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Iren ValovaView profile

Professor

• Artificial Intelligence
• Brain Modeling
• Data Mining
• +4 more

Iren Valova, PhD, is a Professor and Associate Dean of the College of Engineering at the University of Massachusetts Dartmouth. She holds a PhD in Computer Science from Tokyo Institute of Technology (1997), an MS in Applied Mathematics from Sofia Technical University (1993), and a BS in Computer Science from Sofia Technical University (1991). Her research focuses on artificial intelligence, brain modeling, data mining, expert systems, and image processing. She leads the Neural and Adaptive Research Laboratory (NASLAB), exploring neural networks, medical imaging, and machine learning applications. Dr. Valova has secured over $1.2 million in NSF grants for cybersecurity education and has been recognized as an ELATES Fellow in 2023 for leadership in national programs. Teaching highlights include courses in artificial intelligence, neural computing, algorithms, and game design. She advises students on MS projects and emphasizes academic integrity in coursework. Research contributions span neural network architectures, image segmentation, and olfactory system modeling, with over 50 peer-reviewed publications. Her lab collaborates on interdisciplinary projects involving medical imaging, ecological data analysis, and sensor networks. Current work includes developing adaptive learning algorithms for imbalanced datasets and optimizing CNNs for medical diagnostics. Dr. Valova advocates for innovation in STEM education and cybersecurity training through her administrative and research roles.

Mykel KochenderferView profile

Associate Professor

• Artificial Intelligence
• Aerospace Engineering
• Decision-Making Systems
• +6 more

Mykel Kochenderfer is an Associate Professor at Stanford University's Department of Aeronautics and Astronautics and holds a courtesy appointment in Computer Science. He is a Senior Fellow at the Stanford Institute for Human-Centered AI (HAI) and directs the Stanford Intelligent Systems Laboratory (SISL), focusing on advanced algorithms for robust decision-making systems in aerospace and autonomous systems. His research emphasizes safety and efficiency in uncertain environments, with applications in air traffic control, unmanned aircraft, and automated driving. Education: PhD from the University of Edinburgh (2006), M.S. and B.S. in Computer Science from Stanford (2003). Prior to Stanford, he worked at MIT Lincoln Laboratory on airspace modeling and collision avoidance, leading to the ACAS X program. He co-directs the Center for AI Safety, and is affiliated with SAIL, HAI, the Symbolic Systems Program, Bio-X, and the Wu Tsai Neurosciences Institute. Research interests span decision-making under uncertainty, optimization algorithms, AI safety, and robotics. He authored influential textbooks including Decision Making under Uncertainty (2015), Algorithms for Optimization (2019), and Algorithms for Decision Making (2022). Recognitions include the 2017 DARPA Young Faculty Award. His work bridges theoretical AI with real-world applications in aviation, healthcare, and environmental systems. Labs and collaborations include SISL, the Center for AI Safety, and interdisciplinary teams at Stanford. He advises students from multiple departments and actively participates in policy discussions on AI governance and ethics.

• Marine Ecology
• Climate Change Impacts
• Biodiversity Conservation
• +3 more

Graeme Clark is a Professor at the University of Sydney, affiliated with the Sydney Environment Institute. His research focuses on marine ecology, climate change impacts, and biodiversity conservation. He advises PhD student Jakob Quade on ecosystem connectivity and marine habitats. Clark’s work integrates spatial modeling, citizen science, and environmental monitoring to address challenges like marine debris and Antarctic ecosystem vulnerability. His research spans topics like Antarctic benthic communities, desalination effects on marine life, and the interplay between human activities and coastal ecosystems. Notable projects include the Securing Antarctica's Environmental Future (SAEF) initiative and MyClimate informatics tool development. Clark has co-authored over 50 peer-reviewed articles since 2005, emphasizing interdisciplinary approaches to ecological conservation. Grants: MyClimate: personalising the climate crisis with informatics (2024) Securing Antarctica's Environmental Future (SAEF) (2021) Labs/Teams: Collaborates with interdisciplinary teams at the Sydney Environment Institute and Antarctic research networks.

