Find Academic Supervisors

Rosario B. Jaime-LaraView profile

Assistant Professor

• Nutrition
• Neurophysiology
• Obesity Research
• +5 more

Rosario B. Jaime-Lara is an Assistant Professor at the University of California, Los Angeles (UCLA) School of Nursing. She holds advanced degrees including a PhD in Nursing from the University of Pennsylvania, MSN from Columbia University, and dual BS degrees in Nursing and Biological Sciences from University of Pennsylvania and UC Davis. Her research focuses on neurophysiological mechanisms of eating behavior, nutritional disparities in Mexican-American communities, and chemosensory science. Key themes include obesity research, sensory neuroscience, and translational clinical studies. She employs rodent models and clinical research methodologies to explore taste/smell physiology and metabolic health. Jaime-Lara has received over 17 honors including the 2022 Hommer Memorial Award and multiple diversity fellowships. Her work spans interdisciplinary collaborations in genomics, microbiome research, and health IT interventions for chronic disease management. Recent publications emphasize olfactory dysfunction in coronaviruses, fat taste mediators, and metabolic profiling. Her research bridges basic science and clinical practice, with particular attention to underserved populations.

Amanda ProrokView profile

Professor

• Multi-Agent Systems
• Robotics
• Collective Intelligence
• +6 more

Professor Amanda Prorok leads the Prorok Lab at the University of Cambridge's Department of Computer Science and Technology, focusing on multi-agent and multi-robot systems. Her work integrates machine learning, planning, and control to coordinate intelligent agents in shared environments, with applications in transport, environmental monitoring, and search-and-rescue. She is a Fellow of Pembroke College and holds editorial roles at IEEE Robotics and Automation Letters and Autonomous Robots. Education: Ph.D., EPFL (Switzerland); Postdoctoral Research, University of Pennsylvania (USA). Research Interests: The lab pioneers methods like differentiable communication between learning agents and develops decentralized algorithms for navigation, coverage, and coordination. Key themes include neural diversity in collective learning, resilient swarm systems, and environment-aware control. Notable Achievements: ERC Starting Grant, Amazon Research Award, EPSRC New Investigator Award ABB Prize for Best Thesis in Computer Science (EPFL) Teaching: Leads Computing for Collective Intelligence (MPhil/Part III) modules. Lab & Infrastructure: The Prorok Lab operates the Cambridge RoboMaster platform and develops testbeds for connected vehicles and robot swarms. Recent work emphasizes scalable reinforcement learning and graph neural networks for decentralized decision-making.

• Animal Behaviour
• Social Behaviour
• Fish Ecology
• +3 more

Professor Ashley Ward is a Professor of Animal Behaviour at the University of Sydney's School of Life and Environmental Sciences. His research focuses on social behaviour, collective animal behaviour, and the ecology of fish, particularly in group-living species. He leads the Animal Behaviour Lab and has authored influential books like Sociality: The Behaviour of Group-Living Animals (2016) and Questions and Answers on Aquarium Fishes (2007–2008). His work integrates experimental and theoretical approaches to understand how animals make decisions in groups, navigate risks, and adapt to environmental challenges. Research interests include predator-prey dynamics, immune system effects on social behavior, and the role of social context in camouflage. Notable grants include 'Understanding animals through movement' (2018) and 'Advancing fauna conservation in post-fire landscapes' (2021). His recent studies explore collective decision-making, immune-challenge responses in fish, and the impact of environmental cues on shoaling behavior. Awards and recognitions are not explicitly listed, but his contributions to fish behavior research are widely cited. He advises on grants and collaborates internationally, with over 100 peer-reviewed articles and books. His lab emphasizes field and laboratory experiments, combining behavioral observations with computational modeling to uncover principles of collective behavior and social organization.

Pamela UgwudikeView profile

Professor

• AI Ethics
• Algorithms in Justice Systems
• Trustworthy and Responsible AI
• +3 more

Professor Pamela Ugwudike is a Professor of Criminology at the University of Southampton, with expertise in AI ethics, algorithms in justice systems, and trustworthy AI. She holds fellowships from the Higher Education Academy and the Alan Turing Institute. Her research focuses on data-driven technologies in criminal justice, including predictive policing algorithms and social media analytics. Education includes an LLB (Hons), MA, and PhD. Current projects include the ESRC-funded ProTechThem2.0 (2021–2024), exploring AI ethics and sharenting risks. She co-edits the Criminology & Criminal Justice journal and serves on editorial boards for leading criminology journals. External roles include memberships in the ESRC Peer Review College, Ada Lovelace Institute's biometric data advisory group, and the Howard League for Penal Reform’s Research Advisory Group. Her research spans interdisciplinary AI ethics, with recent publications analyzing sharenting’s digital harms, algorithmic transparency in policing, and AI’s role in sentencing. Supervising three PhD students, she also advises on technology policy through roles like Hampshire Constabulary's Science and Technology Advisory Committee.

