Find Academic Supervisors

Gierad LaputView profile

Adjunct Professor

• Human-Computer Interaction
• Machine Learning
• Sensing Systems
• +7 more

Gierad Laput is a Senior Engineering Manager at Apple, leading the Perception and Interaction Engineering organization under Machine Intelligence Neural Design (MIND) at Apple AI/ML (AIML). He is also an Adjunct Professor at Carnegie Mellon University's School of Computer Science and Human-Computer Interaction Institute. His research sits at the intersection of sensing systems, interactive technologies, and machine learning, with applications in ambient intelligence, wearables, and AI-driven interactive experiences. Ph.D. in Computer Science from Carnegie Mellon University Founding member of CMU's Future Interfaces Group Former Google Ph.D. Fellow, Disney Imagineer, and recipient of 10 Best Paper Awards/Nominations His research agenda at Apple leverages cutting-edge hardware to develop multimodal ML systems for vision, audio, and motion sensors, enabling features like head gesture detection for AirPods, Apple Intelligence integrations, and health monitoring tools. Recent collaborative projects include Vision Pro hand interaction systems and WatchOS updates for cycling/fall detection. Key research areas include human-computer interaction , machine learning for sensor fusion , and smart environment sensing . Over 35 publications cover transformative technologies like ViBand (bio-acoustic sensing), EM-Sense (touch recognition), SkinTrack (arm as touchpad), and ElectroDermis (stretchable electronics). Scientific honors include: Fast Company Innovation by Design Award (2017) Google PhD Fellowship (2018) Swartz Entrepreneurial Fellowship (2018) Best Paper Awards at ACM UIST (2016) and ACM CHI (2015) As an educator, he has taught courses at CMU and the University of Michigan on on-device machine learning , signal processing , and human-centered design . His lab has mentored numerous researchers now pursuing PhDs at MIT, UW, CMU, and Northwestern.

Claire BowernView profile

Professor

• Historical Linguistics
• Language Documentation
• Australian Indigenous Languages
• +3 more

Claire Bowern is Professor of Linguistics at Yale University specializing in historical linguistics, language documentation, and Australian Indigenous languages. Her research employs computational phylogenetics to study language evolution and supports language revitalization through digital archives and fieldwork methodologies. Recent publications address: Phylogenetic signal in lexical evolution across language families Digital infrastructure for endangered language documentation (FLEx software analysis) Decolonizing linguistics pedagogy and research practices Her work consistently integrates linguistic, anthropological, and computational approaches to analyze language diversity and change. She contributes to global databases including Grambank and D-PLACE, examining links between linguistic, cultural, and environmental patterns.

Haijun XiaView profile

Assistant Professor

• Human-Computer Interaction
• Human-AI Collaboration
• Interface Design
• +2 more

Haijun Xia is an Assistant Professor at the University of California, San Diego (UCSD), where he directs the Foundation Interface Lab and contributes to the Cognitive Science and Design Lab. His work focuses on Human-Computer Interaction (HCI) and Human-AI Collaboration , emphasizing the development of dynamic, malleable interfaces that integrate human cognition with intelligent systems to enhance thinking and working paradigms. Education: Bachelor's, Tsinghua University Master's and PhD, University of Toronto His research spans generative AI , interface design , and creative collaboration , with applications in data visualization, programming, and scholarly work. He advocates for treating information as a malleable material that can be flexibly manipulated by users and AI agents. Recent publications highlight advancements in: 2025 CHI Conference - Generative user interfaces, malleable overview-detail designs, and compositional structures for human-AI co-creation 2024 CHI Conference - Structured design space exploration with LLMs, ASCII diagram analysis, and adaptive presentation systems 2023 CHI/UIST Conferences - Data particle visualization, contextual logging tools, and metaphor generation for science communication Scientific recognitions include: Multiple Best Paper Awards and Honorable Mentions at CHI and UIST Hellman Fellowship and grants from NSF , Microsoft , Google , Meta , Apple , and Adobe He actively recruits undergraduate and graduate interns and has an upcoming postdoc position in early 2025 for researchers interested in foundational interface development.

