Tanya Marwah is a Research Fellow at the Simons Foundation , collaborating with Polymathic AI . She earned her PhD from Carnegie Mellon University's Machine Learning Department, co-advised by Prof. Andrej Risteski and Prof. Zachary Lipton, and holds a master's degree from CMU's Robotics Institute. Her research bridges Machine Learning and Scientific Computing , focusing on generative modeling , inverse problems , and building scientific agents . Her work explores theoretical and empirical foundations for applying ML to differential equations, with key contributions in neural operators , memory mechanisms , and edge embeddings in GNNs . Recent publications highlight trends in PDE solvers via LLMs , cross-modal adaptation , and implicit regularization in SGD . She has received the prestigious Siebel Scholar award and actively contributes to top ML venues (NeurIPS, ICML, ICLR, TMLR). Her collaborations span institutions including Carnegie Mellon University, Polymathic AI, and CMU's Robotics Institute.
Carlos Vazquez Hernandez is a Lecturer at the Sydney Business School, University of Sydney, with expertise in behavioral sciences, innovation management, and technology-driven decision-making. His research explores how cognitive processes, social environments, and technological tools interact to influence organizational and societal outcomes. Education BBA Iberoamericana GradCert Educational Studies Higher Education, University of Sydney MIP Melbourne PhD, University of Sydney His research focuses on microfoundations of decision-making, innovation in technology contexts, and narrative-driven behaviors. Key areas include experimental methods in business research, AI-enhanced judgment, and behavioral strategies for sustainable innovation. His work bridges psychological insights with technological understanding to reshape management education. Recent publications (2018-2025) demonstrate a consistent focus on intuitive decision-making in innovation contexts, AI-human collaboration in forecasting, and behavioral economics. Thematic analysis reveals strong emphasis on cognitive biases in product development, cross-cultural business strategies, and empirical validation of heuristic approaches in management. Grants Augmented judgments: Human-AI interaction for better new product success predictions (2023 Business School Pilot Research Grant) He teaches courses spanning Ethical Decision Making, International Risk Management, and Innovation Strategy, preparing leaders for evolving business landscapes through integration of behavioral science and technology concepts.
Ignacio Cofone is a Professor of Law and Regulation of AI at the University of Oxford's Faculty of Law and a Fellow of Reuben College. He is affiliated with the Institute for Ethics in AI, Yale Law School's Information Society Project, and the Quebec AI Institute. Previously, he held the Canada Research Chair in AI Law and Data Governance at McGill University. His research focuses on legal adaptation to AI and data-driven technologies, particularly privacy frameworks, immaterial harms, and human-centered regulatory design. Education: Joint PhD (rerum politicarum) from Hamburg University and Erasmus University Rotterdam, JSD from Yale Law School, and degrees in common/civil law. Visiting appointments include NYU, University of St Gallen, and Tilburg University. He advises governments and organizations on AI regulation, including work with Canada's Privacy Commissioner on PIPEDA reform. Research Interests: AI regulation, data protection law, privacy law, algorithmic decision-making, and the intersection of law with technological change. His book The Privacy Fallacy (2023) critiques privacy frameworks and proposes duty-based reforms. Recent articles address AI fairness, vaccine passport ethics, and privacy class actions. Labs/Teams: Leads the Computers and Law Research Group. Actively supervises doctoral/postdoctoral researchers with interdisciplinary or comparative law backgrounds.