• Optical Character Recognition (OCR)
• Data Science
• Metadata (XMP)
• +1 more

Yan Han is an Instructor and Full Library Faculty member at the University of Arizona's College of Information Science. His research focuses on Optical Characterization Recognition (OCR), Data Science, and Metadata (XMP), with active participation in ISO standards development (e.g., XMP metadata, PDF standards). He leads projects such as the Afghanistan Digital Repository (afghandata.org) and the Pashto-English Dictionary (pashtoenglish.org), and contributed to the U.S. Electronic Theses and Dissertations white paper. His work emphasizes recycling mine tailings, biopolymer-stabilized materials, and sustainable construction solutions. Research interests include metadata standards, OCR applications, and data-driven library systems. He has advised over 12 students who secured roles in tech companies (Amazon, Microsoft) and academic institutions. His technical contributions span geopolymer composites, structural health monitoring, and rock mechanics. Awards and grants are not explicitly listed, but his extensive publications and ISO committee involvement highlight professional recognition. Key collaborations include the ANSI Technical Advisory Group and ISO/TC171. His lab and team efforts focus on digital preservation, metadata interoperability, and environmental applications of geopolymer materials. Current work continues exploring smart materials for construction and library informatics.

David AtkinsonView profile

Professor

• Climate Science
• Arctic Studies
• Coastal and Marine Systems
• +5 more

David Atkinson is a Professor and Acting Department Chair in the Department of Geography at the University of Victoria . His research focuses on climate and weather impacts on coastal and marine systems, with emphasis on the Arctic. He has held faculty positions at the University of Alaska Fairbanks (2004-2010) and postdoctoral roles at Bedford Institute of Oceanography and the University of Ottawa. Education: PhD in Geography from the University of Ottawa (2000). Atkinson's research examines environmental forcing of Arctic coastal zones through storm surges, wave action, and temperature inversions. His work integrates field instrumentation, Indigenous community engagement, and advanced statistical modeling. Key projects include ALPACA-PBL field experiments, ArcticNet collaborations, and NSERC-funded snowpack dynamics studies. His recent publications highlight interdisciplinary approaches to Arctic coastal erosion, climate change impacts, and operational weather metrics for shipping industries. MEOPAR , ArcticNet , and NSERC have provided major funding. His lab trains graduate students in methodology development, fieldwork, and community-based research. Teaching: He instructs graduate quantitative analysis (GEOG 524) and applied climatology (GEOG 373), emphasizing Python programming and real-world operational skills. Collaborations: Partnerships with Environment and Climate Change Canada, Geological Survey of Canada, Campbell Scientific Canada Corp., and Indigenous communities across Alaska and Canada.

Michelle LawingView profile

Associate Professor

• Global Change Biology
• Paleobiology
• Macroecology
• +9 more

Michelle Lawing is an Associate Professor in the Department of Ecology and Conservation Biology at Texas A&M University and a visiting fellow at the University of Pisa, Italy. Her research focuses on biodiversity responses to climate change, integrating conservation biology, ecology, evolution, and paleontology. She holds a B.S. (University of Texas at Arlington), M.S. (same institution), and a Ph.D. (Indiana University) in Biology with a dual focus on Evolution/Geological Sciences. Her expertise includes global change biology, paleobiology, and functional ecology, emphasizing interdisciplinary methods like ecological niche modeling, geometric morphometrics, and phylogenetic analysis. Recent work explores how species and communities adapt morphologically and geographically to environmental shifts, including studies of African megafauna and Miocene reptiles. Education: B.S. Biology, University of Texas at Arlington M.S. Biology, University of Texas at Arlington Ph.D. Double Major (Evolution, Ecology, and Behavior; Paleobiology), Indiana University Her research highlights include developing ecometric models to estimate paleoenvironments and predicting conservation outcomes under climate change. She collaborates globally and mentors students in applying quantitative methods to ecological and evolutionary questions. Key contributions span trait-based paleoenvironmental reconstructions, reptile and mammal morphology, and interdisciplinary data integration. Her lab (lawinglab.org) fosters STEM outreach through field stations and informal education initiatives.

• Machine Learning
• Health Informatics
• Artificial Intelligence
• +3 more

José Luis Castillo Sequera is a Professor at the University of Alcalá, affiliated with the Department of Computer Sciences. His research focuses on machine learning applications in healthcare, environmental science, and telecommunications. He holds a PhD from the University of Alcalá (2010) with a thesis on evolutionary algorithms for document clustering in information systems. His research interests include health computing, intelligent systems, renewable energy forecasting, and data-driven solutions for societal challenges. Notable work includes developing predictive models for solar radiation, disease detection systems, and innovative health monitoring apps like 'Sedentary' for office workers. Recent publications emphasize machine learning applications in diverse domains: from optimizing agricultural yields and reducing water losses to predicting geological hazards using quantum computing. He explores blockchain for healthcare interoperability and VR-based language learning tools. His contributions span over 100 publications since 2010, addressing topics like smart lighting systems, IoT-based health monitoring, and educational technology. He leads the HCIS research group focusing on health computing and intelligent systems.

