Felipe Csaszar is a Professor of Strategy and Chair of the Strategy Department at the University of Michigan's Ross School of Business. His research focuses on decision structures' impact on innovation, financial performance, and social outcomes, with particular attention to cognitive frameworks, organizational processes, and AI's role in decision-making. He holds a PhD and MA from the Wharton School, University of Pennsylvania. Education: PhD in Strategy, University of Pennsylvania (2009) MA in Strategy, University of Pennsylvania (2007) Research Interests: Strategic decision-making under AI integration Cognitive and structural drivers of innovation Organizational decision processes and design Formal modeling and empirical strategy research Editorial Roles: Senior Editor, Strategy Science and Management Science Former Editor, Organization Science Co-editor, Handbook of AI and Strategy Professional Experience: Prior role: Assistant Professor at INSEAD Previous career: CEO of an internet startup and Head of Research at an asset management firm Labs/Teams: Leading the Strategy Science division at INFORMS Co-chair of the SMS Behavioral Strategy division
Trine Krogh Boomsma is a Professor in the Department of Insurance and Economics at the University of Copenhagen's Department of Mathematical Sciences. Her research focuses on optimization under uncertainty with significant applications in energy systems, particularly electricity markets, renewable energy investments, and power system planning. PhD in Mathematics-Economics, Aarhus University (2003-2007) Visiting PhD at University of Duisburg-Essen (2004) Academic career includes positions at Risø National Laboratory for Renewable Energy and Imperial College London Her work spans stochastic programming, real options analysis, and dynamic programming to address energy sector challenges. Key areas include support schemes for renewables, market risk modeling, and operational optimization of hybrid conventional-renewable systems. Recent research explores policy impacts on investment decisions and advanced scenario generation techniques. Major publications (2012-2020) cover renewable energy policy frameworks, power plant valuation models, and sequential market bidding strategies. These works emphasize electricity market dynamics, investment risk quantification, and robust planning under uncertainty. She teaches linear programming, integer programming, and stochastic programming applications in operational analysis, contributing to energy economics education at the department.
Jeff Alstott is a Professor of Policy Analysis at the RAND School of Public Policy, serving as the Director of RAND's Center for Technology and Security Policy (TASP) . He holds a concurrent role as a Senior Information Scientist at RAND and directs a program on technology forecasting and improving R&D investment returns at the National Science Foundation. Previously, he held leadership roles in the U.S. government, including the White House as Director for Technology and National Security at the National Security Council and Assistant Director for Technology Competition and Risks at the Office of Science and Technology Policy. Prior to that, he was a program manager at IARPA, focusing on AI, biosecurity, and science forecasting, and has held academic and research positions at MIT, Singapore University of Technology and Design, the World Bank, and the University of Chicago. Education : Doctorate in complex networks from the University of Cambridge; MBA and bachelor's degrees from Indiana University. Alstott's research spans artificial intelligence , technology forecasting , complex networks , and science and technology policy . His recent publications focus on AI security , existential risk from advanced technologies , and collaborative frameworks for AI safety , reflecting his expertise in balancing technological innovation with national and global security. He has contributed to understanding AI model weight protection , cybersecurity challenges , and policy implications of emerging technologies . Alstott has received recognition for his work, including being part of an award-winning team on technology counterintelligence . His policy recommendations and technical research have informed federal strategies for responding to advanced technologies , emphasizing the need for proactive governance and cross-sector collaboration.
