Lesia Mitridati is an Assistant Professor at the Department of Wind and Energy Systems, Technical University of Denmark (DTU). Her research focuses on optimizing energy systems, particularly in renewable energy integration, energy market design, and prosumer behavior modeling. She leads and collaborates on projects involving smart grids, distributed energy resources, and privacy-preserving market mechanisms. Her work contributes to UN Sustainable Development Goals related to affordable and clean energy. Key projects include AI-driven electricity market optimization, hydrogen-wind trading strategies, and risk-aware energy communities. She supervises multiple PhD students in areas like VPP bidding strategies and market-based heat-electricity coordination. Dr. Mitridati has published widely on energy communities, grid services, and reinforcement learning applications. Notable contributions include dynamic pricing frameworks for grid services and privacy-preserving market mechanisms. She co-organizes annual DTU summer schools on future energy systems and AI-driven optimization. Her research integrates machine learning with operational research techniques to address challenges in renewable energy integration, market design, and system resilience. Current initiatives focus on electrolyzer plant bidding strategies and feature-driven trading of renewable resources.
Douglas H Fisher is an Associate Professor of Computer Science and Computer Engineering at Vanderbilt University's School of Engineering. His research focuses on artificial intelligence, particularly machine learning, and computational sustainability. He holds a Ph.D., M.S., and B.S. in Computer Science from the University of California - Irvine. His work bridges AI with societal challenges, emphasizing sustainability, education technology, and cognitive modeling. Notable areas include integrating sustainability into computing curricula, leveraging AI for peer review systems (pReview), and exploring bias mitigation in neural networks. He has contributed to foundational machine learning techniques, such as rule induction for medical data analysis and decision tree optimization. Fisher's research spans interdisciplinary applications: from geospatial water resource modeling to MOOCs' social incentives. His educational contributions include blended learning frameworks and open educational resources advocacy. He has authored over 100 publications across AI, sustainability, and education, reflecting a commitment to both technical innovation and societal impact.
Dr. Gabor Karsai is a Distinguished Professor of Computer Science and Professor of Electrical and Computer Engineering at Vanderbilt University's School of Engineering. He also serves as Senior Research Scientist at the Institute for Software-Integrated Systems (ISIS), where he contributes to the Executive Council. With over 30 years in software engineering, his research focuses on embedded systems, model-driven development, resilient software platforms, and AI-driven autonomous systems assurance. He holds a PhD from Vanderbilt and degrees from the Technical University of Budapest. Education: Ph.D. in Electrical and Computer Engineering, Vanderbilt University Dr.Tech. in Computer Engineering, Technical University of Budapest M.S. and B.S. in Electrical Engineering, Technical University of Budapest Affiliations: Co-Associate Chair for Computer Engineering External Member of the Hungarian Academy of Sciences His research interests span model-integrated computing , autonomous systems assurance , and radiation-hardened systems . Recent work emphasizes AI integration into engineered systems and radiation effects mitigation for space applications. He has led major projects on distributed control for smart grids and resilient CPS architectures. Over 200 peer-reviewed publications and four patents reflect his contributions to software engineering and systems integration. Awards & Recognition: External Membership in Hungarian Academy of Sciences Leadership roles in ISIS and Vanderbilt's academic governance Advisees & Grants: While no student list is provided, his projects involve collaborative teams across academia and industry. Major sponsors include NSF, NASA, and DARPA. Current work includes the ALC (Assurance-based Learning-enabled CPS) and MIDAS (Model-based Intent-Driven Adaptive Software) initiatives. Labs & Platforms: Co-developer of the RIAPS distributed CPS platform and the SEAM assurance modeling framework. His labs focus on cyber-physical system design, radiation effects analysis, and autonomous system reliability.
