Find Academic Supervisors

Andrew R. MeyerView profile

Associate Professor

• Sport Psychology
• Religion and Sport
• Cultural Sport Studies
• +7 more

Andrew R. Meyer is an Associate Professor in the Department of Health, Human Performance, and Recreation at Baylor University, where he teaches courses in Sport Psychology and Religion and Sport. His academic work bridges the fields of sport sociology, cultural studies, and theology, with a particular focus on the enduring influence of muscular Christianity in American sports. His research interests include: Contemporary American Sport Culture and Religion Muscular Christianity and its modern manifestations Hero-celebrity athletes in popular culture Character development through sport and education Traditional and contemporary Indigenous sport cultures Sport's cultural impact on communities The themes across his publications reveal a consistent exploration of the moral, spiritual, and cultural dimensions of sport. His work analyzes how religious values, particularly those rooted in Christian traditions, shape athlete identity, media narratives, and public perception of sports figures. He has developed the Contemporary Muscular Christian Instrument (2017, 2020) to measure these values across global sport contexts. His articles appear in interdisciplinary journals spanning religion, sociology, public health, and cultural studies, reflecting the broad relevance of his research. Scientific awards and recognitions include: Baylor Teaching Fellow (2017) Dr. Meyer has been active in academic and public discourse, serving as a keynote speaker at international conferences on sport and Christianity and being featured on media outlets such as NPR and the Sport, Faith, Life podcast. His teaching emphasizes character formation in the classroom, aligning with his research on moral development through sport. While specific grant funding is not mentioned, his extensive publication record in peer-reviewed journals and edited volumes indicates sustained scholarly productivity. He has contributed to discussions on mega-sport events, cycling culture, and the redemption narratives of fallen athletes like Lance Armstrong. He is affiliated with the College of Health, Human Performance, and Recreation at Baylor University, contributing to both graduate and undergraduate education in sport studies.

Erik Learned-MillerView profile

Professor

• Machine Learning
• Computer Vision
• Artificial Intelligence
• +11 more

Erik Learned-Miller is a Professor and Chair of the Faculty at the Manning College of Information and Computer Sciences (CICS), University of Massachusetts Amherst. He is based in the Department of Computer Science and leads the Computer Vision Lab, with strong affiliations to the Center for Data Science. His work bridges machine learning and computer vision, focusing on foundational and ethical aspects of visual recognition systems. Education: PhD in Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 2002 MS in Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 1997 BA in Psychology, Yale University, 1988 Learned-Miller's research centers on machine learning methods for computer vision problems, particularly in scenarios with limited labeled data. His work includes one-shot learning , face detection and recognition , image and video segmentation , joint image alignment , and text recognition . He emphasizes unsupervised, self-supervised, and semi-supervised learning paradigms, and is actively involved in addressing societal concerns around the regulation of face recognition technology. His contributions have had a major impact on the computer vision community, most notably through the creation of widely used benchmarks such as Labeled Faces in the Wild and the Face Detection Database and Benchmark , which have become standard evaluation tools in the field. Scientific Awards and Honors: NSF CAREER Award (2006) Mark Everingham Award (2019) Microsoft-MIT Graduate Student Fellowship Learned-Miller has played significant roles in the academic community, including serving as Program Chair for the 2015 Conference on Computer Vision and Pattern Recognition (CVPR) and as a member of the editorial board of the Journal of Machine Learning Research . He has secured competitive research funding, including the NSF CAREER award, supporting his long-term research agenda. While specific advisees are not listed, he mentors graduate students through his lab and departmental roles. He leads the Computer Vision Lab at UMass Amherst, a research group focused on advancing the state of the art in visual understanding through machine learning. The lab is part of the broader research ecosystem within CICS and collaborates with the Center for Data Science, contributing to interdisciplinary efforts in AI and data-driven science.

