Itsik Pe'er is a Full Professor and Vice-Chair in the Department of Computer Science at Columbia University's Fu Foundation School of Engineering & Applied Science, and holds a joint appointment as Professor of Systems Biology at the Vagelos College of Physicians and Surgeons. His research focuses on computational methods in human genetics, including genetic variation analysis, disease association studies, and algorithm development for genomic data. He leads the Itsik Pe'er Lab of Computational Genomics, which develops tools like Xplorigin, Germline, and SEACells to address challenges in genomics and medical research. His work spans machine learning applications in healthcare, microbiome analysis, and cancer genomics. Notable contributions include studies on hypertensive disorders in pregnancy, bias correction in predictive models, and the development of non-Euclidean learning libraries like Manify. Pe'er has advised students including Vladimir Vacic, Anat Kreimer, and Arthi Ramachandran, and collaborates on grants addressing genetic epidemiology and computational biology. His lab's location is in the Computer Science Building at Columbia's Morningside Campus.
Ali Vakilian is a Research Assistant Professor at the Toyota Technological Institute at Chicago (TTIC), with a strong academic background in theoretical computer science and algorithms. He will join the Department of Computer Science at Virginia Tech as an Assistant Professor in Fall 2025. His research bridges algorithmic theory and machine learning, focusing on scalable, fair, and efficient algorithms for massive data. Education: Ph.D. in EECS, Massachusetts Institute of Technology (MIT), advisors: Erik Demaine and Piotr Indyk M.S. in Computer Science, University of Illinois at Urbana-Champaign (UIUC), advisor: Chandra Chekuri B.S. in Computer Engineering, Sharif University of Technology Research Interests: Ali Vakilian's work centers on the algorithmic foundations of machine learning and data science. He develops streaming, sketching, and sublinear-time algorithms for massive datasets, and pioneers learning-augmented algorithms that use machine learning predictions to improve performance while maintaining worst-case guarantees. His research in trustworthy ML includes algorithmic fairness, fair clustering, and learning with strategic agents. He also contributes to combinatorial optimization and approximation algorithms for network design, set cover, and low-rank approximation. His recent publications (2023–2025) show a consistent focus on fair clustering (individual and group fairness), streaming graph algorithms , learning-augmented methods , and frequency estimation . These works appear in top venues such as NeurIPS, ICML, SODA, and ICALP, often with recognitions like oral or spotlight presentations. Scientific Awards: Outstanding Student Paper Highlight Award, AISTATS 2024 Notable-top-25% paper, ICLR 2023 Oral presentation, AISTATS 2024 Spotlight presentation, NeurIPS 2023 Advising and Grants: Ali Vakilian mentors several students and interns, including summer interns at TTIC and Fatima Fellows. His research is supported by the National Science Foundation (TRIPODS program), as noted in the press coverage of his work on LearnedSketch. He actively contributes to the academic community through advising, organizing workshops (e.g., Algorithms with Predictions, Learning-Augmented Algorithms), and serving on program committees (e.g., NeurIPS, ICML, AISTATS). Labs and Teams: He is affiliated with the theory and algorithms group at TTIC and collaborates with researchers at MIT, UIUC, and other institutions. His work on learning-augmented algorithms has led to influential workshops and collaborations with leading figures such as Piotr Indyk and Erik Demaine.
Brett Kolesnik is a Research Fellow in the Department of Statistics at the University of Warwick. His research focuses on probability theory, random structures, bootstrap percolation, and interactions with combinatorics. He has held postdoctoral fellowships at UC Berkeley, San Diego, and the University of Oxford, and was a Senior Demy at Magdalen College. His work includes organizing workshops on bootstrap percolation and collaborating with leading researchers in probability and combinatorics. Education: PhD in Mathematics from the University of British Columbia (advised by Omer Angel). Notable awards include the NSERC Postdoctoral Fellowship and the Florence Nightingale Bicentennial Fellowship in Statistics. Research interests span bootstrap percolation models, random graph dynamics, and stochastic processes. Recent work includes studies on Brownian map geometry, tournament score sequences, and Coxeter group structures. Selected articles explore topics such as critical beta-splitting processes, Catalan percolation, and random walks on algebraic structures. His publications appear in top journals like Electronic Journal of Probability and Annals of Applied Probability . Awards include the Florence Nightingale Fellowship and NSERC Postdoctoral Fellowship. Professional involvement includes organizing the 2024 BIRS workshop on Bootstrap Percolation and contributing to interdisciplinary collaborations in probability and combinatorics.
