Find Academic Supervisors

• Japanese Applied Linguistics
• Second Language Acquisition
• Language Education Policy
• +7 more

Satoshi Miyazaki is a Professor at Waseda University's Graduate School of Japanese Applied Linguistics and affiliated with the Faculty of International Research and Education . With a Ph.D. from Monash University, his work bridges Japanese language education, second language acquisition, and cross-cultural communication. Education Background: Doctor of Philosophy (Japanese Applied Linguistics) - Monash University (1997) Graduate School of Japanese Applied Linguistics - Waseda University (1982) Research Interests focus on Japanese language pedagogy, natural acquisition mechanisms, and interactional strategies for non-native learners. His innovative use of Eye Mark Recorders (EMR) and Event-Related Potentials (ERP) has advanced understanding of cognitive processes in language learning. Recent Articles (2011-2001) reveal trends in Japanese language education reform, cross-cultural learning challenges, and technology-enhanced teaching methods. Key themes include correctional language programs for foreign inmates, immersion environments, and brain processing studies using advanced neuro-linguistic techniques. Research Projects have received continuous funding from Japan Society for the Promotion of Science, including prison language education programs, foreign care worker competence frameworks, and EMR-based lecture comprehension analysis. His work extends to international collaborations with institutions in Australia, the U.S., and Europe.

• Artificial Intelligence
• Multi-agent systems
• Machine learning
• +5 more

Toshiharu Sugawara is a Professor in the Department of Computer Science and Engineering at Waseda University's Faculty of Science and Engineering, School of Fundamental Science and Engineering, a position he has held since April 2007. With a Ph.D. in Engineering from Waseda University, his research spans multiple domains in artificial intelligence and multi-agent systems, maintaining active collaborations across international institutions and contributing significantly to the field through numerous publications and awards. Dr. Sugawara received his BS and MS degrees in Mathematics from Waseda University in 1980 and 1982, respectively, followed by his Ph.D. in 1992. Before joining Waseda University as faculty, he worked as a Research Scientist at NTT Laboratories from 1982 to 2007, with a visiting researcher position at the University of Massachusetts at Amherst in 1992-1993. He also held part-time lecturer positions at University of Electro-Communications (2003-2007), Waseda University (2004-2006), and Tokyo University of Agriculture and Technology (1990-1991). His research interests focus on artificial intelligence with particular expertise in multi-agent systems, machine learning, cooperation and coordination mechanisms, soft computing, computational social science, and social informatics. His work bridges theoretical foundations with practical applications in network management and information systems. Recent publications demonstrate a strong trajectory toward interpretable multi-agent reinforcement learning, efficient path planning algorithms, and modeling social behaviors in complex networks. His research group has made significant contributions to multi-agent path finding, cooperative task execution, and understanding virtual economies in social media platforms. Dr. Sugawara has received numerous prestigious awards including multiple Best Paper Awards at JAWS conferences (2014, 2015, 2018), ACM SAC 2015, and various research paper awards from Japanese academic societies. His work on multi-agent systems has been consistently recognized for its theoretical rigor and practical impact. As an advisor, Dr. Sugawara has mentored numerous students who have become prominent researchers in their own right, with many co-authoring papers that have received awards. His laboratory maintains strong collaborations with industry partners, particularly in the areas of network management and intelligent systems. Current research directions include developing interpretable multi-agent reinforcement learning frameworks, optimizing multi-agent coordination in constrained environments, and analyzing social dynamics in virtual economies.

Diamantis PanagiotopoulosView profile

Professor

• Aegean Bronze Age
• Cultural Interaction in Eastern Mediterranean
• Archaeological Theory
• +4 more

Prof. Dr. Diamantis Panagiotopoulos is a Professor of Classical Archaeology at Heidelberg University's Institute of Classical Archaeology, serving as its Managing Director since 2012. His academic foundation includes a doctorate from Heidelberg University (1996) and a habilitation from Salzburg University (2003), both focused on Aegean Bronze Age archaeology. Panagiotopoulos's research explores Aegean Bronze Age societies, with emphasis on seal practices, iconography, cultural interactions, and archaeological landscapes. His work integrates theoretical frameworks with material culture analysis to examine Minoan-Mycenaean administration, transcultural exchanges in the Eastern Mediterranean, and heritage preservation strategies. His publications reflect a strong trajectory toward computational methods in archaeology and interdisciplinary approaches, with recent articles leveraging digital tools for seal analysis and landscape modeling. Earlier works established foundations in ritual studies and cross-cultural encounters. Awards include fellowships from the German Archaeological Institute (DAI), DAAD, and DFG, recognizing his contributions to Bronze Age archaeology. He directs excavations at Koumasa (Crete) and co-leads the Heidelberg Corpus of Minoan and Mycenaean Seals.

