Majed Elwardy is a Lecturer at the Department of Computer Science , Blekinge Institute of Technology , focusing on Computer Science , Virtual Reality , and Quality Assessment . He works at the Human-Centered Intelligent Realities Laboratory in Karlskrona, Sweden. Email: majed.elwardy@bth.se Phone: 0455-385801 His research investigates 360° video quality assessment in immersive environments using head-mounted displays (HMDs) , analyzing user behavior , quality perception , and subjective testing methodologies . He explores differences in standing vs. seated viewing and consistency of user feedback over time. Majed contributes to the HINTS project (Human-Centered Intelligent Realities) and has published datasets like RQA360 to advance immersive media research. His work includes studies on simulator sickness , VR experience levels , and scene exploration patterns in 360° video evaluation.
David Lindlbauer is an Assistant Professor at the Human-Computer Interaction Institute (HCII) within Carnegie Mellon University's School of Computer Science. He leads the Augmented Perception Lab and co-directs the CMU Extended Reality Technology Center (XRTC) . His research bridges Human-Computer Interaction, Computer Graphics, and Computer Vision to create adaptive interfaces that enhance human-digital interaction. Education : PhD (summa cum laude) from Technische Universität Berlin , MSc and BSc from University of Applied Sciences Upper Austria Previous Affiliation : Postdoctoral Researcher at ETH Zurich (2018-2020) David's research focuses on understanding human perception of digital information and developing computational approaches to optimize AR/VR interface usability. Key areas include: Context-aware adaptive interfaces Visual saliency and attention modeling Spatial audio-haptic systems Optimal placement algorithms Object manipulation in Remixed Reality Diminished/ambient MR interfaces His 15 most recent publications (2024-2025) span topics in adaptive XR interfaces, multimodal notifications, haptic systems, and spatial cognition. These works appear at venues like ACM CHI, ACM UIST, IEEE VR, and Frontiers in VR. Common themes include: Machine learning for interface adaptation Human factors in XR design Real-time environment analysis Privacy-aware display systems Collaborative MR interfaces Accessibility enhancements Scientific Recognition : Best Paper Honorable Mention Award (ACM CHI 2024) Best Paper Award (ACM ISS 2023) ETH Zurich Postdoctoral Fellowship Multiple best paper recognitions at CHI, UIST, and IEEE VR Teaching & Leadership : Course developer for CMU's "Interactive Extended Reality" Mentor for NASA SUITS Challenge team Co-chair roles at CHI and UIST Overseeing PhD students and research interns
Marilena Vendittelli is a Professor at Sapienza University of Rome, affiliated with the Robotics group. Her research spans control systems, biomedical robotics, motion planning, and autonomous systems. Research Areas: Control systems for robotics and nonholonomic systems Biomedical applications (hyperthermia therapy, needle insertion) UAV navigation and obstacle avoidance Haptics and human-robot interaction Adaptive estimation algorithms Recent Publications focus on adaptive control of bio-heat equations, safe UAV motion planning, soft robotics actuation, and haptic feedback in medical procedures.
David Lindlbauer is an Assistant Professor at Carnegie Mellon University's Human-Computer Interaction Institute (HCII), where he leads the Augmented Perception Lab and co-directs the CMU Extended Reality Technology Center. His research focuses on advancing Mixed Reality (MR) and Extended Reality (XR) interfaces through computational interaction methods that optimize spatial, temporal, and multimodal feedback.
David Hsu is Provost's Chair Professor in the Department of Computer Science at the National University of Singapore (NUS) School of Computing, where he founded and directs the NUS Artificial Intelligence Laboratory (NUSAIL) and leads the Smart Systems Institute. His academic leadership includes chairing major conferences such as Robotics: Science & Systems (2015) and IEEE ICRA (2016), alongside editorial roles in IEEE Transactions on Robotics and the Journal of Artificial Intelligence Research. He earned a B.Sc. in Computer Science & Mathematics from the University of British Columbia and a Ph.D. in Computer Science from Stanford University. His research spans robotics, AI, and computational biology, with recent focus on robot planning under uncertainty and human-robot collaboration. Current work integrates machine learning with decision-theoretic planning to enable robust human-robot co-existence in unstructured environments. Analysis of his 2023-2025 publications reveals dominant trends in deformable object manipulation (e.g., clothes handling via semantic keypoints), open-world navigation using scene graphs, and LLM-driven multi-agent reasoning for complex tasks. Key innovations include perspective-aware visual grounding for human-centric interaction and functional object arrangement through compositional generative models, reflecting a strong emphasis on real-world applicability. His scientific contributions have earned prestigious recognition: IJCAI-JAIR Best Paper Prize (2022) for foundational AI research Robotics: Science & Systems Test of Time Award (2021) IEEE Fellowship (2018) for contributions to robotic planning RSS Best Systems Paper Award (2017) RoboCup Best Paper Award at IROS (2015) Humanitarian Robotics Award at ICRA (2015) As director of the Adaptive Computing Laboratory, Hsu drives research on fundamental computational frameworks for human-robot interaction. The lab's work on uncertainty-aware decision-making has secured significant research funding through grants from Singapore's National Research Foundation and industry partnerships with robotics firms. While specific student names aren't publicized, his leadership in the NUSAIL indicates extensive mentorship of doctoral candidates in AI and robotics.