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +4 more

Andrew Zisserman is a Royal Society Research Professor at the University of Oxford's Department of Engineering Science, affiliated with the Visual Geometry Group (VGG). His research focuses on computer vision, artificial intelligence, and neural networks, with significant contributions to multimodal learning, video understanding, and 3D scene analysis. He leads projects exploring visual-language models, audio-visual synchronization, and clinical imaging applications. Key research areas include: Video analysis and temporal modeling Multimodal systems for sign language translation and action recognition 3D shape estimation and physical property inference Foundation models and cross-modal retrieval Recent work highlights: Developed Flamingo and Tapir models for video-language tasks Advancements in spinal MRI analysis and clinical imaging Leadership in EGO4D and VoxCeleb challenges Honors include Fellowship of the Royal Society (FRS) and the ISSLS Prize in Clinical Science 2023 for spinal analysis innovations. His lab collaborates globally, emphasizing real-world applications in healthcare and autonomous systems.

April Yi WangView profile

Assistant Professor

• Human-Computer Interaction
• Educational Technology
• Programming Support
• +4 more

April Yi Wang is a tenure-track Assistant Professor in the Department of Computer Science at ETH Zürich, where she directs the Programming, Education, and Computer-Human Interaction Lab (PEACH Lab). She is a core faculty member at the Institute for Intelligent Interactive Systems and associated with the ETH AI Center. Wang is also an active member of ETH HCI and Swiss CHI communities, contributing significantly to human-computer interaction and educational technology research. Dr. Wang's educational background includes: Ph.D. in Information Science from University of Michigan (2023), advised by Steve Oney and Christopher Brooks M.Sc. in Computer Science from Simon Fraser University (2018), advised by Parmit Chilana B.Eng in Computer Science from Zhejiang University (2016) Dr. Wang's research focuses on human-centered approaches to programming and data science. Her work reimagines programming as a form of literature that communicates with both machines and people, exploring creative representations like text, shapes, animations, and everyday objects. She investigates how to make programming more natural and intuitive through literate programming environments, with applications in professional and educational contexts. Her research spans human-computer interaction, educational technology, and AI-assisted programming tools. Analysis of Dr. Wang's recent publications reveals a strong focus on AI-enhanced educational tools, particularly for programming and data literacy. Her work increasingly integrates large language models to scaffold learning while maintaining user agency. There's a clear trajectory toward developing situated learning approaches that connect abstract concepts to real-world contexts through augmented reality and tangible interfaces. Her research bridges HCI, education, and AI to create more accessible and engaging technical learning experiences. Dr. Wang has received numerous prestigious awards including: 2023 Gary M. Olson Award and Honourable Mention Award at ACM CHI 2022 Rising Stars in EECS and Heidelberg Laureate Forum Young Researcher 2020 Best Short Paper Award at IEEE VL/HCC and Honourable Mention at ACM CHI 2019 Best Paper Award at ACM CSCW Dr. Wang actively mentors students through thesis projects at ETH Zürich, supervising numerous bachelor's and master's students on topics ranging from AI-assisted programming to data literacy tools. Her lab, PEACH Lab, has secured funding including the recent innovedum funding for the Coducate project. She serves on program committees for major conferences including CHI and UIST, and regularly reviews for top HCI and education journals. The PEACH Lab, directed by Dr. Wang, focuses on creating expressive, intelligent, and human-centered systems that make technical topics more accessible. The lab explores textual, visual, and embodied representations for programming, with emphasis on enhancing communication, collaboration, and learning. Current research directions include balancing automation with user agency, supporting diverse learning needs, and developing tools for interdisciplinary technical communication.

