جست‌وجوی استاد و پژوهشگر

Prosper Dovononمشاهده پروفایل

استاد

• Econometrics
• Time Series Analysis
• Financial Econometrics
• +۲ مورد دیگر

Prosper Dovonon serves as a Full Professor in the Department of Economics at Concordia University in Montreal, Canada, where he holds a prestigious Concordia University Research Chair, Tier 1, in Econometrics of Large Datasets. He previously held positions as Associate Professor (2015-2023) and Assistant Professor (2010-2015) at the same institution. Additionally, he maintains an adjunct professorship at the University of Adelaide's School of Economics since 2021 and previously served as a Visiting Professor at HEC Montreal's Department of Finance (2017-2018). His educational background includes a PhD in Economics from Universite de Montreal (2007), an MSc in Statistics and Economics from ENSEA, Abidjan, Cote d'Ivoire (2000), and an MSc in Mathematics from Universite Nationale du Benin, Abomey-Calavi, Benin (1996). Dovonon's research focuses on advanced econometric methodologies, particularly in time series analysis and financial econometrics. His work addresses complex identification issues, develops robust estimation techniques, and creates innovative testing procedures for economic models. He specializes in moment condition models, GMM estimation, volatility modeling, and handling identification failures in econometric frameworks. His publication record shows a consistent focus on theoretical econometrics with practical applications in finance. Recent work emphasizes mixed identification strength scenarios, instrument exogeneity testing, and specification testing under challenging identification conditions. His research demonstrates increasing sophistication in handling complex econometric problems with real-world financial data applications. His notable recognition includes the Concordia University Research Chair, Tier 1, in Econometrics of Large Datasets, highlighting his significant contributions to the field. Dovonon has supervised numerous graduate students and collaborated extensively with leading econometricians worldwide. His research has been supported by institutional funding through his Research Chair position, enabling significant contributions to econometric theory and methodology. He maintains active research collaborations across international institutions and continues to push the boundaries of econometric theory with applications to financial markets and economic modeling.

• Cartography
• Geospatial Analysis
• Urban Planning
• +۴ مورد دیگر

Prof. Liqiu Meng serves as Chair of Cartography and Visual Analytics at the Technical University of Munich (TUM). He specializes in advanced geospatial research, digital cartography, and human-technology collaboration frameworks. Current Faculty at TUM Chair of Cartography and Visual Analytics Research Focus: His work bridges cartographic theory with cutting-edge technology, covering topics like 3D urban modeling, AI ethics visualization, geovisual analytics, and spatiotemporal data interpretation. Urban Morphology Analysis AI Ethics Cartography Geovisual Analytics 3D City Data Integration Location-Based Service Design Publications: Recent works (2025-2024) demonstrate expertise in explainable AI for urban analysis, multi-agent systems for geospatial interaction, and advanced spatial modeling techniques. Contact: liqiu.meng@tum.de | contact.lfk@ed.tum.de

• Computational Biophysics
• Membrane Proteins
• Protein Dynamics
• +۵ مورد دیگر

Régis Pomès is a Professor in the Department of Biochemistry at the University of Toronto, where he leads an active research program since 1999. His work focuses on computational biophysics, studying the structure-dynamics-function relationships of biomolecules. Canada Research Chair (Tier 2) in Physical Chemistry (2001-2011) Teaches courses: BCH 2107H: Introduction to Biomolecular Simulations BCH 2105H: Cystic Fibrosis: The Cause, The Treatment BCH 2024H: Introduction to Biomolecular Simulations JBB2026H: Protein Structure, Folding and Design BCH473Y: Advanced Research Project in Biochemistry BCH422H: Membrane Proteins: Structure and Function Research Interests: The Pomès Lab specializes in computational methods development and their application to biomolecular systems, particularly: Membrane proteins and ion channels Protein-lipid interactions Protein folding and aggregation Statistical mechanics of biomolecular systems Molecular dynamics simulations across multiple scales Structural biology of disordered proteins Awards: Canada Research Chair (Tier 2) in Physical Chemistry (2001-2011)