Xiaohu Guo is a Professor of Computer Science at the University of Texas at Dallas specializing in computer graphics, computer vision, and geometric modeling. His research develops algorithms for 3D/4D reconstruction, virtual reality, medical imaging, and physics-based simulations. Professor Guo has received significant recognition including a Best Paper Award at SIGGRAPH (2023) and an NSF CAREER Award (2012). His current research focuses on dynamic human capture, deformable models, and medical image computation. Education: PhD, Stony Brook University MS, Stony Brook University BS, University of Science and Technology of China Research Funding: Recently secured a $500,000 NSF grant for developing open-source 4D reconstruction frameworks for real-time dynamic human capture (2021). Editorial Roles: Serves on editorial boards of Graphical Models , Computer Animation and Virtual Worlds , and IEEE Transactions on Visualization and Computer Graphics .
Nicholas Mattei is an Associate Professor of Computer Science at Tulane University and Co-Director of the Tulane Center for Community Engaged AI. He holds a Ph.D. from the University of Kentucky (2012) and researches artificial intelligence, machine learning, and decision-making systems. His work combines theory, data, and experiments to develop algorithms supporting individual and group decision-making. Dr. Mattei's research spans AI ethics, fairness in algorithms, computational social choice, and preference learning. He has published over 100 academic articles and received multiple grants from organizations including Google, IBM, and the National Science Foundation, including a 2024 NSF CAREER Award. He co-authored 'Computing and Technology Ethics: Engaging Through Science Fiction' from MIT Press. Prior to joining Tulane, he held research positions at IBM Research, Data61/CSIRO, and NASA Ames Research Center. His teaching portfolio includes courses on Discrete Mathematics, Data Science, Artificial Intelligence, and Multi-agent Systems.
Miguel Nacenta is a Professor in the Department of Computer Science at the University of Victoria (UVic), Canada, and a founding member of the Victoria Interactive eXperiences with Information (VIXI) research group. Previously affiliated with the University of St Andrews (UK), his work bridges Human-Computer Interaction (HCI), Information Visualization, and Cognitive Science. He specializes in designing interactive systems that enhance human cognition, with a focus on Infotypography (using typography to encode data), collaborative problem-solving tools, and perceptual input/output devices. Research Interests: His key areas include cognitive augmentation, visualization techniques for complex tasks, multi-display environments, and tools for constraint problem-solving. Notable projects include the WriteReason tool for essay writing, InfoTypography studies on perceptual typographic parameters, and Solvi for visual constraint modeling. Grants & Collaborations: He collaborates internationally, including with the University of St Andrews on PhD scholarship programs. His work is supported by grants focusing on HCI innovations and accessibility. He actively mentors students (e.g., Adam Binks, Johannes Lang) and supervises postdoctoral researchers. Affiliations: Member of the VIXI group,他曾是St Andrews计算机科学学院的教授, 并参与多个学术服务活动, including conference program committees and journal reviews. Labs & Teams: Leads the VIXI lab at UVic, focusing on interactive technologies for cognitive tasks. Collaborates with industry partners on projects like TypoCartographer for infoTypographic maps and HaptiQ for accessible graph exploration.
Suining He is an Assistant Professor at the University of Connecticut (UConn)'s School of Computing, leading the Ubiquitous and Urban Computing Lab since 2019. Previously, he was a postdoctoral research fellow at the University of Michigan's Real-Time Computing Lab (2016–2019). He holds a Ph.D. in Computer Science from the Hong Kong University of Science and Technology (2016) and a B.Eng. in Mechanical Design from Huazhong University of Science and Technology (2012). His research focuses on Cyber-Physical Systems (CPS), Smart & Connected Communities, Human-Centered Computing, and Urban Computing Cyberinfrastructure, with emphasis on mobility, equity, and AI-driven solutions. He has received prestigious awards including the NSF CAREER Award (2023), Google Research Scholar Program Award (2021), and recognition as a Stanford Top 2% Scientist (2020–2024). His work spans interdisciplinary grants from NSF, USDA, Google, NVIDIA, and industry partners. Recent publications explore autonomous driving simulation, equity-aware mobility prediction, and urban crowd activity modeling. Teaching excellence is reflected in his 2020 UConn Provost Award. He advises on reinforcement learning, CPS, and mobile computing, with openings for 2025/2026 PhD students. His lab collaborates on socially-conscious AI, privacy-preserving learning, and location-based services with industrial impact.
