Fabian Fagerholm is an Assistant Professor in the Department of Computer Science at Aalto University. His work bridges software engineering, human-computer interaction, and empirical research methodologies. He actively participates in research groups such as Software and Service Engineering (SSE) and Human-Computer Interaction and Design (HCID). Fagerholm's research explores: Continuous experimentation in software development Developer cognition and mental models Agile methodologies and team dynamics Low-code platforms and end-user programming Software engineering education and pedagogy His publications reflect a strong empirical focus, with recurring themes of human factors in technical systems and educational innovation. He has received notable awards including: Journal of Systems and Software Best Paper Award (2018) EUROMICRO SEAA Distinguished Paper Award (2017) Teacher of the Year (2013) Nokia Foundation Scholarship (2013) Fagerholm contributes to software engineering infrastructure through tools for experimentation and boundary artifacts, enhancing collaboration in distributed teams.
Pierre Vandergheynst is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Department of Electrical Engineering, with a courtesy appointment in Computer and Communication Sciences. He serves as EPFL’s Vice-Provost for Education since 2015 and leads the Signal Processing Laboratory 2 (LTS2). His research spans harmonic analysis, sparse approximations, mathematical data processing, and applications in signal/image processing, computer vision, machine learning, and graph-based data analysis. PhD in Mathematical Physics (1998), Université catholique de Louvain Postdoctoral Researcher at EPFL (1998-2001) Assistant Professor at EPFL (2002-2007) His research explores geometry/symmetry in high-dimensional data, redundant dictionaries for dimensionality reduction, and computational harmonic analysis on manifolds. Recent work focuses on protein structure modeling, geometric deep learning, and graph-based signal processing. Key article trends include graph neural networks for protein analysis, geometric deep learning in neuroscience, and structured knowledge priors in neural models. His 2023-2025 publications emphasize interpretable AI, long-range dependencies in graphs, and molecular representation learning. Scientific Awards: IEEE Signal Processing Magazine Best Paper Award (2023) Signal Processing Society Best Paper Award (2022) Apple ARTS Award (2007) De Boelpaepe Prize, Royal Academy of Sciences of Belgium (2009-2010) He has supervised over 30 PhD theses and contributed to foundational work in graph signal processing, compressive sensing, and geometric deep learning. His lab develops tools for data science on non-Euclidean structures, with applications in medicine, astronomy, and wireless systems.
Ali H. Sayed is the Dean of the School of Engineering (Faculté des sciences et techniques de l'ingénieur - STI) at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, where he also directs the Adaptive Systems Laboratory (Laboratoire de systèmes adaptatifs). Previously, he served as an emeritus professor and chair of the Electrical Engineering Department at UCLA. He is a highly cited researcher and a member of the US National Academy of Engineering and the World Academy of Sciences. Sayed served as president of the IEEE Signal Processing Society in 2018 and 2019. Professor Sayed's research focuses on adaptation and learning theories, data and network sciences, statistical inference, multi-agent systems, adaptive networks, and optimization. His work bridges theoretical foundations with practical applications in signal processing, machine learning, and network science. He has made significant contributions to distributed learning algorithms, social learning over networks, and adaptive signal processing techniques that have influenced both academic research and practical implementations. His recent publications demonstrate a strong focus on multi-agent systems, distributed learning, privacy-preserving techniques, and social learning over networks. The research trends show increasing emphasis on federated learning with privacy guarantees, graph-based learning approaches, and the intersection of social dynamics with information processing. His work consistently addresses fundamental theoretical questions while maintaining relevance to practical applications in communication networks, social media analysis, and distributed artificial intelligence systems. Professor Sayed has received numerous prestigious awards throughout his career, including: IEEE Fourier Award (2022) Norbert Wiener Society Award (2020) IEEE Signal Processing Society Education Award (2015) Papoulis Award from the European Association for Signal Processing (2014) Technical Achievement Award from IEEE Signal Processing Society (2012) Terman Award from the American Society for Engineering Education (2005) IEEE Donald G. Fink Prize (1996) Multiple Best Paper Awards from IEEE and EURASIP Sayed has authored or co-authored over 570 publications and six monographs. He has mentored numerous PhD students and researchers in the fields of signal processing and adaptive systems. His editorial leadership includes serving as Editor-in-Chief of IEEE Transactions on Signal Processing (2003-2005) and EURASIP Journal on Advances in Signal Processing (2006-2007), as well as Founding Editor-in-Chief of the Open Access Book Series on Information and Learning Sciences. At EPFL, Professor Sayed leads the Adaptive Systems Laboratory, which focuses on developing theoretical frameworks and practical algorithms for adaptive systems, networked learning, and distributed signal processing. The lab's research encompasses both fundamental theoretical investigations and applications to real-world problems in communications, social networks, and computational biology.
