Dr. Jennifer Ahjin Kim is an Assistant Professor in the Department of Neurology at Yale School of Medicine. She specializes in neurocritical care, focusing on quantitative analysis of electroencephalography (EEG) and neuroimaging for early diagnosis and treatment optimization in patients with severe neurologic injuries. Her clinical interests include traumatic brain injury, subarachnoid hemorrhage, stroke, and post-traumatic epilepsy. PhD in Neuroscience, Brown University (2012) MD from Brown University (2012) Neurology Residency, Massachusetts General Hospital/Brigham & Women's Hospital (2016) Neurocritical Care Fellowship, Massachusetts General Hospital/Brigham & Women's Hospital (2019) Dr. Kim’s research integrates multimodal data (EEG, MRI, CT) with machine learning to predict secondary complications after brain injuries. She actively contributes to clinical trials like BOOST3 and ASPIRE, aiming to improve outcomes for patients with traumatic brain injury, stroke, and hemorrhage. Her recent work emphasizes automated detection of epileptiform discharges, predictive modeling for delayed cerebral ischemia, and application of NLP to CT reports. Collaborations include frequent partnerships with Guido Falcone, Lawrence Hirsch, and Emily Gilmore. Dr. Kim leads the Kim Laboratory, which focuses on bedside monitoring technologies and secondary prevention strategies.
Shuhao Fu is a Program Postdoctoral Fellow at the Santa Fe Institute (SFI) researching the intersection of machine learning and cognitive science. He completed his Ph.D. in Psychology at UCLA under advisors Hongjing Lu and Ying Nian Wu, following a B.S. in Computer Science and Mathematics from Hong Kong University of Science and Technology. His research examines human-like relational reasoning in AI systems through cognitive modeling and computational approaches. Research focuses on: Bridging human-machine reasoning gaps via analogical mapping Developing explicit relational representations in vision models Structural cognitive modeling for compositional understanding Multimodal reasoning and scene interpretation Relational knowledge representation in biological and artificial systems Publication trends show concentrated work in computational cognitive science (2021-2025), with evolving focus from visual analogy fundamentals to applications in 3D recognition, social interaction modeling, and mental health diagnostics. Recent work demonstrates increased emphasis on transformer architectures, multimodal integration, and human-AI comparative studies. Professional experience includes research internships at Google X and Mineral.ai, with prior affiliation at Johns Hopkins University's CCVL lab under Alan Yuille. Currently serves as reviewer for ICML, ICCV, and Cognitive Science Society conferences.
Ashok Goel is a Professor of Computer Science and Human-Centered Computing at Georgia Institute of Technology and Chief Scientist at Georgia Tech’s Center for 21st Century Universities (C21U). He also serves as Executive Director of the NSF-funded National AI Institute for Adult Learning and Online Education (AI-ALOE). His research spans cognitive systems, artificial intelligence, and education, with a focus on computational design, creativity, and AI-driven educational technologies. Professor, School of Interactive Computing, Georgia Tech Chief Scientist, Center for 21st Century Universities Executive Director, NSF’s National AI Institute for Adult Learning and Online Education Goel’s research explores the intersection of AI and cognitive science, particularly in computational design, creativity, and biologically inspired design. His recent work emphasizes AI in education, including virtual teaching assistants like Jill Watson (powered by ChatGPT) and frameworks for scalable, human-centric AI-augmented learning. He investigates explainable AI, multimodal educational systems, and bidirectional feedback mechanisms to enhance personalized learning experiences. Award highlights include: AAAI’s Outstanding AI Educator Award Fellow of AAAI and Cognitive Science Society University System of Georgia Regent’s Award for Scholarship of Teaching and Learning Goel leads the Design Intelligence Laboratory at Georgia Tech, mentoring a team of graduate and undergraduate researchers. His contributions to AI education include pioneering Georgia Tech’s Online Master of Science in Computer Science (OMSCS) program and developing blended learning frameworks. He also co-founded the AI-based educational startup Beyond Question (LLC) in 2020.