Christophe Bisson is an Associate Professor at SKEMA Business School (Paris campus) specializing in Strategy, Innovation & Entrepreneurship. His research examines augmented anticipatory systems, competitive intelligence, AI governance, and strategic early warning frameworks. He directs the MSc International Strategy and Influence program and leads research at the SKEMA Center for Artificial Intelligence. Bisson holds a PhD in Competitive Intelligence from Aix-Marseille University and a Postgraduate Certificate in AI from the University of Helsinki. His professional experience includes international strategy consulting and academic positions at universities in Turkey and France. Awards & Honors: Inducted into Council of Competitive Intelligence Fellows (2023) SCIP International Academic Award (2017)
Dr. K. Max Zhang is a Professor in the Sibley School of Mechanical and Aerospace Engineering at Cornell University. He is the director of the Energy and the Environment Research Laboratory (EERL) and a fellow at the Atkinson Center for a Sustainable Future. His research is deeply interdisciplinary, focusing on sustainable energy systems, air quality, and environmental justice, with significant impacts on policy and community development in New York and beyond. Ph.D., Mechanical Engineering, University of California-Davis, 2004 B.S., Thermal Engineering, Tianjin University, 1998 B.A., English Language, Tianjin University, 1998 Dr. Zhang’s research centers on the integration of energy and environmental systems. He investigates air pollution dynamics using advanced numerical models like CTAG, with applications in near-source pollution, indoor air quality, and environmental justice. His work on renewable energy systems includes designing sustainable solar farms and managing distributed energy resources such as heat pumps to enhance grid flexibility. He also leads a pioneering initiative to create the first statewide public IoT network in the U.S., enabling hyperlocal weather forecasting and microclimate monitoring. His recent publications reflect a strong trend toward agrivoltaics, peer-to-peer energy markets, and IoT-based environmental monitoring. These works demonstrate a consistent focus on data-driven modeling, community-scale energy solutions, and the integration of social considerations into technical systems. The keywords across his articles highlight expertise in sustainability, machine learning, air quality, and energy transition. Cornell Town-Gown Achievement Award (2022) Engaged Scholar Prize, Cornell University (2017) People's Choice Sign of Sustainability Award, Sustainable Tompkins (2016) Scientific and Technological Achievement Award, Environmental Protection Agency (2015) Fellow of the American Society of Mechanical Engineers Dr. Zhang is actively involved in mentoring students and securing research grants from agencies such as the National Science Foundation (NSF) and the New York State Energy Research and Development Authority (NYSERDA). His projects often involve interdisciplinary collaboration across eight Cornell colleges and 16 academic departments. He has led initiatives such as the Cornell Atkinson Academic Venture Fund projects and the development of a county-level energy roadmap for Tompkins County. He also teaches courses in engineering thermodynamics, future energy systems, and air quality, emphasizing experiential and community-based learning. Dr. Zhang leads the Energy and the Environment Research Laboratory (EERL) and collaborates with the Atkinson Center for a Sustainable Future. His lab functions as a hub for innovation in sustainable communities, combining advanced modeling with real-world applications. Through partnerships with community organizations, government agencies, and industry, his team develops science-driven solutions to urban and rural sustainability challenges.
Yannis Paschalidis is a Distinguished Professor at Boston University with appointments in Electrical and Computer Engineering, Systems Engineering, Biomedical Engineering, and Computing & Data Sciences. He serves as Director of the Rafik B. Hariri Institute for Computing and Computational Science & Engineering. He holds a PhD (1996) and MS (1993) in Electrical Engineering and Computer Science from MIT, and a Diploma (1991) from the National Technical University of Athens. His interdisciplinary research spans optimization, control systems, machine learning, and data science with applications in healthcare, autonomous systems, and networks. Key focus areas include developing algorithms for autonomous navigation, healthcare analytics for clinical decision support, energy demand optimization, and computational biology for protein interaction modeling. Recent publications demonstrate strong focus on AI robustness (adversarial defenses, distributional robustness), healthcare applications (cognitive impairment detection, epidemic control), and sustainable systems (power networks, ecological forecasting). Methodological innovations center on reinforcement learning, distributionally robust optimization, and geometric analysis of classical algorithms. CAREER Award (NSF) IEEE Fellow (2014) IFAC Fellow (2022) IBM/IEEE Smarter Planet Award IEEE Computer Society Crowd Sourcing Prize IMIA Best Paper Award Charles DeLisi Award (2020) Distinguished Professor of Engineering As primary advisor to 35 PhD graduates, he leads the Network Optimization & Control (NOC) Lab. His research is funded by NSF, NIH, DoD, ARPA-E, and industry partners, including major grants on Neuro-Autonomy (ONR MURI), pandemic preparedness (ARPA-E NewRAMP), and healthcare AI (NIH QuBBD). He directs the Network Optimization & Control Lab focusing on optimization, learning, and control for autonomous systems, healthcare, and networks. The lab develops fundamental methodologies with applications in robotics, computational medicine, and infrastructure systems.