Adriana TapusView profile

Professor

• Robotics
• Human-Robot Interaction
• Machine Learning
• +7 more

Adriana Tapus is a Full Professor at ENSTA Paris, affiliated with Institut Polytechnique de Paris, leading the Autonomous Systems and Robotics Laboratory (SAR) within the Computer Science and Systems Engineering Unit (U2IS). She holds an HDR (Habilitation) and a PhD from EPFL, Switzerland, with postdoctoral experience at USC. Her research focuses on socially assistive robotics, human-robot interaction (HRI), and personalized therapy for individuals with physical/cognitive impairments. She directs the IP Paris Doctoral School and coordinates national/international projects like the EU-funded ENRICHME and SWEET. Education: PhD in Mobile Robotics, EPFL (2005) Habilitation (HDR), ENSTA Paris (2011) M.S. Computer Science, University Joseph Fourier Engineer, Politehnica University of Bucharest Research Interests: Tapus pioneers socially assistive robotics, integrating machine learning, human modeling, and multimodal communication (verbal/non-verbal/para-verbal). Her work addresses adaptive therapies for vulnerable populations using robotics, physiological data interpretation, and context-aware interaction. Key themes include: Human-robot cooperation and trust Emotion recognition and expression Personalized rehabilitation systems AI ethics and human-centered design Publications: Over 150 articles, with recent work exploring humor in HRI, teleoperation trust models, and cross-cultural intelligent vehicles. Notable 2025 contributions include studies on robot laughter efficacy and multimodal facial expression frameworks. Awards: 2025: 4 IROS papers accepted 2016: 25 Women in Robotics recognition 2010: Romanian Academy Award Multiple conference best paper awards (RO-MAN, ICRA, etc.) Advising & Grants: Supervised over 20 PhD students and led projects like EU Horizon 2020 ENRICHME. Current students focus on teleoperation dynamics, robot humor, and haptic interfaces. Active in editorial roles (IJSR, THRI) and conference organization (HRI General Chair 2019). Labs/Teams: Founder of RoboticsByDesign lab and co-initiator of the Hi! Paris interdisciplinary AI center. The SAR lab develops systems for healthcare, education, and human-robot collaboration.

James McCannView profile

Associate Professor

• Textiles Manufacturing
• Computer-Aided Fabrication
• Real-time Systems
• +5 more

James McCann is an Associate Professor at the Carnegie Mellon Robotics Institute, where he leads the Carnegie Mellon Textiles Lab. He has been a faculty member since May 2017 after working at Disney Research Pittsburgh. McCann's academic journey includes a PhD from Carnegie Mellon advised by Nancy Pollard, followed by a postdoc at Adobe Research and a period developing video games. McCann's research focuses on building creative tools that operate in real-time and build user intuition, with particular emphasis on textiles fabrication and machine knitting. His work spans computer-aided fabrication, simulation, graphics, and creative tools development. He has pioneered systems for machine knitting design, including compilers for knitting instructions and tools for automatic conversion of 3D meshes to knitting patterns. His recent publications demonstrate a strong trend toward computational textiles, with significant contributions to knitting semantics, deployable textile structures, and applications of machine knitting in healthcare and robotics. McCann's work bridges computer science, robotics, and textile arts, creating practical systems for once-off manufacturing with industrial knitting machines. McCann actively mentors students, with current PhD candidates working on solid knitting machines, knit calibration, and assistive devices. His teaching portfolio includes courses on Real-Time Graphics, Algorithmic Textiles Design, and Game Programming. He has taught at CMU since 2017, developing innovative courses that blend computer science with physical fabrication. As director of the Textiles Lab, McCann oversees research projects spanning machine knitting, robotic painting, and real-time graphics systems. His lab develops practical tools for creators, emphasizing intuitive interfaces and real-time feedback that lower barriers to advanced fabrication techniques.

