Cheryl Akner-Koler is a Researcher at Konstfack University of Arts, Craft and Design , specializing in interdisciplinary design research that bridges Industrial Design , Culinary Arts , and Haptic Perception . She collaborates with institutions like Örebro University's School of Hospitality, Culinary Arts & Meal Science to explore sensory design applications. Key research areas: Haptics , Multi-sensory Design , Culinary Aesthetics , and Inclusive Technology . Co-developed the HAPTICA project (funded by the Swedish Research Council 2016–2019) to integrate tactile, taste, and somatic experiences across disciplines. Invented tactile aids (Distime, Monitor, Ready-Ride) for deafblind individuals, focusing on environmental perception and mobility. Recent work includes haptic attribute modeling for hybrid food design communities and anticipative co-creation methods for sustainable futures. Her publications span journals like The International Journal of Food Design and conferences such as E&PDE.
Andrew Sung is a Professor at the University of Southern Mississippi (USM) in Hattiesburg, affiliated with the Chain Technology Center (TEC). His distinguished academic career spans over four decades, focusing on the critical intersection of machine learning and cybersecurity, with significant contributions to multimedia forensics and digital security. His educational background includes: PhD from Stony Brook University (1984) MS from University of Texas System Office (1980) BS from National Taiwan Normal University (1976) Professor Sung's research centers on applying machine learning to security challenges, with groundbreaking work in deepfake detection, malware analysis, and steganalysis. His publications demonstrate expertise in developing privacy-preserving techniques like machine unlearning, creating robust deepfake detection frameworks, and innovating Android security solutions. He has pioneered methods in moving target defense for software-defined networking and advanced forensic techniques for identifying digital forgeries in images and audio, addressing evolving threats in AI-generated content and network infrastructure. Analysis of his 15 most recent publications (2019-2024) reveals a dominant research trajectory toward combating AI-generated threats. His work increasingly focuses on deepfake detection through ensemble learning and dataset distillation, machine unlearning for privacy compliance, and sophisticated malware analysis techniques. This trend highlights his strategic pivot toward securing digital ecosystems against next-generation AI threats while maintaining strong foundations in traditional cybersecurity domains like network defense and steganalysis. No scientific awards were mentioned in available sources. Information regarding students advised or research grants was not provided in the available documentation. Professor Sung conducts his research at the Chain Technology Center (TEC) at USM, where he leads investigations into cutting-edge security challenges at the forefront of artificial intelligence and digital forensics.
Professor Eirini Geraniou is a Professor of Mathematics Education at University College London's Institute of Education (IOE), specifically within the Curriculum, Pedagogy & Assessment department. She holds an ORCID identifier 0000-0002-6360-0316 and serves as the Departmental Enterprise and Innovation Lead since August 2022. Her academic journey spans teaching mathematics at secondary school and university levels, along with extensive work on research projects focused on digital tools for mathematics education. Education: Bachelor of Science in Mathematics from University of Crete Master of Science in Mathematics Education from University of Warwick Doctor of Philosophy in Mathematics Education from University of Warwick Qualified Teacher Status from Department for Education (2006) Professor Geraniou's research focuses on the intersection of mathematics education and digital technologies. Her work explores how computational thinking and mathematical thinking interact, the development of mathematical digital competencies for teaching, and the design of effective tasks that leverage technology to enhance mathematical learning. She has made significant contributions to understanding how students develop algebraic generalization through computer-based environments and how augmented reality can enhance spatial skills in geometry learning. Her research often examines the practical implementation of digital tools in real classroom settings, addressing both the potential and limitations of technology in mathematics education. Her recent publications reveal a strong trend toward examining the integration of AI and computational tools in mathematics education, with particular attention to mathematical modeling, assessment practices, and teacher competencies in the digital age. She has been instrumental in defining frameworks for mathematical digital competency and exploring the critical dimensions of technology use in mathematics classrooms. Scientific Awards: UCL Faculty Education Award (2021) UCL Provost Education Team Award shortlist (2021) HEA Senior Fellow (2020) Professor Geraniou actively supervises graduate students, currently guiding 5 PhD students and 1 EdD student, plus one completed MPhil in 2019. She has secured numerous research grants including projects funded by the Education Endowment Foundation, EU Horizon, Royal Society, and Novo Nordisk Foundation. Her leadership extends to significant collaborative projects such as "Transforming Education with Emerging Technologies" (EU Horizon, 2022-2025) and "Programming, computational thinking and mathematical digital competencies" (Novo Nordisk Foundation, 2020-2023). She also serves as an elected Board Member of the European Society for Research in Mathematics Education (ERME) since 2021. Professor Geraniou leads the Mathematics Education Group at UCL IOE and is actively involved with CambridgeMaths. Her work bridges research and practice, connecting with teachers through initiatives like the Teach First Programme where she served as Mathematics Subject Lead (2017-2022) and Subject Tutor (2011-2022). She has developed and led multiple MA modules including "Understanding Mathematics Education," "Mathematics for Teachers," and "Digital Technologies for Mathematical Learning."
