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Jose Julian Toledo MeleroView profile

Professor

• Partial Differential Equations
• Nonlocal Diffusion
• Calculus of Variations
• +7 more

Jose Julian Toledo Melero is a Professor in the Department of Mathematical Analysis at the Faculty of Mathematics, Universitat de València. He is a leading researcher in nonlinear partial differential equations, with a focus on nonlocal diffusion, evolution equations, and optimal transport in metric random walk spaces. His work bridges pure and applied analysis, with strong collaborations with mathematicians such as José M. Mazón and Julio D. Rossi. His research interests include: Nonlinear Partial Differential Equations Nonlocal Diffusion and Operators Calculus of Variations and Optimal Transport Evolution Problems in Metric Spaces Geometric Analysis and Minimal Surfaces Doubly Nonlinear and Degenerate Equations The recent articles show a consistent focus on analytical and geometric properties of nonlocal models, particularly in discrete and metric random walk settings. His work frequently appears in top journals such as Calculus of Variations , SIAM Journal on Mathematical Analysis , and Journal of Evolution Equations , often in collaboration with leading experts. The publications reveal a deep engagement with both theoretical foundations and structural analysis of solutions. He has co-authored influential books such as Nonlocal Diffusion Problems (AMS, 2010) and Variational and Diffusion Problems in Random Walk Spaces (Birkhäuser, 2023), which are key references in the field. His research is highly cited, indicating significant impact in the mathematical community. He advises no publicly listed students in the provided data. There is no mention of specific grants or teaching roles, but his extensive publication record and book authorship reflect a sustained and high-level research career. He maintains an active research profile with upcoming events such as the 2025 CIMPA-UCA School on optimal transport and PDEs, suggesting ongoing leadership and contribution to the international mathematical community.

Álvaro Escribano SáezView profile

Professor

• Macroeconometrics
• Microeconometrics
• Financial Econometrics
• +1 more

Álvaro Escribano Sáez is a Full Professor of Applied Economics at UC3M and serves as Director of the Carlos III International School (C3IS). He is a member of UC3M's Governing Council, the Spanish Productivity Council, the AIReF Advisory Board, the Balia Foundation, and the VIA-Círculo Jefferson Association. His research focuses on econometric modeling in macroeconometrics, microeconometrics, financial econometrics, and industrial economics. PhD in Economics from University of California, San Diego Full Professor at UC3M since 1998 Director of C3IS since 2021 Associate Editor for multiple journals including Macroeconomic Dynamics His recent research involves advanced econometric techniques like score-driven models and Markov-switching frameworks applied to climate forecasting, energy markets, and monetary policy. He has pioneered nonlinear approaches in money demand modeling and cointegration analysis. Scientific Awards: Best Article of the Year 2022 Award (Studies in Nonlinear Dynamics and Econometrics) He has contributed extensively to editorial work and policy-oriented research, particularly in productivity analysis, energy economics, and financial econometrics. His work spans both theoretical developments and practical applications in regulated markets.

• Statistics
• Probability Theory
• Stochastic Modeling
• +3 more

María Del Rosario Rodríguez Griñolo is a Full Professor in the Department of Economics, Quantitative Methods and Economic History at Pablo de Olavide University in Spain. Her academic work focuses on Statistics and Operations Research within the School of Economics. Dr. Rodríguez Griñolo earned her PhD from the University of Seville in 2010 with a thesis titled "A new approach to multivariate lifetime distributions based on the excess-wealth concept: an application in tumor growth," supervised by Dr. Franco Pellerey and Dr. José María Fernández Ponce. Her research interests span multiple areas of statistical theory and applications, with particular expertise in stochastic ordering, multivariate analysis, reliability theory, and risk assessment. She has developed significant contributions to the understanding of multivariate aging concepts, excess-wealth functions, and orthant convex-type stochastic orders. Her work demonstrates both theoretical depth and practical applications across diverse fields including financial risk management, cultural heritage preservation, and biomedical research. Analysis of her recent publications reveals a strong focus on advancing theoretical statistical concepts while applying them to real-world problems. Her work shows consistent development in multivariate stochastic orders, with increasing applications in risk assessment, biomedical research, and educational methodologies. The interdisciplinary nature of her research is evident through collaborations across statistics, economics, biology, and cultural heritage fields. Dr. Rodríguez Griñolo has contributed to educational innovation in statistics teaching, particularly through developing web-based resources for secondary students and implementing new assessment methodologies for statistics courses in environmental sciences. Her academic activities include significant contributions to statistics education and public outreach, including the design of educational web resources for secondary students and the development of science communication initiatives related to cultural heritage preservation.

