Find Academic Supervisors

Guerino MazzolaView profile

Professor

• Mathematical Music Theory
• Musical Creativity
• Gesture Theory
• +6 more

Guerino Mazzola is a Professor of Creativity, Improvisation, and Mathematical Music Theory at the School of Music, University of Minnesota. His academic career includes a PhD in Mathematics from Zurich University and postdoctoral work at Paris-Sud and Rome. He has been a key figure in developing mathematical music theory since 1980, authoring influential books such as The Topos of Music (2002) and Cool Math for Hot Music (2016). Education: PhD in Mathematics (Zurich University), Habilitation in Algebraic Geometry (1980), and Computational Sciences (1993). Research Interests: Mathematical foundations of music, gesture theory, performance analysis, computational musicology, and interdisciplinary creativity. Notable contributions include the Rubato software for performance analysis and the development of gesture theory applied to free jazz. He has received awards such as the Mexican Mathematical Society Medal (2000) and has been a visiting professor at prestigious institutions like the École Normale Supérieure in Paris. Teaching spans advanced courses on gesture theory, performance science, and music informatics, with a focus on bridging mathematical rigor and artistic creativity.

Sungyong AhnView profile

Lecturer

• Digital Ontology
• New Materialism
• Speculative Capitalist Realism
• +8 more

Dr. Sungyong Ahn is a Lecturer in Digital Media and Cultures at the School of Communication and Arts, University of Queensland, and an affiliate of the Centre for Digital Cultures & Societies. His research focuses on digital ontology, new materialism, and speculative (capitalist) realism, examining how smart technologies reshape human perception and governance. He holds a PhD in Media Studies from the University of Illinois at Urbana-Champaign. Education: Bachelor of Philosophy (Yonsei University), Master of Film and Television (Chung-Ang University), PhD in Media Studies (UIUC). His research investigates the interplay between technology, capitalism, and cultural production, particularly through the lens of the Internet of Things (IoT), quantum physics as cultural imagination, and French philosophy (Foucault, Deleuze, Badiou). Recent work includes analyzing IoT’s role in self-governance and the ontological anxieties of digital existence. Publications span journals like Media Theory , Journal of Cultural Economy , and Postmodern Culture . He is the author of Internet-ontologies-Things: Smart Objects, Hidden Problems, and Their Symmetries (2023). Available for supervision in media studies, digital cultures, and science/technology studies. Active in interdisciplinary collaborations, including the Centre for Digital Cultures & Societies.

Michael OppenheimerView profile

Professor

• Climate Science
• Environmental Policy
• Atmospheric Science
• +3 more

Michael Oppenheimer is the Albert G. Milbank Professor of Geosciences and International Affairs at Princeton University, affiliated with the Princeton School of Public and International Affairs (SPIA), the Department of Geosciences, and the High Meadows Environmental Institute (HMEI). He directs the Center for Policy Research on Energy and the Environment (C-PREE) and holds roles in multiple research programs. His work bridges climate science and policy, focusing on climate impacts, adaptation, and scientific assessment frameworks. He has advised international bodies, including the IPCC, and contributed to foundational climate policies like the Kyoto Protocol. Education: Ph.D. in Chemical Physics, University of Chicago S.B. in Chemistry, MIT Research Interests: Climate change impacts, sea level rise, compound hazards (e.g., storms and heatwaves), migration dynamics, and the role of scientific assessments in policy. His work emphasizes actionable solutions for climate adaptation and mitigation. Grants & Awards: 2010 Heinz Award for the Environment AAAS Fellow Recipient of the Nobel Peace Prize (via IPCC, 2007) Advising & Labs: Leads the C-PREE, mentors over 20 graduate students, and collaborates with interdisciplinary teams on climate policy and science. Key labs/centers include C-PREE, HMEI, and the NY City Panel on Climate Change.