• Neuroscience
• Machine Learning
• Neuroimaging
• +4 more

Dustin Scheinost is an Associate Professor at Yale School of Medicine, affiliated with the Department of Radiology & Biomedical Imaging, Yale Child Study Center, Department of Statistics, and Yale Biomedical Imaging Institute. His research focuses on connectomics , machine learning , and neuroinformatics through the Multi-modal Imaging, Neuroinformatics, & Data Science (MINDS) Lab. Radiology & Biomedical Imaging (Primary) Child Study Center (Secondary) Statistics (Secondary) Wu Tsai Institute Yale Stress Center Research Interests include developing novel statistical and machine learning methods for functional connectivity in big neuroscience data, leading the BioImage Suite Web (BISWeb) platform, and advancing early life neuroimaging through the Fetal, Infant, Toddler Neuroimaging Group (FIT’NG). His work is supported by grants from NIMH, NIAA, NIDA, and NHLBI. Selected Scientific Contributions span functional connectivity in laterality preferences, anti-racist AI governance in psychiatry, self-citation trends in neuroscience, and predictive modeling of mood disorders. He collaborates extensively with Todd Constable and others on multimodal neuroimaging studies.

• Distributed Systems
• Machine Learning
• Big Data
• +3 more

Jim Dowling is a distributed systems researcher at KTH Royal Institute of Technology, focusing on large-scale distributed systems, machine learning, and big data. His work emphasizes improving system dependability, performance, security, and scalability through middleware, peer-to-peer systems, and cloud-native solutions. He leads courses such as Advanced Course in Large Scale Machine Learning and Deep Learning and Scalable Machine Learning and Deep Learning , demonstrating his commitment to education in AI and distributed computing. His research spans topics like feature stores, Kubernetes integration, and AI-driven environmental analytics (e.g., ANIARA project for edge infrastructure automation and ExtremeEarth for Copernicus data analysis). He has contributed to scalable ML pipelines, cloud storage systems (HopsFS-S3), and hyperparameter optimization tools like Maggy. Key projects include the Hopsworks platform for machine learning operations and the development of cloud-native tools for big data analytics. His work bridges theoretical distributed systems research with practical applications in AI, healthcare, and environmental science. He has advised on numerous collaborative initiatives but no formal students are listed. His grants and lab activities are centered around Hopsworks and the ANIARA project, reflecting his focus on scalable, self-managing systems.

Sabina OwsianowskaView profile

Lecturer

• Tourism Studies
• Cultural Tourism
• Semiotics of Tourism
• +4 more

Dr. Sabina Owsianowska is a Lecturer at the Faculty of Tourism and Recreation , University School of Physical Education in Cracow , Poland. She has been actively involved in teaching Polish and international students since 2006, particularly through the LLP-Erasmus program. MA in Tourism Studies (University School of Physical Education in Cracow, 2001) PhD in Humanities (University School of Physical Education in Cracow, 2005) Her research focuses on tourism anthropology , examining tourism as a cultural and social phenomenon , with special interest in: Socio-cultural dimensions of leisure Gender issues in tourism Media influence on tourism Semiotics of tourist experiences Cultural and heritage tourism Her academic contributions include: Co-editor of Anthropology of Tourism in Central and Eastern Europe (Rowman & Littlefield, 2018) Editorial board member of Folia Turistica journal since 2014 Co-editor of multiple thematic issues in tourism anthropology Dr. Owsianowska has: Published 9 book chapters Contributed to refereed journals and conference proceedings Authored critical reviews of contemporary tourism research Key collaborative projects include work with: Dr. Marek Banaszkiewicz Prof. Nelson Graburn Prof. Maria Della Lucia Dr. Elena Giudici

