Martial Hebert serves as the Dean of the School of Computer Science at Carnegie Mellon University, a position he assumed in August 2019. He is the sixth dean to lead the school since its inception in 1988 and holds the title of University Professor of Robotics. Professor Hebert is an active participant in global AI discussions, with an upcoming speaking engagement at the AI and Planet session on May 30, 2024, where he will address "Generative AI and regulation: From local implementations to global standards." Professor Hebert is a leading researcher in the fields of computer vision and robotics. His work has contributed significantly to advancing these disciplines, which are critical components of modern artificial intelligence and autonomous systems. His expertise spans theoretical foundations and practical applications of vision systems and robotic technologies, positioning him as a key figure at the intersection of academic research and real-world AI implementation. As Dean of one of the world's most renowned computer science schools, Hebert oversees a diverse academic community dedicated to innovation and excellence in computing education and research. Under his leadership, the School of Computer Science continues to maintain its position at the forefront of technological advancement and academic achievement in the computing field, while also engaging with critical issues around AI governance and regulation.
Nicolò Cesa-Bianchi is a Professor of Computer Science at the University of Milan, where he serves as head of the Computer Science programs. He is also associated with the Department of Electronics, Information and Bioengineering (DEIB) at Politecnico di Milano. Cesa-Bianchi holds significant leadership roles including Board member, Fellow and co-director of the Milan unit of the European Laboratory for Learning and Intelligent Systems (ELLIS), and membership in the prestigious Accademia Nazionale dei Lincei. He is also involved with The European Lighthouse on Secure and Safe AI (ELSA), The European Lighthouse of AI for Sustainability (ELIAS), and The FAIR foundation. Professor Cesa-Bianchi's research focuses on the theoretical foundations of machine learning, with special emphasis on sequential decision making and online learning algorithms. His work spans multiple areas including multi-armed bandit problems, regret analysis, prediction with expert advice, and learning on graphs. He has made significant contributions to understanding the theoretical limits of learning algorithms and developing efficient methods for various learning scenarios. His research has important applications in online markets, social networks, and bioinformatics. His monographs 'Prediction, Learning, and Games' and 'Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems' are considered seminal works in the field. His recent publications demonstrate continued leadership in advancing the theoretical understanding of machine learning, with 2024-2025 papers covering cooperative online learning, multitask learning, fair trade mechanisms, and refined analyses of bandit algorithms. The research shows increasing focus on practical economic applications while maintaining strong theoretical foundations. Google Research Award Xerox Foundation UAC Award Member of the Accademia Nazionale dei Lincei ELLIS Fellow Cesa-Bianchi has been deeply involved in academic service, having served as action editor for the Machine Learning Journal, IEEE Transactions on Information Theory, and the Journal of Machine Learning Research. He currently serves as associate editor for the Journal of Information and Inference and TheoretiCS. He has held leadership positions including President of the Association for Computational Learning and member of the steering committee for the EC-funded Network of Excellence PASCAL2. He was program chair of the 13th Annual Conference on Computational Learning Theory and the 13th International Conference on Algorithmic Learning Theory. He leads the Laboratory for AI and Learning Algorithms (ALGA) at the University of Milan, which focuses on theoretical and applied research in machine learning. His international collaborations are extensive, with visiting positions at UC Santa Cruz, Graz Technical University, Ecole Normale Supérieure in Paris, Google, and Microsoft Research. As an educator, he teaches advanced courses including Reinforcement Learning and Statistical Methods for Machine Learning, and has supervised numerous students through the years.