Chenliang XuView profile

Associate Professor

• Computer Vision
• Audio-Visual Learning
• Vision and Language
• +5 more

Chenliang Xu is an Associate Professor in the Department of Computer Science at the University of Rochester, affiliated with the Goergen Institute for Data Science and Artificial Intelligence (GIDS-AI). His research focuses on computer vision, audio-visual learning, and trustworthy AI. He holds a PhD from the University of Michigan (2016), with prior degrees from Nanjing University of Aeronautics and Astronautics and the University at Buffalo. Notable awards include the Best Paper Award at ACCV 2024 and the James P. Wilmot Distinguished Professorship. His work spans interdisciplinary topics such as video understanding, multimodal reasoning, and robust AI. Key research contributions include audio-visual scene synthesis, bias mitigation in models, and applications in public health. He has secured over $3M in grants, including NIH funding for AI-driven video description tools and public health initiatives. Prof. Xu advises a dynamic research group with 11 PhD students and numerous collaborators. His lab explores cutting-edge projects like egocentric audio-visual understanding, generative AI for avatars, and multimodal defense mechanisms. He teaches courses in machine vision, deep learning, and advanced computer vision.

Nilanjan SarkarView profile

Professor

• Human-Robot Interaction
• Human-Computer Interaction
• Robotics
• +6 more

Nilanjan Sarkar is the Vice Dean and Senior Associate Dean for Faculty Affairs at Vanderbilt University's School of Engineering, holding the David K. Wilson Professorship in Engineering. He is a Professor in Mechanical Engineering, Computer Engineering, and Computer Science. His research focuses on intelligent systems for human interaction, including robotics, virtual/augmented reality, and assistive technologies for neurodevelopmental disorders and aging populations. Education: PhD (Mechanical Engineering, University of Pennsylvania), ME (Indian Institute of Science), BE (Indian Institute of Engineering Science and Technology, Shibpur). Research interests span human-robot interaction, sensor fusion, and rehabilitation engineering. His lab develops systems for autism intervention, stroke rehabilitation, and elderly engagement through socially assistive robotics and VR/AR. Notable projects include robot-mediated therapy for children and AR telepresence systems for long-term care facilities. Labs/Teams: Robotics and Autonomous Systems Laboratory. Key contributions include CoMove, RASSLE, and the Career Interview Readiness in VR platform. His work emphasizes participatory design with end-users for ethical and inclusive technology.

Drew CingelView profile

Associate Professor

• Media Effects
• Child Development
• Adolescent Psychology
• +5 more

Drew Cingel is an Associate Professor and Graduate Advisor in the Department of Communication at the University of California, Davis. His research focuses on the interplay between human development—particularly child and adolescent development—and media effects, with an emphasis on understanding how developmental stages influence media choices and how media exposure impacts development. He regularly consults with media companies and policymakers to design media that supports healthy child and adolescent development. Education: Ph.D., Media, Technology, and Society, Northwestern University, 2016 M.A., Communication, Wake Forest University, 2012 B.A. (two degrees), Media Effects and Psychology, Pennsylvania State University, 2010 Research Interests: Effects of social media on adolescent mental health Children’s learning from media (e.g., television, tablets) Moral reasoning development through media exposure Parental mediation strategies for media use His work bridges experimental and survey methodologies, often addressing societal concerns about digital media’s role in youth development. Publications & Trends: Recent articles explore personalized social media algorithms’ impact on minors, cross-cultural comparisons of media use during the pandemic, and the validation of moral intuition measurement tools. His research consistently highlights the dual-edged nature of media: while media can foster learning and moral development, it also poses risks like mental health challenges and social comparison effects. Teaching & Advising: Teaches CMN 147 (Children, Adolescents, and the Media) and CMN 141 (Media Effects). As Graduate Advisor, he mentors students in communication studies and supports academic programs. Labs & Teams: Affiliated with UC Davis’ Media Labs, which investigates digital media’s societal impacts. Collaborates with the Human Development Graduate Group to integrate interdisciplinary perspectives.