• Machine Learning
• Health Informatics
• Optimization Algorithms
• +3 more

José Manuel Gómez Pulido is an Associate Professor at the University of Alcalá in the Department of Computer Science. His research focuses on machine learning applications in healthcare, optimization algorithms, energy efficiency in smart buildings, and IoT systems. He holds a PhD in Computer Science from the University of Alcalá, completed in 2005 under the supervision of Dr. José Ignacio Alonso Montes. He is a core member of the HCIS Research Group (Health computing and Intelligent Systems), which develops innovative solutions for health monitoring, predictive analytics, and smart healthcare systems. His work spans diverse areas such as: Predictive modeling for infectious diseases in elderly populations Metaheuristic optimization for medical and engineering challenges Energy-efficient HVAC systems in smart buildings IoT and cloud-based architectures for eHealth applications Recent research highlights include solar radiation prediction in arid climates, VR-based therapy for fibromyalgia patients, and fault detection in industrial IoT devices. His publications emphasize practical solutions for healthcare and environmental sustainability, leveraging neural networks, reinforcement learning, and microservices. He has collaborated on projects funded by national and international grants, focusing on: Autonomic management of building systems Cloud databases for rapid disease diagnosis Machine learning applications in emergency department forecasting His contributions bridge theoretical computer science with real-world applications, particularly in improving healthcare delivery and energy efficiency through advanced computational methods.

Kathleen BakerView profile

Professor

• Geographic Information Science
• Physical Geography
• Biogeography
• +7 more

Kathleen Baker is a Professor in the Department of Geography, Environment, and Tourism at Western Michigan University's School of Environment, Geography, and Sustainability. She is based in Wood Hall and has been actively contributing to research and teaching in geographic information science, physical geography, and biogeography. Education: Ph.D. in Geography, Michigan State University, 2002 M.A. in Geography, Western Michigan University, 1998 B.S. in Earth Science, Central Michigan University, 1995 Her research integrates spatial and temporal analysis with field-based studies in geology, soil science, meteorology, climatology, and plant pathology. She specializes in developing GIS-based models for agricultural and environmental applications, particularly in disease forecasting and geocognitive mapping processes. Her work, supported by USDA and NSF funding, focuses on creating multi-scale, multi-crop disease forecasting systems and understanding expert problem-solving in geologic mapping. These efforts contribute to sustainable agriculture and improved geoscience education. Professional Affiliations: Association of American Geographers International Network for Information Technology in Agriculture As a principal investigator on major federally funded projects, she mentors students and contributes to advancing geospatial methodologies. Her certification as a Geographic Information Systems Professional underscores her technical expertise. She is involved in labs and research teams focusing on geocognitive modeling and agricultural informatics, particularly through the GeoBRAENS project and crop disease forecasting initiatives.

• Remote Sensing
• Water Biogeochemistry
• Harmful Algal Blooms
• +7 more

Padmanava Dash is an Associate Professor in the Department of Geosciences at Mississippi State University (MSU), specializing in remote sensing and water biogeochemistry. His work integrates satellite, unmanned aerial systems (UAS), and autonomous surface vessels to monitor and model water quality parameters such as harmful algal blooms, suspended sediments, nutrients, and heavy metals. He leads the development of web-based visualization tools for water quality management and supports federal, state, and coastal community initiatives. Education Ph.D. in Oceanography and Coastal Sciences, Louisiana State University, 2011 M.S. in Geology, Bowling Green State University, 2005 Research Interests Dr. Dash’s research is centered on understanding the drivers and consequences of water quality degradation in aquatic systems. Using multi-scale remote sensing techniques—from handheld spectrometers to satellites—he quantifies spatiotemporal patterns of phytoplankton blooms, dissolved organic matter, acidification, and trace metals. Advanced machine-learning algorithms are routinely integrated to improve retrieval accuracy and predictive capacity, enabling early warning systems for human and ecological health risks. A significant component of his portfolio involves the design and deployment of autonomous platforms (UAS and ASV) for high-resolution, real-time data acquisition. These efforts feed into web portals that translate complex geospatial data into intuitive maps and dashboards for stakeholders, thereby bridging the gap between science and policy. Publication Trends Between 2024 and 2025, Dr. Dash has published extensively on three converging themes: (1) machine-learning-enhanced retrieval of water quality parameters from satellite and UAS imagery, (2) geochemical immobilization of iodine and uranium using phosphate minerals for nuclear waste management, and (3) physical-biological interactions in marine systems such as mesoscale eddies and hypoxia in the Gulf of Mexico and Bay of Bengal. The work spans freshwater lagoons, estuaries, and coastal oceans, demonstrating a commitment to transdisciplinary, data-driven environmental science. Scientific Awards No specific awards are listed in the provided materials. Advising & Funding While current Ph.D. or master’s students are not named in the text, Dr. Dash’s active laboratory and consistent publication record suggest ongoing mentoring and grant support. External collaborations span federal agencies (NASA, NOAA), state departments of environmental quality, and international partners in India’s Chilika Lagoon. Laboratory & Field Infrastructure Dr. Dash operates the Remote Sensing and Water Quality Lab at MSU, equipped with hyperspectral sensors, UAS platforms, autonomous surface vessels, and high-performance computing resources. The lab maintains two public web tools for real-time water quality visualization accessible at water.geosci.msstate.edu .