Bruce N. WalkerView profile

Professor

• Human-Computer Interaction (HCI)
• Auditory Displays
• Data Sonification
• +7 more

Professor Bruce N. Walker holds a joint appointment in the School of Psychology and School of Interactive Computing at Georgia Institute of Technology, within the College of Sciences. His research focuses on human-centered technology design, emphasizing accessibility, auditory displays, and human-AI interaction. He leads the Sonification Lab, pioneering multimodal interfaces and inclusive technology solutions. He earned his Ph.D. in Human Factors and Human-Computer Interaction from Rice University in 2001. Research Interests: Trust in technology, accessible interfaces, sonification, AI-human collaboration, and HCI in non-traditional environments. Current projects include the AccessCORPS VIP initiative to enhance course accessibility and studies on automated vehicle interaction. Awards: Best Paper Award at AudioMostly 2014 for auditory weather reports research. Active in professional organizations like the International Community for Auditory Display and Human Factors and Ergonomics Society. Teaching: Courses include Research Methods for Human Factors, Sensation and Perception, and HCI Foundations. Supervises interdisciplinary teams in the Sonification Lab R&D Studio and AccessCORPS VIP. Labs/Teams: Sonification Lab (multimodal data exploration) and AccessCORPS (disability-inclusive course design). Collaborates on international projects like the Mwangaza initiative for learners with vision impairment in Kenya.

David DanksView profile

Professor

• AI Ethics
• Causal Inference
• Healthcare Technology
• +4 more

David Danks is a Professor of Data Science, Philosophy, and Policy at the University of California, San Diego. His work bridges AI ethics, causal inference, and policy, focusing on governance frameworks for emerging technologies. He leads research on trustworthy AI systems, healthcare technology applications, and sociotechnical risks. Danks is affiliated with the DIVER Lab, exploring interdisciplinary approaches to AI's societal impact. His research spans causal discovery algorithms, ethical AI design, and the intersection of science and policy. Notable themes include mitigating bias in quantum machine learning, dynamic certification for autonomous systems, and addressing unforeseen technological harms. He has contributed to national AI policy through roles like the National Artificial Intelligence Advisory Committee. Publications emphasize ethical challenges in AI development, such as algorithmic fairness, epistemic utility, and moral responsibilities in dual-use technologies. His work frequently intersects with healthcare innovation, including personalized hemodynamic models for surgical risk reduction. While no formal awards or grants are listed, Danks' involvement in high-profile initiatives like the CCC Whitepaper on pandemic prevention underscores his leadership in translational ethics and policy.

• Artificial Intelligence
• Neurocomputing
• Quantum AI
• +5 more

Dr. Richard Jiang is a Senior Lecturer (Associate Professor) at Lancaster University's School of Computing and Communications. His research focuses on Artificial Intelligence, Neurocomputing, Quantum AI, Privacy Computing, and Medical Computing. He has pioneered secure pattern recognition in encrypted domains and quantum neuromorphic computing. With over £1M in research grants from EPSRC and others, he has authored 100+ publications and supervised over 20 PhD students. Dr. Jiang's work includes the Face2Brain method for neurodegenerative assessment and explainable models for brain aging analysis. He contributes actively to academic committees, editorial boards, and conferences like the World Conference on eXplainable AI. His research spans ethical AI frameworks, quantum algorithms for medical imaging, and privacy-preserving biometric systems.

Alexandr LucasView profile

Teaching Professor

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +3 more

Alexandr Lucas is a Teaching Professor in Robotics at the University of Sheffield's School of Computer Science. He joined in September 2019 and holds roles including Deputy Admissions Tutor (General Engineering) and IPE Tutor. His research focuses on developmental neuro-robotics, cognitive assessment via human-robot interaction, and educational robotics applications. He contributes to courses like COM1005 (Machines and Intelligence) and COM3528 (Cognitive and Biomimetic Robotics), developing teaching materials for MiRo robots and simulators. Publications include work on cognitive skill assessment using cloud computing and robotic systems, visual finger-counting for neuro-robotics learning, and public perception analysis of robots in urban environments. He co-founded the SERAI Network CIC and maintains active roles in robotics education and outreach. Lucas has created extensive teaching resources, including guides for MiRoCloud simulation platforms and visual coding tools (MiRoCODE). His work emphasizes practical robotics education and bridging theory with hands-on experiments in undergraduate and postgraduate programs.