Marie-Colette van Lieshoutمشاهده پروفایل

استاد

• Stochastic Geometry
• Spatial Statistics
• Image Analysis
• +۶ مورد دیگر

Marie-Colette van Lieshout is a Professor of Spatial Stochastics at the Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, and a Scientific Staff Member in the Stochastics group at Centrum Wiskunde & Informatica (CWI), Amsterdam. She has been active in research since 1997 and is a leading expert in stochastic geometry, spatial statistics, and image analysis. Her educational and professional background includes positions at the University of Warwick and the Free University Amsterdam. She is currently engaged in advanced research on point processes, random fields, and tessellation models, with applications in seismic hazard, fire risk, and machine learning. Her research interests include: Stochastic Geometry Spatial Statistics Image Analysis Point Process Modeling Seismic Risk Assessment Machine Learning for Spatial Data Her recent publications (2023–2025) focus on spatial intensity estimation, marked point processes, and data-driven risk modeling, showing a strong integration of classical spatial statistics with modern computational and machine learning techniques. Key themes include adaptive kernel smoothing, infill asymptotics, and applications in environmental and public safety domains. She has received significant recognition, including: Elected Fellow, International Statistical Institute (ISI) She has been awarded multiple research grants from NWO and other agencies, including the KLEIN grant for fire risk management and the DeepNL grant for seismicity prediction in Groningen. She has supervised or collaborated with researchers such as C. Lu, Z. Baki, and R. Markwitz. She is also active in academic service, serving on editorial boards (e.g., Methodology and Computing in Applied Probability), advisory boards (InHolland University), and councils of learned societies (Bernoulli Society, KWG). She leads and participates in research clusters such as STAR and contributes to outreach and education through courses and public lectures on earthquake modeling and spatial statistics.

Alexander M. Petersenمشاهده پروفایل

دانشیار

• Science of Science
• Complex Systems
• Computational Social Science
• +۷ مورد دیگر

Alexander M. Petersen is an Associate Professor and Graduate Chair in the Management of Complex Systems (MCS) department and the Ernest and Julio Gallo Management of Innovation Sustainability and Technology (MIST) graduate group at the University of California Merced (UCM). He is affiliated with the School of Engineering and serves as a key faculty member driving research and academic programs in complex systems and innovation management. His educational background includes a Ph.D. (2011) and M.A. (2008) from Boston University, and a B.S. (2003) in physics and mathematics from the University of Utah. Prior to joining UC Merced, he was a tenure-track faculty member at the IMT Institute for Advanced Studies Lucca in Italy. Petersen's research focuses on the evolution of large multiscale socio-economic systems by applying concepts and methods from complex systems, statistical physics, management, and innovation science. His work spans Science of Science, Computational Social Science, Complex Systems, Quantitative Finance, and Sports Analytics. He has made significant contributions to understanding research collaboration networks, innovation dynamics, convergence science, and the impact of socio-economic shocks on various systems. His publication record demonstrates a clear trend toward increasingly complex interdisciplinary work, particularly in convergence science and network analysis. His most recent publications (2024-2025) focus on global science structure, disruption metrics, national park systems, and the impact of crises on economic behavior. These works often integrate multiple disciplinary perspectives and leverage large-scale data analysis. NSF award #1738163 for research on convergence science Petersen has advised numerous graduate students, including current PhD student Andrea del Pilar Montaño Ramirez and former students Felber J. Arroyave Bermudez (PhD in Environmental Systems) and Dr. Dong Yang (MCS Postdoctoral Scholar). His research has been supported by competitive grants and has resulted in publications in high-impact journals including Nature Communications, Science Advances, and Research Policy. He is actively involved in developing new academic programs, including upcoming B.A. majors in Management of Innovation, Sustainability & Technology (2025) and Data Science & Analytics (Fall 2024). His lab focuses on complex systems analysis, with current projects examining university digital media networks, disruption metrics in science, and the integration of regional innovation systems. The research group maintains an active presence in the science of science community, regularly presenting at conferences like the Science of Team Science conference (INSciTS).