John R Anderson is the Richard King Mellon University Professor of Psychology and Computer Science at Carnegie Mellon University (CMU), affiliated with the Department of Psychology within the Dietrich College of Humanities and Social Sciences. His research focuses on understanding higher-level cognition, particularly mathematical problem-solving, through the development of the ACT-R cognitive architecture—a computational framework simulating human cognitive processes. This architecture integrates behavioral, neural, and educational data to model learning and decision-making. Anderson’s work bridges cognitive science, neuroscience, and educational technology. He investigates how brain imaging (e.g., fMRI, EEG) can reveal the temporal dynamics of cognitive processes and improve instructional methods. His research emphasizes analyzing brain activity time courses to uncover underlying mechanisms of problem-solving and skill acquisition. Key Research Themes: Cognitive architectures, neural correlates of learning, computational models of memory, and intelligent tutoring systems. Notable Contributions: Development of the ACT-R architecture, integration of neuroimaging with cognitive modeling, and studies on skill transfer and learning strategies. Anderson’s publications include seminal books like Cognitive Psychology and Its Implications and How Can the Human Mind Occur in the Physical Universe? His work has advanced understanding of associative memory, strategic decision-making, and the application of cognitive models in educational technology. His lab, the ACT-R Research Group, collaborates across disciplines to model complex cognitive tasks and their neural foundations. Current projects analyze real-time brain activity to refine educational interventions and improve human-machine interaction.
Catherine Guastavino is a Professor at McGill University's School of Information Studies (within the Faculty of Arts) and an Associate Member at the Schulich School of Music. She also contributes to the Centre for Interdisciplinary Research in Music Media and Technology (CIRMMT). Her roles include leading research projects and supervising graduate students in music technology and related fields. She holds a PhD in Psychoacoustics from Université Pierre et Marie Curie (Paris VI), an MSc from IRCAM (France), and a BSc in Mathematics from McGill University. Her research focuses on auditory perception, soundscape analysis, human-computer interaction, and urban acoustics. Notable projects include the 'Sounds in the City' partnership addressing urban noise policy and the development of immersive soundscape simulation tools. She leads or co-leads grants from SSHRC, NSERC, and MITACS, emphasizing interdisciplinary approaches to sound in urban environments. Guastavino's work bridges academic research with practical applications, such as designing sound installations for public spaces and improving acoustic environments in transportation and performance venues. She collaborates with city planners, musicians, and engineers, reflecting her commitment to making cities sound better through evidence-based strategies. Key Projects: SSHRC Partnership Grant for urban soundscape management (2018-2023) NSERC Discovery Grant on auditory localization (2019-2024) MITACS projects on Montreal festival soundscapes and noise policy Labs & Partnerships: Multimodal Interaction Laboratory (MIL) Collaborations with Ville de Montréal and Quartier des Spectacles Education: PhD: Psychoacoustics (Paris VI) MSc: Computer Science & Music Technology (IRCAM) BSc: Mathematics (McGill) Her teaching includes courses like Music Information Retrieval, integrating computational methods with artistic and scientific applications.
Somil Bansal is an Assistant Professor in the Department of Aeronautics and Astronautics at Stanford University, part of the School of Engineering. Previously, he served as an Assistant Professor in the Electrical and Computer Engineering (ECE) department at the University of Southern California. He holds a B.Tech. from IIT Kanpur, an MS, and a Ph.D. from UC Berkeley’s EECS department. Research Focus: Development of mathematical tools and algorithms for safety-critical autonomous systems, emphasizing learning-enabled systems’ safety. Key Collaborations: Waymo, Skydio, Google, Boeing, NASA AMES/JPL. Awards: NSF CAREER Award, Eli Jury Award, RSS Pioneer Award, and Outstanding Graduate Instructor Award. His research integrates control theory and machine learning to ensure safety in autonomous systems, focusing on safe learning frameworks, anomaly detection, and real-time safety guarantees. He leads the Safe and Intelligent Autonomy (SIA) Lab, which explores applications in robotics, autonomous driving, and aerospace systems. Teaching: Courses include Introduction to Control Design Techniques and Principles of Safety-Critical Autonomy. He advises doctoral students and supervises research projects in his lab.
