Dr. Nienke Smit is an Assistant Professor in the Department of Education at Utrecht University's Faculty of Social and Behavioural Sciences. Her research focuses on scaffolding processes, adaptive teaching, and classroom observational research from a complex dynamic systems perspective. Specializes in teacher-student interaction Expert in adaptive instruction and observational methods Collaborates with Data Science department Develops tools for classroom observation Research interests include language learning and teaching , judgment and decision-making in education , and professional development of teachers . She leads projects like Eye Get It (joint attention in classrooms) and a replication study on adaptive instruction effectiveness. Her work applies dynamic systems theory to second language development and foreign language pedagogy. Recent publications address translanguaging in multilingual classrooms, replication studies , and process-oriented research . She teaches at Utrecht's Graduate School of Teaching and works on initiatives to reduce teacher shortages through improved training programs.
Dr. Robert Huber serves as a Lecturer at the Department of Environmental Systems Science at ETH Zürich, specializing in Agricultural Economics and Policy. His research focuses on the integration of interdisciplinary knowledge to analyze and evaluate management and policy options that promote sustainable development in the agricultural sector. Institution: ETH Zürich Department: Agricultural Economics and Policy Research Focus: Sustainable Agriculture and Policy Methodologies: Bio-economic and agent-based modeling Dr. Huber's research explores how multifunctional agricultural sectors can provide essential non-market goods and services while maintaining their primary economic function of food production. His work applies bio-economic and agent-based modeling techniques to understand farmers' decision-making processes regarding ecosystem services provision, coupled with economic valuation techniques to analyze societal demand for these services. His research aims to identify effective and efficient policy measures that align supply and demand for ecosystem services at landscape scale. His recent publications demonstrate strong expertise in agricultural policy analysis, climate change mitigation strategies, sustainable farming practices, and innovative modeling approaches. His work spans topics from herbicide-free agriculture using remote sensing technologies to biodiversity enhancement in agricultural landscapes and the cost-effectiveness of different climate change mitigation policies at various scales. Remote sensing applications in sustainable agriculture Behavioral aspects of farmer decision-making Policy evaluation methodologies Climate change adaptation in farming systems Economic valuation of ecosystem services Digitalization impacts on agricultural policy Dr. Huber has published extensively in high-impact journals including Journal of Agricultural Economics, People and Nature, and Q Open. His collaborative research often involves interdisciplinary teams addressing complex agricultural sustainability challenges. His publications demonstrate a consistent focus on practical policy implications derived from rigorous empirical analysis.
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.
Anna Gottard is an Associate Professor of Statistics at the University of Florence, where she leads the Department of Statistics, Computer Science, and Applications. She directs the Florence Center for Data Science (FDS) and participates in the Technical Scientific Committee of the Tuscan Center for Big Data, Data Science, and AI (CBDAI). Her research focuses on multivariate statistical models, particularly graphical models, and extends to statistical machine learning, fair models, and directional data analysis. She is an Associate Editor for the Journal of the Royal Statistical Society Series A (JRSSA) and Statistical Methods & Applications (SMA). Her recent work includes Bayesian approaches for mixed graphical models, uncertainty-aware classification trees, and methodological advancements in latent uncertainty models. Her contributions span theoretical developments and applied research in interdisciplinary areas like biostatistics and sustainability. Her research interests emphasize bridging statistical theory with practical applications, including fairness in machine learning, interpretable models, and tree-based methodologies. She has actively contributed to open-source software, notably the Mix3Trees R package for mixed-effect tree models. Her work addresses challenges in variable selection, graphical model inference, and ethical AI practices. Current projects explore Bayesian frameworks for complex data structures and methodological improvements in graphical model interpretability. Anna has advised on interdisciplinary collaborations, such as studies on GDPR compliance in biobanking and epidemiological modeling of the SARS-CoV-2 pandemic in Tuscany. She collaborates with institutions like the CBDAI to advance data science applications in regional policy and healthcare. Her research trajectory reflects a commitment to both foundational statistical theory and real-world problem-solving across diverse domains.
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.