Alexandre Jacquillat is the Maurice F. Strong Career Development Associate Professor and Associate Professor of Operations Research and Statistics at MIT Sloan School of Management. His research focuses on data-driven decision-making with applications in air traffic management, urban mobility, and decarbonization. He holds a PhD in Engineering and MS degrees from MIT and École Polytechnique. Education PhD in Engineering, MIT MS in Technology and Policy, MIT MS in Applied Mathematics, École Polytechnique Research Interests His work develops scalable optimization models for efficient, equitable, and sustainable operations. Key areas include stochastic optimization, large-scale systems design, and machine learning applications in transportation and public policy. Recent projects explore vertiport planning for urban aerial mobility and prescriptive analytics for pandemic response. Awards Harold W. Kuhn Award (2024) INFORMS Harvey Greenberg Research Award (2023) MIT Jamieson Prize for Excellence in Teaching (2023) Multiple INFORMS Best Paper Awards (2015-2023) Named Leading Academic Data Leader by Chief Data Officer Magazine (2021-2022) Teaching & Grants Teaches courses in optimization (15.093, 15.083) and analytics (15.072). His grants support work in robotic warehousing, air traffic scheduling, and disaster response logistics. Advises on transportation analytics for industry and government. Labs/Teams Leads MIT Sloan's operations research group, collaborating with industry partners like McKinsey & Co. and Booz Allen Hamilton on transportation analytics and optimization projects.
Lisbeth Aggestam is an associate professor at the Department of Political Science and an associate researcher at the Center for European Research (CERGU) at the University of Gothenburg. She serves as the editor-in-chief of the international journal Foreign Policy Analysis , published by the International Studies Association (ISA) and Oxford University Press. Aggestam's research focuses on several interconnected areas: Political leadership and foreign policy Diaspora and foreign policy Foreign policy role theory The European Union as a global actor Her scholarly work demonstrates a consistent focus on leadership dynamics within European foreign policy, particularly examining how the EU navigates its role as a global actor amid shifting geopolitical landscapes. She has developed significant theoretical contributions to role theory in foreign policy analysis, creating frameworks for understanding how states and international organizations perform specific roles on the global stage. Aggestam's publication record reveals several important trends in contemporary foreign policy studies. Her work increasingly examines the intersection of domestic politics and international relations, particularly how diaspora communities influence state foreign policies. Her recent research addresses European strategic autonomy in the context of US foreign policy shifts and the implications of Sweden's NATO membership decision, reflecting her ability to connect theoretical frameworks with pressing real-world issues. As an educator, Aggestam serves as course manager and instructor for several key programs: EU2215: Europe in the World (MA, 15 hec) SK1136: Foreign Policy Analysis (BA, 7.5 hec) EU1145: The European Union in International Affairs (BA, 7.5 hec) Through her leadership of the Swedish Research Council project 'Transnational civil society and foreign policy' (2022-2025) and her editorial role at Foreign Policy Analysis , Aggestam plays a significant role in shaping contemporary discourse on international relations and European foreign policy.
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.