• Robotics
• Mechatronics
• Humanoid Robots
• +4 more

Atsuo Takanishi is a distinguished Professor at Waseda University's Faculty of Science and Engineering, School of Creative Science and Engineering, with appointments in both the Department of Bioscience and Biotechnology and the Department of Modern Mechanical Engineering. With over 700 publications, 10,399 citations, and an h-index of 47, Professor Takanishi has established himself as a leading figure in robotics research. His work spans multiple institutions, including visiting positions at Showa University School of Dentistry and KDDI Research, Inc. Professor Takanishi received his Doctor of Engineering degree from Waseda University in 1985, following his undergraduate studies in the Faculty of Science and Engineering at the same institution, which he completed in 1980. His academic journey began with positions as an Assistant at Waseda University (1985-1988), Lecturer (1988-1990), and Associate Professor (1990-1997) before achieving his current Professor status. Professor Takanishi's research interests center around robotics and intelligent systems, with particular expertise in humanoid robotics, surgical dentistry applications, and biomechatronics. His work demonstrates a unique integration of mechanical engineering principles with biological systems, resulting in innovative robotic platforms that mimic human capabilities. Notably, his laboratory has developed the Waseda Anthropomorphic Saxophonist Robot series and various medical training simulators that have significantly advanced the field of human-robot interaction. His research in experimental psychology complements his robotics work, particularly in human-robot interaction studies. Analysis of Professor Takanishi's recent publications reveals a strong focus on practical applications of robotics technology, particularly in assistive devices for elderly mobility, medical training simulators, and agricultural robotics for Synecoculture environments. His work consistently bridges theoretical robotics with real-world implementation, with recent papers emphasizing reinforcement learning approaches, haptic feedback systems, and computer vision techniques for complex environments. The Waseda Anthropomorphic Saxophonist Robot series represents a culmination of decades of research in humanoid robotics and musical expression. IEEE Fellow (2020-present) Robotics Society of Japan Fellow (2012-present) Japan Society of Mechanical Engineers Fellow (2007-present) Multiple Best Paper Awards from IEEE conferences (ICRA, IROS, ROMANSY) BusinessWeek Asia's Stars Bloomberg (2001) IFToMM Award of Merit (2010) Professor Takanishi has maintained extensive collaborations with research institutions worldwide, particularly with Italian institutions through the Italy-Japan Workshop series. His laboratory has secured substantial funding from Japanese government agencies including the Japan Science and Technology Agency, with particular emphasis on projects bridging robotics with medical applications and human-centered technologies. His international recognition is evidenced by his numerous visiting professorships and collaborative projects across continents. Professor Takanishi leads the Takanishi Laboratory at Waseda University, which maintains specialized facilities for humanoid robotics development, medical robotics research, and human-robot interaction studies. The laboratory has developed several notable robotic platforms including the WABIAN series of bipedal humanoid robots, the WASEDA FLUTIST ROBOT, and various medical training simulators used in clinical education settings. His work on the WASEDA SAXOPHONIST ROBOT represents a unique fusion of musical artistry and robotics engineering.

• Cognitive Robotics
• Developmental Robotics
• Human-Robot Interaction
• +5 more

