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Kelsey CanadaView profile

Assistant Professor

• Developmental Cognitive Neuroscience
• Memory Development
• Hippocampal Development
• +7 more

Kelsey Canada is an incoming Assistant Professor in the Department of Psychological and Brain Sciences at the University of Massachusetts Amherst, starting Fall 2025. She is based in Tobin Hall and will lead a research program focused on developmental cognitive neuroscience. Her research centers on understanding how memory and brain development, particularly in the hippocampus, evolve during childhood and adolescence. She examines how socioeconomic factors influence neurodevelopment, using interdisciplinary methods including MRI , EEG , behavioral testing , and structural equation modeling . Her work emphasizes representative sampling and data integration to uncover modifiable risk and protective factors. Dr. Canada's recent publications highlight her leadership in hippocampal subfield research, quality control in neuroimaging, and collaborative data practices. Her work spans developmental trajectories, methodological innovation, and social determinants of brain health. She earned her PhD from the University of Maryland, College Park, and is establishing her lab at UMass Amherst. Prospective students are encouraged to contact her directly. Research Areas: Developmental Science, Neuroimaging, Cognitive Development Affiliation: Department of Psychological and Brain Sciences, UMass Amherst Dr. Canada has been involved in major collaborative initiatives, including the Hippocampal Subfields Group, and her research is supported by recent publications in top journals such as Human Brain Mapping , Hippocampus , and Developmental Cognitive Neuroscience . While no formal grants or advising roles are listed yet, her active publication record and lab formation indicate a growing research program.

• Unmanned Aerial Vehicles (UAVs)
• Field Programmable Gate Arrays (FPGAs)
• Computational Fluid Dynamics
• +6 more

Ramazan Yeniçeri is a Lecturer at Istanbul Technical University's Department of Aeronautical Engineering. His research focuses on Unmanned Aerial Vehicles (UAVs), Field Programmable Gate Arrays (FPGAs), and computational fluid dynamics, with applications in hardware acceleration and autonomous flight systems. Academic Rank: Lecturer University: Istanbul Technical University Department: Aeronautical Engineering Research Interests: Yeniçeri's work bridges aerospace engineering and computer science, emphasizing: FPGA-based hardware acceleration for aerospace systems UAV communication networks (FANETs) and formation flight Dynamical modeling for 6-DoF systems Autopilot software and real-time operating systems Scientific Awards: He has received the BOEING Academic Work Encouragement Award (2017) and the Best Doctoral Thesis Award (2015) . Project Leadership: As Principal Investigator (PI), he leads projects like: "IHA Kayıt, Takip, Kontrol ve Hava Trafik Yönetim Sistemi" (2024–2025) "FPGA Tabanlı 6DoF Dinamik Hızlandırıcı Tasarımı" (2024) "EU Sürü İHA" (2020–2022) His recent publications highlight trends in UAV communication, FPGA acceleration, and multi-sensor tracking.

• Bioinformatics
• Computational Biology
• Pharmaceutical Drug Discovery
• +6 more

Leonard P. Wesley is an Associate Professor at the Computer Science Department, College Of Science, San Jose State University. With a Ph.D. and M.S. in Computer Science from University of Massachusetts and a B.A. in Physics and Math from Northeastern University, his work spans bioinformatics, pharmaceutical discovery, machine learning, robotics, and evidential reasoning. He has published extensively on SVM/QSAR-based drug prediction, autonomous systems, and uncertainty management. Ph.D., University of Massachusetts - Computer Science M.S., University of Massachusetts - Computer Science B.A., Northeastern University - Physics and Math His research focuses on developing predictive models for drug discovery, autonomous robotics, and data analytics. Recent publications emphasize SVM applications in medical diagnostics and pharmaceutical modeling. He has contributed to conferences in aerospace, robotics, and biotechnology, with invited talks at NASA and Los Alamos National Laboratory. 3D-QSAR & SVM prediction of drug inhibitors Evidential decision analytics Autonomous robotic control PCA/SVM-based sepsis diagnostics Hybrid network congestion management Professor Wesley teaches courses in artificial intelligence, bioinformatics, and advanced programming. His lab investigates applications of machine learning in biotechnology and aerospace, including biomarker identification and CFD expert systems. He has served as session chair at international conferences and collaborated with institutions like NASA and Advanced Decision Systems.

