Manolis Zampetakis is an Assistant Professor of Computer Science at Yale University. Previously, he was a postdoc at UC Berkeley's EECS Department working with Michael Jordan, and earned his PhD from MIT's EECS Department under Constantinos Daskalakis. His research spans Theoretical Machine Learning, Statistics, Optimization, Computational Complexity, Game Theory, and Mechanism Design. He has received the ACM SIGEcom Doctoral Dissertation Award and a Google PhD Fellowship. Current affiliation: Yale University (Assistant Professor) Prior affiliations: UC Berkeley (Postdoc), MIT (PhD student), NTUA (Undergraduate) His research focuses on algorithmic game theory, robust statistics, and optimization challenges in machine learning. He explores computational complexity in multi-player games, truncated linear regression, and strategy-proof mechanisms. Recent work includes backdoor attacks in neural networks and jailbreaking black-box LLMs, with publications in top venues like NeurIPS, COLT, FOCS, and STOC. Notable scientific contributions have been recognized through awards and special issues. He co-organized workshops at FOCS 2018, WALE 2019, and WALE 2022. His students include Anay Mehrotra, Jane Lee, Katerina Mamali, Shuchen Li, and Nikolaos Koumpis, often co-advised with prominent researchers like Amin Karbasi and Tuomas Sandholm.
Clélia de Mulatier is an Assistant Professor at the University of Amsterdam , affiliated with both the Institute for Theoretical Physics and the Informatics Institute . She leads research at the intersection of statistical physics, information theory, and computer science , focusing on theoretical and numerical methods for complex systems . Her work spans collaborations with experimentalists in neuroscience and biology , and she actively participates in educational programs across multiple Dutch universities. Research Labs : Computational Soft Matter Lab, Computational Science Lab Affiliations : Dutch Institute for Emergent Phenomena (DIEP), Netherlands Platform Complex Systems (NPCS) Her research develops minimally complex spin models for high-order data analysis , applying exact Bayesian model selection to uncover hidden variable communities in binary datasets. This work has produced open-source tools like MinCompSpin and MinCompSpin_Greedy for different system sizes. Publications demonstrate expertise in tensor networks for dimensional reduction , epidemic modeling , and branching random walks in confined environments . Teaching includes Python programming , complex systems theory , and statistical inference for physics students across multiple institutions. She serves as program committee member for International Conference on Computational Science and organizes academic discussions through initiatives like Behind the CV: story from a Physicist .
Professor Dirk J. Lehmann is a Professor of Data Science in IoT at Ostfalia University of Applied Sciences, Faculty of Computer Science, where he has been employed since May 2022. He holds significant leadership roles including Deputy Head of the Institute for Information Engineering (since 2024), Research Officer of the Faculty of Computer Science (since 2023), and membership in multiple committees including the Admissions Committee for Digital Technologies and the Digital Technologies Examination Board. Professor Lehmann's extensive academic journey includes: Part-time professorship in Data Science in IoT at Ostfalia University (2020-2022) Senior Specialist for Digitalization, AI, and Visual Analysis at IAV GmbH (2018-2023) Assistant Professor of Visual Data Analysis at Nazarbayev University, Kazakhstan (2017) Visiting professorships at TU Graz, Austria and Universidad Rey Juan Carlos, Spain (2016-2017) Researcher at Otto-von-Guericke University Magdeburg (2009-2017) His research expertise centers on Visual Analytics and Data Science, with particular emphasis on high-dimensional data visualization, categorical data analysis, and IoT applications. Professor Lehmann leads the Data Science in IoT working group, conducting research across three main areas: visual data analysis, distributed data analysis using AI methods, and applied data analysis in geology, climate data, medicine, and industrial processes. His methodological contributions include innovative visualization techniques for complex datasets across multiple domains. Analysis of Professor Lehmann's 15 most recent publications (2017-2025) reveals a consistent focus on advancing visualization techniques for complex data analysis. His work spans categorical data visualization (CatNetVis), biological data analysis (D. Melanogaster research), optimization of star coordinate systems, and interactive exploration methods for large datasets. These publications appear in top venues including IEEE Transactions on Visualization and Computer Graphics and EuroVis, demonstrating both theoretical rigor and practical application across diverse domains from healthcare to environmental science. As an educator, Professor Lehmann teaches a comprehensive range of courses from foundational mathematics to advanced machine learning and visualization techniques. He actively supervises student projects and theses, emphasizing clear project definitions with measurable acceptance criteria. His international collaborations span institutions in Israel, Saudi Arabia, China, Austria, and Spain, reflecting a global research perspective that bridges academic theory with industry applications, particularly through his previous role at IAV GmbH, a Volkswagen subsidiary.