Dr. Yang Zhang is a tenured Professor at the CISPA Helmholtz Center for Information Security . His research focuses on Trustworthy Machine Learning , emphasizing privacy, safety, and security , with additional work on measuring misinformation and unsafe online content like hateful memes. He has published extensively at top conferences (CCS, NDSS, Oakland, USENIX Security) and received multiple awards including the Busy Beaver Award (2022) and NDSS Distinguished Paper Award (2019) . Research Interests : Trustworthy Machine Learning LLM Security, Privacy, and Safety Misinformation and Hate Speech Detection Social Network Analysis Recent Publications examine synthetic data auditing, hate speech detection in LLM-generated content, and privacy risks in curriculum learning, spanning conferences like USENIX Security , IEEE S&P , and ACM CCS . His work often intersects AI security with ethical considerations . Scientific Awards : Busy Beaver Award for “Privacy of Machine Learning” (2022) NDSS Distinguished Paper Award (2019) CCS Best Paper Runner-Up (2022) Best Machine Learning and Security Paper in Cybersecurity Award (2025) Best Paper Finalist at CSAW Europe (2023, 2024) Students in his group include Yixin Wu , Xinyue Shen , and Yiting Qu , the latter recently completing their Ph.D. defense. He actively recruits MSc and PhD students and has contributed to iDRAMA Lab for meme-related research.
Yingdan Lu is an Assistant Professor in the Department of Communication Studies at Northwestern University's School of Communication. She serves as Director of the Computational Media and Politics Lab, co-director of the Computational Multimodal Communication Lab, and is core faculty in Northwestern's Media, Technology, and Society (MTS) and Technology and Social Behavior (TSB) PhD programs. She is also affiliated with the Center for Communication & Public Policy and the Center for Human-Computer Interaction + Design. Her educational background includes a Ph.D. in Communication from Stanford University (2023), where she also earned a Ph.D. minor in Political Science and an M.A. in East Asian Studies. She received her B.A. in Journalism and Communication from Tsinghua University. Dr. Lu's research centers on digital technology, political communication, and authoritarian politics, with two primary focus areas: the role of digital technologies in authoritarian politics, and multimodal communication across different contexts. Her work employs computational and qualitative methods to examine how authoritarian governments use digital media and AI to maintain rule, and how individuals experience technology in different media environments. As a computational social scientist, she advances methods for analyzing multimodal data (images, videos), cross-lingual digital communication, and mixed-method research. Her publications reveal a consistent focus on digital authoritarianism, particularly Chinese state propaganda on social media platforms, multimodal communication analysis, and information manipulation. Her work spans interdisciplinary outlets including Political Communication, New Media & Society, International Journal of Press/Politics, and Proceedings of the National Academy of Sciences. Dr. Lu's research has received funding from the National Science Foundation, Brown Institute for Media Innovation, and the Stanford Institute for Human-Centered Artificial Intelligence. She is the founder of COMputation Island ("计传岛"), a WeChat-based platform serving over 10,000 Mandarin-speaking scholars in computational communication research. She leads the Computational Media and Politics Lab and co-directs the Computational Multimodal Communication Lab, where she develops innovative frameworks for analyzing multimodal data and promotes mixed-method approaches to computational social science research.
Stuart E. Middleton is a Professor in the Electronics and Computer Science (ECS) department at the University of Southampton, where he has been employed since 2003. His research bridges artificial intelligence with practical applications in social science, mental health, and security domains. He leads multiple research projects funded by DTP and CISDnS CDT, focusing on multimodal natural language processing and large language models for social good applications. Professor Middleton's research interests center on Natural Language Processing, Large Language Models, and Human-in-the-loop AI systems. His work spans mental health applications (particularly suicide risk detection and mood change analysis), social media analysis for crisis mapping, geoparsing for location extraction, and argument mining in political discourse. He has developed numerous open-source NLP projects and datasets including CPIQA for climate science, ConversationMoC for mental health monitoring, and M-Arg for multimodal argument mining. His research demonstrates how AI can effectively support human decision-making in critical domains like mental healthcare, defense applications, and crisis management. His recent publications reveal a strong trend toward applying LLMs to high-impact societal challenges, particularly in mental health monitoring and climate science verification. He has pioneered methods for detecting suicidal ideation in social media, identifying moments of mood change, and developing context-aware question answering for climate papers. His work consistently emphasizes the importance of human oversight in AI systems, with numerous publications on responsible AI, regulation, and human-in-the-loop approaches. Ranked 1st in ECAL-2024 shared task on suicidal ideation detection Ranked 1st in NAACL-2022 shared task on suicide risk and mood change classification Winner of 'best paper' award at WWW2002 Semantic Web Workshop Professor Middleton actively supervises PhD students through multiple funded projects including 'Multimodal Natural Language Processing for Computational Social Science', 'Large Language Models for Military Veteran Mental Health', and 'Large Language Models for Human/AI Information Foraging to Combat Digital Human Trafficking into Terrorism'. He has secured significant funding from UKRI, DSTL, and other sources to support his research in responsible AI applications. He organizes major workshops including the RAI UK Workshops on Responsible AI for Mental Health and AIUK workshops on AI for Data Rescue and Defense applications. His research group maintains numerous GitHub repositories with open-source NLP tools and datasets that have been widely adopted by the research community.