Ceren Budak is an Associate Professor at the University of Michigan School of Information and holds a joint appointment as Associate Professor of Electrical Engineering and Computer Science in the College of Engineering. Her work bridges computer science, statistics, and social sciences through computational social science approaches. Her educational background includes a PhD in Computer Science from the University of California, Santa Barbara (2012) and a Bachelors degree in Computer Science from Bilkent University in Turkey (2007). Prior to joining the University of Michigan faculty, she was a Postdoctoral Researcher at Microsoft Research New York. Professor Budak's research centers on computational social science, with particular emphasis on analyzing large-scale datasets to address questions with social, political, and policy implications. Her work spans several interconnected domains: News Media Production & Consumption (examining bias in news outlets and reader preferences), Social Movements & Media (using social media data to study collective action), Social Networks (understanding information diffusion processes), and Measuring and Promoting the Quality of Online Discussions (developing tools to improve online conversations). She teaches SI 608 (Networks) and SI 618 (Data Manipulation and Analysis) at the School of Information. Her publication record demonstrates consistent contributions to understanding how online information ecosystems operate, with recent work focusing on AI-human collaboration, misinformation dynamics, social movement framing, and the application of computational methods to political communication. Her research shows a clear trajectory from foundational work on social network diffusion to increasingly sophisticated analyses of contemporary information challenges. Among her service activities, she has served as Registration chair for COSN (ACM Conference on Online Social Networks) 2015 and as Program Committee Member for numerous prestigious conferences including WWW, ICWSM, WebSci, AAAI, and others. She has also been involved in organizing the MSR NYC Data Science Seminar Series and instructing the Microsoft Research Data Science Summer School.
Andrew Childs is a Professor at the University of Maryland, affiliated with the Department of Computer Science and the Institute for Advanced Computer Studies (UMIACS). He serves as Director of the NSF Quantum Leap Challenge Institute for Robust Quantum Simulation (RQS) and is a Fellow at the Joint Center for Quantum Information and Computer Science (QuICS). His research focuses on quantum algorithms for simulating physical systems, algebraic problems, and quantum walk protocols, with applications in quantum computing and computational complexity. University of Maryland Institute for Advanced Computer Studies (UMIACS) Joint Center for Quantum Information and Computer Science (QuICS) NSF Quantum Leap Challenge Institute for Robust Quantum Simulation Childs' research spans quantum simulation, quantum Fourier transform, phase estimation, and Hamiltonian dynamics. He has developed techniques to reduce quantum computational resources for simulating quantum systems and explored limitations of quantum computers through hidden subgroup problems and non-unitary dynamics. His publications cover diverse areas including quantum walk optimization, Hamiltonian simulation methods, and applications to cryptography and condensed matter physics. Recent works address spatial search algorithms, product formulas for commutators, and quantum routing protocols. As an educator, Childs has taught courses on quantum algorithms and information processing at both the University of Maryland and University of Waterloo, with lecture notes and materials spanning multiple years. Contact: amchilds@umd.edu | Office: ATL 3359 | Affiliated with University of Maryland's quantum research institutes.
Mark Lewis is the Kennedy Chair in Mathematical Biology at the University of Victoria, holding joint appointments in the Departments of Mathematics and Statistics and Biology. His research focuses on spatial ecology and mathematical modeling, addressing ecological challenges such as animal movement, invasive species, and disease dynamics. Lewis earned his D.Phil. in Mathematical Biology from the University of Oxford and has been elected a Fellow of the Royal Society UK. His work integrates mathematical analysis, field studies, and interdisciplinary approaches to solve ecological problems. Current projects include modeling polar bear populations, cyanobacteria dynamics, and the impact of climate change on wildlife. Lewis supervises students across both UVic and his former University of Alberta lab. Education: D.Phil. in Mathematics (Mathematical Biology), University of Oxford Awards: Royal Society Fellowship, CRM-Fields-PIMS Prize, and Okubo Prize Key Research Areas: Animal movement modeling, aquatic ecology, wildlife disease, and invasive species management Publications highlight his contributions to understanding disease spread, parasite dynamics, and ecological responses to environmental changes. Lewis collaborates widely, applying mathematical tools to real-world conservation and health challenges.