Miguel Ángel Bernal Merino serves as a Part-Time Lecturer in the Department of Modern Philology, Translation and Interpretation at the University of Las Palmas de Gran Canaria. Concurrently, he acts as convener of the MA in Translation at the University of Roehampton in London, managing its Specialised Translation, Audiovisual Translation, and Intercultural Communication pathways since 2005. His academic profile bridges theoretical research and industry application in localization disciplines. PhD in The Localisation of Video Games, Imperial College London MSc in The Localisation of Multimedia Interactive Entertainment Software, Imperial College London Certificate in Software Localisation, University of Limerick CELTA, University of Cambridge MPhil in Lip-Synched Dubbing, Universidad de Alicante MA in Hispanic Literature, University of Rhode Island BA in English & Spanish Linguistics, Universidad de Alicante Bernal Merino's research pioneers the expansion of Translation Studies into interactive media domains. His work on video game localization examines interactivity, cultural adaptation, glocalisation, and playability, while his audiovisual translation research investigates creativity in lip-synched dubbing, subtitling, and audiodescription. He extends these frameworks into gaming-based language learning through haptic environments and multisensory reinforcement, exploring physiological aspects of multilingual cognition and polysemiotic neural networks. This multidisciplinary approach fundamentally challenges traditional boundaries in translation theory. His publication trajectory reveals evolving industry-academia integration, shifting from foundational game localization frameworks (2008-2015) toward user-centered research and educational applications (2018-2024). Recent work emphasizes measurable business impacts through quality localization, regional market adaptations, and crowd-sourced methodologies, demonstrating consistent influence on professional standards across gaming and translation sectors. Fellow of the Higher Education Academy Bernal Merino supervises doctoral research on mobile game localization, transmedia fan experiences, and historical game content translation while developing industry-academia pipelines through projects like AHRC's Media Across Borders. His funded initiatives—including Maximising ROI through Quality Game Localisation and Video Games for Language Learning—bridge theoretical innovation with commercial implementation, focusing on glocalisation strategies, interactivity metrics, and playability optimization. These collaborations connect European universities, gaming studios, and localization service providers. As chair of the IGDA Localization SIG and member of GIR Discourse, Communication and Society, he directs international forums for game localization discourse. His work with Routes into Languages promotes audiovisual media in language education, while REF-readiness collaborations enhance research excellence across European institutions through his professional network spanning industry conferences like GDC Summit and Game Global.
Carlos Alvarez Martinez is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture at the Barcelona School of Informatics (FIB). He is a key member of the Programming Models (PM) research group and collaborates closely with the Barcelona Supercomputing Center (BSC). His research focuses on high-performance computing, FPGA acceleration, task-based programming models like OmpSs, and hardware-software co-design for heterogeneous systems. His research interests center on advancing parallel computing through innovative programming models and hardware acceleration. He investigates efficient task scheduling, resource management in multicore and FPGA-based systems, and runtime support for dataflow models. His work enables high-performance execution of complex applications in domains such as scientific computing and cyber-physical systems. He actively contributes to European initiatives like TEXTAROSSA and AXIOM, aiming to develop next-generation exascale supercomputing technologies. The trend in his recent publications shows a strong focus on leveraging FPGAs for HPC, optimizing SpMV operations, improving task scheduling with hardware support, and developing frameworks for multi-FPGA clusters. His work consistently bridges theoretical models with practical implementations, emphasizing performance, scalability, and energy efficiency in heterogeneous computing environments. Scientific Awards: Premi UPC al Compromís Social 2019 Premi Disseny per al Reciclatge 2013 Alvarez Martinez has advised or collaborated with several doctoral students, including Jaume Bosch, Xubin Tan, and Fahimeh Yazdanpanah. He has been involved in numerous competitive R&D projects, often related to high-performance computing and parallel programming models. His work includes significant contributions to educational innovation, particularly in active learning methodologies and formative assessment using interactive systems. He leads and participates in research labs and teams focused on programming models and computer architecture, notably the PM group at UPC/BSC. These teams develop runtime systems, compilers, and hardware accelerators to push the boundaries of parallel computing efficiency and programmability.