Romdhane Rekayaمشاهده پروفایل

استاد

• Animal Breeding and Genetics
• Quantitative Genetics
• Genomics
• +۴ مورد دیگر

Romdhane Rekaya serves as a Professor in the Department of Animal and Dairy Science within the College of Agricultural & Environmental Sciences at the University of Georgia. He also holds courtesy faculty positions in the Department of Statistics and is an associate faculty member with the Institute of Bioinformatics at UGA. His research program focuses on developing statistical and computational tools for analyzing large genetic and genomic datasets with applications in livestock, poultry, and human health. Dr. Rekaya's educational background includes: Agriculture Engineer from the High Institute of Agriculture, Tunisia Master of Science from the International Center for Advanced Studies in Mediterranean Agriculture of Zaragoza, Spain Ph.D. from the Polytechnic University of Madrid, Spain Dr. Rekaya's research interests span quantitative genetics, genomics, biostatistics, and bioinformatics. His work centers on developing statistical and computational methodologies for analyzing big genetic and genomic data sets with practical applications in livestock, poultry, and human health. His research has been particularly focused on addressing critical challenges in animal agriculture including horn fly resistance in beef cattle, water utilization efficiency in poultry, and greenhouse gas emissions in dairy production. His methodological approaches often integrate machine learning, Bayesian statistics, and genomic technologies to solve complex biological problems. Dr. Rekaya's publication record demonstrates a consistent focus on statistical methodology development for genetic analysis, with recent work increasingly emphasizing practical applications of genomic technologies in animal agriculture. His research spans both theoretical statistical development and practical implementation in livestock improvement programs, with particular emphasis on innovative approaches to traditional breeding challenges. Among his professional recognitions: Student Career Success Influencer Award 2024 Mini-Sabbatical Award Student Career Success Influencer Award 2022 Carnegie fellowship Carnegie African Diaspora Fellow Faculty with significant positive impact of at least one graduate student Gamma Sigma Delta outstanding Research Achievements Award Dr. Rekaya has successfully mentored numerous graduate students including PhD candidates Amanda Warner, Mahsa Zare, Koushik Das, and Evan Hartono, along with undergraduate researchers. His research has been consistently supported by major funding agencies including USDA NIFA, USDA ARS, Georgia Agricultural Commodity Commission for Beef, National Academy of Sciences, and industry partners including Tyson Foods and Cobb-Vantress. Current projects include developing genetic solutions to the horn fly problem in beef cattle, improving water utilization efficiency in poultry, and examining associations between greenhouse gas emissions and feed efficiency in dairy cattle. Dr. Rekaya leads an active research laboratory that collaborates with multiple departments and institutions. His lab focuses on applying advanced statistical and computational methods to solve pressing problems in animal agriculture, with particular emphasis on integrating genomic information into practical breeding programs. The lab maintains strong industry connections and international collaborations, particularly with researchers in Africa through the African Animal Breeding Network.

Erhan Bayraktarمشاهده پروفایل

استاد

• Mathematical finance
• applied probability
• machine learning
• +۴ مورد دیگر

Erhan Bayraktar is a Professor of Mathematics at the University of Michigan, holding the Susan Smith Chair. He serves as Director of the Quantitative Finance and Risk Management Masters Program, which he established in 2015. His academic career at the University of Michigan spans since 2004, progressing from T. H. Hildebrandt Research Assistant Professor to his current full professorship. Professor Bayraktar earned his Ph.D. from Princeton University in 2004, following dual Bachelor's degrees in Electrical Engineering and Mathematics from Middle East Technical University in Turkey. His academic journey reflects a strong foundation in both theoretical and applied mathematical disciplines. Bayraktar's research focuses on mathematical finance, applied probability, machine learning, mean field games, stochastic analysis, stochastic control, and optimal stopping. His work bridges theoretical mathematics with practical applications in finance and risk management. He has developed sophisticated mathematical frameworks for analyzing complex financial systems, market behaviors, and optimal decision-making under uncertainty. His contributions to mean field games have provided new insights into large-scale interacting systems, while his work on stochastic control has advanced methodologies for optimal decision processes. His publication record demonstrates a consistent trajectory of high-impact research, with recent work focusing on Wasserstein space analysis, graphon particle systems, and applications of machine learning to financial mathematics. His research shows increasing interdisciplinary connections between traditional mathematical finance and modern computational approaches. Susan M. Smith Professorship (2010-present) National Science Foundation CAREER Grant (2010-2016) SIAM Activity Group on Financial Mathematics and Engineering Early Career Prize (2010) Professor Bayraktar has mentored 14 Ph.D. students (13 graduated) and approximately 40 post-doctoral researchers. His students hold prestigious positions in academia and industry, including tenure-track positions at Boston University, University of Colorado, University of Sydney, and University of Toronto. He has secured continuous funding from the National Science Foundation, including the current grant DMS-2507940 (2025-2028) and previous grants totaling over 15 years of continuous NSF support. As Director of the Quantitative Finance and Risk Management Masters Program, Bayraktar has built a robust academic community through the Financial/Actuarial Math seminar series, which hosts about 10 outside speakers annually, and by organizing international workshops in stochastic analysis for finance and insurance in Ann Arbor.