Virginia de SaView profile

Professor

• Machine Learning
• Neuroscience
• Brain-Computer Interfaces
• +5 more

Virginia de Sa is a Professor in the Department of Cognitive Science at the University of California, San Diego. Her research integrates computational modeling, psychophysics, and machine learning to investigate visual and multi-sensory perception, with a focus on understanding how humans learn and perceive through neural mechanisms. Her work emphasizes the synergy between human learning and machine learning, applying insights from both fields to advance understanding of perception. Notable projects include developing brain-computer interface (BCI) systems and analyzing biases in facial expression recognition algorithms. She leads the de Sa Lab, which explores the neural basis of learning through interdisciplinary methods, including EEG analysis and biologically inspired algorithms. Key research directions include improving BCI usability through adaptive spatial filtering, investigating pain assessment via facial and electrophysiological data fusion, and enhancing AI fairness in facial expression analysis. Dr. de Sa has contributed to grants such as the NSF-funded CHS project to enhance BCI reliability and collaborates on initiatives like AI-READI to improve healthcare data practices. Her lab’s BCI division focuses on interpreting EEG data for assistive technologies, while her work on divisive normalization bridges biological insights with artificial neural network design. Ongoing efforts explore zero-shot learning and the generalization of neural models to unseen tasks. Dr. de Sa’s interdisciplinary approach spans neuroscience, computer science, and engineering, with a commitment to advancing both theoretical understanding and practical applications in human-computer interaction.

Michael D. ErnstView profile

Professor

• software engineering
• programming languages
• type theory
• +6 more

Michael D. Ernst is a Professor in the Computer Science & Engineering department at the University of Washington's College of Engineering. His research aims to make software more reliable, more secure, and easier (and more fun!) to produce. Previously, he was a tenured professor at MIT and a researcher at Microsoft Research. Ernst's primary technical interests are in software engineering, programming languages, type theory, security, program analysis, bug prediction, testing, and verification. His research combines strong theoretical foundations with realistic experimentation, with an eye to changing the way that software developers work. He focuses particularly on programmer productivity and developing practical tools that can be integrated into developers' workflows. Analysis of his recent publications (2018-2025) reveals a continued focus on verification techniques, program analysis, and testing methodologies. His work spans from theoretical foundations of type systems to practical applications of NLP for test generation and LLMs for test oracle creation. A consistent theme is developing lightweight, modular approaches that can be practically applied in real-world development environments. Scientific Awards: ACM Fellow (2014) John Backus Award (2009) NSF CAREER Award (2002) ACM SIGSOFT Impact Paper Award (2013) 8 ACM Distinguished Paper Awards across multiple conferences ECOOP 2011 Best Paper Award Microsoft Academic Search ranked #2 in software engineering research (2013) Ernst has received significant research funding including the NSF CAREER Award, supporting his work on program analysis and verification techniques. His research combines theoretical rigor with practical impact, often resulting in tools that are adopted by the software engineering community. He actively collaborates with researchers across institutions and has served in leadership roles for major conferences in programming languages and software engineering. His research group develops practical tools that address real challenges in software development, with a focus on making verification and analysis techniques more accessible to working developers. Current projects include applying machine learning techniques to software engineering problems while maintaining strong theoretical foundations.

Eli BlevisView profile

Professor

• Sustainable Interaction Design
• Human-Computer Interaction (HCI)
• Visual Thinking in HCI
• +3 more

Eli Blevis is a Professor of Informatics at Indiana University's Luddy School of Informatics, Computing, and Engineering, with a joint appointment as Visiting Professor at Hong Kong Polytechnic University's School of Design. His research focuses on Sustainable Interaction Design, Visual Thinking, and Transdisciplinary Design Theory. He is renowned for integrating sustainability principles into HCI practices and exploring the role of visual media in design and education. Affiliations: Luddy School of Informatics, Computing, and Engineering, Indiana University Visiting Professor, School of Design, The Hong Kong Polytechnic University Research Interests: Sustainable Interaction Design (SID) Visual Thinking in HCI (e.g., photographic ethnography) Design Pedagogy (e.g., Design Challenge-Based Learning) Sustainability in Digital Infrastructure Cross-Cultural Design Practices His work spans over 50 publications, with awards including the CHI Best Paper and Computing Reviews Notable Paper. He advises students on projects like visible mending, nonlinear design thinking, and sustainable food systems. Blevis also curates the Visual Thinking Gallery and contributes to international conferences like NordiCHI and DIS. His lab explores tangible interfaces (e.g., MemoryReel) and eco-conscious design strategies. Awards: CHI Best Paper Award CCC Sustainability Award ACM Computing Reviews Notable Paper DIS Best Paper Award Grants and Funding: Research supported by NSF and industry partnerships.