Ozan CandoganView profile

Professor

• Social and Economic Networks
• Operations Management
• Network Analysis
• +3 more

Ozan Candogan is a Professor of Operations Management at the University of Chicago's Booth School of Business. He previously served as an Assistant Professor at Duke University's Fuqua School of Business. His research focuses on social and economic networks, exploring how network data can inform operational decisions and developing analytical tools for networked systems. Ph.D. and M.S. in Electrical Engineering and Computer Science from MIT His work spans applications in ride-sharing, delivery platforms, supply chains, and online advertising. He has published in top-tier journals like Management Science and Operations Research , and his research has received awards including the Microsoft Research Ph.D. Fellowship. At Booth, he teaches core MBA courses and advanced Ph.D. classes on networks and markets. Recent publications highlight his interest in spatial resource allocation, disease mitigation via network targeting, and dynamic pricing in large networks. His methodological work combines network analysis, game theory, and optimization techniques. George Nicholson Student Paper Competition Finalist (2013) M&SOM Service Management SIG Prize Finalist (2021) Siebel Scholarship (2009) Microsoft Research Ph.D. Fellowship (2012) Professor Candogan has taught courses ranging from Operations Management fundamentals to specialized Ph.D. seminars on network theory and market dynamics.

Virginia MinniView profile

Assistant Professor

• Labor Economics
• Organizational Economics
• Development Economics
• +2 more

Virginia Minni is an Assistant Professor of Economics at the University of Chicago Booth School of Business, specializing in labor, organizational, and development economics. Her research examines how managers and organizational structures shape talent allocation, productivity, and equity within firms through large-scale personnel data collaborations with multinational organizations. She earned her Ph.D. in Economics from The London School of Economics (LSE) and was previously a Post-Doctoral Fellow at the Institute for Fiscal Studies (IFS). Her academic journey reflects a strong foundation in empirical economic research with real-world applications. Minni's research program investigates critical questions about leadership effects on worker outcomes, gender dynamics across development levels, and innovative workplace interventions. Key projects include analyzing manager rotations in multinational firms, randomized trials on workplace meaning initiatives, and cross-country studies of gender norms. Her work bridges theoretical microeconomics with practical organizational challenges, emphasizing causal mechanisms through quasi-experimental designs and field experiments. Her publication portfolio reveals a cohesive focus on human capital optimization, with recent work trending toward gender equity in global labor markets and the cultural transmission of managerial practices. Articles consistently leverage unique administrative datasets spanning 100+ countries, highlighting her methodological innovation in studying organizational behavior at scale. Virginia has received significant recognition for her scholarship: Best Job Market Paper Award by the European Economic Association and UniCredit Foundation W.E. Upjohn Institute for Employment Research Dissertation Award Honorable Mention AIEL Labor Dissertation Prize Ronald Coase Best Dissertation Award by the Society for Institutional and Organizational Economics While specific grant details aren't provided, her research demonstrates substantial institutional support through access to proprietary personnel records from global firms and collaborations with leading economists. Her future work continues to explore leadership dynamics, gender norm evolution, and talent allocation mechanisms, with implications for both economic theory and corporate practice. Minni actively contributes to Chicago Booth's academic ecosystem through workshops and course instruction. Operating within Booth's Economics department, she leverages the university's collaborative research infrastructure and global partnerships. Her work involves interdisciplinary teams spanning economics, organizational behavior, and development studies, often collaborating with institutions like the Institute for Fiscal Studies. This cross-institutional approach enables access to unique datasets and real-world testing environments for economic theories.

