Beata Megyesi is a Professor of Computational Linguistics at Stockholm University , leading groundbreaking work in Historical Cryptology and Digital Humanities . Her research bridges Artificial Intelligence with Philology to decode secret historical documents through projects like DESCRYPT (2025-2032) and DECRYPT (2018-2024), funded by Riksbankens Jubileumsfond and Vetenskapsrådet . Current Chair of Swedish Research Council's Linguistics Review Group (2024-2025) Director of the international Master's Program in AI and Language Advisor to PhD candidates Micaella Bruton and Crina Tudor Research Focus includes: Automatic analysis of 17th-19th century ciphers with AI Development of Linked Open Data infrastructure for cryptology Decryption of papal and diplomatic correspondences from 1500-1965 Creation of HistCorp multilingual historical corpus collection Key Collaborations span institutions in Sweden, Norway, Spain, Germany, Hungary, and the USA , with notable projects like DECODE2LOD and Swe-CLARIN . Her lab has pioneered automated key extraction and neural network-based alignment of encrypted manuscripts to plaintext.
Demetris P. Gerogiannis is a researcher in the Department of Computer Science & Engineering at the University of Ioannina, Greece. His academic work focuses on Computer Vision and Image Processing, with specific expertise in image registration, pointset registration, and feature extraction. He has maintained a consistent publication record from 2007 through 2020, primarily collaborating with Christophoros Nikou and A. Likas. Gerogiannis' research interests span multiple areas within Computer Vision including Image Segmentation and Registration, Pointset Registration, Feature Extraction, Object Detection and Recognition, Pattern Recognition, Automatic Image Annotation, and Machine Learning applications. His work demonstrates a consistent focus on developing robust algorithms for geometric problems in vision, with particular attention to handling noise and outliers in visual data. He has made significant contributions to point set registration, shape representation, and vanishing point detection. His publication history reveals a strong trend toward developing mathematically rigorous approaches to Computer Vision problems, particularly utilizing statistical models like Student's t-distributions for robust registration. His work bridges theoretical computer vision with practical applications, including consumer-facing technologies like QR code systems. The 15 most recent publications show progression from foundational work in image registration to more applied research in texture analysis, word spotting, and biomedical applications. Gerogiannis is also an active entrepreneur, currently coordinating QReca!, a startup focused on consumer engagement through QR codes and NFC technology. He has previously worked on a Video On Demand startup and is developing a spin-off to commercialize his Computer Vision research through an API framework. He maintains a commitment to open academic knowledge by providing Matlab code for non-commercial use related to his publications. He has volunteered for the Athens 2004 Olympic Games and maintains a passion for Mathematics, particularly Number Theory and Euclidean Geometry. His academic profile suggests a researcher who bridges theoretical computer vision with practical applications and entrepreneurial ventures.
Arsha Nagrani is a senior research scientist at Google AI Research , focusing on machine learning for video understanding. She earned her PhD at the University of Oxford under Andrew Zisserman with a Google PhD Fellowship , and completed her undergraduate studies at the University of Cambridge with mentorship from Roberto Cipolla and Richard Turner. Research Interests : Self-supervised and multi-modal machine learning Video recognition using sound and text Computer vision for wildlife conservation Cross-modal self-supervision in biometric matching Transformer-based architectures for temporal modeling Zero-shot learning with frozen models Scientific Awards : ELLIS PhD Award Google PhD Fellowship ICASSP 2021 Outstanding Paper Award INTERSPEECH 2017 Best Student Paper Award Service Contributions : Area Chair for CVPR23, ICCV23 Reviewer for top conferences (CVPR, ECCV, ICCV, BMVC, NeurIPS, ICML, AAAI) Organized workshops: Sight and Sound Workshop @ CVPR [2020-2022] VoxSRC Challenges @ INTERSPEECH Video Understanding Pentathlon @ CVPR 2020 Women in Computer Vision (WiCV) Workshops