Panayotis MavromatisView profile

Associate Professor

• Cognitive Science
• Music Theory
• Computer Science
• +3 more

Panayotis Mavromatis is an Associate Professor of Music and Music Education at New York University's Steinhardt School of Culture, Education, and Human Development. He holds affiliations with the Department of Music and Performing Arts Professions. His work bridges cognitive science, linguistics, and computer science with traditional music disciplines such as Schenkerian theory and post-tonal theory. B.A. and M.A. in Mathematics, Cambridge University M.A. in Physics, Boston University Ph.D. in Music Theory, Eastman School of Music (University of Rochester) His research focuses on mathematical modeling of musical structures, computational modeling of musical skill acquisition, and educational applications via Intelligent Tutoring Systems (ITS). Notable projects include computational models of modern Greek church chant and expressive timing analysis in music performance. Publications emphasize interdisciplinary approaches: applying hidden Markov models to melody production, multi-tiered analysis of performance timing, and integrating process-based learning into ITS for tonal counterpoint. His work spans theoretical contributions to practical educational tools. No scientific awards are explicitly listed in the provided text. Advising/grants section: No specific advisees or grant details are mentioned. His research aligns with NYU Steinhardt’s Music Education programs focusing on pedagogical innovation and cultural transmission. Labs/teams: While no specific lab names are provided, his research involves computational modeling frameworks and collaborations in interdisciplinary musicology.

Chao TianView profile

Associate Professor

• Information Theory
• Distributed Data Storage
• Coding Theory
• +6 more

Dr. Chao Tian is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from Cornell University (2005) and a B.E. from Tsinghua University (2000). His research focuses on information theory, distributed storage systems, coding theory, and machine learning applications, including large language models (LLMs) and privacy-preserving techniques like steganography and private information retrieval (PIR). He has received notable awards, including the 2017 IEEE Jack Wolf ISIT Best Student Paper Award and the 2014 IEEE ComSoc DSTC Best Paper Award. Dr. Tian’s work bridges theoretical foundations and practical applications, such as optimizing storage codes for distributed systems, developing secure PIR protocols, and exploring LLMs in energy and steganographic contexts. His group has pioneered methods for variable-order Markov chain modeling with transformers and designed quantization techniques for perceptual quality. Recent activities include a faculty development leave at MIT/Harvard and delivering distinguished lectures globally on AI and information theory. He advises students on cutting-edge topics like LLM-based steganography and diffusion models. His publications span journals like IEEE Transactions on Information Theory and conferences such as NeurIPS and ISIT, with a focus on coding theory, privacy, and machine learning. He has also contributed to open-source tools like the CAI toolbox for investigating information-theoretic limits. Current projects include optimizing cryptocurrency mining in energy markets and advancing federated learning with adversarial robustness.

Agnieszka RoginskaView profile

Professor

• Immersive Audio
• 3D Audio Technology
• Spatial Sound
• +5 more

Agnieszka Roginska is a Professor of Music Technology at the Steinhardt School, New York University, specializing in immersive audio, spatial sound, and auditory displays. Her work integrates acoustic science with virtual reality and medical applications, including postural stability studies for vestibular rehabilitation. She holds leadership roles as AES President-Elect and co-edited the authoritative book 'Immersive Sound'. Education: B.A. in Piano Performance and Computer Applications in Music (McGill University, 1996); M.M. in Music Technology (NYU, 1998); Ph.D. in Music Technology (Northwestern University, 2004). Research focuses on 3D audio technologies, auditory displays for virtual environments, and sensory integration studies. She leads NYU's Music and Audio Research Lab (MARL) and advises the Society for Women in TeCHnology (SWiTCH) at NYU. Awards include AES Fellowship and leadership in international audio engineering societies. Recent publications emphasize audio's role in postural control, distributed music performance frameworks (Holodeck), and VR sound design. Her work bridges technical innovation with artistic applications, including collaborative music systems in mixed reality.