Chiara Branchini is an Associate Professor at the Department of Comparative Linguistic and Cultural Studies at Ca' Foscari University of Venice. She is actively involved in teaching Italian Sign Language (LIS) and its linguistics to undergraduate and graduate students, and serves as coordinator of the 1st level Master's degree and Advanced Training Course in 'Theory and Techniques of Translation and Interpretation Italian-LIS'. Her research focuses on the syntax of Italian Sign Language , including linear order, interrogative and imperative sentences, complex sentences, sociolinguistic analysis, and bimodal bilingual production . Education: PhD in European Intercultural Studies (2007) from the University of Urbino with thesis 'On relativization and clefting in Italian Sign Language (LIS)'; Master's in Foreign Languages and Literatures (2001) from the University of Urbino with thesis 'The Body at the Service of Language. Analysis of Non-Manual Components in American Sign Language (ASL)' Research: Currently collaborating on the drafting of a grammar blueprint for Italian Sign Language within European research project COST ACTION IS 1006; previously participated in national research project PRIN 2007 'Dimensions of variation in Italian Sign Language' by collecting and analyzing the CORPUS LIS Projects: Scientific director of FSE 2012 research grant 'Development of a video-glossary of specialist terms in Italian Sign Language (LIS)'; collaborator on European project 'Spread the sign: dissemination in Europe of vocational sign language' Publications: Authored numerous articles on LIS syntax, grammar, and bilingualism in outlets like Bilingualism , Sign Language & Linguistics , and Glossa , as well as book chapters in A Grammar of Italian Sign Language (LIS) and SignGram Blueprint Editorial Roles: Member of the editorial board for LiLiS (Languages, Language and Deafness) Edizioni Ca' Foscari Reviewing: Regular abstract reviewer for international conferences
Álvaro López is a Research Associate at the Fraunhofer Heinrich Hertz Institute (HHI) in Berlin, Germany, where he joined the Multimedia Communications Group in 2023. His work focuses on next-generation mobile communications and 5G standardization within the 3GPP RAN2 activities. His educational background includes: B.Sc. in Telecommunications Systems Engineering from Universitat Politècnica de Catalunya (UPC), 2015 M.Sc. in Wireless Communications Systems from Universitat Pompeu Fabra (UPF), 2017 Ph.D. in Information and Communication Technologies from UPF, 2022 Dr. López's research interests center on the intersection of machine learning and wireless communications . He develops autonomous learning techniques for 5G and beyond, with emphasis on Wi-Fi multi-link operations , traffic allocation , and network optimization to enhance next-generation communication systems. His publications (2016-2022) reveal a consistent focus on wireless networking evolution, particularly IEEE 802.11 standards (Wi-Fi 6/7) and 5G. Key trends include machine learning integration for self-organizing networks, multi-link resource management, and performance optimization in heterogeneous wireless environments. No information is available regarding students advised or specific research grants. His 3GPP standardization participation indicates industry-collaborative project involvement. At Fraunhofer HHI, Dr. López contributes to the Multimedia Communications Group's work on video coding, 5GXR, volumetric video, and Versatile Video Coding (VVC), advancing multimedia transmission for emerging applications like virtual reality.
