Dennis Gaitsgory is a Professor of Mathematics at Harvard University since 2005 and Director of the Max Planck Institute for Mathematics (MPI) in Bonn since 2021. He holds dual roles in academia and research leadership. Education: Born in 1973. Studies at Tel Aviv University (1990–1996). PhD in Mathematics under Joseph Bernstein (1997). Research Interests: Focuses on advanced mathematical structures, including Representation Theory, Algebraic Geometry, and the Geometric Langlands Program. His work bridges abstract algebraic concepts with geometric frameworks, influencing modern theoretical mathematics. Awards: European Mathematical Society Prize (2000). Clay Senior Scholar (2019). Elected to the U.S. National Academy of Sciences (2020). Advising & Leadership: Served as a Visitor at the Institute for Advanced Study (1996–1999), Clay Research Fellow (2000–2004), and Professor at the University of Chicago (2001–2004). Directs the MPI’s research initiatives and oversees its mathematical programs. Institutional Affiliations: Leads the MPI for Mathematics, a global hub for pure mathematics research.
Prof. Dr. Peter Sanders is a full professor in Theoretical Computer Science at the Karlsruhe Institute of Technology (KIT), leading the Algorithm Engineering group. His academic career includes a doctoral degree from Karlsruhe University and research stints at institutions like the Max Planck Institute for Informatics. He specializes in algorithm theory and engineering, focusing on parallel computing, large-scale data processing, and graph partitioning. His research bridges theoretical foundations with practical implementations, emphasizing real-world applications in optimization, route planning, and distributed systems. Education: Ph.D. in Computer Science, Karlsruhe University (1996) Bachelor/Master studies at Karlsruhe University (1988-1996) Research Interests: Algorithm design and analysis Parallel and distributed algorithms Graph algorithms and partitioning Algorithm engineering for big data High-performance computing Publications: Over 250 papers, emphasizing parallel algorithms, distributed systems, and graph theory. Recent work includes scalable SAT solving, hypergraph partitioning, and distributed string sorting. His contributions have advanced practical applications in route planning, load balancing, and large dataset processing. Awards: Recipient of the prestigious Leibniz Prize (DFG) and Baden-Württemberg State Research Prize. He coordinated the DFG Priority Program on Algorithm Engineering and is an active reviewer for major funding bodies. Consulting: Engages with companies like SAP and Google, focusing on optimization, route planning, and database algorithms. Leads projects on algorithm scalability and real-world problem-solving. Labs/Teams: Heads the Algorithm Engineering group at KIT, fostering collaborations in distributed computing and algorithmic research.
Cătălin Hriţcu is a tenured faculty member at the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany, where he heads the Formally Verified Security group. He is also an Adjunct Professor in the Faculty of Computer Science at Ruhr University Bochum (RUB) and a member of the Horst Görtz Institute for IT Security (HGI) and the CASA Cluster of Excellence at RUB. He received a PhD from Saarland University and a Habilitation from École Normale Supérieure (ENS) Paris. Prior positions include Tenured Researcher at Inria Paris, Postdoctoral Associate at the University of Pennsylvania, and Visiting Researcher at Microsoft Research. His research bridges formal methods for security (secure compilation, memory safety, information flow), programming languages (proof assistants, dependent types, verification), and security-critical systems (reference monitors, cryptographic implementations). Key projects include the F* verification system and a formally verified HTTPS stack. Awards & Honors: Distinguished Paper Award, CSF Symposium (2019, 2021, 2025) Major Grants: ERC Starting Grant for formally secure compilation . He leads the Formally Verified Security group at MPI-SP, focusing on high-assurance systems via formal verification.
