Mohamed Amara is a full-time Professor at the University of Pau and the Pays de l'Adour (UPPA) since 1996, affiliated with the Laboratory of Mathematics and their Applications (CNRS-UMR 5142). He served as its director (1999-2007), Director of the Doctoral School of Exact Sciences (ED211, 2007-2008), and UPPA's Scientific Council Vice-President (2008-2012). He has been UPPA's President since 2012 (re-elected until 2020). Education: Mathematics from University of Algiers (1973), Pierre and Marie Curie University (DEA 1974, Doctorate 1978, State Doctorate 1983) Academic Roles: Research Associate at Ecole Polytechnique (1978-1982), Algerian Electricity and Gas Company (1983-1992), Professor in Algiers (1988-1994), Tunis (1994-1995), and Associate Professor at Paris 6 (1995-1996) His research focuses on numerical simulation of partial differential equations for environmental/energy applications, including mechanics in porous media (petroleum engineering, geoscience), fluid mechanics (aerodynamics, estuarine hydrodynamics), non-Newtonian flows, and wave propagation. Articles highlight expertise in discontinuous Galerkin methods, Helmholtz problems, finite element discretization, and multiphysics systems. He managed 20 doctoral theses and led national mathematics programs at ANR (2007-2011). He chairs the Cocktail association for higher education IT systems and collaborates with INRIA's Magique 3D team (since 2006).
Professor Daniel Rueckert is a leading academic in Artificial Intelligence and Medical Imaging, holding dual positions at Imperial College London (as Professor of Visual Information Processing) and Technical University of Munich (Alexander von Humboldt Professor for AI in Medicine and Healthcare). He obtained his MSc from Technical University Berlin (1993) and PhD from Imperial College London (1997), followed by postdoctoral work at King’s College London. At Imperial, he led the Department of Computing (2016–2020) and founded the Biomedical Image Analysis group. His research focuses on AI-driven medical image analysis, including algorithms for image reconstruction, registration, and clinical decision support. His research interests span AI applications in healthcare, machine learning for medical imaging, and computational methods for clinical diagnostics. Notable contributions include over 500 publications and 60+ PhD graduates, with key works in federated learning, cardiac motion analysis, and biomarker development. Awards include the Leibniz Prize (2025), Royal Academy of Engineering Fellowship (2015), and multiple ERC grants. He leads the BioMedIA research group and is an editorial board member of Medical Image Analysis . Recent publications highlight advancements in AI-driven medical imaging, such as secure federated learning frameworks and deep learning models for disease prediction. His work bridges academic and industrial sectors through initiatives like IXICO, an Imperial spin-out. Current affiliations include roles at both Imperial and TUM, emphasizing interdisciplinary collaboration in healthcare technology. Advising and grants: Supervised over 60 PhD students and 40 post-docs. Secured grants including ERC Synergy (2013) and ERC Advanced (2020). Active in labs focused on biomedical image computing and AI in healthcare systems. Collaborative efforts include the BioMedIA group and TUM’s AI initiatives. Labs/teams: Leads the Biomedical Image Analysis group at Imperial and the TUM AI in Medicine team. Collaborates extensively on projects like cardiac imaging analysis and federated learning for healthcare.