Xavier Alameda-PinedaView profile

Researcher

• Machine Learning
• Computer Vision
• Audio Processing
• +7 more

Xavier Alameda-Pineda is a Research Director at Inria Grenoble Rhône-Alpes, where he leads the RobotLearn Team. He is affiliated with Université Grenoble Alpes and has been a key member of the Perception team. His work integrates machine learning, computer vision, and audio processing for scene understanding and human-robot interaction. Research Interests: His research lies at the intersection of multimodal machine learning and social behavior analysis. He focuses on developing algorithms for understanding human behavior in natural settings using audio-visual signals, with applications in robotics and AI companions. His work emphasizes real-world challenges such as noisy data, missing modalities, and dynamic environments. Publication Trends: His recent publications reflect a consistent focus on multimodal fusion, particularly combining vision and audio for social scene analysis. Themes include group behavior recognition, sound source separation, and cross-modal learning, often applied in robotics contexts. Scientific Awards: SIGMM Rising Star Award 2018 IEEE TMM Outstanding Associate Editor Award 2022 ACM TOMM Best Paper Award 2020 Best Paper Award, ACM MM 2015 Best Scientific Paper Award, ICPR 2016 Best Student Paper Award, IEEE WASPAA 2015 Outstanding Paper Award, ICMI 2011 Novel Technology Paper Award Finalist, IROS 2017 Advising and Grants: Xavier has mentored students and early-career researchers, evidenced by co-authored student papers. He coordinated the H2020 SPRING project on socially pertinent robots in gerontological healthcare and co-leads an AI chair on audio-visual perception for companion robots, indicating leadership in funded research initiatives. Labs and Teams: He is the leader of the RobotLearn Team at Inria and was previously part of the Perception team. He has also collaborated with the Multimodal and Human Understanding Group at the University of Trento.

• Perception Engineering
• Extended Reality
• Virtual Reality
• +7 more

Mark Bo Jensen is an Assistant Professor (tenure track) at the Department of Engineering Technology and Didactics, Technical University of Denmark (DTU), specializing in Energy Technology and Computer Science. His research is centered on Perception Engineering and Extended Reality technologies, particularly Virtual Reality (VR), with applications in human cognition, computer graphics, and scientific visualization. His research interests lie at the intersection of engineering and cognitive sciences, focusing on creating immersive and convincing extended reality experiences. Jensen applies his over 10 years of expertise in real-time computer graphics to advance VR systems for perception modeling, geometric data visualization, and material appearance simulation. His work contributes to fields such as medical diagnostics, 3D annotation, and photorealistic rendering. The recent publications highlight a strong trend in leveraging VR for scientific tasks, such as anatomical landmark annotation and visual field testing, as well as advancing core graphics techniques like meshlet optimization and diffusion-based stereo image generation. His research integrates computer vision, graphics algorithms, and human-centered design. While no scientific awards are currently listed, his active participation in research projects and consistent publication output indicate a growing academic profile. He has contributed to interdisciplinary collaborations involving medical, biological, and engineering domains. Jensen has been involved in advising and research projects, including serving as a PhD student in the 'Virtual Reality-Based Visualization of Geometric Data' project and currently as a project participant in 'AL-EYE: The Visual Aid'. These projects reflect his focus on applied VR solutions and data understanding. His work is conducted within the Energy Technology and Computer Science division at DTU, where he contributes to advancing perception-driven technologies and their practical implementation in scientific and medical contexts.

Guanrui LiView profile

Assistant Professor

• Aerial Robotics
• Control Systems
• Robot Perception
• +3 more

Guanrui Li is an Assistant Professor at Worcester Polytechnic Institute's Robotics Engineering Department and the director of the Aerial-robot Control and Perception Lab (ACP Lab). He holds a Ph.D. in Electrical and Computer Engineering from NYU (2024), an M.S. in Robotics from the University of Pennsylvania (2018), and a B.E. in Theoretical and Applied Mechanics from Sun Yat-sen University (2016). His research focuses on aerial robotics, including control methodologies for collaborative transportation, human-robot interaction, and perception-aware systems. Key contributions include Hybrid Perception-Aware MPC frameworks, cooperative manipulation algorithms, and simulation tools like RotorTM. Education: Ph.D., Electrical and Computer Engineering, NYU (2024) M.S., Robotics, University of Pennsylvania (2018) B.E., Theoretical and Applied Mechanics, Sun Yat-sen University (2016) Research Interests: Control and perception of aerial robots, human-robot collaboration, cooperative manipulation, and autonomous systems. Notable work addresses challenges in payload transportation, sensor fusion, and safety-critical navigation. Awards: NSF CPS Rising Stars (2023) Outstanding Deployed System Paper Finalist (IEEE ICRA 2022) NYU Dante Youla Award (2022) NYU Outstanding Dissertation Award (2024) Lab & Team: ACP Lab focuses on advancing aerial robotics through interdisciplinary research. Current projects include mixed reality interfaces for human-robot interaction and fault-tolerant control systems. Recent collaborations include workshops on Rust for Robotics (ICRA 2025) and embodied-AI for aerial systems (ICUAS 2025).