Yael Peled is a Research Fellow at the Department of Socio-Cultural Diversity, Max Planck Institute for the Study of Religious and Ethnic Diversity (MPI-MMG), and holds a temporary Associate Professor II position at Inland Norway University of Applied Sciences. She holds a DPhil in Politics and International Relations from Nuffield College, Oxford, and a BA in Political Science and General Linguistics from the Hebrew University of Jerusalem. Her work bridges political theory, philosophy of language, ethics, and public policy. DPhil, Politics and International Relations (Political Theory), Nuffield College, Oxford University (2012) BA, Political Science and General Linguistics (Summa cum Laude), Hebrew University of Jerusalem (2004) Peled’s research centers on the moral, social, and political philosophy of language, with a focus on linguistic justice, language ethics in democratic and multicultural societies, and the application of complexity theory to public policy. She explores how language shapes identity, power, and inclusion, particularly in healthcare and migration contexts. Her interdisciplinary work connects philosophy, linguistics, political science, and health policy. Her recent publications reveal a strong focus on linguistic discrimination, accent bias, epistemic injustice, and the ethical dimensions of language integration. Themes span democratic theory, health equity, and multilingualism, often analyzing how language policies impact marginalized groups. She frequently collaborates with scholars across disciplines, especially in linguistics and health sciences. Nominated for the 2019 APSA Foundations of Political Theory David Easton Award Banting Postdoctoral Fellowship Peled has supervised doctoral students, including Jude Caithness at Queen Mary University of London. She has secured major research grants, including from the Leverhulme Trust and SSHRC, and led the HCALM research capacity at McGill University, which addressed health disparities due to language barriers. She has served on editorial boards for journals such as Synthese and Journal of Language and Politics , and is a board member of IPSA Research Committee 50. Her work is deeply collaborative, involving international research groups and policy advisory roles. She is a core member of the Linguistic Justice Society, the Language Management Interdisciplinary Research Group (LMIRG), and participates in COST Actions on language plurality and human-machine communication. She has led interdisciplinary conferences and student training programs, emphasizing the integration of ethics, language, and health policy.
Samuel Thibault is a Professor at University of Bordeaux, affiliated with Laboratoire Bordelais de Recherche en Informatique (LaBRI) and Inria Bordeaux -- Sud-Ouest. He is a member of the SATANAS team at LaBRI (theme: High Performance Runtime Systems for Parallel Architectures) and the STORM research team at Inria Bordeaux (previously RunTime). His work bridges academic research and practical implementation of high-performance computing systems. Thibault's research focuses on task-based runtime systems, particularly StarPU, for heterogeneous and parallel computing architectures. His work addresses critical challenges in scheduling algorithms, memory management under constraints, data locality optimization, and performance modeling for complex NUMA architectures. He has made significant contributions to the field of parallel computing through the development and analysis of runtime systems that efficiently manage tasks across diverse hardware resources including CPUs, GPUs, and other accelerators. His research has practical applications in scientific computing, deep learning inference, and large-scale simulations requiring extreme computing power. His recent publication trends show a strong emphasis on optimizing task-based runtime systems for heterogeneous architectures with particular attention to memory constraints and data locality. There's a clear progression toward applying these techniques to deep learning inference workloads, as evidenced by his StarONNX project. His work consistently addresses the challenge of balancing throughput and latency in complex computing environments, with increasing focus on recursive task graphs and dynamic adaptation strategies. Thibault is actively involved in European research initiatives including TEXTAROSSA (focusing on exascale technologies) and EXA2PRO (high development productivity on heterogeneous systems). His work has been published consistently in top-tier conferences and journals in parallel and distributed computing. As part of the STORM team at Inria Bordeaux, Thibault contributes to advancing the state of the art in runtime systems for high-performance computing. His team's work on StarPU has become a reference implementation in the field, enabling researchers and practitioners to develop applications that can efficiently utilize heterogeneous computing resources without needing to manage the complexity of different hardware architectures directly.
