Find Academic Supervisors

Nicole LudwigView profile

Assistant Professor

• Machine Learning for Energy Systems
• Probabilistic Machine Learning
• Probabilistic Time Series Forecasting
• +3 more

Nicole Ludwig serves as a Junior Professor and Principal Investigator of the independent research group "Machine Learning in Sustainable Energy Systems" within the Cluster of Excellence – Machine Learning for Science at the University of Tübingen. Education: PhD in Computer Science, 2020, Karlsruhe Institute of Technology MSc in Information Systems and Network Economics, 2016, University of Freiburg BSc in Economics, 2014, University of Freiburg Ludwig's research focuses on applying machine learning techniques to sustainable energy systems, with particular emphasis on uncertainty quantification and the complex relationships between weather, climate, and energy systems. Her work integrates probabilistic approaches with time series analysis to address challenges in energy forecasting and management under uncertain conditions. She actively contributes to the development of methods for probabilistic forecasting and reinforcement learning applications in energy contexts. Ludwig maintains an active presence in the machine learning research community, with contributions to open-source projects related to energy time series analysis. Her GitHub profile shows engagement with repositories focused on graph convolutional networks and energy time series motif discovery.

Tim OatesView profile

Professor

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +7 more

Dr. Tim Oates is a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County . His research spans machine learning, artificial intelligence, and brain-machine interfaces, with a focus on weakly supervised methods, human-in-the-loop reinforcement learning, and grounded policy development for robotics. Ph.D., Computer Science, University of Massachusetts, Amherst, 2000 M.S., Computer Science, University of Massachusetts, Amherst, 1997 B.S., Computer Science and Electrical Engineering, 1989 Current research threads include: Developing non-invasive brain injury severity assessment via medical time series Modeling human brain development through computational frameworks Designing algorithms for autonomous robotic learning Recent publications highlight AI security mechanisms (backdoor detection via tensor decomposition, matrix factorization) Medical applications (3D artery reconstruction, skin lesion diagnosis, EEG denoising) Neuro-symbolic integration (holographic representations, language-guided reinforcement learning) Mathematical reasoning (schema-based problem solving, subitizing algorithms) Contact: oates@cs.umbc.edu | Office: 336 Information Technology and Engineering (ITE) Building

David WanikView profile

Assistant Professor

• Data Science
• Natural Hazards
• Remote Sensing
• +3 more

David Wanik is an Assistant Professor in the Department of Operations and Information Management and Associated Faculty in the Department of Civil and Environmental Engineering at the University of Connecticut. He serves as Academic Director for Business Data Analytics at the Stamford campus and conducts research in the Eversource Energy Center, focusing on data science, natural hazards, remote sensing, and IoT applications in utility systems. PhD, MS, and BS in Environmental Engineering from University of Connecticut His research bridges natural hazard prediction, power grid resilience, and environmental data science. Key themes include: Machine learning for power outage prediction Climate change impact on energy demand Remote sensing for population and environmental monitoring IoT-enabled infrastructure hardening Recent publications emphasize deep learning for nighttime light imagery analysis, hybrid physics-data-driven models for grid resilience, and climate-integrated demand forecasting. His work integrates satellite data, LiDAR, and utility infrastructure records for predictive analytics. Teaching includes courses in business analytics, Python-based data science, and deep learning for the MS Business Analytics and Project Management program.

• Power Systems
• Electrical Engineering
• Renewable Energy Integration
• +6 more

Pavlos S. Georgilakis is a Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), specializing in modern techniques for power system analysis, optimization, and renewable energy integration. He holds a Diploma (1990) and PhD (2000) in Electrical Engineering from NTUA. His career includes roles as Lecturer (2009) and Associate Professor (2018–2023) at NTUA, and Assistant Professor at the Technical University of Crete (2004–2009). Research focuses on power transmission/distribution systems, transformer design, and applying AI/optimization for grid efficiency. He led 10 research projects, including Horizon 2020 initiatives SHAR-Q, WiseGRID, and NobelGrid. He authored 3 books and over 230 publications (SCOPUS citations: >5,500). Editor of IET Smart Grid, Energies, and Electricity journals; senior IEEE member. He supervised 4 doctoral, 9 master’s, and 76 diploma theses. Awards include the 2013 Best Reviewer Award from Electric Power Systems Research. Active in energy storage, smart grids, and decentralized energy resource integration.