Olof Bälter is a Professor in Computer Science at KTH Royal Institute of Technology, affiliated with the Division of Media Technology and Interaction Design within the School of Electrical Engineering and Computer Science. He is the founder of the Technology-Enhanced Learning research group and holds a focus on learning engineering and human-computer interaction. His research interests center on technology-enhanced learning , question-based learning , learning analytics , AI in education , and inclusive pedagogy . A consistent theme in his work is improving efficiency in education and daily life through digital tools. He developed the Pure Question-Based Learning (Pure QBL) methodology, a digital Socratic approach that enhances student engagement and learning outcomes. His work extends to wellness in education through initiatives like walking seminars, and he investigates digital interventions for mental health, such as online Cognitive Behavioral Therapy (CBT) courses. His recent publications highlight trends in AI-generated educational content , learning efficiency , digital pedagogy , and inclusive course design , with applications in computer science education, language instruction, and global development. His research often employs experimental and data-driven methods, including randomized controlled trials and learning analytics. Teacher of the Year at the Surveying program KTH's Pedagogical Prize Higher Education Hero STINT Excellence in Teaching Scholarship (2008 and 2013) Olof Bälter has supervised numerous courses in programming, computer science, media technology, and learning engineering. He has collaborated with institutions such as Stanford University, Williams College, Region Stockholm, Stockholm University, and organizations like Promobilia and Begripsam. His projects aim to scale effective learning methods globally and make education more accessible and efficient. He leads research on the effectiveness of Pure QBL for students with ADHD and is involved in developing digital tools for health literacy and professional development in Ethiopia and Rwanda. His work bridges theory and practice, aiming to transform educational delivery through innovation and evidence-based design.
Sherry Tongshuang Wu is an Assistant Professor at Carnegie Mellon University's School of Computer Science, with primary appointments in the Human-Computer Interaction Institute (HCII) and secondary affiliation with the Language Technology Institute (LTI) . Trained at the University of Washington under Jeffrey Heer and Dan Weld, she bridges HCI and NLP to study human interactions with AI systems across diverse user groups. Her educational background includes a Ph.D. (2016-22) and M.S. (2016-18) in Computer Science and Engineering from the University of Washington, and a B.Eng. (2012-16) from Hong Kong University of Science and Technology. Industry experience includes research internships at Google Brain, Microsoft Research, and Apple. Wu's research focuses on three interconnected pillars: Real-world AI Evaluation (developing frameworks like SPHERE for systematic assessment), Task-specific AI Test & Distill (optimizing general-purpose models for specific use cases), and Human-AI Task Delegation (designing optimal collaboration between humans and AI). Her work emphasizes practical deployment, user-specific net gains, and error recovery mechanisms. Analysis of her 15 most recent publications reveals a strong trend toward evaluation frameworks (35%), human-AI collaboration systems (30%), and specialized model distillation (25%), with growing emphasis on educational applications (10%). Key methodological themes include checklist-based evaluation, perspective-aware retrieval, and structural analysis of AI outputs. Google Academic Research Award (2024) Amazon Research Awards (2024) AIED 2024 Best Paper Award ACL 2020 Best Paper Award Rising Stars in EECS Workshop (2020) Wu actively mentors 19 students across PhD, Master's, and undergraduate levels, with notable projects including synthetic data generation, LLM literacy tools, and retrieval system optimization. She leads significant grant-funded work through Amazon Research Awards and Google Academic Research Awards, focusing on deployable model generation and human-AI collaboration frameworks. Her lab develops practical tools like Promp2Model and SPHERE that bridge theoretical research with industry applications.
Brad Knox is a Research Associate Professor in the Department of Computer Science at the University of Texas at Austin . His work bridges machine learning, human-computer interaction, and computational cognitive science, with a focus on developing systems that learn from human feedback. Key research areas: Reinforcement Learning, Human-AI Interaction, Reward Design, Autonomous Systems Notable contributions: TAMER framework for human-guided learning, empirical studies on reward misdesign, and human preference modeling for autonomous agents Research Trends : His recent work (2023-2025) emphasizes reward alignment, safety in autonomous systems, and preference-based learning frameworks. Earlier studies (2012-2020) established foundational methods for integrating human feedback into reinforcement learning architectures and exploring behavioral signatures in decision-making. Scientific Honors : Bert Kay Dissertation Award (2013) Victor Lesser Distinguished Dissertation Award (IFAAMAS, Runner-up, 2013) NSF SBIR Grant (PI, 2016) NSF Graduate Research Fellowship (2008-2011) IEEE Intelligent Systems AI 10 to Watch (2013) Teaching & Leadership : Knox served as Principal Lecturer for MIT's Interactive Machine Learning course (2013) and held organizational roles at major conferences including Reinforcement Learning Conference (Scheduling Chair, 2025) and RLDM workshop (Co-chair, 2022).