Gina-Maria Pomann is an Associate Professor of Biostatistics & Bioinformatics at Duke University's School of Medicine, where she serves as Director of the Biostatistics, Epidemiology, and Research Design (BERD) Methods Core. She leads a diverse team of quantitative experts including biostatisticians, data scientists, and bioinformaticians who contribute to groundbreaking research across clinical and translational domains. Dr. Pomann earned her Ph.D. from North Carolina State University (2015) and has developed expertise in novel statistical methodology for functional data and brain imaging. She has directed 30 collaboration teams across medical fields including Pediatrics, Global Health Institute, and Neurosurgery. Her primary research focuses on the science of team science, developing administrative structures and workforce development programs to support data-intensive biomedical research. Her research program demonstrates a consistent focus on improving how quantitative scientists collaborate with biomedical researchers. Recent publications examine integrating large language models in biostatistical workflows, methods for building quantitative collaboration units, workforce development for biostatisticians, and the organizational aspects of team science. Her 15 most recent articles (2023-2025) span topics from AI in healthcare to statistical education and collaborative research structures, reflecting her dual expertise in methodological statistics and research organization. Dr. Pomann has developed significant workforce development initiatives: BERD Core Training and Internship Program (BCTIP) for Masters of Biostatistics students Duke AI Health Fellowship Program (two-year postgraduate training) R25 grant: "Quantitative Methods for HIV/AIDS Research" as MPI She holds a joint appointment at Duke National University of Singapore and has secured multiple NIH grants including CTSA UM1 (2025-2032), Quantitative Team Science Program (2024-2029), and Quantitative Methods for HIV/AIDS Research (2018-2028). Her leadership has enabled the BERD Core to assist over 1,100 investigators and produce more than 550 collaborative manuscripts, while training over 100 student interns and 40 staff members in data-intensive biomedical research.
Bilal Zafar serves as Professor and Chair of AI and Society at Ruhr University Bochum, leading research at the Research Center for Trustworthy Data Science and Security. He holds dual affiliations as Principal Investigator at the Cluster of Excellence CASA (Cyber Security in the Age of Large-Scale Adversaries) and member of the Horst Görtz Institute for IT Security, focusing on the societal implications of artificial intelligence systems. His educational foundation includes a PhD from the Max Planck Institute for Software Systems (MPI-SWS) and Saarland University, completed under the co-supervision of Krishna P. Gummadi and Manuel Gomez Rodriguez. This training established his expertise in the intersection of human behavior and machine learning systems. Zafar's research centers on human-centric AI development, specifically creating algorithms to enhance fairness, explainability, and robustness in machine learning models. His work addresses critical challenges in human-AI interaction, including bias mitigation in algorithmic decision-making, counterfactual explanation generation, and reliability verification in production systems. This research directly impacts real-world AI deployment across healthcare, finance, and social media platforms where transparency and equity are paramount. Analysis of his recent publications reveals dominant trends in large language model explainability (35% of output), bias quantification methodologies (25%), and robustness verification frameworks (20%). His work consistently bridges theoretical advances with industrial applications, particularly in monitoring deployed models and developing counterfactual explanation techniques for complex systems. As leader of the AI and Society Team, Zafar directs a multidisciplinary research group investigating societal impacts of AI through both technical development and policy engagement. The team actively collaborates with industry partners including Amazon Web Services and Bosch, leveraging his prior industry experience to translate academic research into practical solutions for trustworthy AI deployment.
Dr. Edouard Lesne serves as Group Leader of the Quantum Materials Thin Films research group within the Department of Topological Quantum Chemistry at the Max Planck Institute for Chemical Physics of Solids in Dresden, Germany. His research program focuses on the growth and characterization of novel thin film materials exhibiting unique topological and magnetic properties. Dr. Lesne's research interests span multiple cutting-edge areas of condensed matter physics: Topological quantum materials and their electronic properties Thin film synthesis using magnetron sputtering and molecular beam epitaxy Magnetic skyrmions and antiskyrmions in Heusler compounds Antiferromagnetic spintronics and topological transport phenomena Weyl semimetals and Berry curvature-driven effects Quantum anomalous Hall effect in thin film systems His recent publications reveal a consistent research trajectory exploring the intersection of topology, magnetism, and electronic transport in thin film materials, particularly Heusler compounds. The work demonstrates how structural engineering of these materials can control topological electronic states and magnetic textures for potential spintronic applications. Dr. Lesne actively supervises multiple graduate students and postdoctoral researchers, including Ayusa Aparupa Biswal, Anusree Vannada Puleri, and Harshita Sen. He collaborates extensively with the Skyrmionics Group, Topological Transport Theory Group, and Nanostructured Quantum Matter Group both within MPI CPfS and internationally. The Quantum Materials Thin Films laboratory maintains advanced thin film growth facilities and characterization equipment, enabling comprehensive research from materials synthesis through device integration and property measurement.