Henning Schomerusمشاهده پروفایل

استاد

• Quantum Transport
• Topological Photonics
• Quantum Chaos
• +۵ مورد دیگر

Professor Henning Schomerus is a leading theoretical physicist at Lancaster University , specializing in condensed matter theory with a focus on quantum systems. His research spans topological photonics , non-Hermitian physics , quantum chaos , and mesoscopic transport . He leads the Theory Group and contributes to the Physics Strategy Committee . Education: Dr rer. nat. (University of Essen, 1997) Dipl. Phys. (University of Stuttgart, 1993) Research Interests include: Quantum transport in graphene and topological insulators , exploring disorder effects and quantum pumping Topological lasers and non-Hermitian photonic systems with combined amplification/absorption Quantum chaos and fractal Weyl laws in open systems Many-body localization and quantum noise phenomena Scientific Awards Senior Fellow of the Higher Education Academy (SFHEA) Fellow of the Institute of Physics (FInstP) Studenstiftung des Deutschen Volkes Scholarship JSPS Invitational Fellowship DFG Forschergruppe 760 Fellow Teaching encompasses advanced topics in Quantum Mechanics and Quantum Information Processing , with over 15 years of experience in undergraduate and postgraduate instruction.

• Computational Linguistics
• Historical Linguistics
• Phylogenetic Linguistics
• +۸ مورد دیگر

Prof. Gerhard Jäger holds the Chair of General Linguistics at the Faculty of Humanities, University of Tübingen . He serves as a Principal Investigator (PI) in the Clusters of Excellence Human Origins and Machine Learning for Science , and leads projects like Phylomilia (funded by Volkswagen Foundation) and CrossLingference (ERC Advanced Grant). His career spans multiple institutions, including Bielefeld University (2004-2009) and Stanford University (visiting scholar, 2004). Habilitation (2002) at Humboldt University Berlin with thesis on Anaphora and Type Logical Grammar PhD (1996) at Humboldt University Berlin on Dynamic Semantics His research bridges computational linguistics , phylogenetic analysis , and game theory , focusing on Bayesian models , language evolution , and cross-linguistic typology . Recent work explores phylogenetic inference from acoustic speech data and geographic influences on language trees . Key contributions include 15+ recent publications on topics spanning phylogenetic typology , cognate detection , and Bayesian language modeling . These works employ machine learning , statistical inference , and evolutionary game theory to analyze language change , typological variation , and linguistic stability . Honors include ERC Advanced Grant , Volkswagen Foundation funding , and DFG-Humanities Centre for Advanced Studies participation. He has taught courses in Computational Historical Linguistics , Phylogenetic Methods , and Bayesian Data Analysis across institutions like Tübingen, Bielefeld, and Stanford. He actively contributes to academic communities through workshop organization (e.g., Quantitative Theoretical Linguistics , Game Theory in Pragmatics ) and serves on the faculty council at Tübingen. His team collaborates with institutions like Max Planck Institute for Evolutionary Anthropology , University of Pennsylvania , and LMU Munich .