Cao Jiannong is currently a Chair Professor and Director of the University Research Facility in Big Data Analytics at Hong Kong Polytechnic University . He has held academic roles including Assistant Professor at City University of Hong Kong and University Lecturer at the University of Adelaide and James Cook University. His research spans Cloud and Edge Computing , Parallel and Distributed Systems , Big Data Analytics , and Wireless Sensing . Ph.D. in Computer Science, Washington State University (1990) MSc in Computer Science, Washington State University (1986) BSc in Computer Science, Nanjing University, China (1982) His work focuses on solving theoretical and practical challenges in distributed computing , mobile cloud systems , and wireless sensor networks . Recent projects include coupled network embedding models for heterogeneous networks and SDN architectures for vehicular communication. His research also pioneers WiFi-based non-invasive health monitoring and fault-tolerant sensor deployment for structural health applications. Dr. Cao's publications highlight advancements in network embedding , edge computing , and WSN optimization . Key papers address multi-user computation partitioning , energy-efficient SHM systems , and consensus protocols for mobile networks. These works have been cited over 15,000 times, with an h-index of 60. Ministry of Education (China) Natural Science Award (2018) Distinguished Member, ACM (2017) Fellow, IEEE (2014) Best Paper Awards at IEEE DSAA, SMARTCOMP, and WCNC Dr. Cao has advised multiple PhD students, including Linchuan Xu and Weigang Wu , whose research on WSN-based SHM and coupled network embedding has practical impact. His leadership includes directing Hong Kong Polytechnic University's Big Data Research Facility and serving on technical committees for IEEE INFOCOM and ACM/IEEE conferences.
Elaine Shi is a Professor with a joint appointment in the Department of Computer Science (CSD) and the Department of Electrical and Computer Engineering (ECE) at Carnegie Mellon University (CMU). She is also an Adjunct Professor of Computer Science at the University of Maryland. Her research focuses on cryptography, security, mechanism design, algorithms, blockchains, and programming languages. Shi co-founded Oblivious Labs, Inc., and her work on Oblivious RAM and differentially private algorithms has been adopted by major companies like Signal, Meta, and Google. Affiliations: CMU’s Crypto Group CMU’s Cylab Crypto Seminar Series (co-founder) Research Interests: Cryptography and Security: Focused on oblivious computation, private information retrieval, and secure protocols. Blockchain Technology: Including consensus mechanisms, transaction fee design, and decentralized systems. Privacy-Preserving Algorithms: Such as differential privacy and secure multi-party computation. Algorithm Design: With applications to parallel computing and efficient data structures. Awards: Packard Fellow Sloan Fellow ACM Fellow IACR Fellow Advising & Grants: Current advisees include PhD students like Benjamin Chan, Yanyi Liu, Mingxun Zhou, and Nikhil Vanjani. Past students have taken academic roles at institutions like UVA, University of Michigan, and Duke University. Grants supported research in secure computation, blockchain mechanisms, and privacy-preserving technologies. Labs & Teams: Co-founder of Oblivious Labs, Inc. Leading CMU’s Crypto Group and Cylab Crypto Seminar Series.
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.
Roghayeh (Leila) Barmaki is an Assistant Professor in the Department of Computer & Information Sciences at the University of Delaware, where she leads the Human-Computer Interaction Lab (HCI@UD) and is affiliated with the Data Science Institute. Her research centers on Human-Centered Computing with applications in healthcare and medical education. Education: Postdoctoral Fellowship (2018): Johns Hopkins University, Laboratory for Computational Sensing and Robotics, Whiting School of Engineering PhD (2016): University of Central Florida, Computer Science MSc (2012): Iran University of Science and Technology, Artificial Intelligence BSc: Kharazmi University, Software Engineering Her research spans Multimodal Machine Learning, Virtual and Augmented Reality, and Embodied Interface Design, focusing on innovative applications in healthcare delivery and science education. She teaches core courses including Introduction to Human-Computer Interaction (CISC 482/682) and Introduction to Data Mining (CISC 483/683), emphasizing hands-on project development and theoretical foundations. Dr. Barmaki directs the HCI@UD laboratory, which develops cutting-edge interfaces for medical training and patient care through interdisciplinary collaboration between computer science, engineering, and healthcare domains.