Artur W. Dubrawski is an Alumni Research Professor of Computer Science and Director of the Auton Lab at Carnegie Mellon University's School of Computer Science. He leads interdisciplinary research on Artificial Intelligence, Machine Learning, and Robotics with real-world applications in healthcare, nuclear safety, food safety, and counter-human trafficking. His work focuses on bridging gaps between data-driven AI and empirical sciences through probabilistic modeling, predictive analytics, and time-series intelligence. Lab: Auton Lab (founded 1993) Collaborations: Allegheny County Health Department, USDA, CDC, U.S. Army Research Impact: AI for wastewater-based COVID-19 forecasting, radiological inspection systems, and hospital infection detection His students and affiliates include current PhD candidates Angela Chen, Emma Erickson, Cecilia Morales, Willa Potosnak and past researchers like Benedikt Boecking (co-inventor of Interactive Weak Supervision). The lab has spun off startups like Marinus Analytics (IBM XPrize finalists) and developed open-source tools like auton-survival for survival analysis. Key Grants: $10.5M U.S. Army contract for AI-driven predictive maintenance research.
Charles M. Jones is a Professor of Finance at Columbia Business School, Columbia University, with an extensive publication record spanning several decades. His research focuses on market microstructure, high-frequency trading, short selling, retail investor behavior, and stock market liquidity. His work has appeared in top finance journals including the Journal of Finance, with his most recent publication "Nonstandard Errors" appearing in the June 2024 issue. Professor Jones's research interests center on understanding how financial markets function at a granular level. His work on market microstructure examines the mechanics of price formation, liquidity provision, and the impact of trading technologies on market quality. His research on short selling has been particularly influential, investigating when short sellers trade, what information they possess, and how regulatory interventions like short sale bans affect market functioning. His more recent work has explored the rise of retail trading through platforms like Reddit and its implications for price discovery, particularly during events like the GameStop phenomenon and the COVID-19 pandemic. Analysis of his publication trends reveals a consistent focus on market efficiency and price discovery mechanisms, with increasing attention to retail investor behavior in recent years. His work spans both theoretical modeling and empirical analysis of market data, often utilizing high-frequency datasets to examine intraday trading patterns. The interdisciplinary nature of his research bridges finance, economics, and information science, contributing to both academic understanding and practical market regulation. Professor Jones has collaborated extensively with researchers across the globe, as evidenced by his numerous co-authored papers with scholars from institutions worldwide. His work has significant implications for market regulators seeking to understand the impact of technological changes and regulatory interventions on market quality and efficiency.
Mahdi Khodayar is an Assistant Professor of Computer Science at the University of Tulsa . He holds a Ph.D. in Electrical Engineering from Southern Methodist University (2020) and M.Sc./B.S. degrees in Artificial Intelligence and Software Engineering from K.N. Toosi University of Technology (2015, 2013). His research focuses on AI and machine learning applications in power systems, transportation systems, computer vision, and spatiotemporal pattern recognition. B.Sc. in Computer Engineering (2013), K.N. Toosi University of Technology M.Sc. in Artificial Intelligence (2015), K.N. Toosi University of Technology Ph.D. in Electrical Engineering (2020), Southern Methodist University Khodayar’s research bridges deep learning with energy systems , including fault detection in power grids, renewable energy forecasting, and traffic scene understanding. His work leverages graph neural networks , reinforcement learning , and generative models for robust spatiotemporal analysis. Recent publications highlight his focus on graph-based architectures for power systems, hybrid deep reinforcement learning in energy forecasting, and unsupervised domain adaptation in remote sensing. His work integrates physics-informed modeling with probabilistic frameworks . Scientific Awards : NSF ECCS Division Funding (2022) US DOT FHWA Grant (2023) Zelimir Schmidt Award for Early Career Research (2023) Honors Student Award (2015), K.N. Toosi University Khodayar has been funded by the NSF , US Department of Transportation , and the TU Cyber Fellows program . He serves as an associate editor for IEEE Transactions on Transportation Electrification and other journals.
Mario Berges is an Associate Professor in the Department of Civil and Environmental Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in Electrical and Computer Engineering. He holds leadership roles as Co-Director of the IBM Smart Infrastructure Analytics Lab and Director of the Intelligent Infrastructure Research Lab (INFERLab). His work focuses on applying information/communication technologies to enhance the operational efficiency and resilience of built environments amid evolving resource constraints and climate changes. Education: PhD in Civil & Environmental Engineering from CMU (2010). Research Interests: Berges' research integrates smart infrastructure systems, energy efficiency, and machine learning. Key areas include non-intrusive load monitoring (NILM), structural health monitoring of pipelines, building automation systems, and urban heat risk modeling. He develops data-driven frameworks for energy disaggregation, sensor placement optimization, and real-time infrastructure diagnostics. Awards: Recognized with the 2010 FIATECH Outstanding Early Career Researcher Award and 2015 Dean’s Early Career Fellowship from CMU. Grants & Labs: Leads INFERLab, collaborating with IBM on smart infrastructure projects. His work spans academic-industry partnerships focused on building analytics, smart grid technologies, and sensor networks. Future Directions: Expanding research into AI-driven energy systems, resilient urban infrastructure, and cross-disciplinary solutions for climate adaptation.