Bernhard Schölkopf is a Director at the Max Planck Institute for Intelligent Systems and holds a Professorship at ETH Zurich. He is one of Europe's leading researchers in Artificial Intelligence and serves as co-founder and president of ELLIS (European Laboratory for Learning and Intelligent Systems). His scientific interests focus on machine learning and causal inference, with applications spanning biomedical problems, computational photography, and astronomy. Schölkopf has conducted research at prestigious institutions including AT&T Bell Labs, GMD FIRST in Berlin, and Microsoft Research Cambridge before joining the Max Planck Society in 2001. Schölkopf's recent work examines the tangible risks of AI systems rather than speculative existential threats, highlighting important challenges including algorithmic bias, lack of transparency, privacy violations, worker exploitation, carbon footprint of AI systems, and job displacement across multiple publications in different languages. His scientific achievements have been recognized with numerous prestigious awards: Academy Prize of the Berlin-Brandenburg Academy of Sciences and Humanities Royal Society Milner Award Leibniz Award Koerber European Science Prize BBVA Foundation Frontiers of Knowledge Award Allen Newell Award Fellow of the ACM Fellow of the CIFAR Program "Learning in Machines and Brains" Schölkopf is a member of the German Academy of Sciences (Leopoldina) and co-founded the series of Machine Learning Summer Schools. His work bridges theoretical advances in machine learning with practical applications across diverse domains, while advocating for responsible AI development that addresses real-world challenges rather than speculative existential threats.
Prof. Maciej Lewenstein is a Group Leader and ICREA Professor at the Institute of Photonic Sciences (ICFO), Spain. He leads the Quantum Optics Theory group, focusing on theoretical and computational studies of quantum many-body systems, topological phases, and nonlinear optics. He holds a Dr.rer.nat. in Physics from the University of Essen, Germany. His research interests include quantum simulations, high-harmonic generation, Bose-Einstein condensation, and quantum information processing. He has pioneered work on topological quantum thermometry and ultrafast phase transitions in materials like vanadium dioxide. His group actively explores the intersection of quantum optics with condensed matter physics and quantum computing. Recent articles highlight advancements in tensor network approaches for topological phases, quantum algorithms for many-body systems, and the application of sonification to quantum entanglement dynamics. He has received a prestigious ERC Advanced Grant and the ICREA Professorship, recognizing his contributions to quantum science. Prof. Lewenstein advises over a dozen PhD students and postdoctoral researchers, fostering a dynamic research environment at ICFO. His grants include ERC and ICREA funding, supporting projects on quantum simulations and nonlinear optical phenomena. The Quantum Optics Theory group collaborates internationally, with expertise in light-matter interactions, quantum resource theory, and quantum many-body localization. Their lab develops novel methods for probing quantum systems using high-harmonic spectroscopy and quantum trajectory simulations.
Jacob D. Leshno is an Associate Professor of Economics and Robert H. Topel Faculty Scholar at the University of Chicago Booth School of Business. His research employs game theory, applied mathematics, and microeconomic theory to study allocation mechanisms and marketplace design, with applications spanning school choice systems, patient assignments to nursing homes, and decentralized cryptocurrency protocols. Professor Leshno's academic background includes: PhD in Economics from Harvard University, completed under Nobel laureate Alvin Roth M.Sc. in Pure Mathematics from Tel Aviv University B.Sc. in Pure Mathematics from Tel Aviv University His research program centers on market design theory with two primary strands. The first focuses on matching markets, where he developed tractable cutoff characterizations that clarify market structures for college admissions and medical residency matching (NRMP). His work demonstrates how price discovery mechanisms can streamline inefficient processes like college applications and subsidized housing allocation. The second strand examines cryptocurrencies and blockchain technology, investigating how open-source computer code functions as market rules in decentralized systems. This research explores both the economic security of permissionless consensus and fundamental limitations of proof-of-work protocols. Professor Leshno's publications reveal a cohesive research trajectory applying economic theory to increasingly complex market structures. His work consistently bridges theoretical rigor with practical implementation, evolving from traditional matching markets to the frontier of decentralized digital systems. Publications in top journals like American Economic Review and Journal of Political Economy demonstrate both analytical depth and real-world relevance across education, healthcare, and financial technology sectors. Professor Leshno has received significant recognition for his contributions: ACM SIGecom Test of Time Award for foundational work in matching markets INFORMS Frederick W. Lanchester Prize for outstanding contributions to operations research Prior to Chicago Booth, Professor Leshno served as Assistant Professor at Columbia Business School and completed a postdoctoral fellowship at Microsoft Research New England, following industry experience at Yahoo! and IBM. He teaches MBA courses in Competitive Strategy and Market Design, and developed a PhD seminar bridging computer science theory with economic principles for distributed systems. His research continues to influence both academic theory and practical implementations of market mechanisms across multiple sectors. Professor Leshno maintains active collaborations with leading researchers including Itai Ashlagi, Irene Lo, and Gur Huberman, advancing the theoretical foundations of market design while addressing contemporary challenges in digital marketplaces and allocation systems.