Hilda Ruokolainen is a Senior Lecturer at Åbo Akademi University, affiliated with the School of Business and Economics. Her work focuses on misinformation as a social phenomenon, particularly within asylum seeker contexts, using qualitative methodologies like semi-structured interviews and empirical research. University: Åbo Akademi University School: School of Business and Economics Email: hilda.ruokolainen@abo.fi ORCID: 0000-0003-2521-4390 Her research spans information science, social sciences, and migration studies, emphasizing collaborative qualitative practices and the creation process of data. Recent publications analyze official misinformation, methodological approaches, and network effects in information research. Key trends in her work include social dynamics of misinformation, empirical studies on service workers, and the intersection of information practice with computer science. She advocates for embracing complexity in collaborative research frameworks.
Raquel Fernández is Full Professor of Computational Linguistics and Dialogue Systems at the University of Amsterdam, where she leads the Dialogue Modelling Group at the Institute for Logic, Language & Computation (ILLC). As Vice-Director for Research at ILLC and a Fellow of the ELLIS Society, she bridges computational linguistics, cognitive science, and artificial intelligence through her research on language use in multimodal and conversational contexts. PhD in Computational Linguistics from King's College London Prior research positions at University of Potsdam and Stanford University's CSLI Her work explores how cognitive constraints, social interaction, and perception shape language use, with a focus on: Visually-grounded language processing Multimodal dialogue modeling Model uncertainty and calibration Language grounding in multimodal data Language learning and semantic change Dialogue reference resolution Recent publications analyze multimodal reasoning limitations, cross-lingual knowledge consistency, and uncertainty modeling in dialogue systems. She has received multiple accolades including an ERC Consolidator Grant , NWO VENI/VIDI/Aspasia fellowships , and EMNLP/GenBench awards . Outstanding Paper Award (EMNLP 2023) Best Data Award (GenBench Workshop 2023) ELLIS Society Fellow ERC Consolidator Grant #819455 recipient NWO VENI/VIDI/Aspasia awardee As a leader in academic service, she serves on the SIGDAT Executive Committee and chairs multiple conference committees. Her lab develops models for multimodal dialogue, visual storytelling, and grounded language understanding.
Roberto Manduchi is a Professor of Computer Science and Engineering at the University of California, Santa Cruz, within the Baskin School of Engineering. His primary affiliation is with the Computer Science and Engineering department where he leads research in assistive technology for visual impairments. He holds a Dottorato di ricerca in Electrical Engineering from the University of Padova, Italy, and previously worked at Apple and NASA JPL before joining UCSC in 2001. His research focuses on mobile computer vision, inertial sensors, and location-aware systems to enhance spatial awareness and information access for blind and low-vision individuals. Key research areas include indoor navigation systems, screen magnification for low-vision readers, obstacle detection using augmented reality, and text accessibility assessment through specialized OCR pipelines. His work bridges computer vision, human-computer interaction, and accessibility design. Analysis of his recent publications (2022-2025) reveals strong emphasis on inertial-based indoor navigation (e.g., PALMS localization system, backtracking algorithms), screen magnification usability studies, and novel approaches to scene text access for blind users. His research consistently targets practical applications for visual impairment, with significant contributions to pedestrian dead reckoning, magnetic signature localization, and gaze-contingent interfaces. Manduchi serves on the scientific advisory board of Aira and is a board member of the Vista Center for the Blind and Visually Impaired. He leads the UCSC Computer Vision Lab where his team develops accessible computing solutions. His work includes both theoretical contributions to computer vision and tangible assistive applications, with recent projects focusing on smartphone-based inertial odometry, multi-scale tactile maps, and real-time obstacle cueing systems.