Yuan ChaiView profile

Lecturer

• Voice Quality
• Phonetics
• Language Documentation
• +3 more

Yuan Chai is a post-doctoral fellow in the Department of Linguistics at the University of Washington, supervised by Professor Richard Wright. They serve as instructor-of-record for courses including Introduction to Linguistic Phonetics and Computational Methods for Linguists . Education: Ph.D. in Linguistics, UC San Diego M.A. in Linguistics, University of Colorado Boulder Dr. Chai specializes in experimental phonetics with emphasis on voice quality (breathy, creaky, falsetto), tone systems, and checked syllables. Their fieldwork documents endangered languages like Yateé Zapotec (Oaxaca, Mexico) and Xiapu Min (Fujian, China), analyzing phonation-tone interactions through acoustic and perceptual studies. Current projects include building a Living Dictionary for Yateé Zapotec with community audio recordings. Recent publications demonstrate consistent focus on multi-dimensional phonetic contrasts, particularly how glottalization position/duration affects perception in Zapotec, and tone sandhi neutralization in Xiapu Min. Work spans journal articles (JASA, Languages), conference presentations (ICPhS, ASA), and collaborative book chapters. Dr. Chai collaborates extensively with native speakers (Briseida Mendez, Adrián Fernández) and researchers (Patricia Keating, Marc Garellek, Jody Kreiman) across institutions. They maintain active GitHub repositories for computational linguistics tools and prioritize open-access language documentation resources.

• machine learning
• robotics
• machine vision
• +9 more

Dr. Hamidreza Mohades Kasaei is an Associate Professor in the Department of Artificial Intelligence at the University of Groningen, Netherlands. He holds positions in both the Faculty of Science and Engineering and the Faculty of Medical Sciences/UMCG, focusing on Robotics and image-guided minimally-invasive surgery. His work bridges theoretical advances in machine learning with practical robotic applications. Dr. Kasaei's research focuses on developing algorithms for adaptive perception systems through interactive environment exploration and open-ended learning. His specific interests include 3D object perception, grasp affordance detection, object manipulation, and active perception. He has evaluated his research on various robotic platforms including PR2, UR5e, Kinova, Franka robotic arms, and humanoid robots. His work enables robots to learn from past experiences and intelligently interact with non-expert human users using data-efficient techniques. Analysis of his recent publications reveals strong trends toward increasingly sophisticated manipulation capabilities, particularly in dual-arm coordination and handling dense clutter. There's a clear progression toward integrating language models with robotic control systems, as seen in works like 'Lifelong Robot Library Learning' and 'Towards Open-World Grasping with Large Vision-Language Models.' His research consistently addresses real-world challenges in agricultural robotics, assistive technologies, and service robotics applications. Gratama Science Award (2022) Google Research Scholar Award in Machine Learning (2023) Outstanding Associate Editor for IEEE Robotics and Automation Letters (2023) Dr. Kasaei has successfully supervised multiple PhD students including Zhenxing Zhang (thesis on 'Generative Adversarial Networks for Diverse and Explainable Text-to-Image Generation') and Hamed Ayoobi (thesis on 'Explain What You See: Argumentation-Based Learning and Robotic Vision'). His research is supported by significant grants including the Google Research Scholar Award for 'Continual Robot Learning in Human-centered Environments' and various conference organization roles including workshops at RSS 2023 and NeurIPS 2022. He leads the Lifelong Interactive Robot Learning Lab (IRL-Lab), which focuses on six key research directions: Perception and Perceptual Learning, Object Grasping and Manipulation, Lifelong Interactive Robot Learning, Dual-Arm Manipulation, Dynamic Robot Motion Planning, and Exploiting Multimodality. The lab develops cutting-edge approaches for robots to learn in open-ended fashion through interaction with non-expert human users, with applications in assistive robotics for people with disabilities.