Tetsuya Ogata is a Professor at the Faculty of Science and Engineering, School of Fundamental Science and Engineering at Waseda University. He also holds joint appointments as a Fellow at the Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), and as a visiting professor at the National Institute of Informatics (NII). Professor Ogata serves as director of the Institute of AI and Robotics at Waseda University, chairperson of the AI Robot Association (AIRoA), and research supervisor of JST CREST "Fundamentals and Core Technologies for Embodied AI" since 2025. Professor Ogata received his B.S., M.S., and D.E. degrees in mechanical engineering from Waseda University in 1993, 1995, and 2000, respectively. He began his academic career as a Research Associate at Waseda University (1999-2001), followed by positions as a Research Scientist at RIKEN Brain Science Institute (2001-2003), Lecturer (2003-2005) and Associate Professor (2005-2012) at Kyoto University's Graduate School of Informatics, before becoming a Professor at Waseda University in 2012. He was also a JST PRESTO Researcher from 2009 to 2015. Professor Ogata's research focuses on cognitive developmental robotics, deep predictive learning, and the integration of vision, language, and action (VLA) for human-robot interaction. His work explores how robots can learn through experience and interaction with humans and environments, developing models that allow for flexible behavior generation and adaptation. His research has significant implications for creating robots that can understand human intentions, collaborate effectively in real-world settings, and continuously learn from their experiences. The integration of multimodal information processing with predictive models forms the core of his approach to embodied artificial intelligence. Analysis of Professor Ogata's recent publications reveals a strong focus on deep predictive learning frameworks that enable robots to anticipate future states and generate appropriate actions. His research spans multiple domains including soft object manipulation, bimanual coordination, transparent object grasping, and integration of large language models with robotic systems. A notable trend is the increasing emphasis on multimodal integration, combining visual, tactile, and linguistic information to create more robust and adaptable robotic systems. His recent work also shows growing interest in the application of foundation models to robotics, aiming to create more general-purpose robotic capabilities. Best Paper Award, Advanced Robotics, The Robotics Society of Japan (2024) Best paper award Nomination Finalist, IEEE/SICE International Symposium on System Integration (2024) Frontiers of Science Awards, The International Congress for Basic Science (2023) Minister of Education, Culture, Sports, Science and Technology Award (2023) Fellow, Japan Society of Mechanical Engineering (2023) Fellow, The Society of Instrument and Control Engineers (2022) Fellow, The Robotics Society of Japan (2022) IBM Academic Awards (2017) Professor Ogata has been instrumental in establishing collaborative research frameworks between academia and industry, serving in leadership roles for multiple professional organizations including the Robotics Society of Japan, Japanese Society for Artificial Intelligence, and Japan Deep Learning Association. His research has been supported by significant grants including JST CREST funding for "Fundamentals and Core Technologies for Embodied AI." He has mentored numerous researchers who have gone on to contribute to the field of robotics and AI, with many of his former students now holding positions in leading academic and industrial research institutions. Professor Ogata directs the Ogata Laboratory at Waseda University, which focuses on cognitive developmental robotics and deep predictive learning. The lab maintains strong collaborations with industry partners including major robotics companies and technology firms. In 2025, he played a key role in establishing the AI Robot Association (AIRoA), which brings together companies like Toyota, Nissan, and KDDI to advance the development of practical AI robotics technologies. His research group is known for its interdisciplinary approach, combining insights from neuroscience, cognitive science, and engineering to create more human-like robotic capabilities.

Andrea MartinelliView profile

Lecturer

• Optimal Control
• Reinforcement Learning
• Decentralized Control
• +4 more

Dr. Andrea Martinelli is a Lecturer and Postdoctoral Researcher at the Automatic Control Laboratory (IfA), ETH Zurich. He holds a PhD in Automatic Control from ETH Zurich (2024) under Prof. John Lygeros, an MSc in Control Engineering (2017) from Politecnico di Milano, and a BSc in Management Engineering (2015). His research focuses on optimal control, reinforcement learning, and decentralized control strategies for large-scale systems, emphasizing scalability and applicability to renewable energy systems. He received the ETH Medal for his doctoral thesis on data-driven control methods. Education: BSc in Management Engineering, Politecnico di Milano (2015) MSc in Control Engineering with Honours, Politechnico di Milano (2017) PhD in Automatic Control, ETH Zurich (2024) Research Interests: Optimal control and reinforcement learning Data-driven methods for control systems Decentralized control of interconnected systems Dissipativity theory and passivity-based approaches Applications in renewable energy systems (DC microgrids) Teaching & Outreach: Program Manager for the CAS ETH in Automation Teaching a post-graduate course on automation in 2025 Professional Activities: Worked at Laboratoire d'Automatique (EPFL) during MSc thesis (2017) Research Assistant with Prof. R. Scattolini, Politecnico di Milano (2018)