• Decision Making
• Risk Literacy
• Cognitive Psychology
• +5 more

Dr. Jinan Allan is an Assistant Professor at Clemson University's College of Behavioral, Social and Health Sciences, Department of Psychology. She holds a PhD in Cognitive Psychology from the University of Oklahoma (2021), an MS from the same university (2018), and a BS in Mathematics-Statistics from Michigan Technological University (2015). She completed a postdoctoral fellowship at the Max Planck Institute for Human Development. Her research focuses on cognitive abilities, statistical numeracy, and their impact on decision-making in contexts like medical scenarios, cybersecurity, and extreme weather risks. She is affiliated with the Media Forensics Hub and teaches courses on judgment and decision-making. Education: PhD Cognitive Psychology, University of Oklahoma, 2021 MS Cognitive Psychology, University of Oklahoma, 2018 BS Mathematics-Statistics, Michigan Technological University, 2015 Research emphasizes behavioral science and psychometric methods to develop decision-making metrics. Key themes include risk communication for tornado/hurricane warnings, climate change judgments, and cybersecurity decision-making. Her work bridges cognitive psychology with real-world applications in disaster preparedness and public health. Recent publications analyze extreme weather response patterns, numeracy's role in science comprehension, and probabilistic risk communication strategies. She is currently reviewing graduate student applications for 2025-2026. Affiliations include the Media Forensics Hub at Clemson University, indicating interdisciplinary collaboration in digital security and information integrity.

David R. ThemensView profile

Associate Professor

• Space weather
• Ionospheric modeling
• Radio communications
• +7 more

Dr. David R. Themens is an Associate Professor in Space Environment within the Space Environment and Radio Engineering (SERENE) group in the School of Engineering at the University of Birmingham. He specializes in modeling and mitigating the impacts of space weather on radio communications and navigation systems, with a particular focus on the ionosphere's effects on these technologies. Dr. Themens earned his academic credentials from Canadian institutions: BSc (Hons) in Physics from the University of New Brunswick (2011) MSc in Atmospheric and Oceanic Science from McGill University (2013) PhD in Physics from the University of New Brunswick (2018) His research primarily focuses on four interconnected areas: ionospheric modeling, ionospheric physics, measurement techniques, and radio propagation. Dr. Themens is particularly interested in the interaction between the ionosphere and the atmosphere, specifically how lower atmospheric forcing drives variability within the ionosphere and the interactions between the ionosphere and thermosphere. He is the principal developer of the Empirical Canadian High Arctic Ionospheric Model (E-CHAIM) , a high-latitude alternative to the International Reference Ionosphere (IRI) used for HF/UHF signal propagation modeling. His work includes exploring synergistic properties of different earth observation instruments, measurement technique development, data assimilation, and empirical modeling. Analysis of Dr. Themens' recent publication record reveals a strong emphasis on space weather phenomena, ionospheric modeling, and radio propagation. His work spans from fundamental ionospheric physics to practical applications in navigation and communication systems. Key themes include the development and validation of ionospheric models, analysis of space weather events (including the May 2024 geomagnetic superstorm), and the impact of solar phenomena on Earth's upper atmosphere. His research increasingly incorporates advanced data assimilation techniques and leverages multiple observational platforms including radar systems, GNSS networks, and satellite measurements. Dr. Themens holds significant leadership positions in the international space science community: Co-Chair of IAG-GGOS Joint Study Group on Understanding Ionospheric and Plasmaspheric Processes (2023-present) Chair of URSI Data Assimilation Working Group (2023-present) Co-Chair of IAGA Geospace Data Assimilation Working Group (2023-2027) URSI Commission G Early Career Representative (2023-2029) Chair of Canadian Association of Physicists Division of Atmospheric and Space Physics (2022-present) Dr. Themens actively mentors graduate students and is 'always looking for new Ph.D. students interested in the ionosphere, data assimilation, and radio propagation.' His research has been supported through contracts with Defence Research and Development Canada (DRDC) and various international collaborations. He leads the Canadian High Arctic Ionospheric Models (CHAIMs) project, which builds upon his doctoral work developing the E-CHAIM model. At the University of Birmingham, he teaches courses in Space System Engineering and Design, Space Mission Analysis and Design, and Space Environment.

