Hugo Duminil-Copin is a Full Professor at the University of Geneva and a permanent professor at the Institut des Hautes Études Scientifiques (IHES) since 2016. His research focuses on Mathematical Physics , Combinatorics , and Probability Theory . Education : École Normale Supérieure (ENS) Paris, University of Paris-Saclay Awards : 2022 Fields Medal for work in statistical physics Research Trends : Probabilistic aspects of lattice models Phase transitions and critical phenomena Conformal invariance and percolation theory Collaborations : Active collaborations with researchers such as R. Panis, S. Goswami, I. Manolescu, and others. Teaching : Offers courses in mathematical physics and probability at the University of Geneva. His recent publications emphasize critical models, random-cluster models, and Gaussian free fields, with applications in planar and high-dimensional systems. He supervises doctoral students including Emile Averous , Aman Markar , and Tiancheng He .
Esther Duflo is the Abdul Latif Jameel Professor of Poverty Alleviation and Development Economics at MIT's Department of Economics. She co-founded and co-directs the Abdul Latif Jameel Poverty Action Lab (J-PAL) and holds the Chaire Pauvreté et politiques publiques at the Collège de France. A graduate of École Normale Supérieure (Paris) and MIT (PhD, 1999), her work focuses on experimental approaches to combat poverty through health, education, financial inclusion, environment, and governance research. She received the 2019 Nobel Prize in Economics for her experimental methods in development economics, alongside Abhijit Banerjee and Michael Kremer. Other honors include the John Bates Clark Medal (2010), MacArthur Fellowship (2009), and Princess of Asturias Award (2015). Duflo has advised governments and international bodies, including the U.S. President’s Global Development Council (2013–2021). Her books Poor Economics and Good Economics for Hard Times popularized her research. She also authored children’s books explaining poverty, aiming to demystify socioeconomic challenges for younger audiences.
George Musgrave is a Senior Lecturer in Cultural Sociology and Creative Industries at the Institute for Creative and Cultural Entrepreneurship (ICCE) at Goldsmiths, University of London. An interdisciplinary sociologist of culture, he specializes in researching musicians' psychosocial working lives with a focus on mental health and wellbeing in the music industry. His work has directly influenced industry practices and government policy, including the establishment of the Music Minds Matter helpline in 2017. Dr. Musgrave holds a PhD from the Centre for Competition Policy (UEA), an MA in Politics, Philosophy and Economics, and a Cambridge MA (Cantab) in Social and Political Science. His academic journey reflects his interdisciplinary approach that bridges sociology, psychology, and economics in understanding creative work. His research interests center on the psychological experiences and working conditions of creative careers, particularly examining how the music industry impacts mental health. Musgrave investigates the paradox where music-making can be therapeutic while building a music career often proves detrimental to wellbeing. His work explores the intersections of identity, class, gender, and economic precarity in creative labor, with particular attention to how social media, streaming platforms, and industry structures affect musicians' mental health. Analyzing his recent publications reveals a consistent focus on mental health in the music industry across multiple dimensions: epidemiological studies of anxiety and depression among musicians, interventions and policy solutions, the impact of digital platforms, and the cultural narratives surrounding musical work. His research spans methodological approaches from quantitative surveys to qualitative interviews and critical policy analysis, consistently highlighting the need for structural changes in the music industry to support artist wellbeing. Fellow of the Royal Society for Public Health (FRSPH) Fellow of the Royal Society of Arts (FRSA) Fellow of the Higher Education Academy (FHEA) Editorial Board member of Cultural Trends journal Royal Musical Association Music and Mental Health Group Research Coordinator Dr. Musgrave has supervised 68 MA dissertations to completion and seven doctoral projects, including Dr. Steven Sparling (2021). His current PhD supervision spans interdisciplinary topics connecting music, psychology, and sociology. He has secured significant research funding from diverse sources including UKRI (ESRC), the Mayor of London, Help Musicians UK, and the Danish Partnership for Sustainable Development in Music. His major projects include 'Can Music Make You Sick?' and 'When Music Speaks,' which represent the largest studies to date on musicians' mental health in the UK and Scandinavia respectively. Beyond academia, Musgrave maintains an active music career, having signed with EMI/Sony/ATV and performed at major festivals including Reading and Leeds.
