Daniel Sierra Ramos is an Adjunct Professor at IE University, Spain, and Co-Founder of Fitizens, a startup developing AI-driven wearables for physical activity quantification. He has over 6 years of experience as a Lead Data Scientist at Telefónica and Synergic Partners, specializing in AI applications for industries like banking, retail, and telecommunications. His expertise includes machine learning models for customer segmentation, predictive maintenance, and demand forecasting, alongside Big Data and cloud technologies (AWS/Azure). Education: Master in Telecommunication Engineering (Carlos III University, 2015) Master in Multimedia and Communications (Carlos III University, 2015) Bachelor in Telematic Engineering (Carlos III University, 2013) His research focuses on integrating AI into wearable devices and industry-driven data solutions. He trains professionals in AI from technical and business perspectives, emphasizing autonomy in data-centric environments. Active in corporate roles since 2015, he bridges technological innovation with practical business applications.
Shiri Azenkot is an Associate Professor at the Jacobs Technion-Cornell Institute at Cornell Tech and Cornell University, affiliated with the Information Science Department and Technion’s Computer Science Department. Her research focuses on accessibility innovations in emerging technologies, particularly addressing needs of people with vision impairments and neurodiverse populations. She holds a PhD from the University of Washington, advised by Richard Ladner and Jacob Wobbrock, and has been recognized with prestigious awards including the NSF Graduate Research Fellowship. Education: PhD in Computer Science & Engineering from University of Washington (advisors: Richard Ladner, Jacob Wobbrock). Research interests include VR/AR accessibility, assistive technology design, inclusive education tools, and ethical AI applications. Current projects explore social VR avatars for invisible disabilities, AR navigation aids, and AI moderation systems against ableist hate speech. Her work is funded by NSF, AOL, Verizon, and Facebook. Recent publications address video accessibility for ADHD users, AI-driven scene descriptions for blind users, and tactile learning materials co-designed with educators. She leads the XR Access initiative promoting inclusive virtual/augmented reality. Awards include the UW Graduate Medal (2020), NSF CRII Award (2017), and AT&T Labs Fellowship (2014). Her research bridges academic innovation with industry impact through collaborations with tech companies and disability advocacy groups.
Prof. Mark Heitmann is a Professor of Marketing & Customer Insight at the University of Hamburg Business School. He holds a visiting appointment at Nova School of Business and Economics. His research focuses on AI-driven marketing strategies, social media analytics, brand equity, and consumer decision-making. He has held academic roles at institutions including the University of St. Gallen and Christian-Albrechts-University in Kiel before joining Hamburg in 2011. Academic Career: Holds a PhD (2004) and post-doctoral Habilitation (2007) from the University of St. Gallen. Visiting researcher at Columbia University and the Max Planck Institute for Human Development. Research Interests: Applications of artificial intelligence in marketing Social media and digital transformation Ethical consumer behavior Brand self-expression and visual design Marketing technology (MarTech) innovations Key Achievements: Over 20 peer-reviewed publications in top journals like Journal of Marketing Research and Harvard Business Review . Award-winning research includes the MSI/H. Paul Root Award and IJRM-EMAC Best Article Award. Active in translating research into scalable commercial solutions through software-as-a-service ventures. Team & Collaborations: Leads a research team including M.Sc. candidates Sammar Rath, Julia Rosada, Maximilian Witte, Tijmen Jansen, Claus Hegmann-Napp, and Magdalena Heynicke. Engages in interdisciplinary projects with industry partners.