• Decision Making
• Information Sampling
• Machine Learning Applications
• +4 more

Gaël Georges Marcel Le Mens is a Full Professor at Pompeu Fabra University (UPF), holding a position in the Department of Economics and Business. He is also affiliated with the Barcelona School of Economics and serves as academic co-director of the Executive Master in Business Administration (EMBA) at the UPF Barcelona School of Management. His academic journey includes teaching roles at INSEAD, London Business School, ESADE, and the University of Lugano, alongside positions at the universities of Southern Denmark and New York. Education: Doctor in Business Administration, Stanford Graduate School of Business MSc in Management Science and Engineering, Stanford University Diploma in Engineering, Supélec Bachelor of Economics, University of Paris XI His research focuses on decision-making processes, information sampling, machine learning applications in semantics, and organizational behavior. Key themes include cognitive heuristics, social media impact on political expression, and the interplay between popularity and evaluation dynamics. He has explored how feedback mechanisms shape political communication and developed methodologies to compare human and machine conceptual judgments using models like BERT. His publications span journals such as PNAS , Psychological Review , and Industrial and Corporate Change , reflecting his interdisciplinary approach. Though no explicit awards are noted, his prolific output highlights sustained academic impact. He has advised multiple institutions on curriculum design and executive education, leveraging his cross-university teaching experience. Le Mens is affiliated with the Barcelona School of Management’s research teams and contributes to initiatives bridging artificial intelligence and social sciences. His work often addresses practical challenges in organizational decision-making and digital communication strategies.

Blake MillerView profile

Assistant Professor

• Computational Social Science
• Text Analysis
• Social Media Politics
• +7 more

Blake Miller is an Assistant Professor of Computational Social Science in the Department of Methodology at the London School of Economics (LSE), affiliated with the Data Science Institute. Their research focuses on computational methods applied to political communication in authoritarian regimes, particularly China, and the intersection of social media with political violence and identity politics. They hold a PhD from the University of Michigan (2018) and conducted postdoctoral research at Dartmouth College. Key substantive areas include: China's surveillance-driven security state and information control mechanisms Political mobilization through moral outrage and outgroup targeting Technological adaptations in authoritarian governance Methodological expertise spans machine learning, text analysis, and fairness in AI applications. Their book project Platforms and Power examines how authoritarian states delegate censorship to private platforms. Teaching focuses on quantitative text analysis and machine learning in political contexts. Research outputs include influential work on: Censorship patterns during China's zero-COVID protests Moral-emotional triggers for violence support Evaluation of active learning algorithms for text labeling Blake's work has been featured in The Washington Post , China File , and the CSIS Pekingology Podcast. They maintain an active presence in interdisciplinary research communities.

Bernadette BucherView profile

Assistant Professor

• Embodied AI
• Vision-Language Grounding
• Mobile Manipulation
• +3 more

Bernadette Bucher is an Assistant Professor in the Robotics Department (primary) and Computer Science and Engineering Department at the University of Michigan. Her research focuses on embodied AI, vision-language grounding, and mobile manipulation, with an emphasis on interpretable visual representations and uncertainty estimation for robotics tasks. She previously worked at Boston Dynamics AI Institute, NVIDIA Research, and Lockheed Martin Corporation. Her academic background includes a PhD in Computer Science from the University of Pennsylvania (GRASP Lab) under advisors Kostas Daniilidis and Nikolai Matni, alongside MA degrees in Mathematics and Economics from the University of Alabama (2014). Research interests include robotics, computer vision, and machine learning intersections, particularly autonomous mobile manipulation. Her work emphasizes uncertainty-aware systems and deployable learning-based methods. Notable achievements include the Best Paper in Cognitive Robotics at ICRA 2024. Her research spans projects like EVORA for off-road autonomy and ASHiTA for hierarchical task analysis. She has contributed to open-source projects like RoboNet and actively publishes in top conferences (CVPR, CoRL, ICRA). Key projects: EVORA, ASHiTA, Vision-Language Frontier Maps (VLFM) Grants and funding: Honda Research Institute (Curious Minded Machines project) Labs/Teams: Active participation in robotics labs at University of Michigan and prior collaborations with industry partners