Alan Montgomery is a Professor of Marketing at Carnegie Mellon University's Tepper School of Business, where he has held a tenured position since 2018 (previously as Associate Professor from 2005-2017). He also maintains an affiliation with the Machine Learning Department at CMU's School of Computer Science, demonstrating his interdisciplinary research approach at the intersection of marketing, economics, and computational methods. Dr. Montgomery earned his educational credentials from prestigious institutions: Ph.D. in Marketing/Economics, University of Chicago (1994) MBA, University of Chicago (1994) BS in Economics, University of Illinois at Chicago (1989) His research focuses on applying advanced quantitative methods to marketing problems, with particular expertise in consumer behavior modeling, clickstream data analysis, pricing strategies, and micro-marketing. Dr. Montgomery's work bridges traditional marketing theory with computational approaches, making significant contributions to both academic literature and practical business applications. His research often involves large-scale data analysis to uncover patterns in consumer decision-making processes, with recent work exploring mental accounting, bandit algorithms, and the impact of digital phenomena like movie piracy on traditional markets. Dr. Montgomery has received notable recognition including the 1999 Mitchell Prize from the American Statistical Association for his paper "Estimating Price Elasticities with Theory-based Priors." His work has been published in top-tier journals across marketing, economics, and computer science disciplines, demonstrating the interdisciplinary impact of his research. As an educator and mentor, Dr. Montgomery has advised numerous PhD students and collaborated extensively with researchers across multiple institutions. His interdisciplinary approach has led to collaborations with computer scientists studying web browsing behavior and economists examining consumer decision frameworks. His research has been supported by various grants throughout his career, enabling extensive data collection and analysis projects. Dr. Montgomery's work spans multiple research environments, including collaborations with the Machine Learning Department at CMU's School of Computer Science. His research group likely focuses on applying computational methods to marketing problems, particularly in the areas of consumer behavior modeling, clickstream analysis, and data-driven marketing strategies. His recent work shows increasing integration of machine learning techniques with traditional marketing research methodologies.
Reinhard Heckel is a Tenured Associate Professor (equivalent to Professor) of Machine Learning at the Department of Computer Engineering, Technical University of Munich (TUM), and Adjunct Faculty in Electrical and Computer Engineering at Rice University. He was previously an Assistant Professor at Rice (2017–2019), a postdoc in the Berkeley Artificial Intelligence Research (BAIR) Lab at UC Berkeley, and a researcher at IBM Research Zurich. Education: PhD, 2014 – ETH Zurich Visiting PhD student – Department of Statistics, Stanford University Research Interests: His work centers on machine learning and information processing with three major thrusts: (1) developing algorithms and theoretical foundations for deep learning, especially for accelerated magnetic resonance imaging ; (2) establishing rigorous mathematical and empirical underpinnings for modern machine-learning systems; and (3) leveraging DNA as a digital information-storage medium , including error-correction coding and system design for DNA-based storage. Across more than 100 peer-reviewed papers since 2017, Heckel’s research exhibits a strong interdisciplinary blend of computational imaging , machine-learning theory , and molecular data storage . Recent 2024–2025 publications show intensive focus on robust MRI reconstruction using diffusion priors, evaluation of bias in large web-text corpora, and state-of-the-art error-correcting codes for DNA storage channels. A forthcoming book, Deep Learning for Computational Imaging (Oxford University Press), consolidates his contributions to the field. Outreach & Media: Keynote and panel talks at DLD, TUM, and major ML conferences Op-eds in Frankfurter Allgemeine on ChatGPT and DNA storage Science features on Netflix, BBC, and German television (Galileo, “Gut zu Wissen”) Research Environment: At TUM he leads a group investigating theoretical and applied aspects of deep learning, compressed sensing, and coding for DNA storage. Open-source repositories on GitHub (e.g., dna_data_storage , supplement_deep_decoder ) provide code and data supplements accompanying his publications.