Peter Burke is a Professor of Electrical Engineering and Computer Science (joint appointments in Biomedical Engineering and Materials Science and Engineering ) at the Samueli School of Engineering, University of California, Irvine . His research bridges nanoelectronics with biotechnology , focusing on carbon nanotubes , graphene devices , and mitochondrial bioenergetics . He has received prestigious Young Investigator Awards from the Office of Naval Research and Army Research Office. Education: B.A. in Physics, University of Chicago (1992) Ph.D. in Physics, Yale University (1998) His work spans quantum electronics , high-speed semiconductor devices , and bio-nano interfaces . Recent publications highlight drone technology , mitochondrial electrical activity , and AI-driven nanoscale sensing . Research trends include terahertz spectroscopy , super-resolution imaging , and open-source medical devices like the NanoStat potentiostat . Scientific Awards Young Investigator Award, Office of Naval Research Young Investigator Program Award, Army Research Office As director of the BurkeLab , he develops nano-electronic interfaces for biological systems, including mitochondrial membrane potential assays and graphene-based biosensors . His lab's innovations in carbon nanotube arrays and scanning microwave microscopy have advanced bio-nano applications.
Dr. Ali Arya is an Associate Professor at the School of Information Technology , Carleton University, Canada. His work bridges Human-Computer Interaction , Educational Technologies , and Virtual/Augmented Reality systems. Funded by NSERC, SSHRC, and OCE, he has designed graduate programs in Digital Media and organized the Global Game Jam since 2009. Education: B.Eng. Electrical Engineering, Tehran Polytechnic Ph.D. Computer Engineering, University of British Columbia Research Interests: Immersive VR/AR for education and health Affective and social computing Personalized learning systems Wearable interaction technologies Game design and development 3D virtual environments Research Trends: Recent publications focus on inclusive VR design, educational applications of immersive environments, anxiety-reduction technologies, and culturally responsive systems. His work combines machine learning, multimodal interaction, and pedagogical innovation across STEM and social contexts. Scientific Awards: OCUFA Teaching Award (2023) Carleton Provost's Fellowship (2020) Faculty Teaching Excellence Award (2019) Graduate Mentorship Award (2019) Professional Service: Associate Dean (2018-2022), Program Chair FDG (2021), IEEE/ACM conference committees. He maintains the Interactive Media Group (iMG) research lab and contributes to open-access educational resources including his "Anyone Can Code" book series.
Jungeun (Jenny) Won is an Assistant Professor of Research in the Department of Biomedical Engineering at the School of Engineering and Applied Sciences, University at Buffalo. Her research focuses on optical imaging , biomedical device development , medical image analysis , and artificial intelligence in OCT . She leads the Translational Biophotonics Laboratory , where she develops advanced OCT techniques for medical applications such as diabetic retinopathy , otitis media , and biofilm analysis . Contact: 215J Bonner Hall, Buffalo NY 14260, jungeunw@buffalo.edu Related Links: CV PDF , Google Scholar , Lab Website Her recent work involves high-resolution OCT for longitudinal studies on retinal degeneration, VISTA OCTA for blood flow analysis, and 3D motion correction algorithms to enhance image quality. She also explores multimodal imaging combining OCT with Raman spectroscopy for bacterial differentiation and microplasma-based therapies for ear infections.