Kai Han is an Assistant Professor at The University of Hong Kong's School of Computing and Data Science, where he directs the Visual AI Lab. His research focuses on computer vision, machine learning, and artificial intelligence with specific interests in open-world learning, 3D vision, generative AI, and foundation models. He aims to achieve principled visual understanding and build reliable AI systems that close the intelligence gap between machines and humans. Dr. Han's research interests span multiple areas in visual AI, with particular emphasis on developing methods for open-world visual understanding. His work addresses fundamental challenges in category discovery, visual correspondence, 3D reconstruction, and generative modeling. He has made significant contributions to novel category discovery, open-set recognition, and visual correspondence problems, with his AutoNovel framework being particularly influential in the field. His current research explores the intersection of generative models and visual understanding, particularly focusing on how foundation models can be leveraged for comprehensive visual analysis. His publication record demonstrates a clear evolution from traditional computer vision problems toward more challenging open-world scenarios and generative approaches. Early work focused on 3D reconstruction of transparent and mirror surfaces, while more recent publications explore category discovery, visual correspondence, and generative AI. The trend shows increasing focus on foundation models, large language model integration with vision systems, and creating more robust visual understanding systems that can handle real-world open-set scenarios. Best Paper Runner-Up Award at CVPR Workshop on Continual Learning in Computer Vision, 2022 Outstanding Reviewer for ICCV 2021 (top 5%) Outstanding Reviewer for CVPR 2021 Outstanding Reviewer for CVPR 2020 Travel Award, ICLR 2020 Doctoral Consortium Travel Grant, ICCV 2017 Dr. Han actively mentors PhD students and postdocs, with numerous students appearing as first authors on his publications. His lab has secured multiple funding opportunities including HKU-PS, HKPFS, PGS, HKU-BICI, and HKU-ASTRI scholarships. He serves as Area Chair for major conferences including CVPR 2026, ICLR 2026, and AAAI 2026, demonstrating his standing in the research community. His lab, the Visual AI Lab, focuses on creating robust visual understanding systems that can handle real-world scenarios beyond closed-set recognition.
Vicky Kalogeiton is a Professor in AI at École Polytechnique's Computer Science Laboratory (LIX) and heads the VISTA team. She is a core member of ELLIS Paris, contributing to multimodal generative AI with focus on efficiency, structured outputs, and medical applications. Her work appears in top venues like CVPR, ICCV, ECCV, and IJCV, emphasizing open science and slow research principles. PhD from University of Edinburgh/INRIA Grenoble with Vittorio Ferrari and Cordelia Schmid Research Fellow at VGG, University of Oxford Organizer of CVPR 2025 and Hi!Paris Summer School Her research spans generative AI (diffusion models, flow matching), medical imaging (renal transplant analysis, brain disorders), and cinematic AI (camera control, humor detection). Key projects include E.T. dataset for camera trajectories, FunnyNet-W for multimodal humor analysis, and SCAM for semantic image generation. Recent work demonstrates state-of-the-art performance in visual geolocation (CVPR 2024), optical video generation (AKiRa), and character-aware camera motion (E.T. dataset). She has secured grants from ANR, Hi!Paris, and Microsoft. Program Chair, CVPR 2027 Diversity Chair & Area Chair, ICCV 2025 Best Paper Awards (ICCV-W 2021, ACCV 2022 Honorable Mention) Outstanding Reviewer Awards (ICCV 2021, ECCV 2020) She supervises PhD candidates in generative modeling , medical AI , and reinforcement learning . Collaborations span institutions including Inria, MBZUAI, and MPI.