Frank TongView profile

Professor

• Cognitive Neuroscience
• Visual Perception
• Visual Cognition
• +8 more

Frank Tong is a Professor of Psychology at Vanderbilt University in the College of Arts and Science. He leads an active research laboratory investigating the neural mechanisms of human visual perception, cognition, attention, and working memory. His work integrates behavioral experiments, high-resolution fMRI, and computational modeling to decode how visual information is represented and maintained in the brain. Department: Department of Psychology Office: Wilson Hall, Room 531 Email: frank.tong@vanderbilt.edu Phone: 615-322-1780 Education: B.S. in Psychology, Queen's University, Kingston, Canada (Advisor: Barrie Frost) Ph.D. in Psychology, Harvard University (Advisors: Ken Nakayama, Nancy Kanwisher) Postdoctoral Fellow, UCLA (McDonnell-Pew Fellowship, Advisor: Steve Engel) Frank Tong's research centers on understanding how early visual representations interact with higher cognitive functions such as attention and working memory. He has developed pioneering fMRI decoding methods to reconstruct visual features like orientation and object categories from brain activity patterns in the human visual cortex. His lab has demonstrated how these techniques can reveal the neural bases of visual working memory and object-based attentional selection. Current work includes using deep convolutional neural networks as models of human visual processing. His recent publications show a consistent focus on decoding mental states, visual features, and memory contents from brain activity, particularly using fMRI pattern analysis. The research spans visual working memory, attentional modulation, scene perception, and the application of machine learning to neural data. These studies frequently appear in top journals such as Nature , Nature Neuroscience , and Annual Review of Psychology . Scientific Awards: McDonnell-Pew Training Fellowship (1999) Robert K. Root Preceptorship, Princeton (2003) Scientific American Top 50 Award (2004) Young Investigator Award, Cognitive Neuroscience Society (2006) Chancellor's Award for Research, Vanderbilt (2008) Young Investigator Award, Vision Sciences Society (2009) Troland Research Award, National Academy of Sciences Frank Tong has advised numerous graduate students and postdoctoral fellows, many of whom have gone on to successful academic and research careers. His lab has received significant research funding to support its work in cognitive neuroscience and brain imaging. He has also served on the editorial board of the Annual Review of Psychology and as a board member of the Vision Sciences Society, reflecting his leadership in the field. He teaches undergraduate courses including Psy 3760 (Mind and Brain), Psy 3765 (Social Cognition and Neuroscience), and Psy 3780 (The Visual System). His lab continues to explore the interplay between early visual processing and higher cognition using advanced neuroimaging and computational techniques.

Zhu-Tian ChenView profile

Assistant Professor

• Data Visualization
• Human-Computer Interaction
• Augmented Reality
• +7 more

Zhu-Tian Chen is an Assistant Professor in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities, where he leads research in data visualization, human-computer interaction, and augmented reality. Prior to this, he held postdoctoral positions at Harvard University and UC San Diego, working with leading researchers in visual computing and interactive design. Ph.D. in Computer Science, Hong Kong University of Science and Technology B.Eng. in Software Engineering, South China University of Technology His research focuses on augmenting human intelligence through hybrid human-AI systems, particularly in everyday and outdoor environments. He specializes in designing intelligent AR interfaces, embedded visualizations, and language-oriented interactions for applications in sports analytics, education, and data analysis. His work integrates human-centered design with applied machine learning to create intuitive and effective visualization tools. The recent trend in his publications shows a strong emphasis on intelligent AR systems for dynamic scenes, LLM-based code generation interfaces, and real-time augmentation of sports videos using natural language and gaze-based interactions. His work frequently appears in top-tier venues such as IEEE VIS, ACM CHI, and UIST. Best Paper Award, ACM CHI'23 Best Short Paper Honorable Mention, EuroVis'23 Best Paper Honorable Mention, IEEE VIS'22 (twice) Certificate of Distinction and Excellence in Teaching, Harvard University Hong Kong Ph.D. Fellowship Dr. Chen actively mentors undergraduate, master’s, and PhD students, as well as visiting scholars and interns, and is building a new research lab focused on visualization for intelligent AR systems. He has served on program committees for major conferences including ACM CHI, IEEE VIS, and EuroVis, and has been invited to speak at institutions such as Apple, JP Morgan, and multiple universities worldwide. He also contributes to the academic community through grant reviewing for NSF and the Department of Energy. He leads research projects in intelligent AR systems for sports, language-oriented interactions with LLMs, and immersive data visualization, often in collaboration with institutions like Harvard, UC San Diego, and HKUST. His lab welcomes students and collaborators interested in visualization, HCI, and applied AI.