Fikile R. Brushett is a Professor and holds the Chevron Chair of Chemical Engineering at the Massachusetts Institute of Technology (MIT), within the Department of Chemical Engineering under the School of Engineering. His research focuses on electrochemical energy storage systems, particularly redox flow batteries, catalyst synthesis, and environmental applications like CO₂ capture. Brushett has received numerous accolades, including the Allan P. Colburn Award and Charles W. Tobias Young Investigator Award for his impactful publications. His work integrates experimental and computational methods to advance energy technologies, emphasizing sustainable materials and system optimization. He leads projects addressing global energy challenges through innovations in battery design, electrolyte development, and process modeling. Education: Ph.D. in Chemical Engineering, University of Illinois at Urbana-Champaign (2010) M.S.E. in Chemical Engineering, University of Illinois at Urbana-Champaign (2009) B.S.E. in Chemical Engineering, University of Pennsylvania (2006) Research: Brushett’s lab explores electrochemical energy conversion/storage, microfluidics, and interfacial phenomena. Key areas include redox flow battery optimization, scalable carbon materials for energy storage, and CO₂ capture via electrochemical methods. His team develops novel diagnostic tools (e.g., microelectrode sensors) and models for system efficiency. Awards: Over two dozen honors, including named chairs and fellowships from organizations like ACS and GEM. Notable recognitions include the 2024 Chevron Chair and 2022 AIChE Colburn Award. Grants & Labs: Active in MIT’s Electrochemical Energy Lab, focusing on grid-scale storage solutions. Collaborates on projects funded by DOE, industry partners (e.g., Chevron), and foundations. Leads initiatives in material sustainability and battery lifecycle analysis. Publications: Over 140 peer-reviewed articles (2022–2025) emphasize interdisciplinary innovations in energy storage, electrolyte design, and environmental electrochemistry. Recent work highlights advancements in non-aqueous systems, CO₂ capture, and Bayesian-optimized material synthesis.
Ashish Cherukuri is an Associate Professor at the University of Groningen's Faculty of Science and Engineering, affiliated with the Optimization and Decision Systems group. His research focuses on optimization-based control, game theory, and multi-agent systems applied to energy, transportation, and robotics. He holds a Ph.D. from UC San Diego and postdoctoral experience at ETH Zurich. Education: Ph.D., University of California, San Diego (2012–2017) M.Sc., ETH Zurich (2008–2010) B.Tech, Indian Institute of Technology Delhi (2004–2008) Research Interests: Data-driven optimization, distributed algorithms, networked cyber-physical systems, and uncertainty handling in energy and transportation systems. Recent work emphasizes stochastic optimization, game-theoretic routing, and risk-aware control. Awards: Robert E. Skelton Dissertation Award (2017) Outstanding Graduate Student Award (2016) Focht-Powell Fellowship (2012–2015) Grants & Service: Editor for the IEEE Control Systems Society, organizer of Energy-Open 2019, and member of professional societies (IEEE, INFORMS, SIAM). Active in conference organization and academic leadership roles. Labs/Teams: Part of the Jan C. Willems Center for Systems and Control and the Engineering and Technology Institute Groningen (ENTEG). Research integrates theoretical advancements with practical applications in energy networks and smart systems.
Dr. Svetlana Yanushkevich is a Professor in the Department of Electrical and Software Engineering at the Schulich School of Engineering, University of Calgary. She is also a Full Member of the Hotchkiss Brain Institute and the Mathison Centre for Mental Health Research and Education. Her research focuses on biometric technologies, decision support systems, biomedical applications, and computational intelligence. She leads the Biometric Technologies Laboratory, developing strategies for risk assessment in biometric systems and healthcare monitoring through machine reasoning and signal processing. Education : BSc/MSc in Electrical Engineering (1989), State University of Informatics and Radioelectronics, Minsk PhD in Electrical Engineering (1992), same institution Dr. Habilitated in Technical Sciences (1999), Warsaw University of Technology Research Interests : Dr. Yanushkevich’s work spans biometric system design (e.g., gait analysis, facial attributes), decision support via probabilistic models (Bayesian networks, causal inference), biomedical applications (stroke rehabilitation, wearable sensors), and computational intelligence for data science. She emphasizes fairness, bias mitigation, and trustworthiness in AI systems, particularly in healthcare and accessibility contexts. Recent Research Trends : Her recent publications address causal modeling for accessibility barriers, UAV operator cognitive workload, and medical device optimization in radiation therapy. She explores AI ethics, stress contagion in human-robot teams, and cross-spectral biometric systems. Awards & Recognition : 2024 FEIC Fellow (Engineering Institute of Canada) 2019 Research Excellence Award (Schulich School of Engineering) 2001 Senior IEEE Membership Advising & Grants : She coordinates courses like ENCM 509 (Biometric Systems Design) and ENEL 610 (Biometric Technologies). Her research is supported by grants focusing on healthcare AI, accessibility technologies, and computational epidemiology. Labs & Collaborations : Her Biometric Technologies Lab collaborates with institutions like Hokkaido University and the IEEE Computational Intelligence Society. Projects include wearable health monitoring, decision support platforms, and AI-driven epidemiological modeling.