Gül Varol is a permanent researcher (equivalent to Associate Professor) at École des Ponts ParisTech, where she is part of the IMAGINE group within the Laboratoire d'Informatique Gaspard-Monge (LIGM). She holds additional affiliations as an ELLIS Scholar and a Guest Scientist at the Max Planck Institute (MPI). Her academic journey includes a postdoctoral position at the University of Oxford's Visual Geometry Group (VGG) under Andrew Zisserman, and a PhD from the WILLOW team at Inria Paris and École Normale Supérieure. Her educational background includes BS and MS degrees from Boğaziçi University, followed by doctoral studies at Inria Paris and École Normale Supérieure. During her PhD, she spent research periods at MPI, Adobe, and Google, gaining diverse industry and academic experience. Dr. Varol's research focuses on the intersection of computer vision and language processing, with particular expertise in video representation learning, human motion synthesis, and sign language technologies. Her work bridges theoretical advances with practical applications, especially in accessibility technologies like audio description for visually impaired audiences and sign language recognition systems. She has pioneered approaches for text-driven 3D human motion generation, sign language translation with contextual cues, and film-grammar-aware audio description generation. Her research often leverages large language models and vision-language models to solve complex multimodal problems without requiring extensive training data. Analysis of her recent publications reveals a clear trajectory toward more sophisticated multimodal systems that integrate temporal understanding, contextual awareness, and fine-grained control. Her work increasingly focuses on practical applications in accessibility, with significant contributions to sign language technologies and audio description systems. She has demonstrated leadership in developing large-scale datasets like BOBSL (BBC-Oxford British Sign Language Dataset) and advancing methods for zero-shot and training-free approaches that can be deployed without extensive fine-tuning. Her scientific achievements have been recognized with several prestigious awards: ELLIS 2020 PhD Award AFRIF 2019 PhD thesis award from the French association for pattern recognition Google Research Scholar award (2023) Best application paper award at ACCV'20 Dr. Varol has received significant research funding including an ANR JCJC project "CorVis" and research gifts from Google and Adobe. She actively mentors students and researchers, with prospective students encouraged to apply through her lab's application form. Her leadership in the academic community is evident through her service as Program Chair for ECCV'24, Senior Area Chair for CVPR 2025, and associate editor for the International Journal of Computer Vision (IJCV). She is a key member of the IMAGINE research group at École des Ponts ParisTech, which focuses on image analysis and computer vision. Her collaborative work extends to multiple institutions including MPI, Oxford, and various industry research labs. She has co-organized numerous workshops on specialized topics including sign language recognition, vision transformers, and foundation models for 3D humans, fostering community building in these emerging research areas.
Sérgio Pequito is an Associate Professor in the Department of Electrical and Computer Engineering at Instituto Superior Técnico, University of Lisbon, and a principal investigator at the Institute for Systems and Robotics. He previously held faculty positions at Uppsala University (2022-2024) and Technische Universiteit Delft (2020-2022), and was a faculty member at Rensselaer Polytechnic Institute (2017-2020). His academic journey includes a PhD in Electrical and Computer Engineering from Carnegie Mellon University and a Doctorate in Electrical and Computer Engineering from Universidade de Lisboa. Pequito's research focuses on understanding the global qualitative behavior of large-scale systems through structural and parametric descriptions, with applications spanning neuroscience, biomedicine, and control theory. His work leverages dynamical systems, control theory, and artificial intelligence to develop new analysis tools for brain dynamics aimed at personalized medicine and improved bidirectional brain interfaces. His specific interests include fractional-order systems, structural systems theory, network controllability, and neurotechnology applications for epilepsy treatment and brain-computer interfaces. His recent publications reveal a strong emphasis on applying fractional calculus to model brain dynamics, developing control frameworks for neurotechnology, and advancing structural systems theory for large-scale networks. His work demonstrates a consistent trajectory toward bridging theoretical control systems with practical biomedical applications, particularly in understanding and controlling brain dynamics for therapeutic purposes. 2016 O. Hugo Schuck Award in the Theory Category by the American Automatic Control Council 2009 Best student paper finalist in the 48th IEEE Conference on Decision and Control 2023 Royal Swedish Academy of Engineering Sciences 100 List 2023 European Laboratory for Learning and Intelligent Systems (ELLiS) Research Scholar Multiple Trustees Faculty Achievement awards from Rensselaer Polytechnic Institute Pequito actively mentors numerous PhD and Master's students across multiple institutions, with research topics spanning cyber-neural systems, structural control theory, fractional-order optimization, and neurotechnology applications. His laboratory work focuses on developing theoretical frameworks for large-scale system control while maintaining strong connections to practical applications in biomedical engineering and neuroscience, particularly in developing new diagnostic tools and treatment strategies for neurological diseases through control systems theory.