• Multimedia Modeling
• Machine Learning
• Computer Vision
• +7 more

Dr. Stevan Rudinac is a Researcher at the University of Amsterdam's Faculty of Economics and Business , Section Business Analytics . His work focuses on interactive learning systems and multimodal data analysis, particularly in urban contexts and multimedia modeling. Education: PhD in Multimedia and Information Retrieval from Delft University of Technology (2013). Research Interests: Stevan specializes in multimedia modeling , hypergraph learning , and interactive video search . He develops frameworks for scalable analysis of social networks, urban imagery, and large multimodal datasets, bridging machine learning with practical applications in city planning and financial social media. Recent Trends: His 2024-2025 publications highlight large language model optimization , diffusion model evaluation , and dynamic graph embedding for meme stocks. Collaborative projects include the CASTLE 2024 dataset and Exquisitor , a system for 100 million image exploration. Labs & Teams: He contributes to the Business Analytics group at UvA, collaborating with Prof. Marcel Worring and Dr. Björn Þór Jónsson. He co-organized the UrbanMM'21 workshop and participates in ACM Multimedia and MMM conferences.

Tauhidul AlamView profile

Assistant Professor

• Artificial Intelligence
• Robotics
• Motion Planning
• +1 more

Tauhidul Alam serves as Assistant Professor in the Department of Computer Science within the College of Arts and Sciences at Louisiana State University Shreveport (LSUS), where he has taught since 2019. His research focuses on advancing autonomous robotic systems through innovations in artificial intelligence and cyber-physical applications. Dr. Alam holds a Ph.D. in Computer Science awarded in 2018. His scholarly work centers on robotics challenges including motion planning for underwater vehicles, multi-robot coordination under resource constraints, and energy-aware autonomous navigation. Key research domains span artificial intelligence, cyber-physical security using blockchain, and persistent monitoring in constrained environments. Analysis of his 14 recent publications reveals a strong emphasis on solving real-world robotics problems in marine and aquatic settings. His work consistently addresses uncertainty handling, multi-agent coordination, and security vulnerabilities, with increasing integration of data-driven methodologies across autonomous systems research. Scientific recognition includes: Best Student Paper Finalist at MTS/IEEE OCEANS Conference (2018) Dr. Alam teaches undergraduate courses including Computer Architecture (CSC 242), Database Systems (CSC 315), and Artificial Intelligence (CSC 465), alongside graduate-level instruction in Programming Languages (CSC 620) and Cloud Computing (CSC 690). Information regarding advised students, research grants, or laboratory affiliations is not specified in available materials.

• Earth Observation
• Synthetic Aperture Radar (SAR)
• Remote Sensing
• +6 more

Dr. Armando Marino is a Senior Lecturer in Earth Observation at the University of Stirling’s Department of Biological and Environmental Sciences since 2018. He holds an MSc in Telecommunication Engineering (2006, Universita’ di Napoli) and a PhD in Polarimetric SAR Interferometry (2011, University of Edinburgh). His research focuses on synthetic aperture radar (SAR) for environmental monitoring, including maritime pollution, forest degradation, agricultural productivity, and coastal erosion. Education: MSc Telecommunication Engineering, Universita’ di Napoli ‘Federico II’ (2006) PhD in Remote Sensing, University of Edinburgh (2011) Marino develops machine learning algorithms for SAR data analysis and conducts fieldwork with custom-built radar systems. He collaborates with institutions like ESA, JAXA, and NASA, leading projects such as PlasticSurf (microplastic detection) and MoLaDy (ALOS-4 land monitoring). His work integrates optical and SAR satellite data for flood mapping and vegetation analysis. He has received accolades including the RSPSoc Best PhD Thesis (2011) and University of Stirling’s Outstanding Collaborator award (2022). Current projects involve £180,000+ in funding for radar-based environmental solutions. Scientific Awards: Best PhD Thesis 2011 (RSPSoc) Outstanding PhD Thesis (Springer Verlag) Outstanding Collaborator 2022 (University of Stirling) Marino’s methodologies combine SAR polarimetry, computer vision, and environmental field measurements. He actively mentors interdisciplinary teams and contributes to global initiatives on climate hazard mitigation.