Aram Harrow is a Professor of Physics at the Massachusetts Institute of Technology (MIT) , affiliated with the MIT Center for Theoretical Physics and MIT Center for Quantum Engineering . He focuses on quantum information science and quantum algorithms , with additional interests in representation theory and optimization . His recent work explores quantum computing applications in chemical physics and statistical mechanics . Undergraduate and graduate degrees in Physics at MIT Faculty positions: MIT (2013-present), University of Washington (2010-12), University of Bristol (2005-10) Research Interests: His work bridges quantum information theory and many-body physics , including: Quantum algorithm design for chemistry and optimization Quantum circuit complexity and t-designs Entanglement dynamics in quantum systems Quantum-classical hybrid computing models Key Publications: Recent articles demonstrate quantum speedups for biomolecular free energy calculations , Hamiltonian simulation , and jet clustering algorithms . His research combines quantum complexity theory with practical implementations on near-term quantum devices. Scientific Awards: 2023 Simons Investigator 2018 APS Bennett Award 2017 IEEE Best Paper Award 2016 Kavli Frontiers Fellow Mentorship: He advises current PhD students Shankar Balasubramanian , Angus Lowe , and Norah Tan , with 12 former advisees including Anand Natarajan and Saeed Mehraban . His 2026 recruitment seeks one new graduate student.
Henrik Boström is a Professor of Computer Science specializing in Data Science Systems at the Division of Software and Computer Systems, KTH Royal Institute of Technology. His research focuses on trustworthy machine learning , with emphasis on conformal prediction (for confidence-calibrated predictions) and explainable AI . He is the developer of Python packages crepes (conformal classifiers/regressors) and xrf (explainable random forests). His primary research domains include: Developing robust methods for uncertainty quantification in predictive models Creating interpretable machine learning frameworks Optimizing ensemble techniques for high-dimensional data Applying ML to healthcare informatics and industrial diagnostics Analysis of his recent publications reveals strong emphasis on: (1) advancing conformal prediction theory for trustworthy AI, (2) enhancing interpretability of complex models like random forests and GNNs, and (3) developing efficient algorithms for uncertainty-aware learning in domains including healthcare, graph data, and high-dimensional regression. He serves as examiner for multiple degree projects and teaches courses including Programming for Data Science (ID2214) and Research Methodology and Scientific Writing (II2202) . He leads development of open-source tools for conformal prediction and model interpretation.
Chandra Chekuri is the Paul and Cynthia Saylor Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign, situated within the Grainger College of Engineering. He has been actively contributing to theoretical computer science for over two decades, with significant leadership roles including serving as Editor-in-Chief of the prestigious SIAM Journal on Computing since May 2025. His academic journey began with a B.Tech in Computer Science from the Indian Institute of Technology, Madras in 1993, followed by a Ph.D. in Computer Science from Stanford University in 1998. Prior to joining UIUC, he spent eight years as a Member of Technical Staff at Bell Labs, Lucent Technologies. Chekuri's research focuses on theoretical computer science with particular emphasis on the design and analysis of algorithms, discrete and combinatorial optimization, approximation algorithms, mathematical programming, and graph theory. His work explores fundamental connections between discrete structures and optimization problems, with applications spanning network design, data analysis, and computational complexity. His recent publications demonstrate a continued focus on hypergraph algorithms, submodular function optimization, and graph partitioning problems, showing how theoretical insights can yield practical algorithmic improvements. His approach often combines continuous relaxations with discrete rounding techniques to develop approximation algorithms for NP-hard problems. As Editor-in-Chief of SIAM Journal on Computing, Chekuri leads one of theoretical computer science's most respected publications, which covers analysis and design of algorithms, algorithmic game theory, computational complexity, and other mathematical aspects of computer science. His editorial leadership follows previous service as Associate Editor for several major journals including SIAM Journal on Computing, Mathematics of Operations Research, and Mathematical Programming. ACM Fellow (January 2024) Scott Fisher Teaching Award (for year 2022-23) from CS Department Chekuri has advised numerous PhD students to completion, including Kent Quanrud, Vivek Madan, Shalmoli Gupta, and Chao Xu, with several currently in progress such as Tanvi Bajpai, ElFarouk Harb, Rhea Jain, and Weihao Zhu. His teaching portfolio includes graduate courses on Randomized Algorithms, Approximation Algorithms, Algorithms for Big Data, and Combinatorial Optimization. He has served as Director of the Graduate Program in the Department of Computer Science from May 2014 to August 2017, demonstrating significant administrative leadership within the department.