Sebastian Raisch is a Full Professor of Strategic Management at the Geneva School of Economics and Management (GSEM), University of Geneva, where he also serves as Director of the Executive MBA program. He is affiliated with the Institute of Management and holds a Ph.D. from the University of Geneva. His academic work is deeply rooted in understanding how organizations balance stability and change, particularly in the face of digital transformation. Research Interests: His primary areas of expertise include strategic management, organizational ambidexterity, organizational paradox, artificial intelligence in organizations, and digital transformation. His research investigates the dynamics of corporate growth and decline, strategic renewal, and the integration of human and artificial intelligence in organizational decision-making. He is especially interested in how firms navigate tensions between competing demands such as exploration and exploitation, automation and augmentation, and stability and innovation. Publications and Trends: His recent publications (2023–2025) show a strong shift toward the role of artificial intelligence in management, with a focus on hybrid human-AI systems, ethical AI, and the strategic implications of AI adoption. Earlier works center on ambidexterity, paradox theory, and corporate turnaround, establishing him as a leading scholar in these domains. Scientific Awards: Strategic Management Society’s Best PhD Paper Award Emerald Publishing Group’s Citation of Excellence Award Journal of Management’s Scholarly Impact Award Editorial and Professional Service: Sebastian Raisch serves as Associate Editor at the Academy of Management Review and sits on the editorial boards of the Academy of Management Journal and the Strategic Management Journal . He has advised numerous doctoral students and supervised research projects on organizational innovation and digital strategy, though specific names are not listed in the provided materials. He has not received explicit mention of research grants, but his extensive publication record suggests active research funding. Labs and Research Teams: While no formal lab is mentioned, Raisch leads or contributes to research initiatives within the Institute of Management at GSEM, particularly in strategic management and digital innovation. He collaborates with scholars across Europe and the U.S., including notable co-authors like J. Schad, S. Krakowski, and M. Tushman.
Prof. Raffaello D'Andrea is a Full Professor at ETH Zürich's Department of Mechanical and Process Engineering, affiliated with the Institute for Dynamic Systems and Control. His research focuses on bridging digital and physical worlds through robotics, control systems, and autonomous systems. He has pioneered work in aerial robotics, swarm systems, tactile sensing, and soft robotics. His philosophy emphasizes solving 'easy' problems with scalable, robust solutions, prioritizing simplicity and replicability. Key research areas include UAV navigation, distributed control, tactile sensor design, and fault-tolerant systems. He has founded multiple organizations and led roles as CTO/CEO, emphasizing cross-disciplinary innovation. His work has commercial applications in logistics, healthcare, and automation, driven by a belief in technology's role in improving human life. Notable projects include the Cubli robotic cube, aerial vehicle swarms, and optical tactile sensors for robotics. His lab emphasizes collaboration and team leadership, aiming to translate theoretical insights into practical, scalable technologies. Scientific awards: None explicitly listed in the provided texts. Advising and grants: No students listed in the provided texts; grants information not detailed. Labs/Teams: Leads research at ETH Zurich's Institute for Dynamic Systems and Control, collaborating with industry and academic partners globally.
Jose Miguel Abito ("Mike") is an Associate Professor in the Department of Economics at The Ohio State University (OSU), serving as Director of Undergraduate Studies. He holds a PhD from Northwestern University, with prior roles including Assistant Professor at the Wharton School, University of Pennsylvania, where he won a teaching award. His research focuses on applied microeconomics, particularly industrial organization, regulation, and environmental economics. He examines topics such as regulatory incentives, consumer misinformation effects, and antitrust methodologies in sectors like electricity, extended warranties, and infant formula. Education: PhD in Economics from Northwestern University (Evanston, IL), graduate studies in Econometrics and Mathematical Economics at Toulouse, France, and undergraduate studies in Singapore. Research Interests: Mike explores how regulatory frameworks impact market efficiency, consumer welfare, and firm behavior. Recent work analyzes renewable energy market design, electricity procurement dynamics, and demand spillovers in welfare programs like WIC. His methodologies bridge theoretical models and empirical analysis to address complex policy questions. Professional Activities: Currently serves as a University Senator (2023-2026), member of the Salmon P. Chase Center Academic Advisory Committee, and faculty advisor for the Quantitative Finance Club at OSU. Previously involved in inclusive teaching initiatives at Wharton. Awards: Recognized with a teaching award during his tenure at Wharton School. Grants & Advising: Advises on projects related to infant formula markets, wage dynamics, and environmental policy. Engaged in collaborative research on green jobs, regulatory auditing frameworks, and productivity analysis.