Dr. Uri Maoz is an Associate Professor at Chapman University, affiliated with the Crean College of Health and Behavioral Sciences, Schmid College of Science and Technology, and Fowler School of Engineering. His research bridges computational neuroscience, decision-making, and moral philosophy, focusing on volition and the neural underpinnings of conscious action. He holds visiting roles at UCLA (Department of Anesthesiology) and Caltech (Biology and Bioengineering). Educations: Bachelor of Science in Computer Science and General Humanistic Studies, The Hebrew University of Jerusalem Ph.D. in Neural Computation, The Hebrew University of Jerusalem Research Interests: Dr. Maoz investigates how consciousness influences voluntary actions through empirical methods (EEG, intracranial recordings) and theoretical models. He explores ethical and legal implications of neuroscience, particularly regarding free will and decision-making. His work integrates machine learning for real-time neural data analysis. Key Projects: Leading the COVID-Dynamic longitudinal study on pandemic-related behavioral and emotional changes. Developing computational models of volition and neural correlates of intention. Collaborating across disciplines to address neuroethical questions. Visiting Roles: Visiting Assistant Professor at UCLA’s Department of Anesthesiology Visiting Associate in Biology and Bioengineering at Caltech Labs/Teams: Active in Chapman’s Institute for Interdisciplinary Brain and Behavioral Sciences and collaborates with the Anderson School of Management at UCLA.
Paul E. Hand is an Associate Professor of Mathematics and Computer Science at Northeastern University, affiliated with both the College of Science and the Khoury College of Computer Sciences. He holds a Bachelor of Science in Applied and Computational Mathematics from the California Institute of Technology (2004) and a PhD in Mathematics from the Courant Institute at New York University (2009), where he received the Kurt O. Friedrichs Prize for outstanding dissertation. PhD in Mathematics, Courant Institute, NYU (2009) BS in Applied and Computational Mathematics, Caltech (2004) His research focuses on developing theoretical frameworks and algorithms for machine learning and artificial intelligence, particularly in signal recovery, phase retrieval, and vision/imaging. He also explores intersections of deep learning with convex optimization and has contributed to bilinear recovery problems. Recent publications emphasize generative models, inverse problems, and robust optimization techniques. Key themes include deep learning with provable recovery guarantees , convex programming for signal inversion , and manifold-based optimization . Kurt O. Friedrichs Prize for Outstanding Dissertation (2009) NSF CAREER Grant DMS-1848087 He has taught courses on Deep Learning, Machine Learning, and Signal Processing at Northeastern University since 2016, previously holding academic roles at Rice University (2016-2018) and MIT (2009-2016). He directs educational outreach initiatives and developed the educational resource Leading Lesson for multivariable calculus problem-solving.
Kevin Stange is a Professor of Public Policy and Education (by courtesy) at the University of Michigan's Ford School of Public Policy, where he serves as co-director of the Education Policy Initiative and director of the PhD Program. He is also a Research Associate at the National Bureau of Economic Research and a faculty affiliate of the Center for the Study of Higher and Postsecondary Education at the Marsal Family School of Education. His educational background includes: Bachelor of Science in Mechanical Engineering and Economics, Massachusetts Institute of Technology (1999) Ph.D. in Economics, University of California, Berkeley (2008) Stange's research focuses on empirical labor and public economics within education contexts. He investigates how higher education shapes labor market trajectories, geographic mobility, and responses to skill demand shifts. Current projects examine Michigan's financial aid programs and college major impacts on career outcomes, utilizing large-scale datasets like College and Beyond II to analyze postsecondary pathways and institutional effectiveness. His recent publications (2023-2025) emphasize college major effects on earnings inequality, financial aid efficacy, community college transfer pathways, and graduate mobility patterns. These works leverage administrative data to address policy-relevant questions about educational equity and labor market alignment. Key recognition includes: Robert Wood Johnson Scholar in Health Policy Research Stange has secured major funding from the U.S. Department of Education, National Science Foundation, Spencer Foundation, W.T. Grant Foundation, Robert Wood Johnson Foundation, and Russell Sage Foundation. During 2022-2023, he served as Senior Advisor to the Under Secretary at the U.S. Department of Education, applying his research expertise to federal policy. He co-directs the Education Policy Initiative, which produces evidence-based research for education practitioners, and spearheaded the College and Beyond II dataset containing 50 million course records from 19 universities. This resource enables comprehensive analysis of student pathways and institutional effectiveness across diverse higher education contexts.