Albert Presto is a Research Professor in the Department of Mechanical Engineering at Carnegie Mellon University with a courtesy appointment in Civil and Environmental Engineering. He serves as Director of the Center for Atmospheric Particle Studies (CAPS) and is a Faculty Affiliate at the Steinbrenner Institute for Environmental Education and Research. His work spans multiple interdisciplinary collaborations across environmental science, engineering, and public health domains. Department of Mechanical Engineering, College of Engineering Center for Atmospheric Particle Studies (CAPS) Steinbrenner Institute for Environmental Education and Research Africa Clean Air Network Advisory Council Health Effect Institute's Energy Review Committee Presto's research focuses on pollutant emissions from energy extraction and consumption and their subsequent atmospheric transformations. His work examines how gas and oil wells emit methane, how vehicles emit carbon monoxide and particulate matter, and how these pollutants undergo oxidation in the atmosphere to create secondary pollutants like ozone and secondary particulate matter. His approach combines ambient measurements, laboratory experiments, source testing of pollution sources, and atmospheric models to provide a holistic view of pollutant emissions and transformations. His recent publications reveal a strong emphasis on air quality monitoring technologies, particularly low-cost sensor networks deployed internationally. There's a clear trend toward studying air pollution in African cities (especially Nairobi, Kampala, and Addis Ababa), examining spatial and temporal variability of particulate matter, and investigating the health impacts of air pollution exposure. His work increasingly integrates machine learning approaches for pollution forecasting and leverages international collaborations to address global air quality challenges. Member of Africa Clean Air Network Advisory Council Collaborator with US embassies worldwide on air quality data collection Quoted in The Guardian on inequality of air pollution exposure, specifically noting how people of color are more likely to be exposed to harmful air pollution Collaborated on research regarding chemicals released during the Ohio train derailment Presto actively collaborates with medical professionals to develop neighborhood-by-neighborhood studies of pollutant exposure and to understand relationships between emissions and health effects like childhood asthma. His work includes investigating racial-ethnic exposure disparities to airborne pollutants and examining how proximity to pollution sources like fast-food restaurants affects pediatric asthma outcomes. He has received seed funding from the Scott Institute for Energy Innovation for research in clean hydrogen production, AI hardware efficiency, and decarbonizing transportation. As Director of CAPS, Presto leads a research team focused on atmospheric particle studies with applications ranging from local Pittsburgh air quality to global monitoring initiatives. His lab develops and deploys advanced air quality monitoring technologies, including low-cost sensor networks that have been implemented in multiple cities across Africa. The CAPS research group works at the intersection of environmental engineering, atmospheric science, and public health to address critical air quality challenges worldwide.
Ming Hu is a Professor and University of Toronto Distinguished Professor of Business Operations and Analytics at Rotman School of Management, University of Toronto. He serves as Area Coordinator for the Operations Management & Statistics Area and holds editorial leadership roles including Editor-in-Chief of Naval Research Logistics and Associate Editor for multiple top journals. MS in Applied Mathematics, Brown University (2003) PhD in Operations Research, Columbia University (2009) His research focuses on sharing economy , social operations , and platform economics , examining how operational decisions can maximize societal benefit. Key areas include crowdfunding , two-sided markets , crowdsourcing , and group buying , with applications to DEI , sustainability , and AI-empowered operations . Recent work analyzes spatial operations in delivery systems, algorithmic fairness , and climate change adaptation in agricultural supply chains. His publications span top journals like Management Science and Operations Research , covering topics from blockchain traceability to quantum-inspired optimization . Scientific recognitions include: Wickham Skinner Early-Career Research Award (2016) Best Operations Management Paper in Management Science (2017) 2018 Poets & Quants Best 40 Under 40 MBA Professors As an Amazon Scholar (2022–) and Chair of Chain Analytics Institute (2023–), he bridges academic research with industry applications in AI-driven logistics and sustainable operations.