Jean-François Le Gall is a full Professor at Université Paris-Saclay and a member of the Orsay Mathematics Laboratory (LMO) since 2006. He has held prominent positions at Pierre and Marie Curie University (1988-2006) and École Normale Supérieure (1997-2007). A Senior Member of the University Institute of France (2007-2017) and an elected member of the Academy of Sciences since 2013, he served as Vice-President of Research for the Mathematics Department at Orsay (2020–present) and led the ERC Advanced Grant GeoBrown (2017–2023). Education: Ecole Normale Supérieure (1978–1982), PhD in stochastic differential equations (1982), State Doctorate on Brownian motion (1987) Research Interests focus on probability theory , particularly Brownian motion , superprocesses , random trees , planar maps , and their connections to PDEs and geometric models. His work bridges stochastic analysis , branching processes , and coalescence phenomena . Selected Publications include foundational studies on the Brownian map , random geometry , and spatial branching processes . His 2025 paper on The area of spheres in the Brownian plane explores fractal properties of random metric spaces, while the 2020 Growth-fragmentation processes work links Brownian trees to fragmentation models. Scientific Distinctions : 1986 Rollo Davidson Prize 1997 Loève Prize in Probability 2005 Sophie Germain and Fermat Prizes 2019 Wolf Prize in Mathematics 2022 BBVA Frontiers of Knowledge Award Academic Leadership includes directing the Probability and Statistics Team (2013–2019) and the Master 2 in Probability and Statistics (2007–2015). He chairs editorial roles in Grundlehren der mathematischen Wissenschaften (since 2020) and Probability Theory and Related Fields (2005–2010).
Sebastian U. Stich is a tenured faculty member at the CISPA Helmholtz Center for Information Security , where he has been since December 2021. He is also a member of the European Lab for Learning and Intelligent Systems (ELLIS) since June 2020. His research focuses on optimization methods for machine learning, collaborative learning algorithms, privacy and security in distributed systems, and theoretical foundations of deep learning. Stich received his PhD in Theoretical Computer Science from ETH Zurich (2014), following a Master's in Mathematics at the same institution (2010-2014). Prior to CISPA, he worked as a research scientist at EPFL (2016-2021) and held positions at ETH Zurich and ICTEAM/CORE. He has been awarded the ERC Consolidator Grant 2024 , Google Research Scholar Award (2023), and Meta Privacy-Enhancing Technologies Research Award (2022). His team includes Dr. Anton Rodomanov (since 2023), Dr. Rotem Mulayoff (since 2024), Xiaowen Jiang (2023), Yuan Gao (2023), and notable alumni like Anastasia Koloskova (defended 2023). Stich actively organizes workshops (e.g., NeurIPS OPT 2024) and serves on editorial boards ( Journal of Optimization Theory and Applications , Transactions on Machine Learning Research ). He teaches advanced courses in optimization at Saarland University and has held visiting positions at MIT. Key scientific contributions include: Developing ProgFed for progressive federated learning (2021) Creating ProxSkip to accelerate communication in federated settings (2022) Formalizing SCAFFOLD with control variates for FL (2020) Introducing RelaySum mechanism for decentralized learning (2021) Proposing Lookahead-Minmax for GAN training (2021) His work addresses fundamental challenges in: Decentralized optimization theory Communication-efficient algorithms Privacy-preserving model training Handling heterogeneous data distributions Stochastic gradient dynamics Second-order optimization methods
Song Mei is an Assistant Professor in the Department of Statistics and Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. She received her Ph.D. from Stanford University in 2020 under Andrea Montanari and maintains active research collaborations with institutions including Amazon (as a 2023 Amazon Research Award recipient) and OpenAI (where she is currently on leave). Her research spans the intersection of statistics, machine learning, information theory, and computer science, with particular emphasis on foundational theories for modern AI systems. Key interests include language models, diffusion models, quantum algorithms, and high-dimensional statistics, often leveraging insights from statistical physics literature. Analysis of her recent publications reveals a strong focus on theoretical underpinnings of generative AI, with significant contributions to understanding contrastive pre-training (CLIP), attention mechanisms in LLMs, and mathematical foundations of diffusion models. Her work demonstrates consistent interdisciplinary connections between statistical theory and practical AI development. Sloan Research Fellowship (2025) Noether Early Career Scholar Award (2025) Google Research Scholar Award (2024) Amazon Research Award (2024) She actively advises graduate students through MA programs and has secured significant research grants including Amazon Research Awards. Her work with AGI Labs at Amazon demonstrates applied impact of theoretical research. Current projects focus on mechanistic interpretability of large language models and mathematical frameworks for generative AI. Professor Mei leads research on the statistical principles behind frontier AI models, with particular focus on developing rigorous theoretical frameworks for understanding emergent behaviors in large-scale systems.