Stella YuView profile

Professor

• Computer Vision
• Artificial Intelligence
• Robotics
• +6 more

Stella Yu is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. She serves as the Vision Group Director at the International Computer Science Institute (ICSI) and is a Senior Fellow at the Berkeley Institute for Data Science (BIDS). Her research bridges computer vision, human perception, and artistic vision. Ph.D. in Robotics (2003), Carnegie Mellon University Dr. Yu's work spans artificial intelligence, signal processing, and computational neuroscience. Her research focuses on multi-perspective visual perception, integrating robotic vision, medical imaging, and artistic analysis. Recent projects explore hyperbolic data visualization and computational MRI. Her publications reflect contributions to data visualization and medical imaging. She has received prestigious recognition including the NSF CAREER award. She has advised graduate students in EECS, such as Ke Wang (2023) and Pat Virtue (2019). Her affiliations include the Berkeley Deep Drive (BDD) center and the FHL Vive Center for Enhanced Reality, reflecting her interests in applied vision science.

• Machine Learning
• Explainable AI (XAI)
• Deep Learning
• +8 more

Haoyi Xiong is an active academic researcher in artificial intelligence, machine learning, and data science, with extensive publications in top-tier journals and conferences including IEEE TPAMI, NeurIPS, ICML, KDD, and AAAI. His work spans explainable AI, graph neural networks, diffusion models, remote sensing, and large language models. Research Interests: Explainable AI (XAI) and model interpretability Graph Neural Networks and contrastive learning Diffusion models and generative AI Medical and remote sensing image analysis Large language models and autonomous agents Learning to rank and web search His recent publications (2023–2025) show a strong trend toward self-supervised learning , model robustness , and integration of LLMs with structured data and knowledge graphs . He frequently collaborates with researchers from major tech and academic institutions. Scientific Awards: No explicit awards mentioned in the provided text. Advising and Grants: While no direct mention of students or grants, his role as a senior author on numerous papers suggests he advises graduate students and likely leads funded research projects in machine learning and AI. His work on frameworks like COLTR , GS2P , and MUSCLE indicates leadership in developing scalable AI systems. Labs and Teams: Though not explicitly stated, his frequent collaboration with Jiang Bian, Dejing Dou, and Dawei Yin suggests affiliation with a well-established AI research lab or industry-academia partnership focused on data mining, intelligent systems, and large-scale learning.

Valeria BruschiView profile

Researcher

• Audio Engineering
• Signal Processing
• Spatial Audio
• +5 more

Valeria Bruschi is a Researcher at the Department of Information Engineering (DII) within the Faculty of Engineering at Università Politecnica delle Marche (UNIVPM) in Ancona, Italy. Her academic profile was last updated on April 13, 2024, and she maintains her office at the Engineering Faculty on via Brecce Bianche, with contact information including phone +39 071-220-4486 and email v.bruschi@staff.univpm.it. Dr. Bruschi's research spans multiple domains within audio and signal processing, with particular expertise in spatial audio systems, automotive human-computer interaction, and biomedical signal applications. Her work bridges theoretical signal processing techniques with practical implementations across diverse fields including automotive safety systems, hearing aid technology, sleep medicine, and agricultural monitoring. She has made significant contributions to head-related transfer function (HRTF) processing, real-time audio enhancement algorithms, and innovative monitoring systems that utilize acoustic signals for various applications. Analysis of Dr. Bruschi's recent publications reveals a strong trajectory in developing practical audio processing solutions with real-world applications. Her work shows increasing integration of machine learning techniques with traditional signal processing approaches, particularly in areas like driver monitoring systems, snoring detection and cancellation, and spatial audio rendering. A notable trend is her focus on creating lightweight, real-time implementations suitable for embedded systems and practical deployment scenarios, while maintaining high performance standards. Her research consistently demonstrates interdisciplinary collaboration, connecting audio engineering with fields as diverse as automotive safety, sleep medicine, and agricultural technology. Dr. Bruschi actively contributes to advancing audio engineering through her research on equalization techniques, noise reduction systems, and immersive audio technologies. Her work on pulse compression techniques for hearing aid distortion measurement represents an important contribution to audiological assessment methodologies. Her publication record demonstrates consistent scholarly output with increasing impact across multiple application domains, reflecting her ability to translate theoretical signal processing concepts into practical engineering solutions.