Dr. Armin Mustafa is an Associate Professor in Computer Vision and AI at the University of Surrey, where he holds a prestigious Royal Academy of Engineering Research Fellow position. He is affiliated with the Centre for Vision, Speech and Signal Processing (CVSSP), the School of Computer Science and Electronic Engineering, and the Surrey Institute for People-Centred Artificial Intelligence (PAI). His research focuses on developing AI systems for visual understanding of complex dynamic scenes, with applications in entertainment, autonomous systems, and augmented/virtual reality. Dr. Mustafa completed his PhD in general dynamic scene reconstruction from multi-view videos in 2016 from the University of Surrey under the supervision of Prof. Adrian Hilton. Prior to his doctoral studies, he worked for three years (2010-2013) at Samsung Research Institute in Bangalore, India, in the field of Computer Vision. His research expertise spans Computer Vision, Scene Understanding, 3D/4D Vision, Virtual Reality, Light Fields, Machine Learning, Video Captioning, Augmented Reality, Artificial Intelligence, and Audio-visual Video Understanding. Dr. Mustafa has pioneered advances in 4D vision, NLP, and Scene Understanding over the past decade, with a particular focus on enabling machines to model and interpret real-world environments for socially beneficial applications. His work bridges theoretical advances in computer vision with practical applications in media production, virtual reality, and autonomous systems. Analysis of Dr. Mustafa's recent publications reveals a strong focus on multimodal learning, particularly the integration of audio and visual information for scene understanding. His work spans diverse areas including shadow detection and removal, audio event classification, video captioning, person image generation, and dynamic scene reconstruction. A notable trend is his exploration of transformer architectures for both vision and audio tasks, as well as the application of self-supervised learning techniques to reduce dependency on labeled data. Dr. Mustafa has received numerous prestigious awards: 2018 - Research Fellowship, The Royal Academy of Engineering, UK 2017 - Young Researcher award, CVPR 2016 - Doctoral Consortium grant, CVPR 2015 - BMVA travel grant for ICCV 2014 - Set-Squared Research to Innovator grant 2013 - Overseas Research Scholarship, FEPS, The University of Surrey 2010 - Cadence Silver Medal, Indian Institute of Technology, Kanpur As a dedicated mentor, Dr. Mustafa supervises several PhD students working on cutting-edge topics including multi-person reconstruction, audio-visual scene understanding, and automatic storyboard generation. His research is supported by significant grants including a £15 million UKRI Prosperity Partnership with the BBC (AI4ME), a 5-year Royal Academy of Engineering fellowship (4D Vision for Perceptive Machines), and multiple projects with industry partners such as Figment Productions and Foundry. Dr. Mustafa is an active member of the Centre for Vision, Speech and Signal Processing (CVSSP), one of the world's leading research centers in vision, speech, and signal processing. He also contributes to the Surrey Institute for People-Centred Artificial Intelligence (PAI), where he serves as a Surrey AI Fellow. His work often involves collaboration with industry partners and other academic institutions across Europe.
Jonathan Ginzburg is a Researcher in the Department of Linguistics at Université Paris Diderot, affiliated with the CLILLAC-ARP research group. His work bridges linguistics, cognitive science, and computational models, focusing on dialogue semantics, pragmatic functions, and developmental language acquisition. Education details are not explicitly provided in the text, but his research spans formal semantics, syntax, and multimodal interaction. His studies on laughter dynamics and infant communicative behaviors have advanced understanding of pragmatic functions in early development. Key research interests include dialogue structure, question-response systems, and the integration of cognitive science with linguistic theory. Recent work explores neural correlates of language processing through type-theoretical semantics and corpus-based methodologies. He has pioneered the analysis of non-sentential utterances and their role in social interaction. His contributions to dialogue systems design for low-resource languages and the DUEL corpus (analyzing disfluencies, exclamations, and laughter) reflect his interdisciplinary approach. Current projects investigate laughter's pragmatic functions across developmental stages and cross-linguistic variations in exclamatory constructions. Laboratory affiliations include the CLILLAC-ARP laboratory, where he develops computational tools for analyzing dialogue semantics and cognitive interactions. His work on 'response spaces' for questions integrates machine learning and formal pragmatics to model conversational coherence.