Yongmin LiuView profile

Professor

• Nano Optics
• Nanomaterials Engineering
• Plasmonics
• +5 more

Yongmin Liu is a Professor in Mechanical and Industrial Engineering and Electrical & Computer Engineering at Northeastern University, and a member of the Cross-College Magnetics Center. He holds a PhD in Applied Science and Technology from UC Berkeley (2009), with earlier degrees from Nanjing University. His research focuses on nano-optics, metamaterials, plasmonics, and their applications in optical devices and systems. His interdisciplinary work bridges engineering, physics, and AI, with notable contributions to metasurface design and optical neural networks. Education: PhD, Applied Science and Technology, UC Berkeley (2009) M.S. and B.S., Physics, Nanjing University (2003, 2000) Research Interests: Nano-optics, nanoscale materials engineering, metamaterials, plasmonics, and applied physics. His group develops novel optical materials and devices for applications like super-resolution imaging, efficient light harvesting, and biomedical detection. Recent projects include AI-driven photonic materials design and meta-optical neural networks. Key Achievements: Recipient of the Søren Buus Outstanding Research Award (2024) NSF CAREER Award (2017) and ONR Young Investigator Award (2016) Elected SPIE Fellow (2023) and Optica Fellow (2023) Lab & Collaborations: Head of the Yongmin Liu Research Group, collaborating with institutions like Georgia Tech and Purdue University. Recent grants include a $1.5M NSF DMREF grant for AI-driven photonic materials and a $468K NSF grant for meta-optical neural networks.

Sushmita RujView profile

Associate Professor

• Cryptography
• Blockchains
• Data Privacy
• +6 more

Sushmita Ruj is an Associate Professor in the School of Computer Science and Engineering at the University of New South Wales (UNSW), Sydney. She serves as the Faculty of Engineering Lead for the UNSW Institute for Cybersecurity (IfCyber) and as the Taste of Research (ToR) Coordinator within the School of Computer Science and Engineering. Her academic journey includes previous positions as a Senior Research Scientist at CSIRO's Data61 (2019-2022), Associate Professor at the Indian Statistical Institute, Kolkata, and Assistant Professor at the Indian Institute of Technology (IIT), Indore. Dr. Ruj's primary research interests focus on applied cryptography, post-quantum cryptography, cybersecurity, blockchains, and data privacy. She designs practical, efficient, and provably secure protocols for real-life applications, with particular emphasis on critical infrastructure including smart grids, cloud computing, ad hoc networks, and data sharing frameworks. As quantum technology advances, her work increasingly focuses on developing quantum-safe algorithms to ensure a more secure Internet infrastructure. Her research spans multiple domains including cryptographic key management, proofs of storage, verifiable computation, vector commitments, and privacy-enhancing technologies for cloud and IoT environments. Her recent publications demonstrate a strong trend toward post-quantum cryptography solutions, with particular emphasis on blockchain applications, DNS security, and privacy-preserving protocols for industrial IoT. The research shows increasing focus on practical implementations of theoretical cryptographic concepts, with applications across multiple sectors including finance, healthcare, and critical infrastructure. Her work bridges the gap between theoretical cryptography and real-world security challenges, with growing emphasis on the transition from classical to quantum-resistant systems. Best Paper Award at ACISP 2024 JNCA Best Survey Award (2023) NSW Innovation Award (iAward) Merit Winner (2022) Women in Science Award from CSIRO (2020) ACM Senior Member (2016) IEEE Senior Member (2015) Samsung GRO award (2014) Dr. Ruj has successfully mentored numerous PhD and Master's students, with many of her former students now holding academic positions at institutions like IIT Indore, TU Wien, and CISPA Helmholtz Center. She has secured significant competitive funding including multiple Australian Research Council (ARC) grants, Samsung GRO Award, NetApp Faculty Fellowship, Cisco Academic Grant, and IBM Research grant. Her current research portfolio includes projects on blockchain-based quantum-safe digital medical passports, embedding trust in digital IDs, and resilience of supply chain unstructured data. As Faculty of Engineering Lead for IfCyber, Dr. Ruj plays a key role in UNSW's cybersecurity research initiatives. She has served on editorial boards for prestigious journals including IEEE Transactions on Information Forensics and Security and has held leadership positions in major conferences such as ACISP 2021 and Indocrypt 2020. She was also a member of the working group on "Blockchain For Cybersecurity" for the National Blockchain Roadmap of Australia and the first Blockchain Working group set up by the Reserve Bank of India.