• Solid State Physics
• Quantum Mechanics
• Analytical Mechanics
• +۷ مورد دیگر

Matti Selg is an Associate Professor at the Institute of Physics within the Faculty of Science and Technology at the University of Tartu, Estonia. He has been actively teaching graduate courses in Quantum Mechanics, Analytical Mechanics, and Mathematical Physics since 2009, with increasing responsibility over the years. Currently, he oversees the entire teaching of three mandatory courses: Master's Course in Quantum Mechanics, Analytical Mechanics, and Theory of Complex Variables. Dr. Selg completed his education at the University of Tartu, earning a diploma in physics. He received his Doctor's Degree in 1981 from the University of Tartu with a dissertation titled "Relaxation and hot luminescence of self-trapped excitons in rare gas crystals," supervised by Vladimir Hižnjakov and Rein Kink. His additional qualification includes a PhD in solid state physics from the Institute of Physics of the Estonian Academy of Sciences. Professor Selg's research spans several interconnected areas of theoretical physics. His primary interests include quantum mechanics, particularly scattering theory and inverse problems, as evidenced by his numerous publications and textbooks on quantum scattering. He has made significant contributions to the understanding of reflectionless potentials and the Marchenko equation. More recently, he has focused on classical mechanics problems, particularly exploring Binet's equation and its connections to Newtonian and Einsteinian gravity theories, as well as revisiting historical problems like Galileo's swiftest descent problem. His work bridges mathematical physics with practical applications in molecular and solid-state physics. Analysis of his recent publications reveals a clear trajectory from quantum scattering theory toward classical mechanics and historical physics problems. While maintaining his expertise in quantum systems, particularly with hydrogen molecules and diatomic systems, he has expanded into historical and mathematical analyses of foundational physics concepts. His 2023-2025 publications show a particular focus on exact solutions to classical mechanics problems and their connections to modern gravitational theory. Dr. Selg has served in several administrative roles, including as a member of the Science Council of the Institute of Physics at the University of Tartu since 2001 and as a member of the Expert Commission for Exact Sciences of the Estonian Science Foundation (2003-2006). He has successfully led research projects funded by the Estonian Science Foundation, including studies on excimers in rare gases and their crystals. As an educator, Professor Selg has developed and taught advanced courses that integrate deep theoretical concepts with practical applications. His textbooks on quantum scattering theory demonstrate his commitment to making complex topics accessible to students. His recent work on the mathematical and physical perspectives of foundational problems suggests an evolving research program that connects historical scientific developments with contemporary theoretical challenges.

Diana Tunaruمشاهده پروفایل

مدرس

• Empirical Finance
• Interest Rate Modeling
• Dynamic Estimation and Forecasting
• +۴ مورد دیگر

Dr. Diana Tunaru is a Lecturer in Finance at Kent Business School , University of Kent, with expertise in empirical finance and quantitative methods. Her research focuses on dynamic financial modeling and forecasting, particularly in interest rate structures and volatility spillover effects. Education: Diploma in Mathematics (University of Craiova), Master in Banking and Finance (Middlesex University), PhD in Finance (University of Westminster) Research Interests include multi-factor continuous-time models, Gaussian estimation techniques, and the analysis of financial variables like interest rates, equities, and derivatives during economic distress. She applies her industry experience in investment banking to bridge theoretical and practical finance. In publications , Diana has explored topics ranging from agricultural commodity forecasting to green finance, with recent work emphasizing sustainability-driven portfolio performance and market interconnectedness. Her methodological focus on statistical accuracy tests and spillover effects reflects her commitment to advancing financial modeling. Teaching: Quantitative Methods for Finance, Introduction to Econometrics, Financial Investments, Economics of Financial Markets Diana serves as Senior Tutor at Kent Business School (Medway campus) and contributes to postgraduate education through her supervision of undergraduate dissertations on empirical finance and financial time series analysis.

• Probability Theory
• Stochastic Processes
• Mathematical Physics
• +۳ مورد دیگر

Balint Toth is a distinguished academic with dual affiliations: a Research Professor at the Alfréd Rényi Institute of Mathematics in Budapest and a Professor of Probability (Heilbronn Chair) at the University of Bristol 's School of Mathematics. His work bridges Probability Theory , Mathematical Physics , and Statistical Mechanics , focusing on stochastic dynamics, random walks in complex environments, and scaling limits. Key Roles: Co-Editor-in-Chief of Probability Theory and Related Fields , organizer of probability seminars in Budapest-Vienna and Bristol, and former leader of the BME Stochastics Seminar (1999–2020). Teaching: Delivers advanced courses like Probability 2 , Stochastic Differential Equations , and Percolation , emphasizing rigorous mathematical foundations. Research Themes include hydrodynamic limits, self-interacting random walks, diffusion in random media, and symmetry breaking in spin systems. His recent publications explore non-equilibrium stochastic models, anomalous diffusion, and connections between probability and physics. Teaching Materials span bilingual resources (Hungarian/English) for undergraduate and graduate courses in probability and stochastic analysis.