Michalis Vazirgiannis is a Professor at LIX, École Polytechnique (France) leading the Data Science and Mining (DaSciM) group. With academic backgrounds in Physics (Athens University), AI (Heriot-Watt University), and Informatics (Athens University), he has conducted research at Fraunhofer, Max Planck MPI, and INRIA/FUTURS while teaching at institutions across Greece, France, China, and Spain. His research spans Machine/Deep Learning for Graphs (GNNs, graph kernels, embeddings) Text Mining & NLP (Graph-of-Words, biomedical text analysis) Combinatorial Optimization for pandemic forecasting and energy systems Event/Anomaly Detection in time series and sensory data Industrial collaborations with Airbus, Google, Tencent, and BNP . He has supervised 29 completed PhD theses, published over 250 papers, and received prestigious awards including Marie Curie and Tencent Rhino-Bird Fellowships. His team leads the ANR-HELAS Chair (2020-2025) focusing on heterogeneous data deep learning.
Jordi Perelló Muntan is an Associate Professor in the Department of Computer Architecture at the Universitat Politècnica de Catalunya (UPC), Barcelona, Spain, where he is also affiliated with the Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona (ETSETB). He is a member of the Broadband Communications Systems and Architectures (CBA) and IDEAI-UPC research groups, focusing on advanced optical and future internet networking technologies. Research Interests: His research spans telecommunications networks, optical fiber and optical networking, resource optimization, network architectures, and the Future Internet. He investigates performance optimization in 5G transport networks, Spatial Division Multiplexing (SDM), Recursive Inter-Network Architecture (RINA), elastic optical networks, and cognitive networking. His work integrates SDN, network virtualization, and green networking principles for scalable and efficient infrastructures. Publication Trends: His recent publications focus on probabilistic constellation shaping in multicore fiber networks, cognitive strategies for optical margin reduction, RINA-based QoS assurance, and migration planning toward spectrally-spatially flexible optical networks. These reflect a strong trend toward intelligent, adaptive, and energy-efficient network design for future communication systems. Scientific Awards: Co-recipient of the 2020 Fabio Neri Best Paper Award Runner-up (Elsevier Journal of Optical Switching and Networking) Co-recipient of the ONDM 2021 Best Paper Award Co-recipient of the 2019 IEEE Communications Society Charles Kao Award Co-recipient of the ONDM 2012 Best Student Paper Award Advising and Grants: He has advised multiple PhD students on topics including RINA, optical network planning, and virtual provisioning. He has led or participated in major European (H2020, FP7) and national (PID, TEC) research projects such as SLICENET, PRISTINE, TRAINER, and ALLIANCE, focusing on 5G, RINA, and sustainable network infrastructures. Labs and Teams: He is an active member of the CBA research group at UPC, contributing to experimental and theoretical advancements in optical and programmable networks. His team collaborates internationally on testbed development and standardization efforts in next-generation networking.