Hyemi KimView profile

Adjunct Professor

• low frequency climate variability
• tropical meteorology
• ocean-atmosphere interaction
• +4 more

Hyemi Kim is an Adjunct Professor at the School of Marine and Atmospheric Sciences (SoMAS), Stony Brook University. Her research focuses on climate variability across subseasonal to decadal timescales, including topics like the Madden-Julian Oscillation (MJO), tropical-extratropical interactions, and extreme weather events such as atmospheric rivers and tropical cyclones. Education: Ph.D., 2008, School of Earth and Environmental Sciences, Seoul National University, South Korea Research Interests: Hyemi Kim's work spans four primary areas: (1) Climate prediction from subseasonal to decadal scales, (2) Tropical-extratropical interactions, (3) Extreme events (atmospheric rivers, storm tracks, tropical cyclones), and (4) Machine learning applications for subseasonal-to-seasonal (S2S) prediction. Publication Trends: Her research output emphasizes the MJO, its interactions with other climate modes (QBO, ENSO), and implications for extreme weather. Recent works analyze atmospheric rivers, storm tracks, and tropical cyclone activity, often linking these to large-scale climate variability. Publications frequently employ climate models (e.g., CESM1, SubX, NMME) to assess predictability and improve forecasting frameworks. Labs & Teams: She collaborates with institutions like the National Center for Atmospheric Research (NCAR) and contributes to multi-model experiments such as the Subseasonal Experiment (SubX) and North American Multi-Model Ensemble (NMME).

• Space Physics
• Magnetospheric Dynamics
• Plasma Turbulence
• +6 more

Brian Walsh is an Associate Professor of Mechanical Engineering at Boston University, with affiliations in the Departments of Astronomy and Electrical and Computer Engineering. He holds a Ph.D. in Mechanical Engineering from Boston University (2011) and a B.A. from Colgate University (2006). His research focuses on experimental space physics and spacecraft instrumentation, particularly studying solar wind-magnetosphere interactions, magnetopause reconnection, and X-ray imaging techniques. His work investigates energy transfer from the Sun to Earth's space environment, with a primary focus on the magnetopause. Walsh develops instruments for NASA and ESA missions, including the LEXI lunar X-ray imager and the CuPID CubeSat. He has contributed to missions like SMILE and pioneered compact solar energetic particle telescopes. His research spans CubeSat-based observations and large-scale space missions, emphasizing cross-scale coupling and global magnetospheric dynamics. Key research interests include plasma turbulence, magnetosheath dynamics, and exosphere variability. Walsh collaborates on projects like the Trans-Heliospheric Survey and the Magnetospheric Constellation (MagCon), aiming to advance understanding of heliospheric plasma behavior. His work also addresses space weather prediction and instrument calibration, such as the Carruthers Observatory Student Solar Monitor (COSSMo). Walsh's recent studies explore solar wind acceleration, ionospheric outflow asymmetry, and the role of recirculated plasmasphere material in ring current dynamics. His contributions to instrumentation and mission design highlight his dual role as a researcher and engineer in aerospace and space physics.

Elisa PerroneView profile

Assistant Professor

• Statistics
• Mathematical Statistics
• Dependence Modeling
• +4 more

Elisa Perrone is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology. Her research focuses on dependence modeling, copula theory, and their applications in fields such as public transport analysis, environmental risk assessment, and renewable energy forecasting. Academic Rank: Assistant Professor University: Eindhoven University of Technology (TU/e) Department: Mathematics and Computer Science Elisa’s work explores discrete copulas, zero-inflated data, optimal experimental design, and uncertainty quantification. She has contributed to modeling dependence structures in complex datasets, particularly in transportation systems and climate science. Recent research outputs highlight copula-based statistical post-processing for weather forecasts, analysis of multi-way contingency tables, and uncertainty reduction in LED health management. Her publications span top-tier journals and conferences in statistics and applied mathematics. Scientific Award : Second Best Poster Presentation Award (2015) Elisa actively organizes workshops like the Eurandom Workshop on Dependence Modeling and contributes to editorial activities. She teaches courses on linear statistical models, regression models, and dependence modeling.

