, artificial intelligence, unmanned vehicles, control systems, and quantum computing. Dr. Li completed his Ph.D. at the University of Waterloo and has conducted research internationally at institutions including EPFL (Switzerland) and University of Pavia (Italy). He has industry experience developing training systems for fighter aircraft and control systems for autonomous vehicles. His research interests span robotics, autonomous systems, quantum computing, and AI applications in mechatronics. Recent publications demonstrate focus on LiDAR simulation, deep learning architectures, satellite imagery analysis, and geospatial algorithms. Dr. Li has secured over $1,000,000 in research grants from agencies including NSERC, CFI, and DRDC. He chairs the IEEE Autonomous Robotics Group and contributed to the first IEEE robotics standard. Harrison McCain Foundation Young Scholars Award NSERC Postgraduate Scholarship IEEE Senior Member recognition He established the COBRA research group and open-source Quad Rotor Simulator. All his former postdocs/PhDs secured positions at prestigious institutions including MIT and NASA.

Rakesh MishraView profile

Adjunct Professor

• Remote Sensing
• Computer Vision
• Artificial Intelligence
• +1 more

Rakesh Mishra is an Adjunct Professor in the Department of Geodesy and Geomatics Engineering at the University of New Brunswick and Chief Technology Officer at SceneSharp Technologies. His expertise includes multi-sensor data fusion, computer vision, and AI-based object detection using LiDAR, SAR, and satellite imagery. Dr. Mishra completed his PhD at UNB and has research experience with institutions including Indian Institute of Technology Kanpur and IIRS-ISRO. He has collaborated with Canadian government agencies including DND, NRC, and NRCan. His research focuses on LiDAR simulation, deep learning applications in remote sensing, and geospatial algorithm development. Recent publications demonstrate advancements in physics-aware deep learning for LiDAR and super-resolution techniques for satellite imagery.

Deanne RogersView profile

Professor

• Planetary Geology
• Remote Sensing
• Infrared Spectroscopy
• +3 more

Deanne Rogers is a Professor of Geosciences at Stony Brook University, specializing in planetary geology and remote sensing. She leads the Earth and Planetary Remote Sensing Laboratory within the Center for Planetary Exploration, focusing on Mars surface processes, mineralogy, and planetary analog studies. Her work integrates infrared spectroscopy, thermal modeling, and field observations to investigate planetary evolution. Education: B.S. from College of Charleston (1998), M.S. and Ph.D. from Arizona State University (2001, 2005), followed by a postdoc at Caltech (2005–2007). She has been a faculty member at Stony Brook since 2007. Research interests include planetary surface compositions, remote sensing techniques, laboratory spectroscopy of Mars analog materials, and applications of machine learning in planetary science. She has advised numerous graduate students and postdocs, contributing to studies of Martian bedrock, asteroid Bennu mineralogy, and subsurface processes. Rogers’ lab houses specialized equipment for spectral analysis, thermal modeling, and field-based studies. Her team has collaborated on NASA missions, including Mars Science Laboratory and OSIRIS-REx, and contributed to over 80 peer-reviewed publications.

Julia DobrosotskayaView profile

Associate Professor

• Harmonic Analysis
• Partial Differential Equations (PDE)
• Variational Methods
• +3 more

Julia Dobrosotskaya is an Associate Professor at the Department of Mathematics, Applied Mathematics, and Statistics, College of Arts and Sciences, Case Western Reserve University. Her research focuses on harmonic analysis, partial differential equations (PDE), variational methods, sparse representations, and signal/image processing. She specializes in developing mathematical models for image analysis with applications in biomedical imaging, remote sensing, and computational vision. Her work integrates theoretical mathematics with practical challenges in data science, emphasizing multiscale analysis, wavelet-based techniques, and variational calculus. Notable research includes modeling retinal photo-bleaching kinetics, hyperspectral image classification, and PDE-free variational methods for image segmentation. Her publications span applied mathematics, signal processing, and biomedical imaging, addressing topics from optical flow velocimetry to data-adaptive multiscale representations. She collaborates across disciplines, bridging pure mathematics with engineering and medical applications.