Albert Atserias is a Professor in the Department of Computer Science at the Universitat Politècnica de Catalunya (UPC), affiliated with the Faculty of Informatics of Barcelona (FIB) and the ALBCOM research group (Algorithms, Bioinformatics, Complexity, and Formal Methods). He is also associated with the Institut de Matemàtiques de la UPC-BarcelonaTech. His research is central to theoretical computer science, with a strong emphasis on logic and complexity. Atserias's research interests span Computational Complexity, Logic in Computer Science, Finite Model Theory, Proof Complexity, and Constraint Satisfaction Problems . His work explores the fundamental limits of computation, the expressive power of logical languages over finite structures, and the complexity of proving mathematical statements. He investigates the algebraic and combinatorial properties of proof systems, the limits of efficient algorithms for constraint solving, and the theoretical foundations of databases. His research often bridges logic, algebra, and combinatorics to provide deep insights into computational phenomena. The trends in his recent publications show a sustained focus on the logical and algebraic underpinnings of computational problems. Key themes include the consistency and complexity of database queries , the power and limitations of proof systems (like resolution and sum-of-squares), and the expressive power of homomorphism counts in graph theory. His work on the hardness of automating resolution and the development of circular proof systems are particularly significant contributions to proof complexity. The 2024 PODS Best Paper Award for work on relational consistency underscores the impact and timeliness of his research. Among his notable scientific awards are the prestigious ICREA Acadèmia , the PODS 2024 Best Paper Award , the Premi Extraordinari de Doctorat (Extraordinary Doctoral Prize), and the Kleene Award for Best Student Paper . These accolades reflect both the excellence of his early work and his continued leadership in the field. Atserias has been a principal investigator on numerous competitive research projects, including funding from the European Research Council (ERC) and the Spanish Ministry of Science. He has advised doctoral students, such as Toni Hakoniemi, whose thesis on proof complexity he supervised. His extensive collaborative network includes leading researchers like Phokion Kolaitis, Anuj Dawar, and Victor Dalmau. He has also served on the scientific committees of major conferences, contributing to the academic community. He is a core member of the ALBCOM research group , a leading team at UPC focused on theoretical aspects of computer science, which provides a vibrant environment for research in algorithms, complexity, and formal methods. His work is also connected to the broader Institut de Matemàtiques de la UPC, fostering interdisciplinary collaboration between computer science and mathematics.
Susana Paton Alvarez is an Associate Professor at the Department of Electronic Technology within the College of Engineering at the Universidad Carlos III de Madrid. She is affiliated with the Microelectronic Design and Applications (DMA) research group and the University Institute on Gender Studies . Her contact information includes email addresses susana.paton@uc3m.es and spaton@ing.uc3m.es , and her office is located at 1.2.C06 - Agustin De Betancourt in Leganés. Her research focuses on analog and mixed-signal circuit design , with a particular emphasis on capacitance-to-digital converters , voltage-controlled oscillators (VCO) , and biomedical sensors . Recent work includes advancements in time-encoded sensors for physiological monitoring and noise analysis in multi-bit SigmaDelta modulators. She also explores applications in flexible electronics and edge computing for biosignal acquisition. Notable publications include contributions to IEEE Sensors Letters , IEEE Transactions on Circuits and Systems II , and IEEE Sensors Journal , reflecting her expertise in microelectronic design and biomedical engineering. Her research often bridges theoretical circuit analysis with practical implementations in nanometer CMOS technologies. Dr. Paton Alvarez actively participates in collaborative projects and conferences, such as Modularos and Short Range Wireless Front-Ends initiatives. While no scientific awards are explicitly listed, her publications highlight sustained contributions to the field of analog and biomedical circuit design. Her academic roles include teaching Computer Science and Electronics subjects. She is involved in the University Institute on Gender Studies , indicating an interest in interdisciplinary research or outreach in gender-related academic issues.
Carles Padro Laimon is a Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Mathematics and the School of Telecommunications Engineering. He is a leading researcher in cryptography and information security, focusing on secret sharing schemes, combinatorial structures, and cryptographic protocols. His work integrates discrete mathematics, coding theory, and algorithmic design to address security challenges in digital systems. Padro leads the MAK Research Group (Mathematics Applied to Cryptography) and the ISG-MAK Information Security Group. He has been involved in numerous competitive research projects, including initiatives on post-quantum cryptography and secure multi-user systems. His contributions span over 211 documented activities, including articles, theses, and conference participations. His research interests include the theoretical foundations of cryptography, with a focus on optimizing secret sharing schemes, analyzing matroid-based structures, and developing secure communication protocols. He has collaborated extensively with institutions like the UPC and European research networks, contributing to both academic and practical advancements in cybersecurity. Padro holds a PhD in Mathematics from UPC and has supervised doctoral theses and mentored researchers in his field. His work frequently appears in top journals like IEEE Transactions on Information Theory, Designs, Codes and Cryptography, and SIAM Journal on Discrete Mathematics.