Monika Henzinger is Professor at the Institute of Science and Technology Austria (ISTA), heading the research group of Theory and Applications of Algorithms. She also serves as Vice President for Technology Transfer at ISTA since 2024. Previously, she held professorships at the University of Vienna (2009-2023) and EPFL, Switzerland (2005-2009), was Director of Research at Google (1999-2005), and served as Assistant Professor at Cornell University. Professor Henzinger's research centers on efficient algorithms and data structures with three main thrusts. First, she investigates dynamic settings where program inputs are repeatedly updated, seeking solutions faster than restarting computations. Second, she develops privacy-preserving algorithms that add minimal noise to protect input data while maintaining efficiency. Third, she translates theoretically optimal algorithms into practical implementations for dynamically changing inputs. Her work consistently addresses resource conservation in data processing, particularly computing time and memory space, while exploring the theoretical limits of possible savings. Henzinger's recent publications (2024-2025) reveal strong trends in dynamic algorithms, differential privacy, and graph theory. Her research consistently bridges theoretical computer science with practical applications, focusing on algorithms that adapt to changing inputs while preserving computational efficiency and data privacy. She has made significant contributions to problems like dynamic matching, minimum cut computation, and privacy-preserving data analysis across various domains. Professor Henzinger has received numerous prestigious awards and honors: Wittgenstein Award (2021) Two ERC Advanced Grants (2014, 2021) Carus Medal of the German Academy of Sciences Leopoldina (2019) SIGIR Test of Time Award Fellow of the Association of Computing Machinery (2016) Member of the Austrian Academy of Sciences (2017) CAREER Development Award of the National Science Foundation Best paper Award at the Symposium on Discrete Algorithms (2024) Professor Henzinger currently advises PhD students Bardiya Aryanfard, Antoine El-Hayek, and Roodabeh Safavi Hemami, along with postdocs Anamay Chaturvedi and Niklas Hahn. Her research is supported by multiple significant grants including an ERC Advanced Grant for 'Design and Evaluation of Modern, Fully Dynamic Data Structures,' the FWF Wittgenstein Prize, and the WEAVE Project on 'Static and dynamic hierarchical graph decompositions.' She also serves as Principal Investigator for the FWF project 'Fast algorithms for a reactive network layer,' providing substantial funding for her innovative work in algorithms and data structures. Professor Henzinger leads the Theory and Applications of Algorithms research group at ISTA, which focuses on developing practical algorithms for dynamic environments. Her team investigates resource conservation in data processing, specializing in dynamic algorithms that efficiently handle changing inputs, privacy-preserving algorithms that minimize noise while protecting data, and translating theoretical algorithms into practical implementations. The group maintains a strong presence in theoretical computer science through regular publications in top conferences and journals, and collaborates extensively with institutions worldwide to advance algorithmic research.
James Glass is a Senior Research Scientist at the Massachusetts Institute of Technology (MIT) and heads the Spoken Language Systems Group within MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). He is also affiliated with the Harvard-MIT Division of Health Sciences and Technology. His research spans automatic speech recognition, multimodal learning, and spoken language understanding, with applications in healthcare and video analysis. Education: SM and PhD in Electrical Engineering and Computer Science from MIT His work focuses on paralinguistic speech analysis, health markers in speech, and the intersection of speech and natural language processing. Recent trends emphasize audio-visual alignment, recursive reasoning, and AI applications in cognitive disorder diagnosis. Scientific awards include IEEE Fellow, ISCA Fellow, and Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence. His group explores unsupervised learning, speaker verification, and social text analysis. James leads the Spoken Language Systems Group at CSAIL, collaborating with institutions like IBM and Harvard-MIT Division of Health Sciences and Technology. His research integrates vision-language models, neural audio codecs, and self-supervised frameworks.
Neil Selwyn is a Professor in the Faculty of Education at Monash University with over 30 years of research experience focusing on the integration of digital technology into educational settings. His work centers on the 'real-life' constraints and problems faced when implementing technology-based education, with expertise in AI, automation, data-driven education, and digital degrowth. His primary research interests span digital technology and society, the use of digital technologies in educational settings, digital sociology of education, and environmental sustainability issues associated with digital technology use. Professor Selwyn is recognized internationally for his critical approach to educational technology and has led numerous nationally-funded projects examining educational data, learning analytics, and AI technologies. His recent publications reflect a growing focus on the environmental implications of AI in education, digital degrowth, and the practical challenges teachers face when implementing AI tools in classrooms. This work demonstrates a shift toward examining the sustainability and ethical dimensions of educational technology. Fellow of the Academy of Social Sciences in Australia (2024) Professor Selwyn serves as a media commentator for major outlets including the NYT, WSJ, CNN, Guardian, BBC, and ABC, and hosts the 'Education Technology Society' podcast. He maintains significant international collaborations, currently holding visiting positions at Lund University (2025), University of Oxford (2023-2027), and previously at the University of Gothenburg (2016-2017).