• Reinforcement Learning
• Dynamic Resource Allocation
• Manufacturing Systems
• +6 more

Dr. Fouad Bahrpeyma is a faculty member at the Faculty of Informatics / Mathematics with a distinguished research career focused on artificial intelligence applications in industrial systems. His work bridges theoretical machine learning advancements with practical implementations in manufacturing and petroleum engineering sectors. His research interests span multiple domains: Reinforcement Learning and Multi-Agent Systems for industrial applications Dynamic Resource Allocation and Scheduling in manufacturing environments Quality of Service Management in Service-Oriented Architectures Active Learning methods for petroleum reservoir analysis Time Series Prediction and meta-learning approaches Analysis of his publication trends reveals a strategic evolution from foundational cloud computing research (2013-2016) toward specialized industrial applications. Since 2022, his work has focused intensely on multi-agent reinforcement learning frameworks like YoloRL and EncodedRL for dynamic scheduling problems, while simultaneously developing active learning methodologies for hydrocarbon reservoir characterization. His research demonstrates strong industry relevance, particularly evident in collaborations with platforms like Nvidia Omniverse for robotics applications. Dr. Bahrpeyma's scholarly contributions show consistent productivity with numerous publications in high-impact venues across computer science and engineering disciplines.

David HutchinsonView profile

Research Fellow

• Paleoclimate Modeling
• Climate Change
• Ocean Circulation
• +7 more

Dr. David Hutchinson is a Research Fellow at the Climate Change Research Centre within the Faculty of Science at the University of New South Wales (UNSW). He is also a long-term visitor at ANU Earth Sciences as part of the Climate and Fluid Physics group. Currently undertaking an ARC DECRA fellowship from 2022 to 2025, his work focuses on paleoclimate modeling and Earth system dynamics during geological warm periods. His educational background includes a PhD from the University of New South Wales (2011-2016) and a PhB (Hons) from the Australian National University (2005-2008). Dr. Hutchinson specializes in paleoclimate modeling, with particular expertise in past warm climate intervals including the Eocene (56-34 Ma), Oligocene (34-23 Ma), and Miocene (23-5 Ma). His research investigates the long-term evolution of deep ocean circulation and the development of the Antarctic Circumpolar Current. He has developed sophisticated simulations using the coupled climate model GFDL CM2.1 and is extending these to new intervals in the Oligocene and Miocene. His work also includes simulations of the Last Interglacial (127 ka), examining the impacts of partial melting of the West Antarctic ice sheet. Analysis of his recent publications reveals a strong focus on Eocene climate dynamics, with particular attention to ocean circulation patterns, climate variability during geological warm periods, and model-data comparisons. His research spans multiple disciplines including paleoceanography, climate modeling, and biogeochemical cycles, with significant contributions to understanding how Earth's climate system responds to elevated temperatures. Scientific Awards: ARC DECRA fellowship DE220100279: Did ocean circulation changes build the Antarctic ice sheet? (2022-2024, $453,000 AUD) FORMAS Mobility grant 2018-01621: Climate Impacts of Paleogeography at the Eocene-Oligocene Transition (2019-2021, 3,260,925 SEK) Dr. Hutchinson has secured significant research funding including his current ARC DECRA fellowship and a previous FORMAS Mobility grant. His current research involves developing equilibrium simulations of the Oligocene (30 Ma) and Miocene (15 Ma) warm climates using GFDL CM2.1, developing snapshot simulations using ACCESS-ESM1.5, enabling offline tracer simulations of Neodymium isotopes, and simulating partial loss of the West Antarctic Ice Sheet during the Last Interglacial. His work is highly collaborative, with numerous co-authored publications across leading climate science journals. As part of the Climate Change Research Centre at UNSW, Dr. Hutchinson contributes to one of Australia's leading climate research institutions, working alongside experts in paleoclimate modeling, climate dynamics, and Earth system science. His research integrates with broader efforts to understand past climate states as analogs for future warming scenarios.