Wolfgang EpplerView profile

Researcher

• Artificial Intelligence
• Digital Transformation
• Technology Assessment
• +5 more

Dr. Wolfgang Eppler is a Researcher at the Institute for Technology Assessment and Systems Analysis (ITAS) at the Karlsruhe Institute of Technology (KIT), where he has been working since 2023. His research focuses on the societal implications of digital technologies, particularly artificial intelligence and digital transformation. Prior to his current position, he served in various leadership roles related to staff representation at KIT and its predecessor institutions. Dr. Eppler received his education at the University of Stuttgart, where he completed his computer science studies from 1980 to 1986. He then worked as a research assistant at the University of Karlsruhe and the Research Center for Information Technology (FZI) from 1987 to 1993, during which time he earned his doctorate on the topic of "Pre-structuring of neural networks with fuzzy logic." Dr. Eppler's research spans multiple domains at the intersection of technology and society. His early work focused on neural networks, fuzzy logic, and their applications in areas such as electronic noses, medical imaging, and high-energy physics data processing. In recent years, his research has shifted toward technology assessment, particularly examining the societal impacts of artificial intelligence, digital transformation, and the governance of emerging technologies. His interdisciplinary approach combines technical expertise with social science perspectives to address complex questions about the role of technology in society. Analysis of Dr. Eppler's recent publications reveals a strong focus on the ethical, governance, and societal implications of artificial intelligence. His 2024-2025 work addresses critical issues such as EU AI regulation, generative AI for technology assessment, the grounding of large language models, and algorithmic bias. This represents an evolution from his earlier technical work on neural networks and data processing systems toward more policy-oriented research that bridges technical and social dimensions of technological change. Throughout his career, Dr. Eppler has been actively involved in institutional governance and staff representation. From 2009 to 2023, he served as Chairman of the Staff Council at KIT, and from 2005 to 2009 as Chairman of the Works Council at the Karlsruhe Research Center. His publications on university governance, particularly regarding the KIT merger and models for democratic science institutions, reflect his practical experience and theoretical interest in participatory decision-making in academic settings. Dr. Eppler is a member of the Research Group "Digital Technologies and Social Change" at ITAS, where he contributes to projects examining the societal dimensions of technological innovation. His interdisciplinary background enables him to bridge technical and social science perspectives in assessing emerging technologies.

• Volunteered Geographic Information (VGI)
• OpenStreetMap
• Spatial Data Analysis
• +3 more

Peter Mooney is a Lecturer in the Department of Computer Science, Faculty of Science & Engineering at Maynooth University. His research focuses on Volunteered Geographic Information (VGI), OpenStreetMap, spatial data analysis, and geospatial data integration in applications such as environmental monitoring and pervasive health systems. Institution: Maynooth University School: Faculty of Science & Engineering Department: Computer Science Role: Lecturer Mooney's research explores the use of crowdsourced geospatial data, particularly through OpenStreetMap, analyzing data quality, community roles, and integration into location-based services. His work bridges technical analysis with policy considerations in geospatial data management. Recent publications highlight his contributions to understanding spatial data dynamics, including attribute changes in OpenStreetMap, characteristics of edited objects, and applications of VGI in environmental systems. He also investigates the intersection of haptics and GIS for novel interaction methods. Contact: peter.mooney@mu.ie

