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Hyunsun LeeView profile

Senior Lecturer

• Statistical data analysis
• Stochastic branching process in wireless ad-hoc network
• Sound generation mechanism for aircraft engine nozzle
• +2 more

Hyunsun Lee is a Senior Lecturer at the School of Design , University of Massachusetts Amherst , affiliated with the Department of Mathematics and Statistics . She contributes to the M.S. Option in Statistics and holds a PhD in Mathematics from Stony Brook University, alongside M.S. and B.S. degrees in Mathematics from Yonsei University. Education: PhD in Mathematics, Stony Brook University M.S. in Mathematics, Yonsei University B.S. in Mathematics, Yonsei University Research Interests span statistical data analysis, stochastic branching processes in wireless networks, aircraft engine sound generation mechanisms, homogenization for heterogeneous composites, and Rayleigh-Taylor instabilities. These areas intersect applied mathematics, fluid dynamics, and computational modeling. Scientific Awards include the 2017 Golden Apple Award for Excellence in Mentoring at Hawaii Pacific University and a 2021-22 Distinguished Teaching Award nomination at UMass Amherst.

Dhireesha KudithipudiView profile

Professor

• Artificial Intelligence
• Neuromorphic Computing
• Energy-Efficient Machine Learning
• +5 more

Dhireesha Kudithipudi is a Professor in the Department of Computer Science at the University of Texas at San Antonio (UTSA) and Director of the MATRIX AI Consortium. Her work focuses on advancing neuromorphic computing, energy-efficient machine learning architectures, and lifelong learning systems. She holds a Ph.D. in Electrical and Computer Engineering from UTSA and an M.S. in Computer Engineering from Wright State University. Her research interests include AI algorithms, neuromorphic hardware design, spiking neural networks, and memristor-based systems. She leads initiatives in neuromorphic benchmarking (NeuroBench), energy-efficient computing roadmaps (EES2), and collaborative frameworks like the Neuromorphic Commons (THOR). Her lab develops neuromorphic chips with on-device learning capabilities, such as the Genesis chip, and explores applications in edge computing. Dr. Kudithipudi has pioneered techniques for continual learning in spiking networks, probabilistic metaplasticity, and low-precision numerical formats (e.g., PositCL). Her work bridges theoretical neuroscience principles with practical hardware implementations, emphasizing sustainability and scalability. She also contributes to NSF-funded projects like EFRI BRAID and NAIAD, advancing interdisciplinary AI research. Her lab’s collaborations include developing the NeuroBench framework for fair benchmarking and exploring neuromorphic systems for tasks like video/activity recognition and time-series forecasting. She advises on hardware-software co-design strategies for efficient neural network deployment on constrained devices.

Babak JamhiriView profile

Researcher

• Structural Health Monitoring
• Geotechnical Engineering
• Machine Learning Applications in Civil Engineering
• +3 more

Babak Jamhiri holds a Doctoral Researcher position with expertise in structural health monitoring and reliability analysis. He is affiliated with the 3DCP and Construction Automation Research Group, focusing on AI-driven simulations, infrastructural risk assessment, and probabilistic modeling. His research spans geoenvironmental reliability, machine learning algorithms for signal processing, and risk assessment in construction materials. Education: BSc, MSc, and PhD in relevant disciplines (institutions unspecified). Current projects include structural health monitoring of 3DCP components, machine learning for signal processing, and probabilistic crack propagation in problematic soils. Recent work involves performance assessment of post-tensioned concrete beams and developing compressed earth bricks with marl sediment. Research interests emphasize geotechnical challenges such as expansive soils, desiccation cracking, and soil stabilization using industrial byproducts. He explores Bayesian inference, Markov decision chains, and fractal approaches in clay-based materials for nuclear waste disposal applications. Contributions include YouTube tutorials on information value theory, signal processing for anomaly detection, and probabilistic modeling with R. His work bridges theoretical advancements with practical engineering solutions, emphasizing uncertainty quantification and decision-making under uncertainty.