Michael D. Smith is a Professor of Information Technology and Public Policy at Carnegie Mellon University, with joint appointments at Heinz College and Tepper School of Business. His research employs economic and statistical methods to analyze digital markets, focusing on firm and consumer behavior in online environments. Education: B.Sc. in Electrical Engineering (Summa Cum Laude), University of Maryland M.Sc. in Telecommunications Science, University of Maryland Ph.D. in Management Science and Information Technology, MIT Research Interests: Professor Smith investigates the economics of digital information markets, consumer behavior in online platforms, and policy implications of technological disruption. His work spans copyright enforcement, digital advertising, and the impact of piracy on legal media consumption. Publications: His recent studies examine AI’s role in copyright policy, effectiveness of anti-piracy measures, and market dynamics in digital streaming. Articles often bridge economics, computer science, and public policy. Awards: National Science Foundation CAREER Award Multiple Best Teacher Awards at CMU Recognized as a Top 100 Emerging Engineering Leader (NAE, 2020) Best Paper Runner-Up (Information Systems Research, 2006) Editorial and Industry Roles: Served as Senior Editor at Information Systems Research and Associate Editor at Management Science . Prior to academia, he worked in telecommunications at GTE and Booz Allen Hamilton, earning a patent for AI applications in network design. Contact: mds@cmu.edu | Office: 4800 Forbes Avenue, Hamburg Hall 2204, Pittsburgh PA 15213
Lenya Ryzhik is a Professor in the Department of Mathematics at Stanford University, specializing in analysis and partial differential equations with applications in various physical contexts. His research spans stochastic processes, wave propagation, and front dynamics in random media, with significant contributions to understanding reaction-diffusion systems and their applications in mathematical biology and physics. Professor Ryzhik's research interests focus on the mathematical analysis of partial differential equations arising in physical systems. His work particularly emphasizes stochastic PDEs, wave propagation in random media, front propagation in reaction-diffusion systems, and homogenization theory. He investigates how randomness and complex structures affect wave propagation, front speeds, and transport phenomena, with applications ranging from combustion theory to population dynamics and quantum mechanics. The publication record demonstrates a consistent focus on understanding propagation phenomena in complex environments. Ryzhik's research shows a progression from classical PDE analysis toward increasingly sophisticated stochastic frameworks, particularly examining high-dimensional systems and random media. His recent work has focused on KPZ fluctuations, random heat equations, and non-local reaction-diffusion models, revealing deep connections between probability theory and partial differential equations. Alfred P. Sloan Research Fellowship (2002-2004) AFOSR NSSEFF Fellowship (2010-2015) Ryzhik has advised graduate students including Alexandra Stavrianidi, and has secured substantial research funding throughout his career. His grant history includes multiple NSF awards (DMS-9971742, DMS-0203537, DMS-0604687, DMS-0908507, DMS-1311903), ONR funding (N00014-02-1-0089, N00014-04-1-0224), and FRG support for collaborative research on nonlinear evolution problems. He co-organized a Summer School and Workshop on 'Recent Advances in PDEs and Fluids' at Stanford in 2013. Ryzhik maintains an active research group collaborating with leading mathematicians worldwide, particularly with researchers at institutions like NYU, Chicago, and various European universities. His work frequently involves interdisciplinary collaborations bridging mathematics with physics and biology.
Wojciech Jarosz is an Associate Professor of Computer Science at Dartmouth College, affiliated with the College of Engineering and Computer Science. His research focuses on computer graphics, particularly light transport simulation, rendering algorithms, and digital fabrication. He co-founded the Visual Computing Lab and previously led the rendering group at Disney Research Zürich. Jarosz holds a Ph.D. and M.S. from UC San Diego and a B.S. from the University of Illinois Urbana-Champaign. His educational background includes studies in computer science and engineering, with a strong emphasis on graphics and rendering. Research interests span light transport simulation, Monte Carlo methods, appearance capture, and fabrication. Notable achievements include the Eurographics Young Researcher Award (2013) and the NSF CAREER Award (2019). Jarosz's work integrates theoretical rigor with practical applications, such as real-time rendering techniques and volumetric light transport. His lab develops tools for artistic authoring, including intuitive metaphors for volumetric lighting in animated films. Recent projects explore wave-optics BSDF models, optical heterodyne rendering, and unifying radiative transfer models. Key awards include the SIGGRAPH 2024 Best Paper Award and Neukom Institute prizes. His teaching includes courses on rendering algorithms, computer graphics, and computational photography. Jarosz collaborates with industry (e.g., Disney, NVIDIA) and advocates for diversity in computer graphics research.
Olof Bälter is a Professor in Computer Science at KTH Royal Institute of Technology, affiliated with the Division of Media Technology and Interaction Design within the School of Electrical Engineering and Computer Science. He is the founder of the Technology-Enhanced Learning research group and holds a focus on learning engineering and human-computer interaction. His research interests center on technology-enhanced learning , question-based learning , learning analytics , AI in education , and inclusive pedagogy . A consistent theme in his work is improving efficiency in education and daily life through digital tools. He developed the Pure Question-Based Learning (Pure QBL) methodology, a digital Socratic approach that enhances student engagement and learning outcomes. His work extends to wellness in education through initiatives like walking seminars, and he investigates digital interventions for mental health, such as online Cognitive Behavioral Therapy (CBT) courses. His recent publications highlight trends in AI-generated educational content , learning efficiency , digital pedagogy , and inclusive course design , with applications in computer science education, language instruction, and global development. His research often employs experimental and data-driven methods, including randomized controlled trials and learning analytics. Teacher of the Year at the Surveying program KTH's Pedagogical Prize Higher Education Hero STINT Excellence in Teaching Scholarship (2008 and 2013) Olof Bälter has supervised numerous courses in programming, computer science, media technology, and learning engineering. He has collaborated with institutions such as Stanford University, Williams College, Region Stockholm, Stockholm University, and organizations like Promobilia and Begripsam. His projects aim to scale effective learning methods globally and make education more accessible and efficient. He leads research on the effectiveness of Pure QBL for students with ADHD and is involved in developing digital tools for health literacy and professional development in Ethiopia and Rwanda. His work bridges theory and practice, aiming to transform educational delivery through innovation and evidence-based design.