Fabiano PallonettoView profile

Professor

• Smart Grids
• Sustainable Mobility
• Energy Systems Optimization
• +3 more

Fabiano Pallonetto is a Professor at Maynooth University's School of Business, with affiliations to the Hamilton Institute and Innovation Value Institute (IVI). He combines academic research with industry experience in energy, IT, and transport sectors. Role: Professor Location: Room 314, Maynooth University Contact: Fabiano.Pallonetto@mu.ie His research focuses on smart grid integration, energy system optimization, and sustainable development. Key projects include: NexSys (Funded Investigator): Developing net-zero energy pathways FLOW (Principal Investigator): Flexible EV-grid integration RES4CITY (Coordinator): Workforce upskilling for renewables Recent publications analyze energy flexibility software, deep learning optimization models, phase change materials for thermal storage, and blockchain security frameworks. His work spans smart cities , renewable integration , and AI-driven energy systems . Student Supervision: Currently advising MR B. Mohseni-Gharyehsafa (PhD research).

• Internet of Things
• Fog Computing
• Edge Computing
• +9 more

Amirhosein Taherkordi is a Professor in the Networks and Distributed Systems group at the Department of Informatics, University of Oslo, Norway. His research focuses on resource-efficiency, scalability, adaptability, dependability, mobility and data-intensiveness of distributed systems for emerging computing technologies including Internet of Things (IoT), Fog/Edge/Cloud Computing, and Cyber-Physical Systems (CPS). Dr. Taherkordi received his Ph.D. from the Informatics Department at the University of Oslo under the supervision of Prof. Frank Eliassen, with his thesis titled "Programming Wireless Sensor Networks: From Static to Adaptive Models." He holds an M.Sc. in Information Technology Engineering (Software Engineering) from University of Science and Technology and a B.Sc. in Computer Engineering from Sharif University of Technology. His research spans multiple domains of distributed systems with emphasis on practical applications. He investigates energy efficiency in wireless sensor networks, communication optimization in IoT systems, and adaptive resource allocation in edge computing environments. His work addresses critical challenges in network traffic classification, federated learning for vehicular networks, and data processing across heterogeneous platforms. Analysis of his recent publications reveals a strong trajectory toward communication-efficient federated learning techniques for vehicular networks, energy-aware protocols for IoT data collection, and advanced machine learning approaches for network traffic analysis. His research consistently focuses on optimizing resource usage while maintaining system performance and privacy in distributed architectures. Dr. Taherkordi actively contributes to several research initiatives including the CPS Lab at UiO for Cyber Physical Systems, DILUTE: Fluid Service Abstraction for Large-Scale Cloud IoT Systems, and the Gemini Centre on IoT at UiO. His work bridges theoretical advances with practical implementations in transportation systems, environmental monitoring, and industrial automation.

Manohar N. MurthiView profile

Associate Professor

• Machine Learning
• Quantum Computing
• Signal Processing
• +5 more

Manohar N. Murthi serves as an Associate Professor in the Department of Electrical & Computer Engineering at the University of Miami's College of Engineering. His academic profile shows active engagement across both technical engineering domains and social science research, with recent publications spanning quantum computing applications, neural network models for biomedical signal processing, and political conspiracy theories. Dr. Murthi's research interests bridge multiple disciplines, with primary focus areas including machine learning, quantum computing, signal processing, belief theory, and conspiracy theory research. His work demonstrates a unique interdisciplinary approach that connects electrical engineering methodologies with social science applications, particularly in analyzing belief systems and misinformation patterns. The breadth of his research is evident in publications ranging from technical algorithms for Dempster-Shafer belief theory to sociological studies on White Replacement theory and QAnon conspiracy beliefs. Analysis of his recent publications (2020-2024) reveals two distinct but complementary research trajectories: technical work in quantum tensor networks, graph neural networks, and belief theory frameworks; and social science applications examining conspiracy theories, political extremism, and misinformation. His technical papers often develop novel computational frameworks for uncertainty quantification and data analysis, while his social science work applies these methodologies to understand belief formation and political behavior. This dual focus creates a distinctive research profile that connects engineering rigor with social science insights. Dr. Murthi maintains active research collaborations, particularly with Kamal Premaratne, across multiple publications in both engineering and political science journals. His work has been published in venues including IEEE transactions, The Journal of Politics, and Politics, Groups and Identities, demonstrating successful cross-disciplinary scholarship. His research appears to be supported by collaborative grants, though specific funding sources aren't detailed in the available information. While specific laboratory information isn't provided in the source material, Dr. Murthi's research suggests involvement in computational laboratories focused on machine learning, signal processing, and data analysis. His work with sEMG signals for gesture recognition indicates potential connections to biomedical engineering labs, while his belief theory research suggests computational theory groups. His interdisciplinary approach likely involves collaboration across multiple research teams within and beyond the College of Engineering.