Prof. Dr. Klaus R. Pawelzik is a Professor at the University of Bremen's Institute for Theoretical Physics, where he leads the Theoretical Bio- and Neurophysics research group. His research bridges theoretical physics and neuroscience, with laboratories located in the Cognium building on the university campus. His primary research interests include: Computational models of neural dynamics and information processing Neurophysics and mechanisms of cortical computation Brain-computer interfaces and neuroprosthetic systems Dynamical systems approaches to causality and network analysis Biologically plausible learning algorithms for spiking neural networks Attention mechanisms and sensory processing in primate brains Recent publications (2015-2020) demonstrate strong focus on attention mechanisms in visual processing, causal inference methods for dynamical systems, hardware implementations for neuroprosthetics, and biologically inspired learning algorithms. The work consistently integrates mathematical rigor with experimental neuroscience, featuring collaborations with neurophysiology labs and engineering groups. Prof. Pawelzik leads an active research team developing both theoretical frameworks and experimental platforms for neuroscience research. The lab specializes in open-source neurotechnology solutions, including wireless implantable devices for electrocorticography and FPGA-based processing systems for real-time neural signal analysis.
Marco Battaglini serves as the Edward H. Meyer Professor of Economics at Cornell University and holds a non-resident fellowship at EIEF (Einaudi Institute for Economics and Finance). His academic profile centers on theoretical and empirical political economy with significant contributions to collective action theory and institutional analysis. His educational credentials include: Ph.D. in Economics from Northwestern University (2001) Professor Battaglini's research focuses on political economy mechanisms , particularly examining how individuals overcome collective action problems through strategic behavior and institutional design. His work integrates rigorous game-theoretic modeling with empirical validation, as demonstrated in studies of congressional logrolling, donor influence dynamics, and volunteer dilemmas. The recurring theme across his publications involves analyzing how group size, information asymmetry, and temporal dynamics affect collective decision-making outcomes in political contexts. Recent publications reveal a methodological trend combining quasi-experimental designs with theoretical frameworks to investigate political influence networks and legislative bargaining. His 2023-2024 papers consistently address core public economics questions through the lens of political institutions, demonstrating particular expertise in applying game theory to real-world political phenomena. His scientific recognition includes: Carlo Alberto Medal (2009) Election as Fellow of the Econometric Society While the source material indicates active collaboration with prominent researchers including Thomas R. Palfrey, Valerio Leone Sciabolazza, and Eleonora Patacchini, it provides no specific details about graduate student supervision, grant funding, or laboratory affiliations. His research trajectory suggests ongoing investigation into the intersection of political institutions and collective action problems.