Ranga PitchumaniView profile

Professor

• Energy Systems
• Renewable Energy
• Materials Processing
• +7 more

Ranga Pitchumani is the George R. Goodson Professor in the Department of Mechanical Engineering at Virginia Tech since 2009, following prior roles including the John R. Jones III Professor at Virginia Tech (2009–2014) and Distinguished Professor at the University of Connecticut (2003–2008). He directs the Advanced Materials and Technologies Laboratory (AMTL), focusing on energy systems, materials processing, and uncertainty quantification. Education: Ph.D., Mechanical Engineering, Carnegie Mellon University (1992) M.S., Mechanical Engineering, Carnegie Mellon University (1988) B.S., Mechanical Engineering, Indian Institute of Technology (1986) Research Interests span energy conversion and storage , micro-/nanotechnologies , grid integration of renewables , and transport phenomena . His lab develops fractal-textured surfaces for solar absorbers, corrosion-resistant coatings for CSP systems, and machine learning tools for grid optimization. Sponsors include AFOSR, DOE, NSF, and ONR. Scientific Awards include the 2022 ASME Frank Kreith Energy Award, 2022 ASES Charles Abbot Award (for R&D in solar energy), and being named a Fellow of ASME in 2004. Earlier recognitions include the 1996 ONR Young Investigator Award and multiple University of Connecticut honors. AMTL research emphasizes dual-use innovations like solid-infused surfaces for condensation enhancement (5× improvement) and corrosion-resistant coatings that match nickel-based alloys' performance at lower costs. Key projects involve thermal control of battery packs using probabilistic forecasting and fractal-textured surfaces delaying freezing seven-fold.

Tim MarjoribanksView profile

Senior Lecturer

• Hydrodynamics
• Water Engineering
• Eco-hydrodynamics
• +4 more

Dr. Tim Marjoribanks is a Senior Lecturer in Water Engineering with a focus on numerical modeling and eco-hydrodynamic processes. His research integrates computational fluid dynamics, flume experiments, and fieldwork to study flow-vegetation interactions in rivers and coastal environments, emphasizing fluvial geomorphology and natural flood management. Current projects include saltmarsh vegetation dynamics, swimming pool circulation efficiency, and the representation of vegetation in hydraulic models. Recent work explores climate extremes and blue-green infrastructure through large ensemble simulations. Education: Holds an MSci, PhD, and PGCAP, indicating expertise in academic teaching and advanced water engineering research. His work has been supported by NERC through a PDRA role in reducing uncertainties in flood prediction. Research Interests: Key areas include vegetation hydraulics, sediment-ecology relationships, and public health implications of water systems. His methodologies span high-resolution modeling, analytical frameworks, and community engagement strategies for sustainable infrastructure projects. Grants & Projects: Led NERC-funded research on vegetation representation in hydraulic models. Collaborates with interdisciplinary teams on ecohydraulic processes and climate adaptation. Labs/Teams: Affiliated with research groups studying hydraulics, coastal systems, and climate extremes, though specific lab names are not disclosed.

• Artificial Intelligence
• Explainable AI (XAI)
• Computational Argumentation
• +8 more

Antonio Rago is a Researcher at Imperial College London's Department of Computing (Faculty of Engineering). His work focuses on explainable AI (XAI), computational argumentation, and their applications in engineering design, Formula One strategy, and healthcare. He is affiliated with the ADIX, XAI, and Computational Logic & Argumentation research groups. Education: PhD (Computational Logic & Argumentation) from Imperial College London (2019), MEng in Automotive Engineering from Loughborough University (2012) Professional Experience: Former Race Strategy Engineer at Mercedes AMG Petronas F1 Team (2012-2014), Quantitative Development roles in industry Research interests span argumentation frameworks, counterfactual explanations, and ethical AI systems. He organizes international workshops like ArgXAI and Arg&App, and co-leads tutorials on Argumentative Explanations in AI at conferences including KR and ECAI. His publications emphasize robust AI systems, human-centric explanations, and applications in high-stakes domains like motorsport strategy and clinical decision-making. He collaborates with institutions such as Sapienza University of Rome under the UK-Italy Trustworthy AI Programme.