Zakia Hammal is an Assistant Research Professor with dual appointments at Carnegie Mellon University, holding positions in the Robotics Institute within the School of Computer Science and the Department of Biomedical Engineering in the College of Engineering. Her work bridges computer science, machine learning, artificial intelligence, and social/behavioral psychology to advance computational models for human behavior analysis. Dr. Hammal's educational background includes a PhD in Computer Science, a Master of Artificial Intelligence and Algorithmic with specialization in Image Processing, and an Engineer's degree in Computer Science with specialization in Computer Systems. Her academic journey has positioned her at the intersection of technical expertise and healthcare applications. Her research focuses on multimodal human behavior modeling in social interaction, with particular emphasis on health informatics and affective computing (Emotion AI). Dr. Hammal's work has pioneered computational models for multimodal assessment of psychiatric disorders, including depression severity evaluation, automatic pain intensity measurement, assessment of expressiveness in children with facial abnormalities, analysis of non-verbal communication in mother-infant interaction, and identification of behavioral markers in autism spectrum disorder. Her approach integrates computer vision, machine learning, and behavioral psychology to create systems that can objectively measure human behaviors that are often subjective in clinical settings. Analysis of her recent publications reveals a consistent trajectory toward more sophisticated multimodal approaches to healthcare challenges, particularly in pain assessment and mental health diagnostics. Her work increasingly emphasizes interpretable AI models that can translate complex behavioral patterns into clinically meaningful insights, with growing attention to applications for vulnerable populations including infants, elderly patients, and those with craniofacial abnormalities or autism spectrum disorder. Women in AI Awards North America 2023 – AI Researcher of the Year Award Outstanding Reviewer Award at FG 2015 Best Paper award at ACII 2015 Outstanding Paper award at ICMI 2012 Dr. Hammal has secured significant research funding, primarily from the U.S. National Institutes of Health, including an R01 grant for developing a Multimodal Behavioral AI platform for pain assessment and management, and additional grants for automatic pain assessment in older adults with dementia. Her leadership extends to mentoring through her involvement in organizing workshops and conferences that train the next generation of researchers in affective computing and health informatics. As an active leader in her field, Dr. Hammal serves as ACM ICMI Steering Board Committee Member, Associate Editor for IEEE Transactions on Affective Computing and IEEE Transactions on Multimedia, and has organized numerous influential workshops including the International Workshop on Automated Assessment of Pain and Face and Gesture Analysis for Health Informatics. She is set to serve as Program Chair for FG 2025, ACII 2025, and ICMI 2026, demonstrating her growing influence in shaping the future direction of research in multimodal interaction and affective computing.
Xiaoming Liu is the Anil K. and Nandita Jain Endowed Professor of Engineering and MSU Foundation Professor in the Department of Computer Science and Engineering at Michigan State University . Holding a Ph.D. from Carnegie Mellon University (2004), he leads cutting-edge research in computer vision and machine learning. Research Interests : Computer Vision Pattern Recognition Image and Video Processing Machine Learning Medical Image Analysis Multimedia Retrieval Recent Research Trends : Focus on 3D object detection and depth estimation Development of robust biometric recognition systems Integration of radar-camera fusion for autonomous systems Advancements in self-supervised and multimodal learning Exploration of adversarial AI security Creation of interpretable forgery detection frameworks Teaching : Spring 2013: CSE891-006 Computer Vision Seminar Fall 2012-2015: CSE803 Computer Vision Spring 2014-2017: CSE 471 Media Processing and Multimedia Contact Information : Email: liuxm@cse.msu.edu Office: EB 3137, Michigan State University Phone: +1 (517) 355-2359
Hossam Hassanein is a Professor and Director of the School of Computing at Queen's University. He received his B.Sc. in Electrical Engineering from Kuwait University in 1984, M.Sc. in Computer Engineering from the University of Toronto in 1986, and Ph.D. in Computing Science from the University of Alberta in 1990. He joined Queen's University School of Computing in 1999 and has established himself as a leading researcher in telecommunications and networking. Dr. Hassanein's research interests span wireless sensor networks, mobile ad hoc networks, edge computing, Internet of Things (IoT), radio resource management, and data-centric networks. His seminal contributions include pioneering work on WSN planning, load-balanced routing protocols, and energy-efficient network designs. He has championed research in IoT, developing frameworks for smart spaces that use contextual information to enhance IoT applications in healthcare, transportation, and infrastructure. His recent publications (2023-2025) demonstrate a strong focus on cutting-edge areas including extreme edge computing, vehicular networks, and AI/ML integration in networking. Research trends show increasing emphasis on practical applications in telesurgery, digital twins, and industrial IoT, addressing challenges in resource allocation, task offloading, and real-time processing in constrained environments. Dr. Hassanein has received numerous recognitions for his work: Fellow of the IEEE Queen's University School of Graduate Studies Award for Excellence in Graduate Student Supervision (2015) Multiple best paper awards from top international conferences As founder and director of the Telecommunications Research Lab (TRL), Dr. Hassanein has supervised over 75 students who have made substantial contributions in academia and industry. The TRL is one of Queen's largest research groups with extensive international collaborations. Dr. Hassanein has successfully attracted significant research funding from government and industry sources in the competitive telecommunications field. The Telecommunications Research Lab has developed innovative platforms including SPROUTS, a rugged sensor platform used in mining, steel manufacturing, and smart-grid monitoring. TRL's work has had significant impact in WSN planning, data dissemination, and resource reuse in wireless networks, with contributions featured in IEEE Wireless Communications Magazine.