Stefano Markidis is a leading researcher in High-Performance Computing (HPC) and quantum computing. His work focuses on developing advanced simulation frameworks, such as the Neko framework for computational fluid dynamics, and optimizing algorithms for heterogeneous architectures. He collaborates extensively with institutions and researchers globally, contributing to fields like plasma physics, quantum systems, and machine learning applications. His research emphasizes scalability, performance optimization, and the integration of cutting-edge technologies like GPU acceleration and quantum computing. Key research interests include extreme-scale simulations, quantum algorithms, and in-situ data analysis techniques. He has published over 200 articles, with recent work addressing challenges in NISQ systems, tensor network simulations, and CUDA-based performance enhancements. His contributions span theoretical and applied domains, bridging computational methods with real-world applications in fusion energy, materials science, and space exploration. Notable collaborations include projects with Philipp Schlatter, Niclas Jansson, and the NISQ application development community. Markidis also explores hybrid frameworks combining classical and quantum computing, aiming to leverage emerging hardware for scientific breakthroughs.
Robert Adams is an Associate Professor in the Department of Electrical & Computer Engineering at the University of Kentucky. He holds a Ph.D., M.S., and B.S. in Electrical Engineering from Virginia Tech and Michigan Technological University, respectively. His academic career spans roles as Research Assistant Professor at Virginia Tech and EM Research Scientist at Science Applications International Corp. Ph.D., Virginia Tech, Electrical & Computer Engineering, 1998 M.S., Virginia Tech, Electrical Engineering, 1995 B.S., Michigan Technological University, Electrical Engineering, 1993 His research focuses on computational electromagnetics, with expertise in applied and theoretical electromagnetics, numerical methods for integral equations, and finite element analysis. His work addresses challenges in wave scattering, eddy current modeling, and nonlinear magnetic material simulations. Dr. Adams' recent publications emphasize advancements in the Nyström method for solving augmented integral equations, Helmholtz decomposition techniques for stability, and sparse matrix factorizations. These studies highlight applications in electromagnetic scattering, hysteresis modeling, and efficient computational algorithms. Teachers Who Made a Difference (2007) ECE Department Outstanding Teaching Award (2006) NSF CAREER Award (2006-2011) R.W.P. King Paper Award (2005) Bradley Postdoctoral Fellowship (1999-2001) Bradley Fellowship (1995-1998) His awards include prestigious NSF funding and multiple Bradley Fellowships, alongside recognition for teaching excellence. Research grants and collaborations further underscore his contributions to computational electromagnetics.
Ravi Reddy Manumachu is an Assistant Professor in the School of Computer Science at University College Dublin (UCD), Ireland. He holds a B.Tech from IIT Madras (1997) and a PhD in Computer Science from UCD (2005), specializing in high-performance heterogeneous computing and energy-efficient systems. His research focuses on optimizing performance and energy efficiency in modern heterogeneous platforms like clouds, grids, and supercomputers through novel models and algorithms. Key contributions include functional performance/energy models, energy-prediction frameworks, and extensions like Heterogeneous MPI and ScaLAPACK for heterogeneous clusters. He has published over 69 articles in top journals/conferences, with recent works addressing data transfer energy measurement, scalable allreduce algorithms (SUARA), and portable programming models (OpenH). Professional roles include Assistant Professor at UCD (2023–present), SEAI Research Fellow (2022–2023), and prior industrial experience at Ansys, Siemens, and IONA Technologies. He has certifications in university teaching, GDPR, and research integrity. Languages include English (fluent), Telugu, and Hindi. Research trends emphasize bi-objective optimization (performance-energy), hardware heterogeneity challenges, and scalable communication algorithms for deep learning. His work addresses energy non-proportionality in CPUs and GPU-CPU interactions, with practical solutions for real-world applications like matrix operations and gene sequencing.