Jonathan A. Kelner is a Professor of Applied Mathematics in the Department of Mathematics at the Massachusetts Institute of Technology (MIT) and a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on applying techniques from pure mathematics to solve fundamental problems in algorithms and complexity theory, with the goal of developing practical algorithms for real-world questions. Dr. Kelner received his undergraduate degree from Harvard University and his Ph.D. in Computer Science from MIT in 2006. Before joining the MIT faculty, he spent a year as a member of the Institute for Advanced Study. His educational background has provided a strong foundation for his interdisciplinary research spanning mathematics and computer science. His research interests include combinatorial optimization, mathematical programming, spectral graph theory, distributed computing, machine learning, computational geometry and topology, computational biology, signal processing, and random matrix theory. Kelner's work demonstrates how deep theoretical insights can lead to practical algorithmic improvements, particularly in graph algorithms and optimization problems. His approach often involves connecting seemingly disparate areas of mathematics to create novel algorithmic techniques. Analysis of his recent publications reveals a strong focus on spectral graph theory, optimization algorithms, and the Sum-of-Squares method. His work frequently addresses fundamental questions in theoretical computer science with practical implications for algorithm design. A notable trend in his research is the development of nearly-linear-time algorithms for various graph problems, which represents significant improvements over previous approaches. NSF CAREER Award Alfred P. Sloan Research Fellowship NEC Award for Research in Computers and Communication Sprowls Doctoral Dissertation Award Best Student Paper Award at STOC 2004 Best Paper Award at STOC 2011 Kokusai Denshin Denwa Junior Faculty Chair 2008 Harold E. Edgerton Faculty Achievement Award 2011 School of Science Award for Excellence in Undergraduate Education 2012 Professor Kelner has been actively involved in mentoring students and has received recognition for his teaching excellence, including the School of Science Award for Excellence in Undergraduate Education in 2012. His research has been supported by prestigious grants including the NSF CAREER Award. He has collaborated extensively with researchers across multiple institutions, often working with other leading figures in theoretical computer science to produce groundbreaking results in algorithm design. At MIT, Kelner is part of both the Mathematics Department and CSAIL, positioning him at the intersection of theoretical mathematics and practical computer science. This dual affiliation reflects the interdisciplinary nature of his work, which bridges pure mathematical theory with concrete algorithmic applications.
Huiyan Sang is a Professor and Director of the Undergraduate Program in the Department of Statistics at Texas A&M University (College of Arts & Sciences). She earned her Ph.D. in Statistics from Duke University and a B.Sc. in Mathematics and Applied Mathematics from Peking University. Her research focuses on spatial statistics, Bayesian nonparametric methods, machine learning, computational statistics, and applications in environmental sciences, geosciences, urban planning, and biomedical research. Her interdisciplinary work integrates statistical methodologies with real-world challenges, such as analyzing extreme environmental events, optimizing urban infrastructure, and modeling complex systems like human mobility during pandemics. She has contributed to advancing spatio-temporal modeling, Gaussian processes, and Bayesian hierarchical frameworks for large datasets. Recent publications highlight innovations in nonparametric regression, spatial functional data analysis, and stochastic frontier analysis, often leveraging computational efficiency and scalability. Her work addresses critical societal issues, including the impact of community design on public health and environmental monitoring through remote-sensing data. No scientific awards are explicitly listed in the provided texts. She advises no students or grants in the current dataset but collaborates widely on interdisciplinary projects. Her research lab focuses on developing cutting-edge statistical tools with applications in engineering, public health, and environmental science.