• Computational Neuroscience
• Neuroimaging Analysis
• Cognitive Rehabilitation
• +3 more

Noelia Martinez Molina is a postdoctoral fellow at Universitat Pompeu Fabra (UPF), supported by the Beatriu de Pinós Fellowship Programme. Her research focuses on understanding brain structural and functional organization in health and disease, with an emphasis on music-based interventions for cognitive and speech rehabilitation in traumatic brain injury (TBI) and chronic stroke patients with aphasia. She employs neuroimaging analysis and in silico stimulation models to identify biomarkers of treatment response for patient stratification in clinical trials. Her work integrates computational neuroscience approaches to study whole-brain dynamics and the neurobiological mechanisms underlying music therapy. Current projects involve analyzing data from randomized controlled trials to evaluate the efficacy of music interventions in neurological rehabilitation. Awards: Beatriu de Pinós Fellowship Programme Research interests span neurorehabilitation, stroke recovery, TBI, and the application of music as a therapeutic tool. Collaborative efforts aim to bridge neuroscience and clinical practice for translational outcomes.

Ignacio CascudoView profile

Associate Professor

• Cryptography
• Secure Multiparty Computation
• Secret Sharing
• +5 more

Ignacio Cascudo is an Associate Research Professor at the IMDEA Software Institute in Madrid, Spain. Previously, he held positions at Aalborg University (Denmark) as Associate and Assistant Professor, and at Aarhus University and CWI (Netherlands) as a postdoc. He earned his Ph.D. in Mathematics from the University of Oviedo (Spain). His research focuses on cryptography, particularly secure multiparty computation, secret sharing, and cryptographic protocols. He explores connections to error-correcting codes, finite fields, algebraic number theory, and algebraic complexity. He has advised PhD students such as Jaron Skovsted Gundersen and co-supervised Diego Mirandola. Recent projects include leadership in Spain's SecuRing grant and collaborations on Confidential6G (Horizon Europe), as well as research on verifiable computation, homomorphic encryption, and secure randomness generation. His work appears in top venues like CRYPTO, EUROCRYPT, and ASIACRYPT. Teaching includes courses on cryptographic protocols, computer security, and algebraic foundations at universities in Spain and Denmark. He contributes to interdisciplinary projects like SECURE at Aalborg University.

• Theoretical Computer Science
• Algorithm Design
• Distributed Computing
• +3 more

Yannic Maus is a Professor at the Faculty of Computer Science and Biomedical Engineering at Graz University of Technology (TU Graz), Austria, where he heads the Institute of Algorithms and Theory (founded in 2025). He is also a co-leader of one of the five Fields of Expertise (FoE) at TU Graz, specifically in Information, Communication & Computation. Dr. Maus received his PhD from the Algorithms and Complexity group at the University of Freiburg (Germany) under the supervision of Fabian Kuhn. Prior to joining TU Graz, he was a postdoctoral researcher at the Technion in Israel in the group of Keren Censor-Hillel. His research focuses on theoretical computer science and algorithm design, particularly problems arising in distributed computing. He is fascinated by clean mathematical questions and the techniques involved in solving them, especially when these questions are motivated by real-world systems. His specific interests include distributed graph algorithms, efficient algorithms, data structures, complexity theory, and geometric algorithms. Dr. Maus's work bridges theoretical foundations with practical applications in large-scale networked systems. Analysis of his recent publications reveals a strong emphasis on distributed algorithms for graph problems, with significant contributions to graph coloring, symmetry breaking, and vertex cover problems across various computational models. His research demonstrates increasing focus on massively parallel computation (MPC) models alongside traditional distributed computing frameworks, addressing challenges related to bandwidth limitations, memory constraints, and adaptive algorithms. The interdisciplinary nature of his work connects theoretical computer science with practical system constraints. GI Dissertationspreis 2018 Wolfgang-Gentner-Nachwuchsförderpreis 2019 2020 Principles of Distributed Computing Doctoral Dissertation Award Best Paper Award at PODC 2016 Best Paper Award at SIROCCO 2016 Best Paper Award at DISC 2017 Dr. Maus actively mentors students at all levels, offering bachelor's and master's theses as well as PhD positions in his research group. His research is supported by multiple competitive grants including FWF grants P36280-N (2023-2027), DOC 183 (2024-2028) and I6915 (2024-2028), and FFG grant No. 59263962. His group maintains strong international collaborations with research teams across Germany, Finland, Iceland, and Israel, facilitating knowledge exchange and joint research initiatives. The group provides fully funded PhD positions with excellent working conditions, including competitive salaries and travel support. The Institute of Algorithms and Theory, under Dr. Maus's leadership, is part of TU Graz's rapidly growing Faculty of Computer Science and Biomedical Engineering. The research group maintains an inclusive environment that actively encourages women to apply and supports work-life balance for all members. Located in Graz, Austria's second-largest city, the institute benefits from the city's high quality of life, cultural vibrancy, and proximity to both the Alps and Mediterranean regions, providing an excellent environment for academic work and personal life.