Zahraa Abdallah is a Senior Lecturer at the School of Engineering Mathematics and Technology, University of Bristol. She holds a PhD and BSc in relevant fields. Her research focuses on Machine Learning, Data Science, Time Series Analysis, and their applications in Health Informatics, Neuroscience, and Bioinformatics. She leads projects on wearable technology integration for diabetes management and EEG-based disease classification. Her work emphasizes explainable AI and multimodal approaches. Zahraa is affiliated with the Bristol Doctoral College Initiative (BDFI) as an Academic Co-Director and collaborates with experts like Prof. Raul Santos-Rodriguez. Contact: zahraa.abdallah@bristol.ac.uk | Website: zahraa-abdallah.com Research Interests: Time Series Clustering & Forecasting EEG-based Disease Detection (Parkinson’s, Alzheimer’s) Smartwatch-Driven Healthcare Systems Explainable AI in Biomedical Applications Key Projects: Development of the CSTS benchmark for time series clustering Investigating insulin needs using automated delivery data Gene essentiality classification via graph neural networks Collaborations: Professor Raul Santos-Rodriguez (BDFI) Lucia Marucci (Systems & Engineering Biology)
Dr. Yizi Chen is a Researcher affiliated with the Professorship for Cartography at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering. Their work focuses on advancing cartographic techniques through AI-driven methods, historical map analysis, and geospatial technologies. Key contributions include automated map vectorization, semantic segmentation of historical maps, and integrating multimodal data for robotic systems. They have published extensively in top-tier journals and conferences, addressing challenges in deep learning applications for geomatic engineering. Education details are not explicitly provided in the text. Research interests include semantic segmentation, generative AI for cartography, and steganography in image translation. Notable publications span topics from eye-tracking segmentation to urban land use mapping, reflecting a strong interdisciplinary approach. Dr. Chen collaborates on projects involving historical map digitization and benchmarking datasets for computer vision tasks. No awards or grants are mentioned. Their work contributes to advancing geomatic engineering through innovative solutions in digital mapping and spatial data analysis.
Dr. Rafeef Garbi is a Professor at the Department of Electrical and Computer Engineering, University of British Columbia, and the Founder/Director of the Biomedical Signal and Image Computing Laboratory (BiSICL). Her multidisciplinary research integrates artificial intelligence, computer vision, and medical imaging for clinical applications in pediatric orthopedics, oncology, and neurology. PhD (Chalmers University, Sweden), MSc (with distinction), Technical Licentiate Research Focus: Specializing in Medical Image Computing and Visual Computing , her lab develops AI-driven solutions for: Automated segmentation and analysis of multi-dimensional biomedical data Clinically-translatable biomarkers for disease assessment Computer-aided intervention systems in surgical contexts Scientific Leadership: UBC Killam Faculty Research Fellow Peter Wall Institute for Advanced Studies Early Career Scholar Senior IEEE Member & Founding IEEE EMBS Vancouver Section Member Key Collaborations: Active in the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society and CAIDA: UBC ICICS Centre for Artificial Intelligence Decision-making and Action. Her team bridges engineering, medicine, and computational biology through translational research.
Zhanna Sarsenbayeva is a Lecturer in the School of Computer Science at the University of Sydney. Previously, she held a Doreen Thomas Postdoctoral Research Fellowship at the University of Melbourne. Her research focuses on Human-Computer Interaction (HCI), Ubiquitous Computing, Accessibility, and Affective Computing. She earned a PhD in Engineering from the University of Melbourne, an MSc in Computer Science and Engineering from the University of Oulu, and a BSc in Computer Science from University College London. Research Interests: Dr. Sarsenbayeva explores how technology can enhance accessibility, improve emotion recognition in mobile contexts, and address situational impairments. Her work spans wearable sensors, mobile health applications, and ethical AI methodologies. Awards & Honors: 2022–2023: Australia-Germany Joint Research Cooperation Scheme 2021: CIS ECR Grant 2020: Doreen Thomas Postdoctoral Fellowship 2019: Gaetano Borriello Outstanding Student Award Advising & Grants: She currently supervises five PhD students researching topics like mixed reality collaboration and emotion recognition. Her grants include projects on accessibility standards and fairness in AI. International Collaborations: Engages with researchers at Aalborg University (Denmark), University of Oulu (Finland), and LMU Munich (Germany) on interdisciplinary projects.