Yang Shen is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, affiliated with the Department of Computer Science and Engineering and the Institute of Biosciences and Technology. He holds a B.E. in Automation from the University of Science and Technology of China (2002) and a Ph.D. in Systems Engineering from Boston University (2008). His research focuses on algorithms for modeling biological molecules, systems, and data, with applications in protein docking, drug design, systems biology, and omics. He has received prestigious awards such as the NSF CAREER Award (2020) and MIRA Award (2017). His work integrates machine learning, optimization, and graph theory to address challenges in computational biology. Notable contributions include generative AI for protein design, interpretable models for compound-protein affinity prediction, and Bayesian active learning for protein docking. Shen has advised numerous students, including Yuning You, Mostafa Karimi, and Arghamitra Talukder, who have received awards like the Chevron Scholarship and NSF Graduate Fellowships. His lab actively collaborates on projects in drug discovery, synthetic biology, and precision medicine. Shen has led funded projects totaling over $3.5 million from NIH and NSF, exploring topics like molecular mechanisms of cancer mutations and AI-driven drug design. He serves on editorial boards for journals like the Journal of Biological Systems and has organized workshops such as the International Workshop on Biomedical Informatics with Optimization and Machine Learning (BOOM). His research bridges computational methods and biological systems, advancing both theory and practical applications in healthcare and biotechnology.
Dr. Chao Tian is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from Cornell University (2005) and a B.E. from Tsinghua University (2000). His research focuses on information theory, distributed storage systems, coding theory, and machine learning applications, including large language models (LLMs) and privacy-preserving techniques like steganography and private information retrieval (PIR). He has received notable awards, including the 2017 IEEE Jack Wolf ISIT Best Student Paper Award and the 2014 IEEE ComSoc DSTC Best Paper Award. Dr. Tian’s work bridges theoretical foundations and practical applications, such as optimizing storage codes for distributed systems, developing secure PIR protocols, and exploring LLMs in energy and steganographic contexts. His group has pioneered methods for variable-order Markov chain modeling with transformers and designed quantization techniques for perceptual quality. Recent activities include a faculty development leave at MIT/Harvard and delivering distinguished lectures globally on AI and information theory. He advises students on cutting-edge topics like LLM-based steganography and diffusion models. His publications span journals like IEEE Transactions on Information Theory and conferences such as NeurIPS and ISIT, with a focus on coding theory, privacy, and machine learning. He has also contributed to open-source tools like the CAI toolbox for investigating information-theoretic limits. Current projects include optimizing cryptocurrency mining in energy markets and advancing federated learning with adversarial robustness.
Rudy Guerra is a Professor and Chair of the Department of Statistics at Rice University, where he has been since 2000. His research spans biomedical applications of statistics, including bioinformatics, statistical genetics, and medical imaging, alongside sociological research in education and Mexican migration. He holds academic leadership roles, including Director of the Data Science Minor and member of the BRIDGE and Doerr Institute steering committees. Guerra earned his Ph.D. in Statistics from UC Berkeley, M.A. in Mathematics from UC Berkeley, and B.S. in Applied Mathematics from UT San Antonio. Education: Ph.D., Statistics, UC Berkeley (1992) M.A., Mathematics, UC Berkeley (1987) B.S., Applied Mathematics, UT San Antonio (1984) Key Roles: Department Chair, Statistics (2019–present) Associate Chair, Statistics (2016–2019) Former Jones College Magister (Residential College Leader, 2005–2011) Research Interests: Dr. Guerra’s work integrates statistical methods with biomedical and social science challenges. His biomedical focus includes cancer genomics (e.g., osteosarcoma metastasis, biomarker discovery), medical imaging (e.g., CT ventilation analysis), and bioinformatics. In social sciences, he examines educational inequities and Mexican migration impacts on health. His recent projects include collaborations with Texas Medical Center institutions and sociologists at Rice. Articles Trends: His publications emphasize interdisciplinary applications, combining statistical rigor with domain-specific insights. Recent work spans oncology, public health, and computational biology, reflecting a commitment to bridging theory and practical medical/sociological challenges. Awards & Roles: Panel Member, Ford Foundation Fellowship (2016–present) Associate Editor, BMC Genetics (2014–present) Former Residential College Master of Jones College (2005–2011) Advising & Grants: Guerra advises students on statistical research and curricula. He has led initiatives like the Keck Center for Quantitative Biomedical Sciences Training and co-founded the Empowering Leadership Alliance (ELA) to support underrepresented minorities in STEM. His grants include funding for bioinformatics consortia and educational outreach programs. Labs & Teams: Active in the Gulf Coast Consortia for Bioinformatics and collaborates with multidisciplinary teams at MD Anderson, Baylor College of Medicine, and UT Health Science Center.