• Neuropsychology
• Neuroimaging
• Parkinson's Disease
• +7 more

Naroa Ibarretxe Bilbao is a Professor in the Department of Psychology at the Faculty of Health Sciences, University of Deusto. She serves as Director of the University Master's Degree in Clinical Neuropsychology and is Principal Investigator of the Neuropsychology of Severe Medical Disorders research group. Her work focuses on neuroimaging and neuropsychology related to Parkinson's disease, multiple sclerosis, and cognitive rehabilitation. Dr. Ibarretxe Bilbao earned her Psychology degree from the University of Deusto in 2005 and her Doctor of Medicine from the University of Barcelona in 2009. During her doctoral studies, she conducted research at the Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB) at the University of Oxford in 2007. Her doctoral thesis examined limbic brain changes and their consequences on emotional, olfactory, and memory dysfunctions in Parkinson's disease patients using magnetic resonance imaging, for which she received the Lluis Barraquer i Bordas award in 2010. Psychology Degree: University of Deusto (2005) Doctor of Medicine: University of Barcelona (2009) Research Stay: Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford (2007) Dr. Ibarretxe Bilbao's research primarily focuses on neuropsychological assessment and neuroimaging in neurodegenerative disorders. Her work investigates brain structural and functional changes associated with cognitive, emotional, and sensory dysfunctions in Parkinson's disease and multiple sclerosis. She has made significant contributions to understanding how cognitive rehabilitation can improve outcomes for patients with neurological disorders. Her recent work has expanded to include studies on preterm birth outcomes and the application of non-invasive brain stimulation techniques for cognitive enhancement. Analysis of Dr. Ibarretxe Bilbao's recent publications reveals a strong focus on multimodal neuroimaging approaches to understand brain structure-function relationships in neurological disorders. Her work frequently examines Parkinson's disease from multiple angles including cognitive decline, fatigue, emotional processing, and quality of life impacts. There's also a growing interest in the application of transcranial stimulation techniques for cognitive enhancement and rehabilitation. Her research increasingly incorporates longitudinal designs and multi-site collaborations to enhance the robustness of findings. Lluis Barraquer i Bordas 2010 award for best doctoral thesis Dr. Ibarretxe Bilbao has supervised multiple doctoral theses focused on cognitive rehabilitation in Parkinson's disease, multiple sclerosis, and schizophrenia. She has been involved in numerous research projects investigating brain-behavior relationships in neurological conditions. Her work often involves interdisciplinary collaborations with neurologists, psychiatrists, and neuroscience researchers across Spain. She has contributed to the standardization of neuropsychological assessment tools for Spanish-speaking populations, including the Montreal Cognitive Assessment and Wisconsin Card Sorting Test. As principal investigator of the Neuropsychology of Severe Medical Disorders research group at the University of Deusto, Dr. Ibarretxe Bilbao leads a team employing various methodologies including structural and functional MRI, cognitive assessment, and non-invasive brain stimulation techniques. Current research directions include understanding the neural mechanisms of cognitive rehabilitation, identifying biomarkers for cognitive decline in Parkinson's disease, and developing interventions to improve quality of life for patients with neurological disorders.

Cameron FreerView profile

Researcher

• Probabilistic Computing
• Random Structures
• Mathematical Logic
• +4 more

Cameron Freer is a Research Scientist at the Massachusetts Institute of Technology's Probabilistic Computing Project. His academic career spans multiple institutions including Keio University, Harvard University, and University of Hawaii at Manoa, with roles ranging from Postdoctoral Fellow to Project Associate Professor. Current affiliation: MIT Probabilistic Computing Project Past academic roles: Keio University SFC (2021–2024), MIT Brain and Cognitive Sciences (2013–2015), MIT Mathematics (2008–2010) Research interests focus on probabilistic computing , random structures , and their intersections with logic, mathematics, and artificial intelligence. Key contributions include foundational work on Markov categories , graphons , and feedback computability . Recent publications explore probabilistic programming systems , random graph modeling , and computable aspects of measure theory . Collaborators include prominent researchers from Harvard, Oxford, and MIT. Professional engagements include committee memberships in major conferences like POPL (2024), LAFI (2024–2025), and PLDI (2024). Holds PhD in Mathematics from Harvard University (2008).