Richard Lewis is the John R. Anderson Collegiate Professor of Psychology, Linguistics, and Cognitive Science at the University of Michigan. He directs the Weinberg Institute of Cognitive Science, a hub for interdisciplinary research. His work focuses on computational models of human cognition, particularly language processing architectures, and he co-founded the cognitive science undergraduate major at U-M. Lewis was honored as an Arthur F. Thurnau Professor (2021) for his transformative contributions to undergraduate education. Education: Ph.D., Carnegie Mellon University Research interests span cognitive neuroscience, psycholinguistics, and computational linguistics. His projects investigate how humans process sentences cross-linguistically and adapt predictive models during comprehension. Notable achievements include developing programs that integrate computer science, philosophy, and psychology to study the mind. His articles explore topics ranging from neural noise effects on decision-making to AI's capacity for analogical reasoning. Awards include the Thurnau Professorship, recognizing his dedication to student engagement and curriculum innovation. Lewis advises on interdisciplinary initiatives at the Weinberg Institute and collaborates on projects like icon array communication for public health. His work bridges theoretical models with empirical studies, emphasizing the computational foundations of human cognition.
Dr. Devindri Perera is a Senior Lecturer at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences (EECMS) within the Faculty of Science and Engineering. He is also affiliated with the Office of the Provost, demonstrating administrative and academic leadership roles. His research focuses on interdisciplinary areas including biosecurity systems, veterinary epidemiology, statistical modeling, and genomics. Dr. Perera's work spans environmental science (biosecurity border management), veterinary medicine (equine health and endocrinology), and computational methods (data clustering, statistical algorithms). His biosecurity research emphasizes risk assessment and spatial-temporal analysis of non-indigenous species, while his veterinary studies investigate equine physiology and disease mechanisms. In genomics, he contributes to identifying genetic markers for neurological disorders like multiple sclerosis. His publications from 2006–2023 reflect a strong focus on statistical methodologies in interdisciplinary contexts, including genome-wide association studies, algorithm validation for genotyping, and applied econometric models. This demonstrates a blend of theoretical rigor and practical problem-solving across diverse domains. Though no specific awards or grants are listed in this profile, his prolific publication record and cross-disciplinary approach highlight sustained academic engagement. His research collaborations span institutions globally, addressing both environmental and biomedical challenges.
Konstantinos Tsagarakis is a Professor at the School of Production Engineering and Management, Technical University of Crete. His office is located at Δ5.107, DPEM Building (Δ5), 1st Floor, and he can be contacted via email at ktsagarakis@tuc.gr or by telephone at +302821037252. His research focuses on interdisciplinary sustainability solutions, with primary expertise in: Circular economy implementation and policy analysis Sustainable production systems and environmental management Renewable energy integration and prosumer models Data-driven approaches to sustainability using social media analytics Industry 4.0 applications for sustainable development Analysis of his recent publications reveals three dominant research streams: 1) Advanced data mining techniques applied to sustainability metrics across social media platforms, 2) Policy and institutional analysis of circular economy frameworks in various industrial sectors, and 3) Technological enablers (IoT, AI, cloud computing) for sustainable transformation. His work consistently demonstrates methodological innovation through mixed-methods approaches combining empirical analysis with computational techniques.