Peter Winkler is William Morrill Professor of Mathematics and Computer Science at Dartmouth College, conducting research in discrete mathematics, probability, and theoretical computer science. His work connects combinatorial problems with statistical physics and algorithmic complexity. Key research areas include: Probabilistic methods in combinatorics and game theory Phase transitions in discrete structures Geometric probability and optimization Mathematical puzzles and paradoxes Winkler's publications resolve fundamental questions in pursuit-evasion theory, geometric set optimization, and combinatorial phase transitions. His work on mathematical puzzles has influenced both academic research and popular mathematics. Current projects explore limit permutations, abelian networks, and new puzzle collections. Honored with the Mathematical Association of America's Lester R. Ford Award and David P. Robbins Prize, Winkler has held visiting positions at the Institute for Advanced Study and Mathematical Sciences Research Institute.
Ahmed M. Attia is a computational mathematician at the Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA. He is also a member of the Laboratory for Applied Mathematics and Numerical Software (LANS) at Argonne. Previously, he was a postdoctoral researcher at Argonne and a research fellow at SAMSI, with affiliation to the Department of Mathematics at North Carolina State University. Education: Ph.D. in Computer Science and Applications, Virginia Tech, 2016 M.S. in Statistics and Computer Science, Mansoura University, 2008 B.S. in Mathematics, Statistics and Computer Science, Mansoura University, 2004 His research spans computational science and engineering, focusing on data assimilation, uncertainty quantification, optimal experimental design, PDE-constrained optimization, Bayesian inference, and high-performance computing . He integrates machine learning and statistical methods into scientific computing frameworks. His work enables robust and scalable solutions for inverse problems in complex physical systems. The primary trend in his recent publications centers on the development of PyOED, an open-source framework that unifies variational and Bayesian data assimilation with optimal experimental design, featuring novel optimization and machine learning solvers. This work bridges applied mathematics, computational science, and software engineering. Scientific Awards: No awards explicitly mentioned. Advising and Grants: Ahmed has mentored and collaborated with researchers such as Abhijit Chowdhary and Shady E. Ahmed on the PyOED project. His research is supported by the U.S. Department of Energy (DOE), particularly through the Office of Science and the Advanced Scientific Computing Research (ASCR) program. Labs and Teams: He is an active member of the Laboratory for Applied Mathematics and Numerical Software (LANS) at Argonne National Laboratory, contributing to national efforts in applied mathematics and scientific computing.
Affiliations & Roles Lionel P. Robert Jr. is a Professor of Information and Robotics at the University of Michigan, holding joint appointments in the School of Information and the College of Engineering's Robotics Department. He directs the Michigan Autonomous Vehicle Research Intergroup Collaboration (MAVRIC) and is an affiliate faculty member at the National Center for Institutional Diversity and Indiana University's Center for Computer-Mediated Communication. His roles include editorial board positions at journals like the Journal of Computer Information Systems and leadership in professional organizations such as ACM and IEEE. Education Ph.D. in Information Systems, Indiana University (BAT Fellow, KPMG Scholar) M.B. from Indiana University, Bloomington M.S. degrees from Clemson University and University of Louisiana, Lafayette B.S. from University of Louisiana, Lafayette Research Focus Robert's research bridges collaboration through technology, with a focus on human-robot interaction, autonomous vehicles, and virtual teams. His work addresses trust in automated systems, human-AV communication, and the sociotechnical implications of robotics in workplaces and public spaces. Recent projects explore explanations for automated vehicles, security robots' societal acceptance, and AI ethics in healthcare and labor. Key Contributions He has published over 100 peer-reviewed articles in journals like MIS Quarterly and conferences such as CHI and HRI. His research has been funded by NSF, Toyota Research Institute, and the Army Research Laboratory. Notable outcomes include frameworks for AV trust repair, models of human-robot team performance, and critiques of AI-driven labor practices. Awards & Recognition ACM Distinguished Member IEEE Senior Member Carnegie Junior Faculty Development Fellowship 3× Teaching Commendation (2006–2008) Grants & Labs Current grants include studies on explainable AI, human-AV trust dynamics, and security robot design. The MAVRIC lab focuses on AV-pedestrian interactions while the CCMC explores digital communication's societal impact.