Takako FujiokaView profile

Associate Professor

• Auditory Perception
• Neural Oscillations
• Auditory-Motor Coupling
• +5 more

Takako Fujioka is an Associate Professor of Music at Stanford University, affiliated with the Center for Computer Research in Music and Acoustics (CCRMA). Her research focuses on the neural mechanisms underlying auditory perception, auditory-motor coupling, and music-supported therapy for neurorehabilitation. She holds a Ph.D. in Physiology from the Graduate University for Advanced Studies, Japan, and M.Sc./B.Eng. degrees in Electrical Engineering from Waseda University. Her work combines neurophysiological techniques such as MEG and EEG to study brain plasticity in development, aging, and stroke recovery. Notable contributions include investigating how music influences motor and cognitive recovery in stroke patients, as well as exploring the neural basis of musical perception through rhythmic synchronization and pitch discrimination studies. Supported by awards from the Canadian Institutes of Health Research during her postdoctoral work at the Rotman Research Institute, her research bridges clinical neuroscience and music cognition. Dr. Fujioka’s expertise spans auditory neuroscience, neurorehabilitation, and technology-assisted music therapy. She has pioneered studies on tactile mapping for cochlear implant users and networked music performance systems, emphasizing cross-modal perception and human-technology interaction. Her findings contribute to both theoretical understanding of auditory processing and practical applications in medical and educational settings. Awards: Canadian Institutes of Health Research Awards (postdoctoral phase) Labs/Teams: CCRMA, Stanford Music Perception Laboratory, Rotman Research Institute collaborations Key Themes: Neuroplasticity, Music-Mediated Rehabilitation, Auditory-Motor Integration, Multisensory Processing Her recent work examines aging-related changes in binaural hearing and the role of beta/gamma oscillations in rhythmic processing. She advocates for translational research that connects neural mechanisms with real-world therapeutic interventions.

Raquel FernándezView profile

Professor

• Computational Linguistics
• Dialogue Systems
• Cognitive Science
• +3 more

Raquel Fernández is Full Professor of Computational Linguistics and Dialogue Systems at the University of Amsterdam, where she leads the Dialogue Modelling Group at the Institute for Logic, Language & Computation (ILLC). As Vice-Director for Research at ILLC and a Fellow of the ELLIS Society, she bridges computational linguistics, cognitive science, and artificial intelligence through her research on language use in multimodal and conversational contexts. PhD in Computational Linguistics from King's College London Prior research positions at University of Potsdam and Stanford University's CSLI Her work explores how cognitive constraints, social interaction, and perception shape language use, with a focus on: Visually-grounded language processing Multimodal dialogue modeling Model uncertainty and calibration Language grounding in multimodal data Language learning and semantic change Dialogue reference resolution Recent publications analyze multimodal reasoning limitations, cross-lingual knowledge consistency, and uncertainty modeling in dialogue systems. She has received multiple accolades including an ERC Consolidator Grant , NWO VENI/VIDI/Aspasia fellowships , and EMNLP/GenBench awards . Outstanding Paper Award (EMNLP 2023) Best Data Award (GenBench Workshop 2023) ELLIS Society Fellow ERC Consolidator Grant #819455 recipient NWO VENI/VIDI/Aspasia awardee As a leader in academic service, she serves on the SIGDAT Executive Committee and chairs multiple conference committees. Her lab develops models for multimodal dialogue, visual storytelling, and grounded language understanding.

Yunjie YangView profile

Associate Professor

• AI-powered sensing and imaging
• Machine learning
• Soft sensors & electronics for robotics
• +5 more