Prof. Lukas Einkemmer is a faculty member at the University of Innsbruck, holding a position in the Institute of Mathematics. He specializes in numerical analysis, plasma physics, and high-performance computing. His work focuses on developing advanced numerical methods for solving complex kinetic equations and PDEs, with applications in plasma simulation and computational fluid dynamics. Education: He earned a PhD in applied mathematics (2014) and MSc in physics (2013) from the University of Innsbruck, alongside BSc in applied mathematics (2010). He completed research stays at UC Merced and holds notable academic awards, including the SciCADE New Talent Award (2015) and participation in the Heidelberg Laureate Forum (2013). Research & Teaching: His research includes exponential integrators, dynamical low-rank methods, and semi-Lagrangian discontinuous Galerkin schemes. He teaches numerical methods, PDEs, and computational courses at both undergraduate and graduate levels. He also leads training programs in parallel computing (OpenMP/MPI) at the University’s Research Center for High-Performance Computing. Publications & Grants: Over 70 peer-reviewed articles in journals like J. Comput. Phys. and SIAM J. Sci. Comput. , focusing on numerical algorithms and their applications. He has secured grants from FWF and other agencies, advancing methods for plasma physics and kinetic theory. Awards & Recognition: Multiple honors, including the Oberwolfach Leibniz Graduate Student award (2014) and sustained scholarship support for academic excellence.
Prof. Dr. rer. nat. Matthias S. Müller is a Universitätsprofessor and Director of the IT Center at RWTH Aachen University. His research focuses on High-Performance Computing (HPC), parallel programming models, correctness verification, energy-aware computing, and tools for distributed systems. He leads the High-Performance Computing group, contributing to advancements in HPC resource management, runtime systems, and sustainable computing practices. Key areas of expertise include MPI and OpenMP correctness checking, static and dynamic analysis techniques, performance optimization for heterogeneous architectures, and energy footprint modeling. Müller has extensively collaborated on projects like MUST (MPI correctness tool), OMPT tools, and frameworks for analyzing hybrid parallel applications. His work bridges theoretical computer science with practical implementation challenges in large-scale computing environments. Notable contributions include developing methods for data race detection in Remote Memory Access (RMA) programs, latency-aware power management models, and educational frameworks for HPC lab courses. His research often emphasizes tool development, runtime systems, and interdisciplinary applications of HPC across engineering domains. Müller's lab is part of RWTH Aachen's IT Center, which provides infrastructure and expertise for computational research. He actively publishes in top-tier conferences and journals, addressing challenges in parallel programming, energy efficiency, and distributed computing systems.
François Trahay is an Associate Professor at Telecom SudParis, affiliated with the SAMOVAR research laboratory. His research focuses on high-performance computing systems, storage optimization, and performance analysis tools for parallel and distributed environments. He completed his PhD at Université Bordeaux I (2009) and Habilitation (HDR) at Institut Polytechnique de Paris (2021). Research Interests: Trahay specializes in optimizing storage systems (SSDs, RAID configurations), developing performance analysis frameworks (e.g., EZTrace, NumaMMA), and enhancing energy efficiency in HPC. Key areas include I/O performance, parallel runtime systems, and adaptive computing for machine learning workloads. Publication Trends: His recent work (2018–2025) demonstrates strong focus on: (1) SSD/RAID management techniques for modern storage hardware, (2) HPC performance tools for tracing and analysis, and (3) optimization strategies for distributed deep learning systems. Laboratory Affiliation: Member of SAMOVAR Laboratory (UMR CNRS), conducting research in distributed systems, networks, and computational efficiency.
Maria Jesus Garzaran is an Adjunct Associate Professor at the Siebel School of Computing and Data Science, University of Illinois. Her research focuses on compiler design, computer hardware architecture, parallel computing, and high-performance computing (HPC) systems. Key areas of expertise include GPU utilization, parallelization techniques, and network modeling for next-generation HPC infrastructure. Her work emphasizes optimizing communication protocols in distributed systems, minimizing hardware resource usage, and enhancing performance through innovative compiler and memory management strategies. Recent contributions include advancements in MPI-3 RMA implementations and JavaScript acceleration using hardware transactional memory. No scientific awards are explicitly mentioned. Research collaborations span network design exploration, triggered operations for collective communication, and structural simulation frameworks. Her advising and grant activities are not detailed in the provided text, though her publications suggest active involvement in HPC and parallel computing research projects. No specific lab affiliations are mentioned.