Mayur Naik is the Misra Family Professor in the Department of Computer and Information Science at the University of Pennsylvania. His research specializes in programming languages and artificial intelligence, with current focus areas including neurosymbolic programming, trustworthy AI for healthcare, and AI-enhanced development tools. He leads the development of the Scallop neurosymbolic programming language and maintains active collaborations in bioinformatics and clinical research. Education: Ph.D. in Computer Science, Stanford University (2008) M.S. in Computer Science, Purdue University (2003) B.S. from BITS Pilani (1999) Research Focus: His work bridges symbolic AI with neural approaches through neurosymbolic programming, enabling interpretable and domain-aware solutions. Current projects include Dolphin (scalable neurosymbolic learning), IRIS (LLM-assisted static analysis), and applications in computer vision and healthcare. Research emphasizes both theoretical foundations and practical system implementations. Publication Trends (Recent Focus): Recent work demonstrates strong emphasis on neurosymbolic learning frameworks (55%), AI-enabled programming tools (30%), and healthcare applications (15%). Key technical threads include LLM integration with program analysis, GPU acceleration for symbolic reasoning, and weakly-supervised learning paradigms. Awards & Honors: Test of Time Award, Foundations of SW Engineering (2023) Distinguished Paper Awards (PLDI 2019, SIGSOFT 2015, FSE 2015, SIGPLAN 2014) NSF CAREER Award (2013) Lockheed-Martin Teaching Excellence Award (2015) Outstanding Junior Faculty Award, Georgia Tech (2016) Academic Leadership: Advises 8 PhD students with research spanning neurosymbolic systems, program analysis, and AI security. Teaches core courses CIS 5470 (Software Analysis) and CIS 5500 (Database Systems). Research supported by NSF and industry partnerships. Labs & Teams: Leads the neurosymbolic programming research group at UPenn, collaborating with Intel Labs, Google AI, and healthcare institutions. Group develops open-source tools including Scallop compiler and LLM-assisted analysis frameworks.
Jianbo Shi is a Professor in the Department of Computer and Information Science at the University of Pennsylvania . He leads research in computer vision with additional interests in artificial intelligence and machine learning . Key projects: First Person Vision , Human Recognition , Image Segmentation , Medical Imaging Developed Normalized Cuts algorithm for image segmentation Research Interests : Focus on first-person vision for social interaction modeling, human behavior analysis through motion and pose estimation, and advanced segmentation techniques using spectral graph theory. His work bridges AI with robotic applications and medical imaging solutions. Scientific Contributions : Received IEEE Longuet-Higgins Prize (2007) NSF CAREER Award (2005) for foundational work in vision algorithms Academic Legacy : Advised 12+ PhD students including Stella Yu (Computational Models of Perceptual Organization) and Katerina Fragkiadaki (Multi-Granularity Human Interaction Models) Developed CIS581 (Computer Vision & Computational Photography) and CIS580 (Machine Perception) courses Software Contributions : Created publicly available Normalized Cuts MATLAB code for image segmentation and data clustering applications.
Yi-Hsin Li is an early-stage researcher affiliated with Mid Sweden University's Department of Computer and Electrical Engineering and collaborates with the Technische Universität Berlin through the PLENOPTIMA project. Her research focuses on light field video compression using gated experts and steered mixture of experts models. Current affiliation: Mid Sweden University Research collaboration: Technische Universität Berlin Her work intersects computer engineering and multimedia systems , emphasizing: Adaptive segmentation techniques Deep learning for image/video regression Model compression optimization Publications demonstrate expertise in machine learning applications for image processing and video coding . Active in the STC Research Centre.