Yvo Pokernمشاهده پروفایل

دانشیار

• Diffusion Processes
• Bayesian Statistics
• Markov Chain Monte Carlo
• +۴ مورد دیگر

Dr Yvo Pokern is an Associate Professor in Statistics at University College London since 2018. His research focuses on computational statistics and machine learning, with expertise in diffusion processes and Bayesian methodology. He earned his PhD in mathematics under Andrew Stuart, a Masters at Paris XI with a dissertation at the Max-Planck-Institute in Leipzig, and was a postdoctoral researcher at Warwick University with Gareth Roberts and Wilfrid Kendall. His primary research interests include statistical inference for diffusion processes (particularly hypoelliptic diffusions and diffusions on manifolds), Bayesian methods such as Markov chain Monte Carlo, and statistical applications in spectroscopy (ENDOR). His work combines theoretical rigor with practical applications in diverse scientific domains. Analysis of his recent publications reveals a consistent theme of developing and applying advanced statistical techniques to complex real-world problems, including traffic flow, fingerprint analysis, and magnetic resonance spectroscopy. Dr Pokern has supervised numerous PhD students, several of whom have gone on to academic careers. Notable former students include Mai Ngoc Bui (now lecturer at the British University Vietnam) and Tjun Yee Hoh (now lecturer at UCL School of Management).

Valentijn M.T. de Jongمشاهده پروفایل

استادیار

• Biostatistics
• Epidemiology
• Medical Research Methodology
• +۳ مورد دیگر

Valentijn M.T. de Jong is an Assistant Professor at Utrecht University, specializing in methodological advancements in biostatistics and epidemiology. His research focuses on causal inference, missing data analysis, and meta-analytical techniques in medical studies. Research Trends: Recent publications highlight his expertise in statistical methods for handling missing data (e.g., Heckman selection models), causal inference in individual-participant data meta-analyses, and enhancing prediction model discrimination in healthcare research. His work spans disciplines like epidemiology, biostatistics, and health data science.

David Steurerمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Algorithms
• Sum-of-Squares Method
• +۵ مورد دیگر

David Steurer is an Associate Professor in the Department of Computer Science at ETH Zurich, where he leads the Institute for Theoretical Computer Science. His research bridges theoretical computer science, mathematics, and practical applications in machine learning and statistics. Steurer has made significant contributions to the understanding of sum-of-squares methods, semidefinite programming, and high-dimensional estimation problems. Steurer earned his B.Sc. & M.Sc. in Computer Science from Saarland University in 2006, followed by a Ph.D. in Computer Science from Princeton University in 2010 under the supervision of Sanjeev Arora. His doctoral dissertation, "On the Complexity of Unique Games and Graph Expansion," received an Honorable Mention for the ACM Doctoral Dissertation Award in 2011. Steurer's research focuses on algorithm design through mathematical programming relaxations, particularly semidefinite programming and the sum-of-squares method. His work addresses computational complexity of high-dimensional estimation problems including tensor decomposition, clustering, Gaussian mixture models, and stochastic block models. He also investigates algorithmic aspects of robustness and differential privacy, especially for estimation tasks. His theoretical contributions have practical implications for machine learning and data analysis in the presence of noise and adversarial conditions. Analysis of Steurer's recent publications reveals a consistent trajectory in advancing the sum-of-squares framework for high-dimensional estimation and robust statistics. His work demonstrates how sophisticated mathematical programming techniques can provide certifiable guarantees for challenging problems in machine learning. The research spans theoretical foundations while maintaining relevance to practical applications, particularly in scenarios involving corrupted or noisy data. A notable trend is the extension of sum-of-squares methods to handle increasingly complex statistical models while providing rigorous performance guarantees. ERC Consolidator Grant (2019) Michael and Sheila Held prize (2018, with Raghavendra) Invited speaker at International Congress of Mathematicians (2018) STOC Best Paper Award (2015) Alfred P. Sloan Research Fellowship (2014) NSF CAREER Award (2014) Microsoft Research Faculty Fellowship (2014) FOCS Best Paper Award (2010) Steurer has advised numerous PhD students and postdocs who have gone on to prominent positions, including Sam Hopkins (now faculty at MIT), Jonathan Shi, and Aaron Potechin. His research is supported by multiple prestigious grants, including an ERC Consolidator Grant, an NSF CAREER Award, and a Microsoft Research Faculty Fellowship. He has served on numerous program committees for top theoretical computer science conferences and has been recognized for his service to the community through roles such as internal member of the Scientific Advisory Committee of the Institute for Theoretical Studies at ETH Zurich. As head of the Institute for Theoretical Computer Science at ETH Zurich, Steurer leads a research group focused on advancing the theoretical foundations of computer science with particular emphasis on mathematical optimization methods. His team explores the boundaries of what can be efficiently computed in high-dimensional settings, with applications spanning machine learning, statistics, and data science. The group maintains strong connections with both theoretical and applied researchers across ETH and the broader academic community.