Julian McAuley is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego's Jacobs School of Engineering. His research spans recommender systems, machine learning, natural language processing, music information retrieval, and multimodal learning. He maintains an active research group with numerous PhD students and postdocs working on cutting-edge AI problems. His research interests focus on developing advanced algorithms for personalized recommendation systems, with particular emphasis on sequential recommendation, multimodal learning, and integrating large language models with traditional recommendation approaches. His work bridges the gap between theoretical machine learning and practical applications across multiple domains including e-commerce, music, and healthcare. McAuley has published extensively in top-tier conferences including NeurIPS, ICML, KDD, SIGIR, and ACL, with his most recent work exploring the intersection of large language models and recommendation systems. His publications reveal a strong trend toward multimodal approaches that combine text, vision, and audio for more comprehensive understanding and recommendation. He has received significant research funding from major technology companies including Google, Amazon, Facebook, Adobe, and Samsung, as well as government agencies like the National Science Foundation and Department of Defense. His work has practical applications across multiple industries, with a focus on improving user experience through better personalization. McAuley advises numerous PhD students who have gone on to successful careers at leading technology companies and academic institutions. His former students include Wang-Cheng Kang and Jianmo Ni at Google DeepMind, Chris Donahue and Zachary Lipton as assistant professors at CMU, and Ruining He at Google Deepmind.
Dr. Lourdes Pena-Castillo is a Professor jointly appointed in the Departments of Computer Science and Biology at Memorial University of Newfoundland's Faculty of Science. Her research focuses on applying machine learning and bioinformatics to study bacterial gene regulation, with emphasis on transcriptomics, gene expression pathways, and microbiology. She leads the Bioinformatics Lab at MUN, developing computational tools like Promotech for promoter prediction and sRNARFTarget for sRNA target identification. Education: BSc in Information Systems Engineering, ITESM-Mexico MSc in Computer Science, University of Alberta PhD in Computer Science (Doktoringenieurin), Otto-von-Guericke Universität Magdeburg Postdoc in Bioinformatics, University of Toronto Research Interests: Bioinformatics, Genomics, Machine Learning, Artificial Intelligence, Transcriptomics, Gene Regulation, Microbiology Her work integrates computational methods with biological data to address challenges in molecular biology, including analyzing bacterial sRNA functions, promoter recognition, and disease diagnostics using machine learning. She has advised numerous graduate students, including PhD candidates Purvikalyan Pallegar and Bonita McCuaig, and MSc students like Ruben Chevez-Guardado and Kratika Naskulwar. Her lab focuses on translational research with applications in both basic science and clinical contexts. Publications span computational methods for bacterial gene regulation, bioinformatics tool development, and interdisciplinary projects in VR and healthcare informatics. Her research has contributed to understanding symbiotic relationships in marine organisms, inflammatory bowel disease diagnostics, and clavulanic acid production in Streptomyces. Grants & Collaborations: Works with interdisciplinary teams across computer science and biology, supported by grants enabling projects in bacterial genomics and computational tool development. Labs & Teams: Leads the Bioinformatics Lab at MUN, fostering collaborations with researchers in microbiology, computer science, and healthcare.
Dr. Aditya Joshi is a Senior Lecturer in the School of Computer Science & Engineering at the University of New South Wales (UNSW). He specializes in Natural Language Processing (NLP), with a focus on sarcasm detection, dialectal NLP, and ethical AI applications in public health and cybersecurity. He joined UNSW in 2023 following industry roles at SEEK, Notiv, and Fractal Analytics, where he developed NLP systems for recommendation engines and meeting analytics. His research has garnered over 3,000 citations (h-index 26) and secured $3.1M in grants, including Defence Trailblazer and Google exploreCSR awards. Education: Joint PhD (2018) from IIT Bombay (India) and Monash University (Australia); MTech in CSE (2011) from IIT Bombay. Research Interests: Making NLP models robust for non-native English speakers and the LGBTI+ community, algorithmic enhancements to transformers, and applications in public health, cybersecurity, and societal issues. His work spans epidemic intelligence (collaborations with EPIWATCH and IFCYBER), cybersecurity tools like AuditNet, and inclusive AI initiatives such as queer-inclusive workshops funded by Google. He designed UNSW's new NLP course (COMP6713) and co-authored a Wiley textbook on NLP. Notable grants include the A$1.4M 'Comprehensive Defence Data Platform' (Lead CI) and A$92K Google exploreCSR grant for benchmarking dialectal sentiment. His awards include the Best PhD Thesis from IITB-Monash and Best Paper accolades at FAccT 2023 and MoMM 2020. He supervises projects on kernel-based attention reformulation, prompt-based sarcasm detection, and multilingual small-scale LLMs. His service roles include Executive Committee Member at ALTA and arXiv moderator for computational linguistics.