Andrea Volkamer is a computational chemist and active principal investigator in the field of computer-aided drug design (CADD), with a focus on kinase targets, druggability prediction, and machine learning applications. She has published extensively in journals such as the Journal of Chemical Information and Modeling and Journal of Medicinal Chemistry , with recent work up to 2025 indicating an ongoing academic research program. Her research group, referred to as 'volkamerlab,' develops open-source tools including DoGSite, KiSSim, KinFragLib, and TeachOpenCADD, which are widely used in both academic and industrial drug discovery settings. Her research interests span computational drug discovery , structural bioinformatics , kinase inhibitor design , off-target and polypharmacology prediction , and educational platforms for CADD . She emphasizes open science and reproducibility, particularly through the TeachOpenCADD initiative, which provides interactive Jupyter Notebooks and KNIME workflows for teaching cheminformatics concepts. The 15 most recent publications reflect a strong trend toward integrating machine learning and deep learning (e.g., transformers, graph neural networks) with structure-based methods such as molecular docking, free energy calculations, and binding site comparison. Her work increasingly addresses real-world challenges in drug discovery, including kinase mutation resistance, selectivity optimization, and in vivo toxicity prediction using conformal and hybrid models. Scientific Contributions and Awards: Development of key computational tools: DoGSite, KiSSim, KinFragLib, TeachOpenCADD. Leadership in open-source and open-education initiatives in cheminformatics. Active publication record in top-tier journals with interdisciplinary impact. Advising and Grants: While specific student names and grant details are not mentioned in the provided text, her role as a corresponding author on numerous publications and the existence of a dedicated research lab ('volkamerlab') imply that she mentors students and postdoctoral researchers. She likely secures competitive funding to support her research in computational drug discovery and method development. Labs and Teams: She leads the Volkamer Lab ('volkamerlab'), which focuses on developing and applying computational methods for drug discovery. The lab collaborates with both academic and pharmaceutical partners and emphasizes open-source software development and educational outreach.
Erik Quaeghebeur is an Assistant Professor at Eindhoven University of Technology's School of Mathematics and Computer Science, focusing on uncertainty modeling in artificial intelligence. His work spans probabilistic circuits, imprecise probability theory, and wind energy applications. PhD in Applied Mathematics (Ghent University, 2002-2009) Master's in Applied Mathematics (Université catholique de Louvain, 2001-2002) Master's in Physics Engineering (Ghent University, 1998-2001) Research interests include probabilistic modeling under uncertainty, with applications in AI and wind energy systems. His recent work explores tensor factorizations, equivariant graph neural networks, and scalable probabilistic circuits. Scientific contributions include 60 research outputs and 2 datasets . Awards encompass the ERCIM Alain Bensoussan Fellowship (2013), BOF Postdoc (2010), and B.A.E.F. Francqui Fellowship (2009). He serves on committees for the Society for Imprecise Probability and acts as editorial board member for related conferences. Foundations of Artificial Intelligence course (since 2020) Uncertainty Representations and Reasoning course (since 2021)
Patrick Jaillet is the Dugald C. Jackson Professor in the Department of Electrical Engineering and Computer Science at MIT's School of Engineering. He holds joint appointments with the Laboratory for Information and Decision Systems (LIDS), the Operations Research Center (ORC), the Operations Research and Statistics Group at MIT Sloan, and the Department of Civil and Environmental Engineering. Previously, he served as Head of Civil and Environmental Engineering at MIT (2002-2009) and Chair of the Department of Management Science and Information Systems at UT Austin (1997-2002). Dr. Jaillet's research focuses on online optimization and learning, sequential decision-making under uncertainty, and security and resilience in complex networks. His work spans theoretical foundations in optimization and machine learning with applications in transportation, online market analytics, and network security. He has developed mathematical frameworks for problems involving uncertainty, dynamic resource allocation, and strategic behavior in complex systems. His recent publications reveal strong trends in bridging theoretical optimization with practical machine learning applications. Key themes include Bayesian optimization for black-box functions, online learning with limited information, mechanism design for resource allocation, and network security applications. His work increasingly integrates large language models with traditional optimization techniques, reflecting the evolving landscape of AI-driven decision-making systems. Fulbright Scholar (1990) Fellow of the Institute for Operations Research and Management Science (INFORMS) Best Applications Paper Award at ICAPS 2019 Long-standing Associate Editor for top journals including Operations Research and Transportation Science Dr. Jaillet has advised over 40 doctoral students who now hold prominent positions in academia and industry, including faculty positions at MIT, Georgia Tech, and ETH Zurich, and research scientist roles at Amazon, Microsoft Research, and Google. His research has been consistently funded by major agencies including NSF, ONR, AFOSR, and international partners like Singapore NRF, with current projects focusing on learning algorithms for autonomous security and fundamental tradeoffs in optimization. He leads a vibrant research group spanning MIT's EECS department and ORC, with current funding supporting work on neural bandits, federated optimization, and network security applications. His research group operates at the intersection of theory and practice, with strong connections to industry through collaborations with IBM, Microsoft, Google, and various transportation and technology companies. The group maintains active partnerships with international institutions, particularly through SMART in Singapore, reflecting Dr. Jaillet's global research impact.