Laurence RozéView profile

Lecturer

• Explainable Artificial Intelligence (XAI)
• Machine Learning Interpretability
• Temporal Automata Learning
• +3 more

Laurence Rozé is a Lecturer at INSA Rennes's Computer Science Department since 1998 and a researcher at IRISA (UMR 6074), affiliated with the LACODAM team. Her work focuses on explainable AI (XAI), temporal automata learning, and time series analysis, particularly in stabilizing explanations and generating counterfactuals. She has co-supervised over 10 PhD students, including recent theses on counterfactual explanations and individual behavior modeling. Key projects include collaborations with Stelantis (2019–2022), Orange Labs (2016–2019), and academic initiatives like the WS-DIAMOND European project (1998–2001). Her research interests combine theoretical advances (e.g., timed automata learning from time series) with industrial applications such as car-sharing fleet optimization and precision livestock farming. Teaching responsibilities include mobile programming, probability, and introductory courses for international students. She has held leadership roles at INSA Rennes, including heading the second-year program since 2024 and leading the third-year IT program from 2004–2015.

• Decision science
• Data science
• Causal machine learning
• +6 more

Wouter Verbeke is Professor of Decision Science at KU Leuven's Faculty of Economics and Business (FEB), where he directs the POC Doctoraatsprogramma Economie en Bedrijfswetenschappen and chairs the Doctoral Committee. He is a core member of Leuven.AI and leads the Information Systems Engineering Research Group (LIRIS), focusing on data-driven business decision-making. His research spans causal machine learning, uplift modeling, and cost-sensitive learning with applications in fraud detection, credit risk, operations management, and pricing. Verbeke pioneers decision-centric frameworks that optimize business outcomes rather than purely predictive accuracy, emphasizing causal inference for resource allocation and fairness in algorithmic decision systems. Analysis of his recent publications reveals a strong trend toward graph-based anti-fraud systems (particularly for AML/smurfing detection), interpretable causal machine learning in finance, and HR analytics using process mining. His work increasingly integrates fairness considerations into operational decision systems while advancing theoretical foundations of treatment effect estimation under network interference. Scientific awards: EURO award for best article in European Journal of Operational Research (Innovative Applications of O.R.) (2014) Verbeke actively supervises doctoral candidates including S. De Vos and M. Reusens, with research funded through major grants like ANUBIS (Aligned oNline and multilevel User and entity Behavior) and prescriptive analytics projects for intelligent organizations. He serves on editorial boards of Decision Support Systems, Business Information Systems Engineering, and INFORMS Journal on Data Science. As leader of the LIRIS research group within FEB, he drives interdisciplinary collaborations between data science and business domains, maintaining strong industry partnerships for real-world validation of fraud analytics, credit risk, and operational decision systems.

Yan ZhouView profile

Research Fellow

• Data Management
• Knowledge Systems
• Environmental Data Science
• +3 more

Yan Zhou is a Research Fellow at the University of Oslo's Department of Informatics (Data and Knowledge Management), affiliated with the Faculty of Mathematics and Natural Sciences. His research focuses on applying data science and machine learning to environmental and energy systems, emphasizing reservoir operations, sustainable energy integration, and climate resilience. Key interests include optimizing hydropower systems, flood forecasting, and mitigating carbon emissions through data-driven approaches. His work spans machine learning applications for hydrological modeling, renewable energy forecasting, and multi-objective optimization frameworks. Recent studies address challenges in reservoir management, wind energy clustering, and urban sustainability through interdisciplinary methods. Notable contributions include hybrid deep learning models for algal bloom prediction and frameworks integrating pumped-storage systems with hydropower. Publications highlight innovations in flood risk assessment, carbon budget analysis, and climate-resilient infrastructure. Yan Zhou collaborates on projects combining physical hydrology with advanced analytics, aiming to enhance energy-water-food nexus synergies in dynamic environmental contexts. No specific awards or grants are listed, though his active research portfolio demonstrates significant contributions to environmental data science.