Rita Cucchiara is a Full Professor at the Department of Engineering 'Enzo Ferrari' of the University of Modena and Reggio Emilia. She leads the AImageLab research laboratory, part of the Artificial Intelligence Research and Innovation Center (AIRI) in Modena. Her research focuses on Computer Vision, Pattern Recognition, Machine Learning, and Multimedia, with applications in video surveillance, medical imaging, human-centered AI, and generative models. She is actively involved in interdisciplinary projects like ELIAS (European Lighthouse for AI Sustainability) and ELSA (European Lighthouse on Secure AI). Her recent roles include being elected Rector of the University of Modena and Reggio Emilia in 2025. She has organized and participated in major AI events, including workshops at NeurIPS, CVPR, and ECCV, and has contributed to advancements in multimodal models, deepfake detection, and trustworthy AI. Education details are not explicitly provided, but her extensive academic and research experience at the University of Modena underscores her expertise. She collaborates with institutions like NVIDIA, CINECA, and industry partners such as Digital Design and NVIDIA's AI Technology Center. AImageLab's projects include developing systems for medical imaging, ethical AI, and generative adversarial networks (GANs) for design surfaces. She co-organizes initiatives like the ELLIS Summer School on Large-Scale AI and contributes to policy discussions on AI ethics and societal impact. Her work spans from foundational research (e.g., vision transformers, continual learning) to applied projects (e.g., DDGan system for surface printing). Key grants and collaborations include the FAIR project and PNRR-M4C2 initiatives. She advises students and researchers in AI, with 7 PhD positions funded under national programs. Her leadership roles in AIRI and AImageLab highlight her commitment to bridging academia and industry, fostering innovation in AI-driven solutions for sustainability and healthcare.
Michael Nebeling is an Associate Professor at the University of Michigan's School of Information (UMSI), leading the Information Interaction Lab. His research focuses on Augmented, Virtual, and Mixed Reality (XR) interfaces, emphasizing usability, accessibility, and privacy. He co-directs the HCI seminar series in the Michigan Interactive and Social Computing (MISC) group and teaches AR/VR courses (SI 559/659). Nebeling has held roles such as UIST 2026 General Co-Chair and CHI 2026 Subcommittee Co-Chair. He pioneered the Extended Reality for Everybody Coursera specialization and the XR MOOC series . His work includes projects like XRDirector , MRAT , and 360proto , addressing challenges in XR design and tool development. Awards include the Disney Research Faculty Award, Mozilla Research Award, and Epic MegaGrant. He advises the student-led Alternate Reality Initiative (ARI) and collaborates with industry partners like Meta, Epic, and Disney. Education: PhD in Computer Science from ETH Zurich (2015), Postdoc at Carnegie Mellon University's Human-Computer Interaction Institute (2016). Research Themes: XR toolkits, privacy/security in AR/VR, inclusive design, educational applications, and ethical considerations in mixed reality. Over 90 publications in venues like CHI, UIST, and ACM Transactions on CHI. Awards: Disney Research Faculty Award (2018), Mozilla Research Award (2018), Epic MegaGrant (2019), Meta Reality Labs Gift (2020). Recognized with Best Paper Awards at CHI and UIST.
Shih-Fu Chang is the Dean of Columbia Engineering and holds the Morris A. and Alma Schapiro Professorship at Columbia University. His research focuses on computer vision, machine learning, and multimedia information retrieval. He is recognized as a foundational figure in the field of content-based visual search and has pioneered innovations in image/video search engines, crime prevention systems, and brain-machine interfaces. His leadership roles include Chair of Columbia's Electrical Engineering Department (2007-2010), Editor-in-Chief of the IEEE Signal Processing Magazine (2006-2008), and Senior Executive Vice Dean at Columbia Engineering, where he drives strategic planning and international collaboration. Dr. Chang has received prestigious awards including the ACM Multimedia Technical Achievement Award, IEEE Signal Processing Technical Achievement Award, and IEEE Kiyo Tomiyasu Award. He is a Fellow of AAAS, ACM, and IEEE, and an Academician of Academia Sinica. His recent work emphasizes multimodal reasoning, few-shot learning, and vision-language systems, with applications in healthcare diagnostics and multimedia benchmarking. His research spans cross-modal understanding, event extraction, and adaptive AI systems. Key contributions include systems like Ferret-v2 for multimodal grounding and RESIN for schema-guided event tracking. He has advised multiple startups and actively contributes to curriculum development in AI and engineering education.