Jens HeggView profile

Assistant Professor

• Ecology
• Fish Migration
• Geochemistry
• +2 more

Jens Hegg is an Assistant Professor in the Department of Biology at Gonzaga University. His work integrates ecology, geochemistry, and conservation biology to study migratory fish species. Education: Ph.D. from University of Idaho (2017) B.A. from Macalester College (2000) Research focuses on fish migration using isotopic analysis of otoliths (ear stones) to reconstruct movement patterns and life history. His studies span Chinook salmon in the Pacific Northwest and Amazonian catfish/sawfish in Brazil, emphasizing environmental tracking and conservation implications . Recent publications highlight innovations in otolith chemistry , data sonification , and trophic niche analysis across freshwater and marine ecosystems. Collaborative work with students like Breanna Graves and Carine G. Moraes demonstrates his commitment to mentorship.

Cai YtsmaView profile

Research Fellow

• Machine learning
• Applications in health
• Data engineering and data science
• +4 more

Cai Ytsma is a Research Fellow and Health Data Scientist specializing in Clinical Epidemiology at University College London. They also hold an Affiliate position at Mount Holyoke College in the United States since September 2019 and serve as a Collaborator on the NASA VERITAS Science Team since June 2023. Ms. Ytsma's research spans multiple interdisciplinary domains with a focus on: Machine learning applications in health data analysis Electronic health records (EHR) and phenotyping for genetic associations Analytical spectrometry including Laser-Induced Breakdown Spectroscopy (LIBS), X-ray fluorescence, and absorption Planetary geology and data analysis for space missions Statistical data science and software engineering for data curation and analysis Ms. Ytsma's recent publications demonstrate a strong interdisciplinary approach, bridging health data science with planetary science. Their work shows expertise in developing computational frameworks for disease phenotyping in large datasets like the UK Biobank while simultaneously advancing analytical techniques for geological and planetary materials. A notable trend is the application of machine learning across both medical and geological domains, particularly in improving accuracy of spectroscopic measurements and disease prediction models. While specific scientific awards are not mentioned in the available information, Ms. Ytsma's work has gained significant attention, with publications picked up by numerous news outlets and cited across various platforms. Ms. Ytsma actively mentors students across disciplines in data science principles despite not having formal teaching responsibilities. They founded Cai Consulting in September 2019, providing data science consultation for diverse research projects. As a Collaborator on the NASA VERITAS Science Team (2023-2026), they contribute to planetary science missions, particularly in calibration and data verification for Venus exploration.

Aniko TothView profile

Research Fellow

• Ecology
• Paleoecology
• Ecosystem Risk Assessment
• +3 more

Dr. Aniko Toth is a Research Fellow at the Centre for Ecosystem Science within the Faculty of Science at the University of New South Wales (UNSW), where she collaborates with Professor David Keith on Antarctic ecosystem risk assessment models. Previously, she completed her PhD at Macquarie University in 2019 and served as a research consultant at the Smithsonian US National Museum of Natural History for five years, contributing to the Evolution of Terrestrial Ecosystems program. Her research spans ecology, paleoecology, and climate change impacts, with focus areas including community assembly processes across geological timescales, ecosystem collapse risk assessment, and Antarctic/Alpine conservation. Current projects involve developing spatial risk models for ice-free Antarctic habitats and standardizing trait data for biodiversity monitoring. Recent publications reveal strong trends in Quaternary mammalian community dynamics, biotic homogenization under climate change, and methodological advances in spatial biodiversity analysis. Her work frequently employs large-scale collaborative frameworks addressing ecosystem vulnerability in polar and alpine regions. Scientific recognition includes: Science Achievement Award for Best Paper of 2019 (Smithsonian, 2020) Student Service Award for digital literacy promotion (Macquarie University, 2019) Science Achievement Award for Best Paper of 2016 (Smithsonian, 2017) Richard L. Hoffman Award for student presentation (Virginia Herpetological Society, 2010) Dr. Toth actively contributes to multiple high-impact research consortia without formal advising responsibilities documented in current sources. She co-leads the Australian Alpine Ecosystem risk assessment working group and participates in the Smithsonian's Evolution of Terrestrial Ecosystems program and iDiv's trait data initiative, focusing on translating paleoecological insights into contemporary conservation frameworks. Her laboratory collaborations span UNSW's Centre for Ecosystem Science, Macquarie University's paleoecology group, and international networks including the Antarctic Science Partnership. Current efforts prioritize operationalizing ecosystem collapse metrics for IUCN Red List of Ecosystems assessments under accelerating climate change.