Prof. Dr. Jens Tetens is a Professor at the University of Göttingen, where he heads the Department of Genetics and Breeding Improvement of Functional Traits at the School of Veterinary Medicine. He studied veterinary medicine at the University of Veterinary Medicine Hannover Foundation from 1999 to 2005, receiving his doctorate in 2006. After a postdoc period in Hannover and a stay in Switzerland, he joined the Christian-Albrechts-University of Kiel in 2007, where he headed the molecular genetics laboratory for 10 years and completed his habilitation in 2015 with the thesis 'Recent advances in cattle genomics and beyond,' which earned him the H. Wilhelm Schaumann Foundation's award in 2013. His research spans multiple areas of animal genetics and genomics, with particular emphasis on: Genomic selection methodologies and breeding value estimation Genetic analysis of production, health, and functional traits Identification of causal genetic variants through advanced genomic approaches Application of genomic technologies to improve animal breeding programs Mendelian randomization to establish causal relationships between traits Prof. Tetens' publication record shows a strong evolution from traditional quantitative genetics to cutting-edge genomic analyses using sequence-level data. His recent work has increasingly focused on understanding genetic correlations between milk production and health traits in dairy cattle, structural variation in livestock genomes, and the genetic basis of behavioral traits in poultry. His research encompasses multiple livestock species including dairy cattle, beef cattle, horses, and poultry. His notable achievements include the H. Wilhelm Schaumann Foundation's award in 2013 and his leadership in developing single-step genomic evaluation methods that have advanced genomic selection practices across multiple livestock species. Prof. Tetens maintains active collaborations with researchers across Europe and has contributed significantly to both fundamental research in animal genomics and its practical implementation in breeding programs worldwide. Prof. Tetens has supervised numerous graduate students and postdoctoral researchers, contributing to the training of the next generation of animal geneticists. His department at Göttingen focuses on integrating genomic technologies with traditional breeding approaches to develop more effective and sustainable animal breeding strategies that balance production with animal health and welfare considerations.
Antonio Filieri is a Senior Applied Scientist at Amazon Web Services (AWS) and holds a Visiting Associate Professor position at the Department of Computing, Imperial College London. Previously, he was a tenured Associate Professor at Imperial College London (2022-2024) and Assistant Professor (2016-2022), and served as Assistant Professor at the University of Stuttgart between 2013 and 2015. His academic career spans over a decade with significant contributions to software engineering research. Dr. Filieri's research focuses on formal mathematical methods for software design, verification, self-adaptation, and security. His primary research areas include static analysis, privacy, and automated test generation for security; exact and approximate methods for probabilistic program analysis; control theory for adaptive software; quantitative verification and model checking; and runtime-efficient and incremental verification. His work bridges theoretical foundations with practical applications in industry settings, particularly in cloud computing and security domains. His recent publications demonstrate a strong focus on probabilistic methods for software analysis, security testing, and performance modeling. The research trends show increasing integration of formal methods with machine learning techniques, particularly in test oracle generation and neural network analysis. There's also a clear emphasis on scalability and practical applicability of verification techniques to real-world systems like serverless computing and microservices architectures. Dr. Filieri has received numerous prestigious awards for his contributions: Best Student Paper Award (2025) for 'Robust Probabilistic Model Checking with Continuous Reward Domains' ACM Distinguished Paper Award (2023) for 'Sibyl: Improving Software Engineering Tools with SMT Selection' Best Paper Award (2022) for 'Enhancing Performance Modeling of Serverless Functions via Static Analysis' Best Artifact Award (2017) for 'Self-adaptive video encoder: comparison of multiple adaptation strategies made simple' Most Influential Paper Award (awarded at SEAMS 2025) for 'Software Engineering Meets Control Theory' ACM SigSoft Distinguished Paper Award (2011) for 'Run-time Efficient Probabilistic Model Checking' Dr. Filieri has advised several PhD students including Donato Clun (2024), Runan Wang (2024), and Xiaotong Ji (expected 2025). His advising focuses on probabilistic program analysis, automated testing, and security verification. His research has been supported by significant grants from both academic and industry sources, enabling collaborations across multiple institutions and contributing to advancements in software engineering practices. While specific lab information isn't prominently featured in the provided materials, Dr. Filieri's work suggests strong connections with research groups focused on formal methods, software verification, and adaptive systems at both Imperial College London and AWS. His research often involves interdisciplinary collaboration between theoretical computer science and practical software engineering challenges.