• Software Engineering
• Computer Supported Cooperative Work (CSCW)
• Human-Computer Interaction (HCI)
• +5 more

Hilda Tellioglu is an Associate Professor and Dean of Academic Affairs for Informatics at TU Wien, leading the Artifact-based Computing and User Research department. She holds a dual role as Head of Research Unit and oversees the Office of the Dean. Her work focuses on Human-Computer Interaction (HCI), Design Thinking, and Digital Transformation, with a strong emphasis on socio-technical systems and participatory design methodologies. Research interests span software engineering, CSCW, ambient assisted living (AAL), and digital co-creation practices in urban planning. She has spearheaded projects like aspern.mobil LAB, EnviSense, and StreetForum, exploring smart mobility, environmental sensing, and citizen participation in urban development. Her work frequently integrates tangible interfaces and model-based design approaches. Key contributions include frameworks for digital transformation evaluation (EEFCET 2020), user-centered design for informal caregivers (TOPIC platform), and crisis management systems using crowdsourcing (RE-ACTA). She has led over 20+ research projects funded by FFG, EU, and national agencies, focusing on mobility innovation, healthcare technology, and sustainable urban systems. Teaching responsibilities include courses on Design Thinking, Digital Transformation, and Research Methods across bachelor's, master's, and PhD programs. She actively promotes interdisciplinary collaboration through initiatives like the Center for Technology & Society (CTS) and Living Lab methodologies.

Cheung Ngai-ManView profile

Associate Professor

• Artificial Intelligence
• Augmented Intelligence
• Data Science
• +4 more

Cheung Ngai-Man is an Associate Professor and Associate Head of Pillar (Education) at Singapore University of Technology and Design (SUTD), part of the Information Systems Technology and Design (ISTD) pillar. He holds a Ph.D. in Electrical Engineering from the University of Southern California (2008) and has held research positions at Stanford University, Texas Instruments, IBM, and others. His research focuses on image and signal processing, computer vision, machine learning, and artificial intelligence. Education: Ph.D., Electrical Engineering, University of Southern California (2008); Postdoctoral research at Stanford University (2009–2011). Research Interests: Develops algorithms for multimedia data processing, explores interdisciplinary applications of signal processing and AI, and addresses challenges in computer vision and generative models. Recent work includes fairness in generative models, few-shot image generation, and adversarial robustness. Publications: Over 100+ peer-reviewed papers in top venues (CVPR, NeurIPS, IEEE TIP, TPAMI) focusing on computer vision, generative models, and AI security. Notable 2023 work includes studies on label-only model inversion attacks and fairness metrics in generative systems. Awards: Best Paper Finalist (CVPR 2019), SAIL Award Finalist (WAIC 2019), Outstanding Associate Editor (IEEE T-MM), Croucher Foundation Fellowship. Students: Supervised postdocs (Hossein Nejati, Fang Lu), research assistants (Mohammad Rostami), and visiting students (Ma Rui). Labs/Teams: Leads research groups in AI, computer vision, and multimedia systems at SUTD. Has spun off AI initiatives for wound care and contributed to Singapore’s National AI Strategy.

Benjamin EysenbachView profile

Professor

• Reinforcement Learning
• Self-Supervised Learning
• Machine Learning
• +2 more

Benjamin Eysenbach leads the Princeton Reinforcement Learning Lab, where he designs algorithms that enable artificial intelligence systems to learn intelligent behaviors through trial-and-error, specializing in self-supervised methods that eliminate the need for human labels. He joined Princeton after completing his PhD in machine learning at Carnegie Mellon University under Ruslan Salakhutdinov and Sergey Levine, supported by the NSF Graduate Research Fellowship and Hertz Fellowship. His research bridges fundamental machine learning principles with practical applications in robotics and decision-making systems. Eysenbach's research focuses on developing self-supervised reinforcement learning algorithms that enable autonomous skill acquisition without external rewards. His investigations span contrastive learning methods, temporal abstraction techniques, and scalable architectures for goal-conditioned behaviors. These innovations aim to create more efficient and generalizable learning systems that can discover useful behaviors from unlabeled experience. Eysenbach's publications demonstrate consistent advancement in self-supervised RL methodologies, with recent work focusing increasingly on temporal abstraction and representation learning theory. His research shows progression from foundational contrastive RL frameworks toward more sophisticated analyses of generalization properties and uncertainty quantification. The 2025 works indicate expanding investigation into hierarchical control, probabilistic alignment, and hyper-deep network architectures. Eysenbach has been recognized with prestigious awards including the Hertz Fellowship and NSF Graduate Research Fellowship, supporting his doctoral research in self-supervised RL methodologies. His work has been presented at top machine learning conferences including NeurIPS, ICML, and ICLR. As director of the Princeton Reinforcement Learning Lab, Eysenbach oversees research initiatives in self-supervised RL, including projects on intention-conditioned modeling, horizon generalization, and contrastive learning frameworks. He has secured funding from the Princeton AI Lab to study neural correlates of temporal contrast in decision-making. Eysenbach teaches courses in reinforcement learning and has developed new benchmarks like JaxGCRL to accelerate research in goal-conditioned RL.