Zhou Zhi-Hua is a Professor at Nanjing University's Department of Computer Science & Technology, serving as Standing Deputy Director of the National Key Lab for Novel Software Technology and Founding Director of LAMDA (Institute of Machine Learning and Data Mining). He holds simultaneous fellowships from ACM, AAAI, AAAS, IEEE, IAPR, IET/IEE, and CCF, reflecting his exceptional contributions to computational intelligence. His educational background includes: B.Sc. in Computer Science from Nanjing University (1996) M.Sc. in Computer Science from Nanjing University (1998) Ph.D. in Computer Science from Nanjing University (2000) Zhou's research pioneers fundamental advances in machine learning theory and applications. His seminal work on ensemble methods established new frameworks for classifier combination, while innovations in multi-label learning and anomaly detection addressed critical challenges in complex data analysis. His research bridges theoretical rigor with practical implementations across diverse domains including biometrics, data mining, and computer vision, resulting in over 150 publications and 18 patents. His textbooks "Ensemble Methods" (2012) and "Machine Learning" (2016) have become standard references in the field. Analysis of his publication trajectory reveals sustained leadership in core machine learning challenges: evolving from neural network ensembles (2002) through semi-supervised learning breakthroughs (2005) to foundational work on multi-instance learning (2012) and theoretical margin analysis (2013). His recent focus demonstrates increasing sophistication in handling complex data structures while maintaining theoretical soundness. His scientific excellence is recognized through: National Natural Science Award of China (2013) PAKDD Distinguished Contribution Award (2016) IEEE ICDM Outstanding Service Award (2016) IEEE CIS Outstanding Early Career Award (2013) Microsoft Professorship Award (2006) Simultaneous fellowships from 7 major international societies Zhou provides extraordinary service to the academic community as Executive Editor-in-Chief of Frontiers of Computer Science and Associate Editor-in-Chief of Science China Information Science. He founded the ACML conference and has chaired premier events including ICDM'16 and PAKDD'14. His leadership extends to serving as General Chair for ICDM'16, Program Chair for IJCAI'15 Machine Learning Track, and Area Chair for multiple top conferences. The available text does not specify student advising details or research grants. He directs LAMDA research group at Nanjing University, which has established itself as a global powerhouse in machine learning research, and contributes significantly to the National Key Lab for Novel Software Technology's mission of developing next-generation intelligent systems.
Sushmita Ruj is an Associate Professor in the School of Computer Science and Engineering at the University of New South Wales (UNSW), Sydney. She serves as the Faculty of Engineering Lead for the UNSW Institute for Cybersecurity (IfCyber) and as the Taste of Research (ToR) Coordinator within the School of Computer Science and Engineering. Her academic journey includes previous positions as a Senior Research Scientist at CSIRO's Data61 (2019-2022), Associate Professor at the Indian Statistical Institute, Kolkata, and Assistant Professor at the Indian Institute of Technology (IIT), Indore. Dr. Ruj's primary research interests focus on applied cryptography, post-quantum cryptography, cybersecurity, blockchains, and data privacy. She designs practical, efficient, and provably secure protocols for real-life applications, with particular emphasis on critical infrastructure including smart grids, cloud computing, ad hoc networks, and data sharing frameworks. As quantum technology advances, her work increasingly focuses on developing quantum-safe algorithms to ensure a more secure Internet infrastructure. Her research spans multiple domains including cryptographic key management, proofs of storage, verifiable computation, vector commitments, and privacy-enhancing technologies for cloud and IoT environments. Her recent publications demonstrate a strong trend toward post-quantum cryptography solutions, with particular emphasis on blockchain applications, DNS security, and privacy-preserving protocols for industrial IoT. The research shows increasing focus on practical implementations of theoretical cryptographic concepts, with applications across multiple sectors including finance, healthcare, and critical infrastructure. Her work bridges the gap between theoretical cryptography and real-world security challenges, with growing emphasis on the transition from classical to quantum-resistant systems. Best Paper Award at ACISP 2024 JNCA Best Survey Award (2023) NSW Innovation Award (iAward) Merit Winner (2022) Women in Science Award from CSIRO (2020) ACM Senior Member (2016) IEEE Senior Member (2015) Samsung GRO award (2014) Dr. Ruj has successfully mentored numerous PhD and Master's students, with many of her former students now holding academic positions at institutions like IIT Indore, TU Wien, and CISPA Helmholtz Center. She has secured significant competitive funding including multiple Australian Research Council (ARC) grants, Samsung GRO Award, NetApp Faculty Fellowship, Cisco Academic Grant, and IBM Research grant. Her current research portfolio includes projects on blockchain-based quantum-safe digital medical passports, embedding trust in digital IDs, and resilience of supply chain unstructured data. As Faculty of Engineering Lead for IfCyber, Dr. Ruj plays a key role in UNSW's cybersecurity research initiatives. She has served on editorial boards for prestigious journals including IEEE Transactions on Information Forensics and Security and has held leadership positions in major conferences such as ACISP 2021 and Indocrypt 2020. She was also a member of the working group on "Blockchain For Cybersecurity" for the National Blockchain Roadmap of Australia and the first Blockchain Working group set up by the Reserve Bank of India.