Nabarun DebView profile

Assistant Professor

• Statistical Inference
• Optimal Transport
• Network Models
• +4 more

Nabarun Deb is an Assistant Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His research focuses on nonparametric statistical methods, optimal transport theory, and dependent data analysis including network and Ising models. Formerly, he was the inaugural PIMS Kantorovich Initiative Postdoctoral Fellow at the University of British Columbia (2022-2023) and completed his PhD in Statistics at Columbia University (2017-2022). Education: PhD in Statistics, Columbia University (2022) Master of Statistics, Indian Statistical Institute, Kolkata (2017) Bachelor of Statistics, Indian Statistical Institute, Kolkata (2015) Research: Specializes in statistical machine learning with applications to optimal transport, kernel methods, and network models. Develops methodologies for causal inference and nonparametric testing under complex dependence structures. Active in theoretical advances for high-dimensional Bayesian regression and measure transportation. Publications: Published in top venues including NeurIPS, JMLR, Annals of Applied Probability, JASA, and Bernoulli. Core research themes: optimal transport applications, network data fluctuations, and generative modeling via PDEs. Recent work explores covariate shifts on approximate manifolds and empirical process trade-offs in nonparametric learning. Awards: PIMS Kantorovich Initiative Postdoctoral Fellowship (2022-2023) at University of British Columbia. Teaching: Teaches Business Statistics and Econometrics and Statistics Colloquium at Booth. Course schedule spans 2024-2026 quarters with emphasis on applied statistical methods.

Ramon Ferrer-i-CanchoView profile

Associate Professor

• Quantitative Linguistics
• Computational Linguistics
• Complexity Science
• +3 more

Ramon Ferrer-i-Cancho is an Associate Professor in the Department of Computer Science at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona School of Informatics (FIB). He leads the Complexity & Quantitative Linguistics Lab and is part of the LQMC research group (Quantitative, Mathematical, and Computational Linguistics). His research focuses on quantitative linguistics, mathematical models of language structure, and computational methods to analyze linguistic universals like Zipf’s law and syntactic dependency distances. Education: PhD in Computer Science from UPC (2003), with a thesis on 'Language: universals, principles and origins.' Editorial Roles: Section editor of Encyclopedia of Language and Linguistics (3rd edition), editorial board member of Glottometrics, Journal of Quantitative Linguistics, and Lingua. Research Interests: Quantitative analysis of syntactic structures, language evolution, cognitive constraints in grammar, and cross-species communication patterns (e.g., chimpanzees, dolphins). He applies principles from complexity science, information theory, and optimization to linguistic problems. Notable Achievements: Recipient of the Joan Lluís Vives Prize (2003) for popular science communication, Barcelona City Research Prize (2003), and UPC’s Best PhD Thesis award (2003-2004). Authored influential papers on dependency distance minimization and Zipfian laws, with over 200 indexed publications. Teaching & Mentorship: Advised Master’s theses on dolphin communication, syntactic dependency distances, and data mining applications. Active in the Master in Innovation and Research in Informatics (MIRI), clarifying academic requirements like SIRI seminar credits. Outreach: Authored popular science books, including Lingüística quantitativa i lleis lingüístiques , and featured in media like National Geographic and Scientific American for bridging linguistics with biology and physics.

Gherardo VarandoView profile

Assistant Professor

• Uncertainty Quantification
• Probabilistic Graphical Models
• Graphical Models for Dynamical Systems
• +4 more

Gherardo Varando is an Assistant Professor at the University of Valencia's Department of Statistics and Operational Research, and a researcher in the Image and Signal Processing Group. His work focuses on causal inference, probabilistic graphical models, and their applications in Earth sciences and health data analysis. He holds a PhD and has been a faculty member since 2024. Research interests include uncertainty quantification, staged event trees, and spatio-temporal causal discovery. Varando has published extensively on topics like causal representation learning, staged tree models, and kernel methods for pairwise causal discovery. His contributions span machine learning theory, statistical methodology, and interdisciplinary applications in climate science and biomedicine. Key achievements include the development of the stagedtrees R package for structural learning of graphical models, and open-source tools like leaflet-map-builder for geospatial visualization. His work bridges theoretical advancements with practical implementations, addressing challenges in causal structure learning and interpretable AI.