Olga KurasovaView profile

Professor

• Machine Learning
• Data Visualization
• Cybersecurity
• +3 more

Dr. Olga Kurasova is a Professor and Senior Researcher at Vilnius University's Institute of Data Science and Digital Technologies, where she leads research in the Cognitive Computing Group. Her work focuses on developing advanced computational methods for real-world applications. Her primary research explores machine learning paradigms including deep learning for cybersecurity (keystroke dynamics, adversarial attacks), medical image analysis (pancreatic cancer detection), industrial monitoring, and explainable AI. She maintains strong collaborations across disciplines, particularly in healthcare and security domains. Analysis of her recent publications (2023-2025) reveals three dominant themes: (1) Advanced biometric authentication systems using behavioral analysis and deep learning, (2) Medical AI applications focusing on pancreatic cancer detection through CT image analysis, and (3) Theoretical advancements in explainable AI methodologies for high-stakes domains. Significant scientific recognition includes: 2021 Lithuanian Science Prize for the cycle 'From Data Science to Artificial Intelligence Technologies' 2024 Vilnius University Rector's Science Prize She has led multiple national research projects, including a 2024-2027 LMT-funded initiative on 'adversarial machine learning for cybersecurity' and coordinated interdisciplinary teams for projects on cognitive computing capabilities and optimal data mining solutions. She directs research within the Cognitive Computing Group, focusing on developing intelligent systems for data analysis, visualization, and decision support across healthcare, cybersecurity, and industrial applications.

Zhi LiView profile

Assistant Professor

• Hydrological Modeling
• Flood Prediction
• Drought Monitoring
• +9 more

Zhi Li is an Assistant Professor at the University of Colorado Boulder's College of Engineering and Applied Science, Department of Civil, Environmental and Architectural Engineering. He leads the newly established Flood Lab, focusing on flood prediction and monitoring through remote sensing and coupled hydrologic-hydraulic models. Joined CU Boulder in Fall 2025 Former Dean's Postdoc Fellow at Stanford University PhD in Civil Engineering & Environmental Science from University of Oklahoma (2022) His research spans hydrological modeling , extreme events , and AI4Science applications, particularly in deep learning and intelligent agents for flood risk assessment. Li’s work also connects floods with public health and economic systems , aiming to develop the Flood-Agriculture-Climate-Economics-Disease (FACED) framework. Key Themes: High-resolution flood modeling Climate change impacts on hydro-meteorology Remote sensing integration Flood-agriculture interdependencies Global health implications Scientific Awards: Dean's Postdoc Fellow, Stanford Doerr School of Sustainability (2023) Hoving Fellowship, University of Oklahoma (2019) Li’s recent publications emphasize improved precipitation estimation (IMERG V07), Brown Ocean Effect studies, and Fourier neural operators for rapid flood forecasting. His collaborative work with NOAA and NASA focuses on comparing ground-based and spaceborne radar systems for extreme event analysis.