Jason Li is an Assistant Professor in the Department of Computer Science at Carnegie Mellon University's School of Computer Science. He teaches advanced algorithms courses including 15-754 Spectral Graph Theory (Spring 2025), 15-451 Design and Analysis of Algorithms (Fall 2024), and 15-850 Advanced Algorithms (Spring 2024). His research focuses on fast graph algorithms , particularly solving longstanding open problems through modern algorithmic techniques. Key research themes include preconditioning and locality , which serve as reductions from worst-case to well-behaved and local instances respectively. His work has produced breakthroughs in deterministic global minimum cut algorithms, all-pairs minimum cut (Gomory-Hu trees), and near-optimal parallel shortest path algorithms. Analysis of his recent publications reveals a consistent trend toward almost-linear time algorithms for fundamental graph problems, with significant contributions to dynamic graph algorithms, minimum cut variants, and parallel computation. His work frequently appears in top venues including STOC, FOCS, and SODA, often with multiple best paper recognitions. EATCS Distinguished Dissertation Award (2021) Best Paper Award at SODA 2024 Invited to HALG 2024 Invited to TALG and JACM for SODA 2024 paper Machtey Best Student Paper at FOCS 2019 Professor Li actively advises graduate students including Henry Fleischmann and George Li. His research is supported by collaborations with leading institutions and frequent invitations to present at major conferences. He maintains an open-door policy for CMU students and collaborators, though notes the high volume of research inquiries he receives weekly.
Prof. Dennis Komm is an Associate Professor at ETH Zurich's Department of Computer Science, leading the group for Algorithms and Didactics. He chairs the Center for Computer Science Education (ABZ) and serves on committees such as the Swiss Maturity Board (Schweizerische Maturitätskommission) and the STEM Commission of the Swiss Academies. Previously, he held roles at RWTH Aachen University (Master's, 2008), ETH Zurich (PhD, 2012), University of Zurich (external lecturer, 2014–2020), and PH Graubünden (including department head and professor of 'Fachdidaktik Informatik'). Education: He completed a Master's in Computer Science at RWTH Aachen (2008), a PhD at ETH Zurich (2012), and studies in Information Technology at Queensland University of Technology (2006). His academic journey includes visiting roles at King's College, Stanford, and Comenius University. He has taught extensively across institutions, emphasizing Python and LOGO-based approaches for beginners. Research focuses on algorithm design, approximation algorithms, reoptimization, and advice complexity in theoretical CS. His work in education explores computational thinking, programming pedagogy (especially for K–12), and interdisciplinary approaches (e.g., robotics in math). Recent trends in his articles highlight advancements in online algorithms, optimization under dynamic conditions, and initiatives to integrate CS into Swiss school curricula sustainably. He actively promotes CS education through platforms like WebTigerPython and collaborates on projects such as CyberQuest and MINTerlink. His outreach includes organizing conferences (e.g., STIU 2025) and workshops on programming and cybersecurity for teachers and students. Despite no listed scientific awards, his contributions to education and theoretical CS are recognized through editorial roles in journals like Informatics in Education and contributions to the TigerJython Group. Grant-related advising includes co-supervising doctoral theses on robotics, USOs, and programming didactics. He advocates for equitable educational opportunities via the Passerelle exam and the Swiss Beaver Competition. His team's work spans teacher training, didactic certifications, and bridging university-school collaborations through initiatives like MINTerlink. Labs and teams: Head of ABZ (ETH's CS education center), collaborator with the Computational Robotics Lab, and part of the TigerJython Group. He also co-organizes the Colloquium on Mathematics, Computer Science, and Education with ETH's Mathematics Department.