• Photovoltaic Materials
• Quantum Dot Systems
• Complex Networks
• +5 more

Lucas Cuadra Rodríguez is a Full Professor in the Department of Signal Theory and Communications at Universidad Alfonso X El Sabio (UAX). He holds a PhD from Universidad Politécnica de Madrid (2004), specializing in intermediate band solar cell development. His research spans quantum dot systems, network science applications in materials and energy systems, and machine learning for environmental and engineering challenges. Key areas include photovoltaic materials, complex network modeling of disordered systems, and renewable energy optimization. Education: Doctorado en Física from Universidad Politécnica de Madrid (2004), supervised by Dr. Antonio Marti Vega and Dr. Antonio Luque López. His work bridges physics, computer science, and engineering, with a focus on interdisciplinary solutions for energy and environmental systems. Research Interests: His primary focus is on developing novel semiconductor solar cell structures, particularly quantum dot-based intermediate band solar cells, and applying network science to study carrier transport in nanomaterials. He also explores machine learning applications in Earth observation, wind energy prediction, and complex system analysis. His recent work emphasizes spatially embedded networks and their relevance to material science and renewable energy infrastructure. Notable Projects: Leading the GHEODE Research Group (Modern Heuristics and Network Design), he investigates optimization algorithms for smart grids and network robustness. Recent studies include modeling electron transport in van der Waals materials and analyzing fog event persistence through nonlinear dynamics. Awards: No specific awards mentioned in the provided text. Advising & Grants: While his PhD supervision details are noted, no current student advisees or grant projects are detailed here. His work often involves collaborative interdisciplinary projects with engineering and environmental science teams. Labs/Teams: Active in the GHEODE Group at UAX, focusing on heuristic algorithms and network-based solutions for engineering challenges.

Panagiotis ToulisView profile

Associate Professor

• Causal Inference
• Experimental Design
• Randomization Methods
• +4 more

Panagiotis Toulis (Panos) serves as Associate Professor of Econometrics and Statistics and John E. Jeuck Faculty Fellow at the University of Chicago Booth School of Business, where his research focuses on model-agnostic causal inference methods for complex networked environments using randomization techniques that provide robustness beyond classical statistical approaches. His academic credentials include: Ph.D. in Statistics from Harvard University (2016) under Edo Airoldi, David Parkes, and Don Rubin Master’s in Computer Science from Harvard University (2011) under David Parkes M.Eng. in Electrical and Computer Engineering from Aristotle University of Thessaloniki, Greece (2006) Toulis pioneers randomization and permutation tests for causal inference under network interference, developing methods applicable to experimental design in complex domains where traditional model-based approaches fail. His work bridges statistics , optimization , and economics , with significant contributions to invariance-based inference for observational studies and machine learning integration through stochastic gradient descent frameworks. This research has practical applications in business analytics, vaccine distribution, and economic policy. Analysis of his 15 most recent publications (2023-2025) reveals dominant trends in extending randomization tests to handle network spillover effects, two-sided market experiments, and temporal dynamics with treatment habituation. A growing emphasis on machine learning assistance for complex treatment effect detection demonstrates his innovative fusion of statistical rigor with computational scalability, consistently providing finite-sample theoretical guarantees uncommon in asymptotic-focused literature. Toulis has earned recognition through prestigious awards: Arthur P. Dempster Award from Harvard University’s Department of Statistics LinkedIn Economic Graph Challenge award 2012 Google United States/Canada PhD Fellowship in statistics He teaches core Booth courses including Applied Regression Analysis (41100), Causal Inference for Business Applications (41207), and the Econometrics and Statistics Colloquium (41600), while maintaining active industry connections through prior software engineering roles at Google and Greek startups. His research appears in top journals including Journal of the Royal Statistical Society and Annals of Statistics, addressing critical methodological challenges in modern data science. No dedicated laboratory structure is documented, but his work inherently involves collaborative research networks across statistics, economics, and computer science disciplines through the Booth School’s academic infrastructure.