Alex Dunlap is an Assistant Professor in the Department of Mathematics at Duke University. His research focuses on probability theory, partial differential equations (PDEs), and applied mathematics, particularly the asymptotic behavior of stochastic PDEs. Before joining Duke in 2023, he was an NSF postdoctoral fellow at NYU Courant, sponsored by Jean-Christophe Mourrat and Yuri Bakhtin. He earned his Ph.D. from Stanford University in 2020 under the supervision of Lenya Ryzhik. His work involves studying nonlinear stochastic PDEs such as the KPZ equation, stochastic Burgers equation, and stochastic heat equations. He is particularly interested in universality phenomena, fluctuation scaling, and invariant measures. Dunlap co-organizes the Duke Probability Seminar and has published extensively in top journals including Annals of Probability , Communications on Pure and Applied Mathematics , and Archive for Rational Mechanics and Analysis . His research is supported by NSF grant DMS-2346915. Notable contributions include work on viscous shock fluctuations, Edwards-Wilkinson universality in 2D systems, and stationary solutions of stochastic Burgers equations. He has collaborated with leading researchers such as Cole Graham, Yu Gu, and Lenya Ryzhik.
David Damanik is the Robert L. Moody, Sr. Professor of Mathematics at Rice University, where he has established himself as a leading researcher in spectral theory, dynamical systems, and aperiodic order. His work bridges pure mathematics with mathematical physics, focusing on the spectral properties of operators arising in quantum mechanics and quasicrystal theory. Dr. Damanik received his academic training at Johann Wolfgang Goethe-Universität in Frankfurt, Germany, earning a Dipl.-Math. in 1995, Dipl.-Inform. in 1996, and Dr. phil. nat. in 1998. His educational background reflects a strong foundation in both mathematics and computer science, which informs his interdisciplinary research approach. His research interests center around spectral theory of Schrödinger operators, particularly those with ergodic, quasi-periodic, and aperiodic potentials. He has made significant contributions to understanding the spectral properties of operators associated with quasicrystals, substitution sequences, and other aperiodic structures. His work often connects spectral properties with dynamical systems concepts, particularly through the study of rotation numbers, Lyapunov exponents, and gap labeling theorems. Damanik's research has profound implications for understanding quantum transport in aperiodic media and the mathematical foundations of condensed matter physics. Analysis of his recent publications (2022-2024) reveals a continued focus on ergodic Schrödinger operators, with two comprehensive monographs providing a systematic treatment of the field. His work spans both theoretical foundations and specific applications, addressing problems in one-dimensional systems, quasi-periodic potentials, and aperiodic tilings. The research demonstrates strong connections between spectral theory, dynamical systems, and mathematical physics, with particular emphasis on the interplay between spectral properties and the underlying dynamics of the potential. Annales Henri Poincaré Prize (2014) for the paper "Continuum Schrödinger operators associated with aperiodic subshifts" Professor Damanik has mentored numerous PhD students and maintains an extensive network of collaborators across the globe, as evidenced by his long list of coauthors. His research has been supported by various grants that enable him to organize workshops and conferences, fostering collaboration in his field. He has been instrumental in organizing major conferences such as the Spectral Theory and Mathematical Physics conference honoring Barry Simon's 80th birthday (scheduled for 2026) and multiple workshops on aperiodic order at prestigious institutions like Banff International Research Station and Mathematisches Forschungsinstitut Oberwolfach. Through his teaching of specialized courses like "Mathematics of Aperiodic Order" and "Ergodic Theory and Topological Dynamics," Damanik has cultivated the next generation of researchers in his field. His leadership in organizing conferences and workshops has established him as a central figure in the international community studying spectral theory and aperiodic structures.