Prof. Robert Meißner is a Professor at the Department of Surface Physics and Technology at TUHH. His research focuses on molecular simulation techniques applied to corrosion processes, energy storage systems, and nanomaterials. He develops computational tools like ELECTRODE and i-PI for electrochemical and advanced molecular dynamics simulations. His work addresses challenges in magnesium battery performance, structural health monitoring of composite materials, and interfacial phenomena in nanoscale systems. Education details are not explicitly provided in the text, but his professional trajectory reflects extensive academic and industrial experience in materials science. Research interests span from fundamental studies (e.g., water imbibition in nanopores, magnetite oxidation dynamics) to applied innovations (e.g., corrosion protection via layered double hydroxides, data-driven electrolyte design). His recent publications highlight trends in data-driven materials discovery, structural health monitoring via vibro-acoustic methods, and computational prediction of corrosion inhibitors. He collaborates on projects involving graphene-based supercapacitors, epoxy resin curing dynamics, and peptide-surface interactions. Advising and grants: While student names are not listed, his research group actively explores corrosion engineering, battery technology, and nanomaterials. Projects include EU-funded initiatives and industry partnerships. Technical expertise includes ATR-FTIR spectroscopy, molecular dynamics, and machine learning for sparse data scenarios. He leads teams focused on surface science and energy storage, maintaining lab facilities for in situ electrochemical analysis and advanced computational modeling. His work bridges theoretical insights with practical applications in materials durability and energy systems.
Mario Braun is an Associate Professor in the Department of Psychology within the Faculty of Social Sciences at the University of Salzburg. He has held this position since 2016, following his role as Assistant Professor at the same institution from 2011-2016. His research is conducted through the Neurocognition Lab, where he investigates the neural mechanisms underlying language and emotion processing. Dr. Braun's educational background includes a PhD in Psychology from Freie Universität Berlin (2006-2009) and a Psychology diploma from Philipps Universität Marburg (1993-1998). Prior to his current position, he served as Scientific and Managing Director of the Dahlem Institute for Neuroimaging of Emotion at Freie Universität Berlin (2009-2011), and held leadership roles in neurocognitive laboratories at both Freie Universität Berlin and Catholic University Eichstätt-Ingolstadt. His primary research interests focus on the processing of written language, particularly how phonology is processed in reading and how emotional content from printed words is extracted and represented by the brain. To investigate these questions, he employs various neurocognitive techniques including eye tracking, EEG, fNIRS, fMRI, TMS, and TES to identify brain regions involved in language and emotion processing as well as their temporal dynamics. His work bridges cognitive psychology, neurolinguistics, and affective neuroscience. Dr. Braun's recent publications demonstrate a strong focus on the intersection of language processing, emotion, and neurological conditions, particularly examining how these processes are affected in juvenile myoclonic epilepsy. His research combines theoretical models with empirical neuroimaging evidence to advance our understanding of cognitive and affective neuroscience across both typical and clinical populations. His scientific contributions include numerous publications in cognitive neuroscience and psychology journals, with recent work exploring structural gray matter predictors of literacy development, impaired semantic categorization during brain stimulation, and emotion processing in neurological conditions. His research often incorporates machine learning approaches and advanced neuroimaging techniques to uncover complex brain-behavior relationships. Dr. Braun leads the Neurocognition Lab at the University of Salzburg, where he continues to investigate the complex relationships between language, emotion, and brain function using cutting-edge neuroimaging and stimulation techniques while mentoring students and collaborating with international researchers in the field.
Ming Fang serves as an Assistant Professor of Instruction in the Department of Finance at Temple University's Fox School of Business and Management. Prior to this role, he held a Lecturer position in Actuarial Science at Columbia University, complemented by extensive industry experience including Vice President at AIG Investment Analytics, Portfolio Manager roles at Zacks Investments/Brevan Howard/Partner-Re, and Senior Quantitative Analyst at Salomon Smith Barney. His academic credentials include: Ph.D. in Finance, Yale University Ph.D. in Applied Mathematics, University of Washington B.S. in Mathematics, Fudan University Dr. Fang's research centers on Asset Pricing (theoretical and empirical frameworks), Financial Econometrics , and Machine Learning applications in finance and insurance. His interdisciplinary approach integrates quantitative finance with advanced computational techniques to address complex problems in investment modeling, risk assessment, and portfolio optimization, bridging academic theory with real-world financial challenges. He teaches diverse courses including Derivatives and Financial Risk Management, Security Analysis and Portfolio Management, and Quantitative Risk Modeling across undergraduate and graduate programs. His industry expertise directly informs pedagogy, though specific student advising details and research grants are not documented in available materials.