Professor Christoph Hanck serves as Chair of Econometrics at the University of Duisburg-Essen's Faculty of Business Administration and Economics since 2012, where he teaches a comprehensive range of statistics and econometrics courses at undergraduate and graduate levels. His academic journey includes positions as Associate and Assistant Professor at the University of Groningen (2009-2012), postdoctoral work at Maastricht University and TU Dortmund, and doctoral studies in econometrics at TU Dortmund. Professor Hanck's research focuses on nonstationary panel data analysis, macroeconometrics, and multiple testing procedures, with recent expansion into educational technology and machine learning applications. His publication record shows consistent output in top econometrics journals with over 40 publications spanning more than 15 years. His most recent work (2023-2025) demonstrates a dual research trajectory: advancing econometric methodology while innovating in digital teaching methods for statistics education. This includes publications on nonlinear cointegration testing, Bayesian econometrics, and educational data mining for assessment integrity and student performance prediction. Professor Hanck collaborates extensively with researchers including Massing, Klenke, Arnold, and Demetrescu across multiple institutions, indicating a strong research network in both methodological econometrics and educational technology applications. His teaching portfolio encompasses core statistics and econometrics courses at all academic levels, with increasing integration of digital assessment methods and computational approaches using R programming.
Professor Ulf Zölitz serves as faculty in the Department of Economics at the University of Zurich and holds the distinguished title of CESifo Affiliate within the global CESifo Research Network. His research is anchored in the Economics of Education and Labour Economics fields, addressing critical policy questions through rigorous empirical analysis. His research portfolio reveals deep expertise in gender economics (analyzing STEM participation gaps and online instruction disparities), peer effect dynamics (personality development and academic choices), and educational intervention evaluation (socio-emotional training and teaching methods). Methodologically, he specializes in causal inference using administrative data to isolate policy-relevant effects. Analysis of his 2015-2025 publications shows increasing focus on digital education equity and gendered outcomes, with recent work (2025) demonstrating how online instruction widens gender performance gaps. His scholarship consistently bridges theoretical economics and practical education policy. Scientific recognition includes: Distinguished CESifo Affiliate status Professor Zölitz actively contributes to the CESifo Economics of Education area led by Hanushek and Woessmann, influencing European education policy discussions. While specific grant details aren't public, his sustained publication output confirms robust research funding. Prospective graduate students should contact him directly through the University of Zurich Department of Economics for supervision opportunities.
Michelangelo Rossi is an Assistant Professor in Digital Economics at Telecom Paris, Institut Polytechnique de Paris, with expertise in Industrial Organization and digital market dynamics. His research examines how quality disclosures impact consumer behavior in online environments, notably through his CESifo visiting position in 2022. His academic credentials include: Bachelor of Science in Economics from the University of Pisa Master of Science in Economics from the University of Pisa and Sant'Anna School of Economics Master of Arts in Economic Analysis Doctor of Philosophy in Economics from Universidad Carlos III de Madrid Dr. Rossi specializes in information economics within digital platforms, investigating how asymmetric information and quality signals like awards alter consumer expectations. His methodological rigor combines industrial organization theory with advanced econometric techniques, particularly non-parametric matching to address selection bias in observational data from online markets. His seminal 2022 work on Academy Award nominations revealed a measurable disappointment effect where heightened expectations from nominations caused a 5%+ rating drop in movie reviews. This research exemplifies his focus on the behavioral economics of digital platforms and has significant implications for platform design and regulation. His advising approach emphasizes empirical rigor and real-world application, with students gaining expertise in causal inference methods for digital market analysis. Though specific grant details aren't public, his CESifo affiliation indicates active engagement with leading economic research institutions.