Philip LeicesterView profile

Teaching Professor

• Renewable Energy Systems
• Solar Photovoltaics
• Energy Storage
• +3 more

Dr. Philip Leicester is a Teaching Fellow at Loughborough University, affiliated with the Wolfson School of Mechanical, Electrical and Manufacturing Engineering. His role focuses on Software Engineering and Energy Systems. He holds a BSc in Chemistry and Polymer Science Technology from Loughborough University (1985) and a PhD in renewable energy technologies, supervised by Drs. Paul Rowley and Chris Goodier. Prior to his academic role, he spent 15 years leading a software company and contributed to the Microgeneration Certification Scheme (MCS) standard for solar PV and battery storage systems. Philip earned his first-class BSc in Chemistry and Polymer Science Technology from Loughborough University of Technology in 1985, complemented by a Diploma in Industrial Studies from his placement at Norddeutsche Affinerie in Hamburg. He later returned to Loughborough to pursue a PhD in renewable energy technologies, completed under the supervision of Drs. Paul Rowley and Chris Goodier. His research interests span renewable energy systems, particularly solar photovoltaics (PV) and energy storage technologies. He applies probabilistic modeling using Bayesian networks to evaluate the social, economic, and environmental impacts of renewable energy deployment. His work also integrates software engineering methodologies for energy system analysis and smart grid technologies. Earlier research involved photochemistry and laser flash photolysis techniques for studying dye interactions on solid substrates. While no specific scientific awards are mentioned in the provided text, his contributions to renewable energy standards and academic research reflect significant professional recognition. Philip has collaborated with industry partners such as Solar Century and Solar Energy UK, contributing to the development of MCS standards for solar PV systems. His research as a former Research Associate at the Centre for Renewable Energy Systems Technology (CREST) explored the energy system impacts of widespread solar PV adoption in the UK. He is part of the Wolfson School’s energy systems research group and the Centre for Renewable Energy Systems Technology (CREST), focusing on interdisciplinary approaches to sustainable energy solutions.

Martina ContiscianiView profile

Research Fellow

• Network Science
• Statistical Inference
• Complex Systems
• +3 more

Martina Contisciani is a Research Fellow at the Department of Network and Data Science, Central European University (CEU), Vienna. Her work focuses on developing statistical methods and algorithms for analyzing complex systems, with expertise in network inference and multiscale modeling. Her academic background includes: Ph.D. in Computer Science from Max Planck Institute for Intelligent Systems and University of Tübingen (2023), supervised by Dr. Caterina De Bacco M.Sc. in Data Science from University of Padua, Italy B.Sc. in Statistics for Business and Economics from University of Padua, Italy Contisciani's research centers on network science, particularly probabilistic generative models for complex systems. She has pioneered work in community detection for multilayer and hypergraph networks, developed the Hypergraphx library for higher-order network analysis, and currently leads migration flow modeling in Austria through the MOMA project. Her methodological innovations bridge theoretical network science with real-world demographic applications. Analysis of her 15 most recent publications (2017-2025) reveals an evolution from foundational work on community detection and network modeling toward impactful applications in migration studies. Her recent output demonstrates increasing integration of statistical inference with spatial and demographic data, particularly through multiscale network approaches applied to Austrian migration patterns. Her scientific recognition includes: Cyber Valley PhD Fellowship IMPRS-IS Fellowship Contisciani actively contributes to the MOMA project under Prof. Tiago P. Peixoto and Prof. Márton Karsai, developing inferential network models for migration analysis. While no formal student advising is documented, her role involves collaborative research within CEU's Network and Data Science department. Her recent presentations at IC2S2’25 and NetSci 2025 highlight ongoing contributions to network science methodology and applications. As a core member of CEU's Network and Data Science research team, she collaborates with leading network scientists on projects combining theoretical innovation with societal challenges. Her current work on Austria's migration flows represents a strategic shift toward data-driven policy-relevant research while maintaining strong methodological rigor.

Ziang ZhangView profile

Associate Professor

• Power Systems
• Energy Storage Systems
• Microgrids
• +3 more

Ziang Zhang is an Associate Professor in the Department of Electrical and Computer Engineering at Binghamton University. His research focuses on complex energy systems, bridging power systems and nonlinear complex networks using control theory, graph theory, and optimization. Key areas include power system stability, energy storage integration, and real-time hardware-in-the-loop (HIL) simulations. He holds a BS from Beijing Institute of Technology, an MS from Purdue University, and a PhD from North Carolina State University, with postdoctoral experience at ABB US Corporate Research. His research emphasizes enhancing grid resilience through advanced algorithms and machine learning. Notable projects include a $1.2 million NSF grant to assess power grid stability with increased renewables. He leads a lab offering PhD opportunities in control theory and nonlinear systems, with applications in power grid stability and inverter design. Publications span topics like transient stability analysis, solar forecasting, and battery energy storage optimization. His work integrates theoretical frameworks with practical applications, addressing challenges in modern grid operations and renewable integration.