Khaled Rasheed is a Professor at the School of Computing, University of Georgia, where he has served in various academic capacities since 2000. His current appointment as Professor in the Franklin College of Arts & Sciences - Division of Physical & Mathematical Sciences, School of Computing began in August 2017. Previously, he served as Associate Professor (2006-2017) and Assistant Professor (2000-2006) at the same institution. Dr. Rasheed also serves as Graduate Program Faculty in the School of Computing since 2003. Dr. Rasheed earned his Doctor of Philosophy in Computer Science from Rutgers State University of New Jersey in 1998, following a Master of Science in Computer Science from the same institution in 1995. His undergraduate education includes a Bachelor of Science in Computer Science from Alexandria University, Egypt, completed in 1990. Dr. Rasheed's research focuses on Artificial Intelligence, with particular expertise in Genetic Algorithms, Evolutionary Computation, Data Mining, and Machine Learning. His work bridges theoretical AI development with practical applications across diverse domains. His research spans Bioinformatics and Health Informatics, Computational Intelligence, Engineering Design Optimization, and specialized applications in Poultry Science and Agriculture. His interdisciplinary approach has led to significant contributions in applying AI techniques to solve real-world problems in agriculture, healthcare, and engineering. Recent work demonstrates his focus on deep learning applications for animal behavior monitoring, crop yield prediction, and protein structure analysis, showing both technical innovation and practical impact. Dr. Rasheed's scholarly output shows a clear progression from foundational AI research toward domain-specific applications. His recent publications (2023-2025) reveal a strong emphasis on agricultural applications of AI, particularly in poultry science and crop management, while maintaining contributions to core AI methodology development. His work demonstrates consistent citation impact across multiple domains, with particular influence in agricultural technology applications of computer vision and deep learning. Student Career Success Influencer Award 2023 Student Career Success Influencer Award 2022 Outstanding Faculty Service Award Second Best Paper Dr. Rasheed has secured multiple significant research grants, including a current project with COBB-VANTRESS INCORPORATED (2025-2027) for developing tracking systems for poultry, and a major USDA NIFA grant (2022-2028) for forest sustainability research. His funded projects demonstrate his ability to translate theoretical AI research into practical applications with economic and environmental impact. His grant portfolio spans multiple funding agencies including NIH, USDA, and industry partners, reflecting the interdisciplinary nature of his work. Dr. Rasheed maintains an active research laboratory focused on evolutionary computation and machine learning applications. His work often involves interdisciplinary collaborations across computer science, agriculture, biology, and engineering. His recent publications and grants indicate a strong emphasis on applying AI to agricultural challenges, particularly in poultry science and crop management, while maintaining a foundation in core AI methodology development.
National and Kapodistrian University of AthensGreece
Gilbert Bernstein is an Assistant Professor in the Computer Science & Engineering department within the College of Engineering at the University of Washington. His research bridges computer graphics and programming languages, with a focus on high-performance domain-specific languages. Previously, he was a post-doctoral scholar at UC Berkeley and MIT working with Jonathan Ragan-Kelley, and received his PhD from Stanford University under Pat Hanrahan. His research interests span Computer Graphics, Programming Languages, High-Performance DSLs, Physical Simulation, Geometry & Topology, Differentiable Programming, Hardware DSLs, Tools for Artists, Fabrication, and Human-Computer Interaction. Bernstein develops languages and compilers that enable efficient computation for creative applications, physical simulations, and graphics rendering systems. His recent publications reveal strong trends in differentiable programming for graphics applications, domain-specific languages for hardware acceleration, and computational approaches to traditional crafts like quilting and knitting. His work consistently combines formal language theory with practical applications in graphics and fabrication. Bernstein actively mentors students across multiple institutions including current advisees Felix Hahnlein (UW Postdoc), Ryan Zambrotta (UW PhD), Haoran Peng (UW PhD), and previous students including Alex Reinking (UC Berkeley PhD 2022, now at Qualcomm) and MacKenzie Leake (Stanford PhD 2021, now at Adobe Research). His lab works on diverse projects including debugging CAD programs, compilers for finite element methods, semantics for knitting machines, algebraic scheduling of tensor programs, and exocompilers for hardware accelerators. Bernstein also collaborates on DSLs for networking, Counterstrike bots, gradient-based optimization, memory management, hardware design, and garment design tools.
Cyprus International Institute of ManagementCyprus
Dr. Luca Ferrarini serves as an Assistant Professor within the Department of Information Technologies at the University of Limassol, Cyprus. With over ten years of industry experience as an IT project manager and data analytics consultant, he bridges academic research with practical applications in healthcare, medical imaging, and business domains through expertise in machine learning, databases, and cloud computing. His educational foundation includes: A Master of Science in Computer Engineering earned cum laude from the University of Modena and Reggio Emilia (Italy), where his thesis was developed at the Burnham Institute in San Diego through collaboration with the University of California San Diego. A Doctor of Philosophy in Computer Science awarded with honor from Leiden University (Netherlands), followed by post-doctoral research at the same institution focusing on computational analysis of MRI brain images for neurodegenerative conditions. Dr. Ferrarini's research program centers on solving industry and social challenges through digital technologies. His primary technical expertise spans machine learning, database systems, and cloud computing infrastructure . Current projects demonstrate applied focus in two key areas: developing natural language processing systems for automatic email classification in hospitality industry customer relationship management, and conducting bibliometric analyses of digital transformation literature using complex network theory to identify research trends and emerging subfields. Analysis of his publication history reveals a strong interdisciplinary trajectory, beginning with biomedical applications (particularly in neuroimaging and vascular biology) and evolving toward business-oriented digital solutions. His work consistently applies advanced computational methods to extract meaningful insights from complex datasets across healthcare and enterprise contexts, demonstrating adaptability in translating core technical skills to domain-specific challenges. Bringing substantial industry experience to academia, Dr. Ferrarini's current research involves collaboration with tier-1 hospitality chains on NLP applications while mapping the evolution of digital transformation research through network analysis. Though specific details of student supervision and grant funding aren't provided, his project descriptions indicate active industry partnerships and applied research orientation.