Danel Ahman is an Associate Professor at the Institute of Computer Science , University of Tartu , Estonia, specializing in programming language theory . His research focuses on dependent/refinement types , computational effects , and verified software . Education PhD in Theoretical Computer Science (University of Edinburgh, 2017) MPhil in Advanced Computer Science (University of Cambridge, 2012) BSc in Informatics (Tallinn University of Technology, 2010) Research Interests : Danel investigates programming languages with algebraic effects and effect handlers for verified software, exploring denotational/operational semantics and fibrational approaches to effects. His work bridges theoretical computer science with practical formal verification. Scientific Awards : Estonian Research Council grant (2025) Marie Skłodowska-Curie Fellowship (2019) PhD dissertation prize (2018) Google/Citrix dissertation awards (2012) Teaching & Supervision : He teaches courses like Logic in Computer Science and Functional Programming at the University of Tartu, and supervised BSc/MSc theses on topics including asynchronous effects and formal verification. Danel also organizes research seminars and guest lectures on F*.
Dae-Jin Lee is an Assistant Professor at IE University’s School of Science and Technology, specializing in statistical modeling and data science. Previously, he served as a Research Line Leader at the Basque Centre for Applied Mathematics (BCAM) and coordinated the Knowledge Transfer Unit in Data Science/AI. His academic background includes a Ph.D. in Mathematical Engineering (2010) from Universidad Carlos III de Madrid and postdoctoral research at CSIRO (Australia). His research focuses on statistical methods for complex data, including penalized splines, tensor product smooths, and applications in biomedicine, epidemiology, environmental science, and sports analytics. He has led multidisciplinary projects funded by public and industry grants, collaborating globally with experts across fields like engineering, medicine, and biology. Key research themes include predictive modeling for health outcomes (e.g., SARS-CoV-2 pneumonia severity), sports injury prevention, and AI in healthcare. His work integrates machine learning with traditional statistical techniques, addressing real-world challenges like pedestrian dynamics simulations and automated medical diagnostics. He is actively involved in scientific organizations, including the Spanish Biostatistics Society and the Statistical Modelling Society. His recent publications highlight innovations in growth curve modeling, AI ethics, and spatiotemporal data analysis, reflecting his commitment to advancing both theoretical and applied statistics.
Alejandro F. Villaverde is a Ramón y Cajal research fellow in the Department of Systems & Control Engineering at the School of Industrial Engineering, University of Vigo, Spain. He also serves as a Research fellow at CITMAga since 2022. Previously, he worked as a postdoctoral researcher at IIM-CSIC from 2016-2020. His research focuses on the modeling of dynamical systems with particular emphasis on biological applications. Villaverde earned his PhD in Systems and Control Engineering from University of Vigo between 2005 and 2009. His academic career has centered at Spanish institutions with a strong interdisciplinary approach bridging engineering, mathematics, and biology. His primary research interests include systems biology, control theory, and mathematical modeling, with specialized expertise in structural identifiability, observability analysis, and computational tools for dynamic modeling of biological systems. Villaverde's work addresses fundamental challenges in building reliable mathematical models of complex biological processes, with applications spanning immunology to microbial communities. His theoretical contributions have practical implications for improving model reliability and predictive power in biological research. Villaverde has published extensively in top journals including PLOS Computational Biology, Bioinformatics, and IEEE/ACM Transactions on Computational Biology. His recent publications (2023-2025) reveal a consistent research trajectory focused on developing theoretical frameworks for biological model analysis, creating practical software tools, and applying these methods to cutting-edge problems. His work shows particular strength in identifying and addressing fundamental limitations in modeling approaches, especially regarding parameter identifiability and model observability constraints. Among the top 2% Scientists Worldwide 2024 (Stanford University list) Recognition as one of the EEI's top valued instructors at University of Vigo's School of Industrial Engineering Villaverde leads multiple significant research projects including DYNAMO-bio (funded by Ministry of Science, Innovation and Universities), SICOMORO (focusing on symmetries in biological communities), and PREDYCTBIO. His group actively develops open-source software tools such as STRIKE-GOLDD for structural identifiability and observability analysis. The laboratory, part of the BICO research group, includes several researchers and students working on various aspects of dynamic modeling in biology, with recent additions including Mahmoud Shams Falavarjani, Adriana González Vázquez, and multiple interns working on specialized projects.