Dr. Yuan Huang is an Assistant Professor in the Department of Biostatistics at Yale School of Public Health. She holds multiple affiliations across Yale University, including the Yale Cancer Center, Yale Center for Analytical Sciences (YCAS), Cancer Prevention and Control program, and the Center for Brain & Mind Health. Her research focuses on developing statistical methods for high-dimensional data, addressing challenges in cancer genomics such as low reproducibility, nonlinearity, and heterogeneity. Dr. Huang specializes in biomarker identification, large-scale network structure estimation, and gene-environment analysis, with recent expansion into neurodegenerative diseases including Alzheimer's, Huntington's, and Parkinson's diseases. Dr. Huang's publication record demonstrates expertise in advanced statistical methodologies including Bayesian mixture models, precision matrix estimation, functional data analysis, and stagewise algorithms for nonconvex optimization. Her work consistently bridges theoretical statistical development with practical biomedical applications. As an active collaborator, Dr. Huang works on clinical trials, genetics, epidemiology, and other biomedical research projects, applying her methodological expertise to solve complex data analysis challenges in these fields.
Johan Comparat is a Researcher at the Max Planck Institute for Extraterrestrial Physics (MPE) in Garching, Germany. His academic background includes a PhD from Marseille focused on baryonic acoustic oscillations with emission line galaxies, followed by research on simulated Universes in Madrid, Spain. He currently studies the Universe's large-scale structure in the X-ray domain at MPE. Research Focus: Comparat specializes in multi-wavelength cosmology, with emphasis on: Observational cosmology and galaxy formation/evolution X-ray astrophysics (eROSITA/eBOSS surveys) Cross-correlations between X-ray sources and galaxies Cosmological simulations and mock catalog development Publication Trends: Recent work (2022-2025) centers on eROSITA all-sky survey data analysis, featuring cluster abundance studies, gravity model tests, and X-ray/galaxy cross-correlations. Earlier contributions include SDSS-IV emission-line galaxy cosmology and luminosity function measurements. PhD Advisees: S. Shreeram (2023–present) Y. Zhang (2021–present) R. Seppi (2019–2023) J. Ider Chitham (2017–2021) F. Albareti (2015–2018) S. Rodriguez-Torres (2014–2017) G. Favole (2012–2016) Collaborations: Leads efforts in eROSITA cosmology working groups, develops simulation pipelines (e.g., Skies and Universes ), and contributes to SDSS-IV/eBOSS surveys. Maintains public repositories for spectral templates and data products.
Ali Mesbah is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), where he leads the SALT lab. His research focuses on software engineering with emphasis on AI-driven software analysis, software testing, and software evolution. Previously, he was a Visiting Research Scientist at Google during 2017-2018. Dr. Mesbah received his BSc/MSc (2003) and PhD (2009) degree cum laude in Computer Science from the Delft University of Technology (TUDelft). After completing a postdoctoral fellowship with the Software Engineering Research Group at TUDelft and a Visiting Researcher position at Fujitsu Laboratories of America, he joined UBC in 2011. His research interests span software engineering with particular focus on AI-driven software analysis, software testing, software evolution, program comprehension, fault localization and repair. His work has significant applications in web application testing, JavaScript analysis, and automated program repair. He has pioneered techniques for testing modern web applications, analyzing JavaScript code, and leveraging AI for software maintenance tasks. His recent publications demonstrate a clear evolution toward integrating large language models with traditional program analysis techniques, focusing on test generation, bug repair, and understanding multi-hunk patches. His work bridges theoretical software engineering concepts with practical applications, particularly in web technologies and AI-assisted development. Amazon Research Award (2023) Killam Accelerator Research Fellowship (KARF) (2020) Killam Faculty Research Prize (2019) NSERC Discovery Accelerator (DAS) award (2016) ACM Distinguished Paper Awards at ICSE (2009, 2014) IEEE Distinguished Paper Award at ICST (2018) Best Paper Award at ESEM (2015) Best Paper Award at ICWE (2013) Dr. Mesbah has advised numerous PhD and MASc students, many of whom have gone on to positions at leading technology companies including Google, Amazon, Apple, Microsoft, and SAP. His research has been supported by various grants including the Amazon Research Award and NSERC funding. He leads the SALT lab at UBC, which focuses on software analysis, testing, and learning, with current research directions including AI-driven software engineering, web application testing, and program repair. The lab maintains active collaborations with industry partners and academic institutions worldwide.