Prof. Dr.-Ing. Rüdiger Daub serves as Professor and Chair of Production Engineering and Energy Storage Systems at the Technical University of Munich (TUM), operating within the Department of Mechanical Engineering. His leadership encompasses research direction, academic supervision, and strategic development of battery production technologies at TUM's Garching campus (Boltzmannstr. 15), with active industry collaborations driving innovation in sustainable manufacturing. Daub's research program pioneers advanced production methodologies for lithium-ion and solid-state batteries, focusing on electrode manufacturing, electrolyte filling, and cell assembly processes. His work investigates critical parameter interdependencies affecting battery safety and performance, developing inline monitoring systems and digital twin technologies for real-time process optimization. Key contributions include moisture control in electrode production, electrochemo-mechanical characterization of solid-state systems, and robotics solutions for deformable object assembly, all integrated with machine learning for quality assurance in industrial settings. Analysis of his 2023-2025 publications reveals a dominant research trajectory toward solving production bottlenecks in next-generation energy storage. The work demonstrates increasing integration of computational modeling with empirical validation, particularly in solid-state battery manufacturing and high-voltage electrolyte systems. A notable trend is the cross-pollination of robotics, computer vision, and uncertainty quantification techniques to address complex assembly challenges and distribution shifts in quality monitoring, reflecting industry's urgent need for adaptable, data-driven production systems. Leading TUM's specialized laboratories for battery cell production, Daub's team maintains comprehensive facilities for electrode calendering, electrolyte filling, and cell assembly with integrated tracking and tracing capabilities. The research infrastructure supports collaborative projects with automotive OEMs and battery manufacturers to develop scalable production processes, emphasizing environmental sustainability through water-based electrode production and footprint optimization. Current initiatives focus on digital factory modeling and prelithiation technologies for next-generation battery systems.
Larissa Schlegel-Pape serves as a Scientific Associate at the Department for Ornamental and Pedigree Poultry within the Farm Animal Clinic of Freie Universität Berlin's Faculty of Veterinary Medicine. Her work focuses on developing innovative methods for assessing chicken welfare through the creation of the "Stressed Chicken Scale," which aims to systematically identify stress indicators in poultry. Her research interests center on animal welfare science, specifically stress assessment in chickens using both behavioral observation and computer vision technology. She investigates how body posture, movement patterns, and other visual indicators can reliably signal discomfort or stress in poultry, with the goal of creating practical assessment tools for veterinarians and poultry farmers. Her work bridges veterinary medicine, ethology, and technological innovation, contributing to refinement research (one of the 3Rs principles) in animal husbandry. Analysis of her publications reveals a strong focus on developing and validating the Stressed Chicken Scale across multiple contexts. Her work spans methodological development, practical implementation studies, and technological integration with computer vision systems. The research demonstrates progression from conceptual framework to validation studies and practical application, with increasing sophistication in assessment techniques and broader implications for animal welfare standards in poultry farming. Schlegel-Pape actively collaborates with the Federal Institute for Risk Assessment (BfR) and participates in interdisciplinary projects involving artificial intelligence applications in agriculture. She presents her findings regularly at major German veterinary conferences including the DVG (Deutsche Veterinärmedizinische Gesellschaft) events, DACh Epidemiology conferences, and specialized poultry medicine gatherings. Her work contributes significantly to advancing animal welfare assessment methodologies and promoting refinement in poultry husbandry practices.