Shipra Agrawal is an Associate Professor at the Department of Industrial Engineering and Operations Research, Columbia University, with affiliations to the Data Science Institute and the Department of Computer Science. Her research bridges optimization and machine learning, focusing on decision-making in uncertain environments. PhD in Computer Science from Stanford University (2011) Researcher at Microsoft Research India (2011–2015) Her work addresses online optimization , reinforcement learning , and game theory , aiming to develop algorithms that balance exploration and exploitation for long-term goals. Applications include internet advertising , revenue management , and resource allocation . Recent publications examine dynamic pricing models, regret bounds in reinforcement learning, and convex knapsack optimization. Her research has been supported by NSF CAREER , Google Faculty Research , and Amazon Research Awards . NSF CAREER Award CMMI-1846792 (2019) Google Faculty Research Award (2017) Amazon Research Award (2017) She has advised PhD students who now hold positions at institutions like Google DeepMind, Amazon, and Facebook. Agrawal serves as an associate editor for Management Science , INFORMS Journal on Optimization , and Journal of Machine Learning Research , and co-chaired major conferences such as COLT 2024 and AISTATS 2025.
Florian Kerschbaum is a Professor and NSERC/RBC Industrial Research Chair in Data Security at the Cheriton School of Computer Science, University of Waterloo. His research focuses on data security and privacy, applied cryptography, and confidentiality in data science. Research interests span data collection/preparation management, secure multi-party computation, homomorphic encryption, differential privacy, and machine learning robustness/privacy. His work develops cryptographic solutions for practical data management challenges in distributed systems.
Allan Larsen is a Professor and Deputy Head of Division at the Department of Technology, Management and Economics (DTU Management) at the Technical University of Denmark. He heads the Operations and Supply Chain Management Section. His academic background includes an MSc in Applied Mathematics and a PhD in Operations Research, both from DTU. He teaches courses in Operations Management and Simulation at both undergraduate and graduate levels. Research Focus: His work applies operations research methodologies to complex planning problems in supply chain management, logistics, healthcare operations, and public transport. Key areas include urban freight transport, healthcare supply chains, and optimization of public transport resources like crew and fleets. Collaborations with industries, especially in freight transport, have been extensive. He previously co-directed the Transport DTU research center and co-chaired Denmark’s Transport Innovation Network. Education: MSc in Applied Mathematics (1989–1995), PhD in Operations Research (1997–1999), both from DTU. Recognition: Recipient of the Hedorf’s transportpris award in 2022 for contributions to transport research. Research Projects: Supervises multiple PhD students (e.g., on electric freight transport, healthcare resource optimization, and Industry 4.0 applications). Active in projects like 'Pioneering Electric Heavy-Duty Freight Transport' and 'Resource optimization in healthcare.' Labs/Teams: Leads the Operations and Supply Chain Management research group and collaborates in initiatives like the Transport Innovation Network, focusing on sustainable transport solutions.
Yi Fang is an Associate Professor of Computer Engineering and an affiliated Associate Professor of Computer Science at New York University Abu Dhabi (NYUAD), and a Global Network Associate Professor at NYU Tandon. He is a core faculty member in the Division of Engineering, specializing in Electrical and Computer Engineering. His research is centered at the intersection of Embodied AI, Robotics, and AI-driven assistive technologies, with strong support from agencies such as the US NSF, UAE ADEK, and ASPIRE. PhD, Purdue University Yi Fang's research interests span 3D Computer Vision, Multimedia Processing, Machine Learning, Deep Learning, and Embodied AI . He focuses on AI-driven perception, learning, and real-world applications, particularly in engineering, medicine, and accessibility. His lab, the Embodied AI and Robotics (AIR) Lab, develops intelligent robotic systems that integrate perception, learning, and decision-making to solve complex societal challenges. His work emphasizes large-scale visual computing, deep visual learning, and cross-domain/multimodal foundation models , with recent innovations in assistive AI for the Deaf and Hard-of-Hearing community. The 15 most recent publications reflect a consistent focus on 3D vision, sketch-based 3D retrieval, point cloud learning, and assistive computer vision . His work leverages deep learning, adversarial training, metric learning, and generative models to bridge modalities such as sketches, depth images, and 3D models. There is a clear trend toward cross-modal understanding, unsupervised representation learning, and real-world assistive applications , especially for visually impaired individuals. Yi Fang actively contributes to the academic community as an Area Chair for top-tier conferences including CVPR, ECCV, ICCV, IJCAI, and IROS. He also serves in peer review and mentoring roles, shaping the future of AI and robotics research. As a dedicated educator, he teaches foundational and advanced courses such as Computer Vision, Applied Machine Learning, Data Structures, and Capstone Design . He mentors students through research seminars and honors projects, fostering innovation and technical excellence. His research is supported by major grants from US NSF, UAE ADEK, and ASPIRE, enabling high-impact interdisciplinary collaborations. He founded and directs the Embodied AI and Robotics (AIR) Lab at NYU Abu Dhabi, a dedicated research space for developing intelligent systems that seamlessly integrate perception, learning, and decision-making. The lab promotes interdisciplinary collaboration across engineering, medicine, and social sciences, advancing the frontiers of Embodied AI.