Mårten Sjöström is a Professor in Signal Processing at Mid Sweden University, where he serves as the highest representative of the research subject Computer and System Sciences and is part of the managerial group of the Department of Information and Communication Systems (IKS). He leads the Realistic 3D research group and has extensive experience in both academic and industrial settings. His educational background includes a Master of Science from Linköping University (Applied Physics and Electrical Engineering, 1992), a Technical Licentiate degree from the Royal Institute of Technology, Stockholm (Signal Processing, 1998), and a PhD from Ecole Polytechnique Federale de Lausanne (Modelling of Non-linear Systems, 2001). He obtained his Docent degree (Associate Professor) in 2008 and Professor's degree in Signal Processing in 2013. His primary research focuses on Multi-Dimensional Signal Processing with emphasis on System Modelling and Identification. He has successfully applied these techniques to Image and Video Processing, Multi-media Communications, and currently specializes in Multi-Scopic 3D and Light Field Technology including capture, processing, coding, and presentation/visualization. His work spans theoretical foundations to practical implementations across various application domains. His recent publication record demonstrates a clear trajectory toward advanced light field and 3D imaging technologies, with significant contributions to compression algorithms, depth estimation techniques, quality assessment metrics, and telepresence applications. His research bridges theoretical signal processing with practical industrial implementations, particularly in remote operation, mining applications, and immersive visualization systems. Best Paper Award at MMEDIA 2013 Quality Reviewer Award at ICME 2013 Professor Sjöström has supervised an extensive number of doctoral and licentiate students, with numerous current PhD candidates expected to complete their degrees in 2025. His teaching portfolio covers a wide range of subjects including Applied Signal Processing, Automatic Control, Computer Hardware and Architecture, and specialized PhD courses in Video Processing and Realistic 3D. He has led numerous research projects both current and completed, including IMMERSE, PLENOPTIMA, and various initiatives in 3D video technology and visualization. As founder and head of the Realistic 3D research group, he directs activities focused on synthesis and capture of 3D images and video, rendering techniques for virtual perspective views, system modeling for 3D capture and presentation, coding of 3D content, quality metrics and assessments, and remote control and measurement systems. The group maintains strong industrial collaborations across multiple sectors.
Aleksej Avramović serves as an Assistant Professor at the Faculty of Electrical Engineering, University of Banja Luka, Bosnia and Herzegovina, holding the academic rank of docent since his election in December 2021. His institutional affiliation places him within the university's primary engineering faculty where he contributes to both undergraduate and graduate academic programs in electrical engineering disciplines. Dr. Avramović's research program demonstrates exceptional breadth across computer vision and machine learning applications. His work spans intelligent transportation systems through advanced traffic sign recognition algorithms, medical imaging innovations for colonoscopy analysis and inflammation detection, and pioneering biohybrid security systems using honeybee activity monitoring for explosive detection. Complementing this, he maintains significant contributions to digital signal processing fundamentals including logarithmic arithmetic circuits and lossless compression techniques for medical images and audio signals, reflecting both theoretical depth and practical implementation expertise. His publication trajectory reveals a clear evolution from foundational signal processing research toward applied computer vision solutions. Early work focused on logarithmic multipliers and compression algorithms, while recent publications emphasize deep learning implementations for real-time systems in transportation, healthcare diagnostics, and security applications. This progression demonstrates strategic adaptation to emerging technological paradigms while maintaining core competencies in efficient algorithm design. Dr. Avramović actively participates in nationally funded research initiatives, currently serving as principal investigator for the active project 'Signal Processing using Embedded Computer Systems and Machine Learning' (2025-2026). His project portfolio includes significant contributions to the NATO-funded 'Biological Methods for Explosive Detection' (2017-2021), the 'Mobile Health Platform NORMEDY' medical project (2019-2021), and multiple image processing initiatives for remote sensing applications, demonstrating consistent success in securing competitive research funding. He maintains robust research collaborations within the Faculty of Electrical Engineering, particularly with the computer vision and biohybrid systems teams led by Professors Zdenka Babić and Vladimir Risojević. These groups operate specialized laboratories for image processing, embedded systems development, and signal analysis, supporting interdisciplinary research that bridges electrical engineering with medical technology and security applications through practical hardware-software integration.