Yoan Hermstrüwer is an Assistant Professor of Legal Tech, Law and Economics, and Public Law at the University of Zurich. He holds affiliations with Yale Law School (visiting researcher), University of Michigan Law School (lecturer), Karlsruhe Institute of Technology (teaching fellow), and the Max Planck Institute for Research on Collective Goods (research affiliate since 2023). Education: Studied law at the Universities of Freiburg im Breisgau, Panthéon-Assas (Paris 2), and Bonn, completing his law degree and First State Examination. Earned a doctorate in economics focusing on experimental economic and empirical studies of public market design after his Second State Examination. His legal dissertation used experimental methods to analyze consent limits under data protection law. Research focuses on interdisciplinary investigations of law's impact on behavior using experimental/empirical methods. Key themes include fair machine learning algorithms, legal implications of algorithmic matching, and public law frameworks. His work bridges law, economics, and computer science through collaborative approaches. No specific grants or awards are listed in the provided information. His research affiliations support interdisciplinary studies in collective goods and legal tech innovation.
Rim Hariss is an Assistant Professor of Operations Management at the Desautels Faculty of Management, McGill University . She holds a PhD in Operations Research from MIT (2019), and degrees from École Polytechnique (MS in Applied Mathematics, 2014; BS in Mathematics & Engineering, 2013). Her research focuses on Big Data Analytics, Dynamic Pricing, Behavioral Operations, and Retail Optimization . Education : PhD in Operations Research, MIT, 2019 MS in Applied Mathematics, École Polytechnique, 2014 BS in Mathematics & Engineering, École Polytechnique, 2013 Research Themes : Combines machine learning with operational decision-making in retail and service systems. Key areas include data-driven pricing strategies, consumer behavior modeling, and optimization under uncertainty. Recent work addresses ticket reselling analytics and markdown pricing mechanisms. Grants & Roles : Principal Investigator: $5,000 SSHRC Grant (2021-2023) for promotional budget optimization in retail McGill Startup Grant ($45k) supporting foundational research Awards : 2022: INFORMS Data Mining Best Theoretical Paper 2019: MSOM Practice-Based Research Finalist Multiple fellowships from French government and MIT Labs/Teams : Actively involved in Data Science for Business Decisions initiatives within Desautels.
Madhur Tulsiani is a Professor at the University of Chicago's Department of Computer Science and a researcher at the Toyota Technological Institute at Chicago (TTIC). His research focuses on theoretical computer science, particularly complexity theory and algorithm design, with applications in coding theory and information theory. He has been supported by NSF grants 1254044, 1816372, and 2326685. Education: Bachelor’s in Computer Science, IIT Kanpur (2001-2005) Ph.D. in Computer Science, UC Berkeley (2005-2009), advised by Luca Trevisan Postdoctoral fellowships at the Institute for Advanced Study (IAS) and Princeton University Research Interests: Mathematical foundations of computation Complexity theory and algorithm design Coding theory and error-correcting codes Sum-of-Squares hierarchies and approximation algorithms Recent Contributions: Pioneering work on list decodable codes and expander-based constructions Advances in approximation algorithms for high-dimensional expanders Lower bounds for Sum-of-Squares algorithms using high-dimensional expanders Teaching: Information and Coding Theory Mathematical Toolkit (linear algebra/probability) Summer REU programs in theoretical computer science Students: Advised PhD students including Fernando Granha Jeronimo, Goutham Rajendran, and Shashank Srivastava Co-advised students with Sasha Razborov, Janos Simon, and others Labs/Groups: Member of the Theoretical Computer Science Group at TTIC and UChicago, contributing to cross-disciplinary research in algorithms and complexity.