Professor Sven Apel holds the Chair of Software Engineering at Saarland University's Saarland Informatics Campus in Germany. He is also the Director of the Saarbrücken Graduate School of Computer Science. His work focuses on software engineering with an emphasis on automation, human factors, and interdisciplinary approaches. Prof. Apel received his Ph.D. in Computer Science in 2007 from the University of Magdeburg. His academic journey includes: Ph.D. in Computer Science, University of Magdeburg (2007) Emmy-Noether Fellowship of the German Research Foundation Heisenberg Professorship of the German Research Foundation Prof. Apel's research centers on empowering software engineering practice to enter an era of intensive automation. His key research areas include software variability and configuration, AI-based program generation and optimization, socio-technical software analysis, and empirical and neurophysiological methods. He pays special attention to the human factor and interdisciplinary research questions, applying his findings to real-world software systems from both open-source projects and industry collaborations with partners like Siemens AG, Bosch Engineering, and Airbus Helicopters. Analysis of Prof. Apel's recent publications reveals a strong focus on configurable software systems, neurophysiological approaches to understanding programming, and the application of AI techniques to software engineering problems. His work often bridges the gap between theoretical foundations and practical applications, with many studies involving industrial collaborations. There's a noticeable trend toward interdisciplinary research combining software engineering with neuroscience, organizational studies, and machine learning. Prof. Apel has received numerous prestigious awards and honors: ERC Advanced Grant "Brains On Code" (2022) ACM Distinguished Member for "Outstanding Scientific Contributions to Computing" (2018) Multiple Most Influential Paper Awards (SPLC'18, ICPC'22, GPCE'23) Multiple Best Paper Awards (SPLC'11, Modularity'15, AOM'18) Heisenberg Professorship and Emmy-Noether Fellowship from the German Research Foundation Prof. Apel has advised numerous Ph.D., Master's, and Bachelor's students throughout his career. His research has been generously funded by multiple grants including an ERC Advanced Grant (2,500,000 Euro, 2022-2027), several DFG projects (CPEC, Congruence, Pervolution), and previous grants like SafeSPL, FeatureFoundation, and Pythia. His work has practical impact through collaborations with industry partners including Siemens AG, Bosch Engineering, and Airbus Helicopters. Prof. Apel leads research in the Chair of Software Engineering at Saarland University, where his team explores the intersection of software engineering, neuroscience, and artificial intelligence. His "Brains On Code" ERC project specifically investigates how programmers' brains process code using neuroimaging techniques. The research group maintains strong connections with both academic and industry partners, facilitating the transfer of research findings into practical applications.
Matthew F. Pusey is a Lecturer in Quantum Information at the Department of Mathematics, University of York. His academic career is deeply rooted in the foundations of quantum mechanics, with a particular focus on quantum contextuality, nonlocality, and the interpretation of quantum states. His work bridges theoretical investigations with experimental implications, making significant contributions to our understanding of the boundaries between classical and quantum phenomena. Pusey's research primarily explores quantum contextuality as a fundamental aspect of quantum theory that cannot be explained by noncontextual hidden variable models. His work demonstrates how contextuality manifests in various quantum phenomena including weak measurements, pre- and post-selection paradoxes, and quantum information processing. His research shows that contextuality serves as a critical resource for quantum advantage in certain computational tasks, and he has developed robust methods to detect contextuality even in imperfect experimental conditions without requiring idealized assumptions. His publication record reveals a consistent trajectory of influential work in quantum foundations, with papers appearing in top journals such as Physical Review Letters, Nature Physics, and Quantum. His research demonstrates increasing sophistication in connecting abstract foundational concepts with practical experimental tests and potential quantum information applications. The evolution of his work shows a progression from foundational questions about quantum state reality to more structured mathematical frameworks for understanding generalized probabilistic theories and contextuality. Pusey completed his PhD at Imperial College London in 2013 with the thesis "Is quantum steering spooky?" and previously earned an MRes in 2010 with research on connections between quantum computation and non-locality. He maintains an active presence in the quantum foundations community through numerous invited talks at major conferences and institutions worldwide.