Professor Jennifer Whitty is an applied health economist at the University of East Anglia , affiliated with the Norwich Medical School and the Centre of Research Excellence in Telehealth . Her research bridges health economics, patient preferences, and health policy, with a focus on quality of life valuation and economic evaluation. Education: Doctor of Philosophy (Griffith University, 2008), Graduate Diploma in Clinical Pharmacy (University of Queensland, 2002), Bachelor of Pharmacy (University of Wales, 1992) Her work emphasizes person-centered methodologies , particularly discrete choice experiments, to evaluate patient and public preferences for healthcare delivery and outcomes. Key research areas include: Treatment burden and quality of life in chronic diseases like cystic fibrosis Economic evaluation of pharmaceuticals and health technologies Asset-based approaches to community health Priority-setting frameworks in health policy Prof. Whitty has secured over AU$27 million in research funding from organizations including the NHMRC and National Institute for Health and Care Research , and serves on editorial boards for Medical Decision Making and Applied Health Economics and Health Policy .
Rina Dechter is a Professor of Computer Science at the University of California, Irvine (UCI), affiliated with the Donald Bren School of Information and Computer Sciences (ICS). She specializes in automated reasoning, probabilistic and constraint-based graphical models, and causal inference. Dechter has held leadership roles, including Co-Editor-in-Chief of Artificial Intelligence since 2011 and editorial board memberships in journals such as the Constraint Journal and Journal of Machine Learning Research . Education : Ph.D., Computer Science, University of California, Los Angeles (UCLA) M.S., Applied Mathematics, Weizmann Institute B.S., Mathematics and Statistics, Hebrew University of Jerusalem Research Interests : Dechter’s work focuses on computational aspects of automated reasoning, constraint processing, probabilistic reasoning, and causal inference. She develops efficient algorithms for graphical models, emphasizing tractable reasoning tasks and anytime search strategies. Her recent projects include causal decision-making frameworks funded by a $5M NSF grant. Awards : Presidential Young Investigator Award (1991) AAAI Fellow (1994) ACP Research Excellence Award (2007) ACM Fellow (2013) Elected to the American Academy of Arts & Sciences (2025) Grants & Collaborations : She leads a multi-institutional NSF-funded project on causal foundations of AI decision-making. Her work emphasizes trustworthiness in AI through causal models, with applications in robotics and public health.
Atsuko Tanaka is an Associate Professor in the Department of Economics at the University of Calgary. Her research focuses on labor markets, health economics, and public policy, particularly examining topics such as female labor supply, skill investment, and pension reforms. She holds a Ph.D. in Economics from the University of Wisconsin-Madison (2013), alongside advanced degrees from Cornell University and the University of Tokyo. Education Ph.D. Economics, University of Wisconsin-Madison (2013) M.S. Economics, University of Wisconsin-Madison (2010) M.S. Applied Economics and Management, Cornell University (2007) B.A. Agricultural Economics, University of Tokyo (2004) Research Interests Tanaka’s work explores how health shocks, financial constraints, and policy interventions influence labor market outcomes. She investigates topics such as post-educational skill accumulation via college loans, strategic medical treatment choices under reimbursement reforms, and the macroeconomic effects of pension system transitions. Her research combines rigorous econometric methods with real-world administrative data to inform evidence-based policy. Awards and Grants SSHRC Connection Grant (2022, co-PI: Apostolos Serletis) National Science Foundation Extreme Science Grant (2021) SSHRC Insight Development Grant (2016) Susan Jane Blake Kocin Award, University of Wisconsin-Madison (2011) Grants and Collaboration Her research has been supported by major grants, including SSHRC and NSF funding. She collaborates with scholars on projects like analyzing physician behavior under reimbursement reforms, auditing firm dynamics, and international aid allocation strategies. Her work bridges theoretical models with empirical evidence to address complex socio-economic challenges.