• Precision Agriculture
• Remote Sensing
• Soil Science
• +3 more

Dr. Patrick Filippi is a Lecturer in Precision Crop Management at the School of Life and Environmental Sciences, University of Sydney. He is affiliated with the Precision Agriculture Laboratory and the Sydney Institute of Agriculture. His work focuses on integrating remote sensing, machine learning, and geostatistics to address challenges in precision agriculture, particularly in crop yield modeling, soil mapping, and environmental monitoring. Research interests include precision agriculture technologies, soil science applications, data-driven crop management, and the use of satellite and proximal sensing for agricultural decision-making. He has contributed to projects funded by the Grains Research and Development Corporation (GRDC) and the University of Sydney, focusing on spatial variability in crop production, soil constraints, and machine learning interpretability. Key achievements include developing the LimeSoDa dataset for soil mapping and winning the 2016 CSIRO AgData Challenge Hackathon. His grants span topics like nitrogen fixation mapping in legumes and frost/heat management analytics. Filippi collaborates closely with industry to translate research into practical tools for farmers. Awards: 2nd Place CSIRO AgData Challenge Hackathon (2016) Labs: Precision Agriculture Laboratory (https://precision-agriculture.sydney.edu.au/) Grants: Includes Strategic Partnership Seeding Grants (2024), GRDC-funded projects (2022–2024), and Start-Up Research Funding (2024).

• Biostatistics
• Public Health
• Epidemiology
• +9 more

Dr. Shuvo Bakar is a Senior Lecturer in the Sydney School of Public Health at the University of Sydney, within the Faculty of Medicine and Health. He holds a PhD in Statistics from the University of Southampton, UK, and has prior experience as an Assistant Professor at Yale University, Lecturer at the Australian National University, and Scientist at Data61 (CSIRO). His research focuses on statistical methods applied to public health challenges, including Bayesian hierarchical modeling, machine learning, spatio-temporal analysis, and their applications in epidemiology, clinical trials, and environmental health. Dr. Bakar's research interests span statistical methodologies such as Bayesian adaptive designs, small area estimation, and spatial risk modeling, alongside applications in child health, infectious diseases, and extreme weather impacts on health. He is an active member of academic communities, including the Royal Statistical Society (RSS Fellow), Statistical Society of Australia, and the Australian Trials Methodology Research Network. His work also involves collaborations on grants totaling millions in funding, addressing topics like climate change impacts on health inequity and cardiovascular disease prevention in remote regions. Education: PhD in Statistics (University of Southampton, UK) Key Research Themes: Obesity, Diabetes, Cardiovascular Disease; Reproductive, Maternal & Child Health Grants/Projects: Includes NHMRC-funded trials on respiratory infections in First Nations children and MRFF grants for cardiovascular risk reduction in regional Australia. Dr. Bakar's contributions extend to editorial roles for Nature Scientific Reports and Discover Public Health , and his research has been published in journals like PloS One , Climatic Change , and Journal of the Royal Statistical Society .

Tao HongView profile

Professor

• Energy Forecasting
• Power Systems Operations
• Renewable Integration
• +7 more