Yangkang ChenView profile

Academic

• Seismic Signal Analysis
• Seismic Modeling and Inversion
• Deep Learning
• +4 more

Yangkang Chen is a Research Assistant Professor at the Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin. He leads AI/ML research at the Texas Seismological Network (TexNet). His expertise includes seismic signal analysis, deep learning, and high-performance computing. Chen holds a Ph.D. in geophysics from UT Austin (2015) and was a postdoc at Oak Ridge National Laboratory (2016-2018). Research Focus: AI-driven earthquake monitoring, seismic modeling, and reproducible research. Key Contributions: Developed EQCCT, TXED datasets, and open-source tools like pyseistr and DRR. Leadership: Editor of JGR: Machine Learning and Computation, and AGU fellowships. Education: Ph.D. in Geophysics, UT Austin, 2015 Postdoctoral Research at Oak Ridge National Lab (2016-2018) Research Interests span AI applications in seismology, seismic inversion, and reproducible methodologies. His work bridges computational methods with real-world seismic challenges, such as induced seismicity detection and earthquake cataloging. Awards include First Prize in GeoHackathon 2024, Clarivate Highly Cited Researcher (2020), and multiple IEEE recognitions. He advises over 20 graduate students/postdocs, focusing on ML-driven geoscience problems. Chen's lab develops tools for seismic data analysis, DAS processing, and machine learning frameworks.

Evelina TrutnevyteView profile

Associate Professor

• Renewable Energy Systems
• Energy Policy Analysis
• Socio-Technical Transitions
• +3 more

Evelina Trutnevyte is an Associate Professor and Head of the Renewable Energy Systems group at the University of Geneva. She specializes in energy systems analysis, focusing on renewable energy integration, long-term projections, and decision-making under uncertainty at the science-society interface. Her work bridges technical modeling with socio-political dimensions of energy transitions. Educated at ETH Zurich (PhD in Natural and Social Science Interface), she has collaborated across institutions including University College London, Carnegie Mellon University, and Vilnius Gediminas Technical University. She leads the SWEET-EDGE consortium (2021–2027), a 22.3 million CHF initiative involving 19 research groups and 60 partners to advance decentralized renewable energy in Switzerland. Her research interests include spatial optimization of energy systems, public acceptance of renewable technologies, and governance frameworks for geothermal energy. She has secured over 7.8 million CHF in grants from Swiss National Science Foundation, European Horizon programs, and industry partnerships. Notable awards include the Eccellenza (2021–2025) and Ambizione Energy (2015–2018) grants. Trutnevyte serves on the Federal Energy Research Commission (CORE) and editorial boards of Renewable and Sustainable Energy Transition and Climatic Change . Her work emphasizes participatory modeling, trust-building in climate policies, and addressing regional disparities in energy transition impacts. Her projects highlight innovative approaches to energy justice, such as analyzing fossil fuel phaseout equity and developing trust inoculation strategies against climate disinformation. She co-leads interdisciplinary efforts to model energy systems' weather resilience and mineral dependency risks, ensuring technical rigor aligns with societal needs.

Sam JohnsonView profile

Assistant Professor

• Cognitive Science
• Behavioral Economics
• Decision Theory
• +5 more

Sam Johnson is an Assistant Professor at the University of Waterloo, specializing in cognitive science and behavioral economics. His research focuses on decision-making under uncertainty, consumer behavior, moral psychology, and the intersection of artificial intelligence with human cognition. He examines how individuals process information, form beliefs, and make choices in complex environments, particularly in contexts involving corporate social responsibility, marketing communication, and societal extremism. His work explores topics such as the psychological underpinnings of conspiracy theories, the impact of precision in information on consumer loyalty, and the role of metacognition in cultivating wisdom through AI systems. Key themes include cognitive biases, social perception, and the application of interdisciplinary approaches to economics and decision theory. Johnson’s recent publications highlight trends in cognitive economics, the analysis of online consumer behavior, and the study of radical uncertainty in decision-making. His research emphasizes the importance of understanding how individuals navigate ambiguity and how institutions can better communicate uncertainty in public policy and corporate contexts. No scientific awards or grants are mentioned in the provided information.