Jeffrey Spike, PhD, is a Clinical Professor at George Washington University School of Medicine and Health Sciences in the Departments of Pediatrics and Clinical Research and Leadership, and serves as the Ethics Scholar-in-Residence at Children’s National Hospital in Washington D.C. His academic background includes graduate studies in philosophy at the University of Chicago and Johns Hopkins University. His educational background includes: Graduate work in Philosophy at the University of Chicago (advisors: Donald Davidson, Paul Ricoeur, Stephen Toulmin) Graduate work in Philosophy at Johns Hopkins University (advisor: Jaegwon Kim) Dr. Spike's primary research interests lie in bioethics, with specific expertise in clinical, research, organizational, environmental, public health, and interprofessional ethics. He has pioneered the use of film in bioethics education, analyzing works like "Wit" and "Lorenzo's Oil," and has developed innovative teaching methods including video documentaries for patient history taking and narrative medicine sequences in clinical clerkships. His recent publications (2016-2021) focus on foundational ethical principles in pediatrics, the distinctions between ethics and politics/religion, models for ethics boards in innovative practice, and principles for public health ethics. His work consistently bridges theory and practice, with a strong emphasis on clinical ethics consultation, informed consent, and interprofessional ethics case studies. Dr. Spike has been actively involved in medical curriculum development for both undergraduate and graduate medical education, including innovative approaches to teaching history taking and narrative medicine. While specific grant details are not provided, his work in bioethics education has been disseminated through publications in MedEdPortal and other venues. Dr. Spike founded the ethics case study series in the American Journal of Bioethics and serves on the Editorial Board of Narrative Inquiry in Bioethics. He has collaborated on projects such as the casebook in interprofessional ethics with R. Lunstroth, demonstrating his commitment to team-based approaches in bioethics education and consultation.
Aniruddha Kembhavi is an Affiliate Associate Professor at the University of Washington's Computer Science & Engineering department and currently serves as Director of Science Strategy at Wayve AI in London, UK. Previously, he led computer vision efforts as Senior Director at Allen Institute for AI (AI2) in Seattle and contributed to Microsoft's Image and Video Search division. His research spans 20+ years in Computer Vision , Robotics , and Embodied AI , focusing on open-source frameworks like AI2-THOR and Molmo. His work emphasizes procedural environment generation , vision-language integration , and 3D asset creation , with large-scale datasets such as Objaverse becoming foundational in 3D computer vision. CVPR 2025 Best Paper (Honorable Mention) CVPR 2023 Best Paper Winner Neurips 2022 Outstanding Paper CoRL 2024 Outstanding Paper IROS 2024 Best Mobile Manipulation Paper ICRA 2024 Best Paper Winner Allen Institute Test Of Time Award 2020 NVIDIA Pioneer Award 2018 His recent publications analyze vision-language models , 3D generation evaluation , and diffusion architectures for unified generation. He actively contributes to community-building as Program Chair for ICCV 2025 and Senior Area Chair for CVPR 2024.
Joni-Kristian Kämäräinen serves as Professor of Signal Processing within the Computing Sciences department at Tampere University, where he leads research in the Vision Group. Previously, he held faculty positions at LUT University's School of Engineering Science for five years before joining Tampere University in 2012 (tenured 2017, promoted to full professor in 2020). His academic journey includes a postdoctoral fellowship at the University of Surrey's Center of Vision, Speech and Signal Processing under Josef Kittler. His research centers on robot vision and robot learning , with significant contributions to computer vision and machine learning. Key focus areas include visual place recognition, RGB-D tracking, color constancy, and anthropometric measurements. His group maintains strong industry collaborations with Huawei, Nokia Technologies, and Business Finland-funded projects. His publication portfolio shows a clear trajectory toward real-world robotic applications, with recent work emphasizing visual place recognition under varying conditions (2022-2024), depth-aware video processing (2023-2024), and reinforcement learning for industrial manipulators (2023-2025). The 2023 textbook Koneoppimisen perusteet (Machine Learning Fundamentals) demonstrates his commitment to education. Expert Statement for Finnish Parliament (2022) on AI solutions Contributor to Finnish Roadmap: Robots and the Future of Welfare Services (2021) Featured in YLE Uutiset (2018), Aamulehti (2021), and multiple technical press outlets He has supervised 20 PhD students since 2007, including Vivienne Huiling Wang (2025), Samu Koskinen (2025), and Fatemeh Shokollahi Yancheshmeh (2024), with alumni placed at Aalto University, Ericsson AB, and Huawei. His group receives funding from the Academy of Finland, EU Horizon 2020, Business Finland, Huawei, and Nokia Technologies. The Vision Group operates from Tampere University's Hervanta Campus, maintaining close ties with industrial partners through applied research projects.