Dr. Tao Hong is the Duke Energy Distinguished Professor and NCEMC Faculty Fellow at the Department of Systems Engineering and Engineering Management, University of North Carolina at Charlotte. He directs the Big Data Energy Analytics Laboratory (BigDEAL) and has been a Founding Chair of the IEEE Working Group on Energy Forecasting (2011-2019). Ph.D., Electrical Engineering & Operations Research (2010), NC State University M.S., Operations Research & Industrial Engineering (2008), NC State University B.Eng., Automation (2005), Tsinghua University His research focuses on Energy Forecasting with applications in power systems operations, renewable integration, risk management, and cross-sector forecasting for healthcare, transportation, and sports. He has led major Delivery point level load analysis (2017-present) Short-term probabilistic forecasting (2016) Demand response modeling using smart meter data (2014-2015) Dr. Hong's scientific contributions include 9+ journal articles on energy forecasting methodologies and 3 major forecasting competitions (GEFCom2012-2017, BigDEAL Challenge 2022). His work has been cited in leading journals like International Journal of Forecasting and IEEE Transactions on Smart Grid . Charlotte Business Journal Energy Education Leader of the Year (2017) IEEE PES PSPI Technical Committee Prize Paper Award (2016) As a dedicated educator , Dr. Hong has advised multiple PhD and Master's students including Shreyashi Shukla (2023), Yike Li (2022), and Jordan McCorey (2021). He teaches specialized courses in energy systems planning and computational intelligence.

Stefan Thor SmithView profile

Professor

• Energy Systems
• Urban Energy Modeling
• Climate Resilience
• +3 more

Professor Stefan Thor Smith is a distinguished academic at the University of Reading , serving as a Professor in the Department of Energy and Environmental Engineering . His work bridges energy systems with urban sustainability , focusing on the integration of social and technical aspects of energy demand , urban energy system modeling , and climate change resilience . Academic Qualifications Postgraduate Certificate in Academic Practice (University of Reading, 2016) PhD in Built Environment (University of Nottingham, 2009) MSc in Computer Science (University of Glasgow, 2002) BSc in Physics (University of Nottingham, 2001) His research interests span the dynamics of energy demand in socio-technical systems, urban heat fluxes, pollution exposure modeling, and climate adaptation strategies. He has developed novel models for energy demand-side management , building environmental control , and urban climate interactions . Recent publications highlight his expertise in areas such as EV charging infrastructure , urban tree radiative performance , phase change material storage , and anthropogenic heat emissions . His work often involves interdisciplinary collaborations with institutions like the Centre for Research into Energy Demand Solutions and the Institute of Physics . Smith supervises a diverse group of postgraduate students and contributes extensively to teaching modules including Numerical Modelling and Programming and Urban Sustainability . His professional affiliations include the Institute of Physics , International Association of Urban Climatology , and the Higher Education Association .

Ida ScheelView profile

Associate Professor

• Bayesian Statistics
• Data Science
• Machine Learning
• +7 more

Ida Scheel is an Associate Professor in Statistics and Data Science at the University of Oslo , Department of Mathematics. She specializes in Bayesian hierarchical modeling, recommendation systems, and stochastic processes on networks. Her research interests include: Bayesian statistics and model diagnostics Data science applications in environmental and health domains Network-based machine learning Uncertainty quantification in predictive modeling Recent publication trends show a focus on Bayesian model validation, machine learning for product adoption prediction, and real-estate analytics. She contributes to interdisciplinary projects like BigInsight and CELS . Scientific awards : Sverdrup Prize for Young Researchers (2011) Advising : Supervised 8 PhD students (main/co-supervisor) in areas spanning Bayesian causal effects, neural network survival analysis, and model conflict detection. Key grants include participation in the Data Science@UiO and Integreat projects. Labs/teams : Active member of the Center for Computational Inference in Evolutionary Life Science (CELS) and the BigInsight center.

• Power Systems
• Renewable Energy
• Smart Grids
• +7 more

Dr. Aris Dimeas is a Researcher at the National Technical University of Athens in the Department of Electric Power and Industrial Applications . He holds a diploma and PhD in Electrical and Computer Engineering from NTUA and has extensive experience in power systems operations, renewable energy integration, and smart grid technologies. Specialized in AI applications for power systems Developed control software for demand side management Consultant for PPC (2007-2012) Research Focus : Smart grids and digital twin implementations Renewable energy market dynamics Microgrid optimization and control algorithms Collaborations : Active participant in EU research projects, collaborating with HEDNO and other energy grid operators on electronic meters and intelligent network deployments. Teaching : Instructs courses on electric energy systems, power system analysis, and energy management.