Joan Bruna is a Full Professor of Computer Science, Data Science, and affiliated Mathematics at New York University's Courant Institute and Center for Data Science. He leads the CILVR group and co-founded the MaD group. His research focuses on mathematical foundations of machine learning, deep learning, signal processing, and their applications in computational science, climate modeling, and geophysics. He holds a Ph.D. in Applied Mathematics from École Polytechnique and has held roles at UC Berkeley, the Institute for Advanced Study, and the Flatiron Institute. Education: Ph.D. (2013) and M.Sc. (2005) in Applied Mathematics, École Polytechnique and ENS Cachan; dual B.Sc./M.Sc. in Telecommunications and Mathematics from UPC Barcelona (2002–2004). Awards include the NSF CAREER Award (2019) and Alfred P. Sloan Fellowship (2018). Research interests span theoretical aspects of deep learning, optimization, and statistical methods, with applications to climate science and inverse problems. He has advised numerous PhD students and postdocs, contributing to seminal work on scattering networks, geometric deep learning, and neural ODEs. Professional service includes editorial roles at TMLR, JMLR, and TPAMI, plus program chairs for major conferences. His work bridges mathematics and machine learning, emphasizing rigorous analysis and real-world impact.
Stefano NASINI is an Associate Professor at the University of Lille 3, specializing in Quantitative Methods within the Economics and Mathematics Sciences. He holds a HDR (Habilitation à Diriger des Recherches) from the University of Lille 3 (2021), a Ph.D. in Statistics and Operations Research from the Polytechnic University of Catalonia (2015), and a Master in Statistics (2011). His research focuses on optimization, complex networks, statistical inference, and microeconomic applications. He has held academic positions including a post-doctoral role at IESE Business School (2014–2016) and a visiting researcher role at the University of Lisbon (2014). His work spans scheduling optimization, network analysis, financial contagion modeling, and energy system planning. Key contributions include specialized algorithms for large-scale optimization problems and frameworks for decentralized portfolio management. He is a member of the LEM research group and teaches courses in optimization, econometrics, and social network analysis at the Grande École and MSc levels. Publications highlight interdisciplinary applications, including network-based diffusion models, multi-market financial strategies, and dynamic choice analysis. His research bridges theoretical advancements in operations research with practical challenges in economics, energy, and transportation systems. No scientific awards are explicitly listed in the provided materials. His advising roles and grants are not detailed here, but his extensive publication record reflects active collaboration within academic and applied domains.
Amit Kumar is the Jaswinder and Tarwinder Chadha Chair Professor in the Department of Computer Science and Engineering at IIT Delhi. His research focuses on combinatorial optimization, online algorithms, and algorithmic fairness. He has taught courses such as Approximation Algorithms (COL 754), Design and Analysis of Algorithms (COL 351), and Numerical Analysis (COL 726). His work spans theoretical computer science with applications to clustering, scheduling, and fairness in evaluation processes. Research Interests Kumar's research emphasizes developing efficient algorithms for online and dynamic settings, particularly in constrained optimization and biased evaluation systems. He explores theoretical foundations of clustering, load balancing, and resource allocation, with recent contributions to fair food delivery systems and coreset constructions. Publications His recent work includes advancements in online convex paging (STOC 2025), consensus clustering (SODA 2025), and fairness-aware algorithms (AAAI 2024). Over 70 papers across top venues like STOC, SODA, and ICML reflect his expertise in algorithm design and analysis. Awards Best Paper Award at ISAAC 2023 for 'Clustering What Matters in Constrained Settings' Teaching & Mentorship Kumar instructs graduate and undergraduate courses in algorithms, data structures, and numerical methods. He advises students through these courses and collaborates with researchers on NSF-funded projects related to approximation algorithms and streaming systems.