Goran Strbac is a Professor of Energy Systems at Imperial College London's Faculty of Engineering, holding the Chair in Electrical Energy Systems. He leads the Department of Electrical and Electronic Engineering and directs the joint Imperial-Tsinghua Research Centre on Intelligent Power and Energy Systems. His roles include IPCC WG 3 Leading Author, OFGEM RIIO-2 Challenging Group Member, and member of multiple EU energy platforms. Strbac's research focuses on multi-energy systems integration, energy market design, renewable integration, distributed energy resources, and grid resilience. Notable contributions include whole-energy system modeling across operation and investment scales, market mechanisms for flexibility services, and security assessments for low-carbon infrastructure. His work emphasizes resilience and decarbonization, with over 500 publications and 4 co-authored books. He advises governments and regulatory bodies on energy policy, including contributions to UK Smart System Forum and European Technology and Innovation Platforms. His recent articles explore AI-driven grid optimization, hydrogen integration, and transactive energy systems. Strbac leads major initiatives like the UK Centre for Grid Scale Energy Storage and chairs resilience-focused research clusters. His research bridges academia and industry, informing policy through rigorous systems analysis and innovative modeling frameworks.
Mark Crowley is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, with a cross-appointment in the Cheriton School of Computer Science. He is a member of the Waterloo Artificial Intelligence Institute (WAII) and the Waterloo Institute for Complexity and Innovation (WICI), and serves as National Secretary of the Canadian Artificial Intelligence Association (CAIAC). His educational background includes a Ph.D. and M.Sc. in Computer Science from the University of British Columbia, where he worked in the Laboratory for Computational Intelligence, and a B.A. in Computer Science from York University. He completed a postdoctoral fellowship at Oregon State University working with Tom Dietterich's machine learning group. Crowley's research focuses on developing dependable and transparent algorithms to augment human decision-making in complex domains with multiple agents, spatial structure, or uncertainty. His work spans Reinforcement Learning , Deep Learning , Ensemble Methods , and Manifold Learning . He frequently collaborates with researchers in applied fields including Computational Sustainability, Sustainable Forest Management, Autonomous Driving, Medical Imaging, and Material Design. His research is motivated by both theoretical opportunities and real-world challenges such as forest fire management, automotive applications, and medical imaging. His recent publications demonstrate a strong focus on addressing challenges in reinforcement learning, particularly around observation costs, multi-agent systems, and causal representation learning. His work on ChemGymRL provides a significant contribution to digital chemistry and material design through reinforcement learning frameworks. The textbook Elements of Dimensionality Reduction and Manifold Learning represents a major contribution to the theoretical foundations of machine learning. Crowley actively supervises graduate students, with recent thesis completions including Shayan Shirahmadi Gale Bagi (PhD, Feb 2025) and Oleksandra Nahorna (MASc, Dec 2024). His lab, UWECEML (Waterloo ECE Machine Learning Lab), focuses on developing new algorithms at the intersection of Machine Learning, Optimization, and Probabilistic Modeling. He teaches courses including ECE 457C (Reinforcement Learning), ECE 657A (Data & Knowledge Modelling & Analysis), and ECE 457B (Fundamentals of Computational Intelligence). His blog Computationally Thinking explores AI, machine learning, and the societal impact of these technologies.
Wei Xiang is an Assistant Professor of Economics at the University of Michigan's Department of Economics, housed within the College of Literature, Science, and the Arts. He earned his Ph.D. in Economics from Yale University in 2024, focusing on international trade, macroeconomics, and environmental economics. His research bridges economic theory with applied analyses, addressing global trade dynamics, macroeconomic policies, and environmental sustainability. Education: Ph.D. in Economics, Yale University (2024). Research Interests: His work explores the intersection of international trade policies, macroeconomic stability, and environmental regulations. He examines how trade agreements influence economic growth and environmental outcomes, with a focus on empirical methodologies to assess policy impacts. Additionally, he investigates macroeconomic models to understand global economic fluctuations and their implications for sustainable development. Publications: His recent articles highlight contributions to vehicle communication systems, signal processing, and wireless technologies, reflecting interdisciplinary engagement with engineering applications. These include real-time prediction models for GPS errors and beamforming optimization in vehicular networks. Labs/Teams: Not explicitly stated, but his research collaborations likely involve interdisciplinary teams in economics, engineering, and environmental science.