Arpit Gupta is an Associate Professor of Finance at the Leonard N. Stern School of Business, New York University, where he has been a faculty member since 2016. His research lies at the intersection of real estate, household finance, and urban economics, with a strong empirical focus on using large datasets to analyze financial behavior and market dynamics. Ph.D. in Finance and Economics, Columbia Business School B.S. in Mathematics and Economics, University of Chicago His research interests include household finance, real estate markets, mortgage default, bankruptcies, urban economics, and the financial impacts of health shocks and pandemics. He explores how financial constraints affect minority borrowers, how remote work reshapes urban real estate, and how public infrastructure influences property values. His recent publications span top journals such as the Journal of Finance , American Economic Review , Journal of Financial Economics , and PNAS . Themes across his work include the economic consequences of the COVID-19 pandemic, the valuation of private equity, and the spillover effects of foreclosures. His research often combines innovative data sources with rigorous econometric methods. Notable scientific awards include the Brattle Prize First Place (2019) and the 2016 Top Finance Graduate Award from Copenhagen Business School. He has advised on policy-relevant topics such as office-to-residential conversions, housing equity, and bail reform, and has received media attention for his insights on urban development and real estate trends. Professor Gupta collaborates with a wide network of researchers and maintains active engagement through a Substack newsletter and public data repositories. He advises students and contributes to academic discourse through conference presentations and policy discussions. He is affiliated with research labs and teams focused on urban economics and financial innovation, often collaborating with scholars at Columbia, NYU, and other institutions. His ongoing projects continue to explore the evolving dynamics of housing, finance, and urban resilience in the post-pandemic era.
Prof. Dr. Wolfgang Nejdl is a Professor at the Institute for Data Science within the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover. He serves as Executive Director of the L3S Research Centre and Leibniz Forschungszentrum Inclusive Citizenship. Web Science Information Retrieval Artificial Intelligence Deep Learning His recent research focuses on AI applications in medicine , multimodal data fusion , and ethical AI systems . Projects include CAIMed (AI in Causal Medicine) and DAISEC (AI & Cybersecurity). His publications span conferences like AAMAS, WWW, and SIGIR. Notable awards include membership in the National Academy of Science and Engineering (acatech) . Former students hold positions at institutions like Stanford, TU Dresden, and ETH Zürich. Current projects involve climate resilience AI , federated learning for healthcare , and quantum-inspired data science .
Dr. Mark Thompson is a Senior Lecturer in Psychology and Researcher of Sport Psychology at London Metropolitan University's School of Social Sciences and Professions. He holds a PhD from the University of Hull and is a Fellow of the Higher Education Academy (FHEA). His academic work bridges sport psychology, healthcare rehabilitation, and youth athlete development. Dr. Thompson's education includes a PhD in Psychology from the University of Hull. His research focuses on emotional processes in elite athletes, doping propensity in youth sports, and post-COVID-19 patient rehabilitation. Notable projects include NHS-funded studies on telerehabilitation and collaborations with the International Olympic Committee and World Anti-Doping Agency. Research Interests: Psychophysiological responses to stress in sports Emotional regulation strategies among athletes Anti-doping education and youth athlete behavior Telehealth applications in post-hospitalization recovery His publications emphasize qualitative methodologies, exploring topics like athlete performance under stress, doping prevention programs, and healthcare professional perspectives on self-management approaches. Awards: Fellow of the Higher Education Academy (FHEA). Dr. Thompson has delivered presentations at major conferences such as the American College of Sports Medicine and contributed to media discussions on doping in elite sport via LoveSport Radio. His teaching spans foundational psychology to advanced modules like cognition and behavior, often serving as module leader.
Vincent Dufour-Décieux is a researcher at the Professorship for Energy and Process Systems Engineering at ETH Zürich , focusing on developing computational methods for material screening in separation processes and global net-zero transitions. He earned his Master's in Materials Chemistry from Ecole Polytechnique (France) and a PhD in Materials Science from Stanford University , where he pioneered statistical methods combining Kinetic Monte Carlo and random graph theory to study planetary diamond formation. Research Highlights: Application of Classical Density Functional Theory (cDFT) for 100x faster adsorption property predictions in porous materials Development of science-based definitions for "hard-to-abate" emissions to guide climate action prioritization Integration of Coulombic interactions in cDFT for CO2 adsorption accuracy Article Trends : His work spans computational materials science (cDFT, random graph theory) and climate policy analysis, with recent publications in Joule , AIChE Journal , and Physical Review E . These studies emphasize scalable solutions for carbon capture, material screening efficiency, and accurate thermodynamic modeling. Collaborations : Active in international conferences (FOA15, MolMod, Gordon Research Conference) and cross-institutional projects with teams at Stanford, ETH Zürich, and industry partners.