Theo Damoulas is a Professor of Machine Learning at the University of Warwick with a joint appointment in the Department of Computer Science and Statistics. He is a Turing AI Fellow (2021-2026) through UK Research and Innovation, an ELLIS member, and a Visiting Professor at New York University's Center for Urban Science and Progress (CUSP). He founded and leads the Warwick Machine Learning Group and has directed major projects at The Alan Turing Institute including Project Odysseus and the London Air Quality project. Education includes: PhD in Probabilistic Multiple Kernel Learning (University of Glasgow, 2009) MSc in Informatics (Distinction, University of Edinburgh, 2004) MEng in Mechanical Engineering (1st Class, University of Manchester, 2003) His research focuses on probabilistic machine learning and Bayesian statistics, emphasizing the integration of structural priors, spatiotemporal dependencies, physical laws, and causal relationships. Key applications include Digital Twins, urban science, and computational sustainability. His work advances robust and scalable inference methodologies for complex real-world systems. Publications demonstrate strong emphasis on Bayesian methods, spatiotemporal modeling, and uncertainty quantification, with applications spanning battery modeling, urban mobility, federated learning, and causal inference. Recent work shows increased focus on physics-informed models, federated learning frameworks, and causal abstraction techniques. Major scientific awards: Turing AI Acceleration Fellowship (2021-2026) Best Paper Awards (Wilkes 2024, AISTATS 2022, IEEE ICMLA 2010) ACM SIGMOD Most Reproducible Paper (2017) Dissertation Award (Classification Society 2012) Teaching Excellence nominations (Warwick 2015-2017) He actively advises PhD students and secured significant grants including the £multi-million Turing AI Fellowship. Current doctoral researchers investigate federated learning, causal inference, and spatiotemporal modeling. He leads the Warwick Machine Learning Group, a cross-departmental team developing foundational ML methods for scientific and societal challenges.
Navid Azizan is the Alfred H. (1929) and Jean M. Hayes Career Development Assistant Professor at Massachusetts Institute of Technology (MIT), holding dual appointments in the Department of Mechanical Engineering (in Control, Instrumentation & Robotics) and the Schwarzman College of Computing's Institute for Data, Systems & Society (IDSS). He is also a Principal Investigator in the Laboratory for Information & Decision Systems (LIDS), and a faculty member of the MIT Statistics and Data Science Center, the Center for Computational Science and Engineering, and the Operations Research Center. Dr. Azizan received his PhD in Computing and Mathematical Sciences from the California Institute of Technology (Caltech) in 2020, his MSc in Electrical Engineering from the University of Southern California in 2015, and his BSc in Electrical Engineering with a minor in Physics from Sharif University of Technology in 2013. Prior to joining MIT, he completed a postdoc at Stanford University's Autonomous Systems Laboratory and was a research scientist intern at Google DeepMind. His research spans the intersection of machine learning, systems and control, mathematical optimization, and network science. Dr. Azizan's work focuses on developing principled learning and optimization algorithms for reliable intelligent systems, with applications to autonomy and sociotechnical systems. His research has significant implications for creating trustworthy AI systems that can operate effectively in complex, uncertain environments. Dr. Azizan's recent publications demonstrate a strong focus on uncertainty quantification, reliable AI systems, constrained optimization, and control-oriented learning. His work bridges theoretical foundations with practical applications, particularly in autonomous systems where safety and reliability are paramount. His research group has made notable contributions to areas including neural network verification, multi-agent reinforcement learning, and adaptive inference techniques for large language models, with several papers featured on MIT News and selected for oral presentations at top conferences. Alfred H. (1929) and Jean M. Hayes Career Development Professorship (2025-present) Frank E. Perkins Award for Excellence in Graduate Advising (2025) List of Outstanding Academic Leaders in Data from the CDO Magazine (2024, 2023) Amazon Science Hub Research Award (2023) Outstanding UROP Faculty Mentor (2023) Esther and Harold E. Edgerton (1927) Career Development Chair (2022-2025) Information Theory and Applications (ITA) Gold Graduation Award (2020) Dr. Azizan has been recognized for his excellence in graduate advising, receiving the Frank E. Perkins Award for Excellence in Graduate Advising in 2025. During the pandemic, he founded and co-organized the 'Control meets Learning' virtual seminar series, connecting researchers across disciplines. His work has attracted significant research funding from industry partners including Google, Amazon, and MathWorks, supporting both fundamental research and practical applications in reliable intelligent systems. The Azizan Lab at MIT brings together researchers from mechanical engineering, computer science, and applied mathematics to tackle challenges at the intersection of learning and control. The lab emphasizes both theoretical foundations and practical implementations, with a particular focus on developing algorithms that provide guarantees of performance and safety. Current research directions include uncertainty quantification in AI systems, constrained optimization for neural networks, and control-oriented learning for autonomous systems, with applications spanning robotics, transportation, and complex sociotechnical systems.