Dr. Yunjie Yang is an Associate Professor at the University of Edinburgh's School of Engineering, with affiliations at the Edinburgh Futures Institute (EFI), the Edinburgh Generative AI Laboratory (GAIL), and the Edinburgh Centre for Robotics. He previously held the Chancellor's Fellow in Data Driven Innovation (2018-2023) and Bayes Innovation Fellow (2023-2024) positions. His research focuses on AI-powered sensing and imaging, machine learning, and soft sensors & electronics for robotics. Yang received his PhD in Engineering Electronics from the University of Edinburgh, MSc in Control Science & Engineering from Tsinghua University, and BEng in Measurement & Control Engineering from Anhui University. After his PhD, he worked as a Postdoctoral Research Associate in Chemical Species Tomography before securing his lectureship. His research interests center on developing intelligent sensing systems that replicate human perception capabilities for robotics and intelligent systems. He pioneers flexible sensing and imaging technologies across various scales through innovative multi-modal sensors, soft electronics, and their modeling using machine learning approaches. His work aims to enable autonomous physical artificial intelligence by bridging the gap between robotic systems and human-like perception. Analysis of his recent publications reveals a strong focus on soft robotics perception, particularly through electrical impedance tomography (EIT) and transformer-based architectures. His research spans medical imaging applications, digital twin modeling for industrial processes, and machine learning approaches for sensor data interpretation. The trend shows increasing integration of physics-informed deep learning with traditional tomographic techniques to achieve higher accuracy and efficiency. European Research Council (ERC) Starting Grant (2024) IEEE J. Barry Oakes Advancement Award (2024) IEEE I&M Society Graduate Fellowship Award (2015) Multiple Best Paper Awards Senior Member of IEEE Fellow of the International Society for Industrial Process Tomography Fellow of the Higher Education Academy ESI highly cited papers Dr. Yang serves as Associate Editor for IEEE Transactions on Instrumentation and Measurement and holds editorial positions with Scientific Reports and IEEE Sensors Journal. His research has been licensed to overseas research institutes and industry partners and received wide media coverage including BBC, EFE, USA Today, and STV. He has secured significant grant funding including the prestigious ERC Starting Grant. He leads the Edinburgh SMART Lab (Sensing/imaging + Machine Learning + Robotics), which aims to replicate human perception capabilities for robotics and advance flexible sensing technologies through innovative multi-modal sensors and machine learning approaches. The lab focuses on enabling autonomous physical artificial intelligence with applications spanning medical diagnostics, industrial monitoring, and advanced robotics systems.

Loli KimView profile

Lecturer

• Asian socio-multimodality
• Korean film analysis
• Cross-cultural communication
• +4 more

Loli Kim is a Lecturer on Modern Korea at the Faculty of Asian and Middle Eastern Studies, University of Oxford, with a Leverhulme Postdoctoral Research Fellowship (2022–2025). She specializes in cross-cultural multimodal analysis of Asian communication systems, particularly Korean cinema, folklore, and representation in media. Education: DPhil in Asian and Middle Eastern Studies (2019–2022), University of Oxford Research Focus: Multimodal discourse analysis, foodscaping theory, haenyeo women’s communication Editorial Roles: Assistant Editor of European Journal of Korean Studies; General Editor of 'Foodscaping Asia' series Her work develops the Segmented Film Discourse Representation Structures (SFDRS) methodology for analyzing Korean films' narrative conventions, including violence in revenge cinema and socio-pragmatic dimensions of fashion. She collaborates globally as a visiting lecturer and conference organizer. Kimi’s recent publications examine multimodal translation gaps, cultural representation in children’s literature, and the sociocultural implications of foodscaping. She has delivered keynote talks at institutions including Edinburgh University and SOAS, and co-organizes the Translating Squid Game talks series.

Parastoo AbtahiView profile

Assistant Professor

• Human-Computer Interaction
• Augmented Reality
• Virtual Reality
• +7 more

Parastoo Abtahi is an Assistant Professor in the Computer Science Department at Princeton University. She leads the Situated Interactions Lab (Ψ Lab) as part of the Princeton HCI Group, focusing on AR/VR and spatial computing. Prior to Princeton, she worked at Meta Reality Labs Research. PhD in Computer Science from Stanford University BSc in Electrical and Computer Engineering from University of Toronto (Engineering Science program) Her research explores wearable interactive technologies that augment human intelligence in physical contexts through: Input on-the-go: Low-effort, privacy-preserving multimodal interfaces Intelligent output: Timely, minimal multisensory systems Situated AI: Personalizable, predictable, safe physical-digital interaction Recent publications analyze: Drone-based haptic illusions for AR/VR Microgestures for mobile AR input Interactivity in explainable AI systems Temporal dynamics in VR haptics Awards include: 2025 Google Research Scholar Award CHI 2019 & 2018 Honorable Mentions She teaches courses like: COS 436: Human-Computer Interaction COS IW: AR Meets AI

Qingguo LiView profile

Professor

• Biomechanical system design
• Energy harvesting
• Wearable sensors
• +4 more

Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .

Ambuj VarshneyView profile

Assistant Professor

• Wireless Communication
• Internet of Things
• Embedded Systems
• +4 more

Ambuj Varshney is an Assistant Professor at the National University of Singapore (NUS) School of Computing , leading the WEISER research group . His work bridges electronics, wireless communication, computer science, and AI with a focus on creating ultra-low-power embedded systems for sustainable IoT deployments. University of California, Berkeley: Postdoctoral Scholar (2020-2022) Uppsala University: PhD in Sustainable Networked Systems NXP Semiconductors: Software Engineer (prior to PhD) Bachelors in Information & Communication Technology Research interests center on overcoming wireless systems' energy asymmetry through tunnel diode oscillators , LiFi-RF hybrid networks , and battery-free communication architectures . His group develops STICORS —sticker-like computers for industrial and medical monitoring. Recent publications demonstrate AudioCast 's FM-band utilization for 130m transmission, TunnelSense 's vital monitoring, and PixelGen 's diffusion model cameras. These works combine IoT sustainability, spectrum efficiency, and hardware innovation . 2024: Google Research Scholar Award 2023: MobiSys Best Demonstration 2021: Berkeley FORM+FUND Fellowship 2019: ABB's $300K Research Award As an educator, he teaches CS4222 Wireless Networking and CS5272 Embedded Software Design . Past students include Wenqing Yan (NUS/UCB PhD), Qiao Yukai , and Kunjun Li . His team collaborates with Prabal Dutta (UCB), Christian Rohner (Uppsala), and Prateek Saxena (NUS).

Daniel RecasensView profile

Professor

• Experimental Phonetics
• Phonology
• Sound Change
• +7 more

Daniel Recasens is a Full Professor in the Department of Catalan Philology at the Faculty of Arts, Autonomous University of Barcelona, where he has been a permanent faculty member since 1985. He is also affiliated with the Institute for Catalan Studies as a researcher. His academic work centers on experimental phonetics and phonology, with a focus on Romance languages, particularly Catalan. Research Interests: Daniel Recasens specializes in Experimental Phonetics , Phonology , Sound Change , and Phonetic and Phonological Universals . His research investigates articulatory and acoustic aspects of speech production, including coarticulation, assimilation, blending, and the phonetic motivations behind diachronic sound changes in Romance languages. He employs advanced techniques such as ultrasound imaging, electropalatography (EPG), and acoustic analysis to study consonant clusters, vowel-consonant interactions, and the evolution of phonological systems. Scientific Publications and Trends: His recent publications (2020–2025) reflect a consistent focus on the articulatory and acoustic foundations of phonological processes. Key themes include dynamic blending in fricative sequences, tongue configuration in stop-liquid clusters, and the phonetic causes of palatalization and vowel changes. His work combines empirical data with theoretical models such as the Degree of Articulatory Constraint (DAC) model, contributing significantly to laboratory phonology and historical linguistics. Scientific Awards: Member, Academy of Europe (Academia Europaea), 2018 Advising and Research Grants: Daniel Recasens has mentored several doctoral and postdoctoral researchers, including Clara Isabel Rodriguez Casado, Aina Espinosa, and Fernando Sánchez-Miret. While specific grant details are not listed, his extensive publication record and leadership in experimental phonetics suggest sustained research funding and collaborative projects in phonetic and phonological research. Labs and Research Teams: He is associated with the Center for Theoretical Linguistics (CLT) at the Autonomous University of Barcelona, a research group dedicated to theoretical and experimental studies of language. His work often involves collaboration with phonetics laboratories and international scholars in the fields of articulatory phonetics and historical linguistics.