Edward LankView profile

Professor

• Human-Computer Interaction
• Intelligent User Interfaces
• Mobile Interaction
• +3 more

Edward Lank was a Professor at the Cheriton School of Computer Science, University of Waterloo, and held an Inria International Research Chair at Inria Lille-Nord Europe (2019–2023). His research focused on Human-Computer Interaction (HCI), including intelligent user interfaces, mobile/multi-touch interaction, gesture recognition, and mathematical software design. He earned a BSc from the University of Prince Edward Island and a PhD from Queen's University. Research Highlights: Pioneered work on gesture-based interaction, including MathBrush for mathematical expression recognition. Explored large-display interaction, powerwall design, and mass user engagement with public displays. Investigated kinematics of user input, endpoint prediction, and mode inference in interfaces. Collaborated on persuasive technology for energy demand management and health-related serious games. Awards & Recognition: National Science Foundation Career Award (2004) Best of CHI Nominee (2008) Inria International Research Chair (2019–2023) Grants & Funding: Supported by Google, NSERC, GRAND NCE, and the ORF Program. His research addressed challenges in wearable tech, VR/AR, and sustainable HCI practices. Legacy: Edward Lank passed away on March 21, 2022. His contributions to HCI, including foundational work on gesture recognition and user-centered design, continue to influence the field. His courses, such as CS 889 (HCI Seminar) and CS 449 (HCI Fundamentals), emphasized user-centered design and empirical methods.

Oscar Meruvia-PastorView profile

Professor

• Interactive 3D Graphics
• Non-Photorealistic Rendering
• Augmented Reality
• +8 more

Dr. Oscar Meruvia-Pastor is a faculty member in the Department of Computer Science at Memorial University of Newfoundland, within the Faculty of Science. He holds a B.Sc. from ITESM-Monterrey, Mexico, an M.Sc. from the University of Alberta, and a Ph.D. from Otto-von-Guericke Universität Magdeburg, Germany. His research focuses on interactive 3D graphics, non-photorealistic rendering, and biomedical visualization, with applications in telepresence systems, augmented reality (AR), and virtual reality (VR). He has developed tools like OMARC for respiratory condition training and GeNET for gene co-expression network analysis. Dr. Meruvia-Pastor has supervised numerous graduate students and contributed to over 50 publications. His work includes evaluating stereo correspondence methods in AR, robot arm manipulation via depth sensors, and smartphone integration in immersive VR. He has been recognized with awards such as the Best HCI Poster at Graphics Interface 2014 and a semi-finalist poster at SIGGRAPH 2015. He teaches courses in computer science, including computer graphics, multimedia development, and introductory science modules. His research lab focuses on 3D telepresence, medical visualization, and human-centered VR/AR solutions. His academic contributions span software tools for medical imaging analysis, interactive visualization systems, and educational technologies. He actively collaborates with health professionals to advance telemedicine and remote procedural training through AR platforms. His work bridges computer graphics with real-world applications in healthcare, education, and environmental advocacy.

Tom BoellstorffView profile

Professor

• Digital Anthropology
• Virtual Worlds
• Queer Theory
• +4 more

Tom Boellstorff is a Professor of Anthropology at the University of California, Irvine. He holds a Ph.D. from Stanford University (2000). His research focuses on digital anthropology, virtual worlds, queer theory, and the intersection of technology with cultural practices. Boellstorff is known for pioneering ethnographic studies in virtual environments like Second Life and has explored topics such as Indonesian LGBTQ+ communities, disability in digital spaces, and the cultural history of early video games like Intellivision. His work spans theoretical frameworks for understanding digital cultures, methodological innovations in virtual ethnography, and critical analyses of technology’s role in shaping identity, politics, and social structures. Boellstorff’s interdisciplinary approach integrates cultural theory, media studies, and queer studies to examine how digital platforms mediate human experience. Recent publications emphasize the anthropology of emerging technologies (e.g., metaverse studies), historical analyses of video game platforms, and the sociopolitical implications of digital exclusion. His documentary work, including Virtual Cultures in Pandemic Times , extends his ethnographic focus into visual media.