Noah P. MolotchView profile

Professor

• Mountain Hydrology
• Snow Hydrology
• Ecohydrology
• +4 more

Noah P. Molotch is a Professor in the Department of Geography at the University of Colorado Boulder, leading the Mountain Hydrology Group and affiliated with the Cryosphere and Surface Processes Lab and Niwot Ridge Long-Term Ecological Research program. His work integrates ground observations, remote sensing, and computational modeling to advance understanding of mountain hydrology, water resources, and ecohydrology in changing climates. His educational background includes: PhD in Hydrology and Water Resources from the University of Arizona (2004) MS in Environmental Science and Management from the University of California, Santa Barbara (2000) BA from the University of Colorado Boulder (1997) Molotch's research focuses on snow hydrology , ecohydrology , and remote sensing with emphasis on snowmelt distribution, soil moisture dynamics, and streamflow generation. He investigates how climate change alters snowpack characteristics and hydrological partitioning, affecting forest productivity and carbon cycling in montane ecosystems. His work spans from the Colorado Front Range to Chilean Andes, addressing critical water security challenges through field studies and satellite data integration. Recent publications (2024-2025) reveal three dominant research thrusts: (1) Advanced snow water equivalent estimation using machine learning and airborne remote sensing, (2) Climate impacts on snowmelt timing and hydrological partitioning across mountain systems, and (3) Ecohydrological feedbacks between snow dynamics and vegetation productivity. These studies increasingly incorporate drone-based multispectral imaging and cross-scale data fusion to resolve spatial heterogeneity in alpine environments. Molotch mentors PhD students Millie Spencer and Emma Tyrrell, with Spencer documenting glacier retreat in Chile through CUAHSI-funded research. His group has secured significant grants from the U.S. Bureau of Reclamation for developing operational snowpack datasets, while collaborating with NASA, NOAA, and water management agencies on real-time monitoring systems. Community engagement includes art-science collaborations like the Colorado Arts Science Environment Program to communicate climate impacts. The Mountain Hydrology Group operates cutting-edge facilities including the Cryosphere and Surface Processes Lab and contributes to the Niwot Ridge Long-Term Ecological Research site. They deploy airborne lidar, drone-based thermal imaging, and wireless sensor networks to map snow properties at unprecedented resolutions, directly informing water resource management during drought and flood events across the Western U.S.

Dawei HanView profile

Professor

• Hydroinformatics
• Flood Risk Management
• Rainfall Simulation
• +4 more

Dawei Han serves as Professor of Hydroinformatics at the University of Bristol's School of Civil, Aerospace and Design Engineering, leveraging advanced computational techniques to address hydrological challenges. Holding a B.Eng. and M.Sc. from Huabei alongside a Ph.D. from Salford, he is recognized as a Chartered Engineer (C.Eng.) and Fellow of the Chartered Institution of Water and Environmental Management (FCIWEM). His academic credentials include: Bachelor of Engineering (B.Eng.) from Huabei Master of Science (M.Sc.) from Huabei Doctor of Philosophy (Ph.D.) from University of Salford Professor Han's research focuses on integrating hydroinformatics with practical water management solutions, particularly in urban environments. His work pioneers applications of machine learning for rainfall nowcasting, radar-based hydrological monitoring, and climate change impact assessment. Key innovations include DREE-RF for rainfall energy estimation and frameworks for urban flood resilience, emphasizing data-driven approaches to enhance prediction accuracy and risk mitigation strategies. Analysis of his 2024-2025 publications reveals dominant themes in urban hydrology (40%), flood risk management (30%), and climate-remote sensing integration (30%). His research spans global contexts from UK catchments to Iraqi rainfall systems, consistently employing computational methods like neural networks and WRF modeling to address data-scarce environments and extreme weather events. Professional recognition includes: Fellow of the Chartered Institution of Water and Environmental Management (FCIWEM) While specific student supervision details are unavailable, his extensive publication record indicates active mentorship in hydroinformatics. Research grants likely support his work on radar remote sensing and urban climate adaptation, though explicit funding sources aren't documented in the source material. His affiliation with Bristol's engineering school positions him within interdisciplinary teams addressing infrastructure resilience, though laboratory-specific information remains unreported.