Prof Lee Kwan Min is a Professor and the inaugural Korea Foundation Professor in Contemporary Korean Society and New Media at Nanyang Technological University’s Wee Kim Wee School of Communication and Information (WKWSCI), where he directs the UX Lab. He holds a Ph.D. from Stanford University and has held leadership roles, including founding director of Interaction Science Research Center at Sungkyunkwan University and Vice President at Samsung Electronics, overseeing UX strategy and innovation. His research focuses on user experience (UX), human-machine interaction (HCI/HRI/HAI), digital culture, and Korean society’s technological evolution. He has secured over $12M in research grants and holds patents in display interfaces and gesture control. Notable awards include ICA Fellowship and USC Mellon Teaching Award. Education: Ph.D. (Stanford University), M.A./B.A. (implied from career timeline). Previous affiliations include Sungkyunkwan University (SKKU) and the University of Southern California (USC), where he was tenured at 40. His work bridges academia and industry, advising startups in fintech, smart mobility, and UX design. Research Interests: UX design and evaluation Social/psychological impacts of ICT Human-robot/vehicle interaction Smart cities and disruptive technologies Korean digital society studies Recent work trends emphasize conversational interfaces in vehicles, metaverse analysis, and Fintech agents. His articles explore topics like autonomous vehicle trust, cross-cultural gaming psychology, and VR advertising. Awards include ICA Fellow status and multiple top-paper recognitions. Grants and Patents: Over $12M funding from NSF, Samsung, and Korean/US governments. Patents span smart displays, gesture control, and multimedia systems. Advises global firms on UX strategies and innovation. Labs/Teams: Director of UX Lab at NTU, leading interdisciplinary research. Collaborates with MIT Cognet and editorial boards of top journals like Journal of Communication and Human Communication Research .
Yuchen Liu is an Assistant Professor in the Department of Computer Science and Department of Electrical & Computer Engineering (by courtesy) at North Carolina State University. He earned his Ph.D. in Electrical and Computer Engineering from Georgia Institute of Technology. His research spans networking, machine learning, and cybersecurity, focusing on wireless systems, digital twins, and networked agentic systems. Research areas: Networking (3D UAV networks, mmWave/THz communication, cybersecurity), Machine Learning (generative AI, LLMs, reinforcement learning), Digital Twins (synchronization optimization, edge caching), Software Development (differentiable simulators, open-source testbeds) His recent publications emphasize neurosymbolic AI, diffusion models for wireless systems, and multi-agent approaches to spectrum sensing. Articles highlight applications in UAV networks, vehicular security, satellite localization, and federated learning defenses. Honors include NSF CAREER (2025), NVIDIA Academic Grant (2025), NCSU Carla Savage Award (2025), and multiple IEEE/ACM Best Paper Awards. Current projects are supported by NSF CNS (#2312138), NSF SaTC (#2350075), and NSF NAIRR Pilot Demonstration (#2506757) grants.
Oswald Lanz is a tenured full professor at the Faculty of Engineering of the Free University of Bozen-Bolzano , leading the Visual Computing Lab . He holds a Ph.D. in Computer Science and a Mathematics degree from the University of Trento. Prior to his current role, he was a researcher and head of research at FBK Trento. He is an endowed professor collaborating with Covision Lab , an AI hub in Bressanone, and coordinates the board of professors for the PhD in Computer Science program since 2025. His research focuses on Computer Vision, Deep Learning, and Video Analytics , with applications in sports technology, medical imaging, and industrial automation. Key achievements include the Amazon AWS Machine Learning Research Award (2020) , ACM Multimedia Best Paper (2015) , and Best Student Paper at ICIAP (2007) . He co-organized the ELLIS-VISMAC Winter School (2025) and chaired ICIAP 2019 . His work spans novel view synthesis, action recognition, and anomaly detection, supported by patents in video tracking and detection. He teaches courses like Deep Learning and Artificial Intelligence in undergraduate and graduate programs. Recent projects such as 5VREAL integrate 5G, edge computing, and AI for sports analysis. His collaborations bridge academia and industry, exemplified by his role in Covision Lab and multidisciplinary initiatives like DSS4LCO for food supply chains. Lanz’s publications emphasize spatiotemporal modeling, neural architecture search, and hybrid machine vision systems.