Simon GoodwillView profile

Research Fellow

• Sports Engineering
• Performance Analysis
• Software Development
• +3 more

Dr. Simon Goodwill is the Head of Research in Sport and Physical Activity and Head of the Sports Engineering Research Group (SERG) at Sheffield Hallam University's Academy of Sport and Physical Activity. He holds a PhD focused on tennis ball-racket impact modeling and has collaborated with the International Tennis Federation. His expertise includes novel photogrammetry techniques, software development for athlete performance analysis, and camera calibration systems like check2d/check3d. He led projects supporting Team GB athletes in multiple Olympics, contributing to 42 medals at Rio 2016. Currently, he oversees the Advanced Wellbeing Research Centre (AWRC), applying sports performance methodologies to health research. His work integrates machine vision, data acquisition hardware, and elite athlete monitoring systems, with notable contributions to the EIS Innovation Partnership and sports tech exhibits like Le Tour Yorkshire's bike simulator. Education & Research: Simon’s PhD (modeling tennis impacts) and extensive software engineering background underpin his research. His projects span biomechanics, sports engineering, and health tech, with collaborations involving UK Sport, FIFA, and Adidas. He has developed systems used by athletes in London 2012, Rio 2016, and Glasgow 2014 Commonwealth Games. Research Themes: His work emphasizes performance analysis through photogrammetry, real-time athlete tracking, and health-related applications of sports tech. Key projects include court pace rating for the ITF, iBoxer2 boxing data tools, and the AWRC’s health innovations. Grants & Impact: The AWRC, funded by UK DoH and ESIF (£15.7M), reflects his role in translating elite athlete tech to public health. His work is cited in UK government reports on Olympic legacy and has received media attention, including BBC features on GB Boxing’s iBoxer software. Advising & Leadership: Supervised 17 PhD students on topics like machine learning in taekwondo, swimming analysis, and tennis racket dynamics. Served as External/External advisor and led 10+ Olympic cycle projects. Active in the International Sports Engineering Association as Director.

Charles A. BoumanView profile

Professor

• Computational Imaging
• Statistical Image Models
• Tomographic Image Reconstruction
• +13 more

Charles A. Bouman is the Showalter Professor of Electrical and Computer Engineering and Biomedical Engineering at Purdue University, with a courtesy appointment in Mathematics. He is a leading researcher in computational imaging, integrating statistical signal processing, physics, and computation for applications in healthcare, scientific, and industrial imaging. Education: B.S.E.E., University of Pennsylvania, 1981 M.S., University of California at Berkeley, 1982 Ph.D. in Electrical Engineering, Princeton University, 1989 His research focuses on computational imaging , including statistical image models, multiscale techniques, tomographic reconstruction, and fast algorithms. Key areas include Model-Based Iterative Reconstruction (MBIR), Plug-and-Play priors, document processing, and multiscale segmentation. His work has led to foundational contributions in total variation regularization and sparse-view reconstruction. The recent publications highlight a strong trend in integrating machine learning with physical models for image reconstruction, particularly through Plug-and-Play methods. His work spans optical tomography, halftoning, image scaling, and document compression, demonstrating consistent innovation in both theory and practical software implementation. Scientific Awards and Honors: Member, National Academy of Inventors Life Fellow, IEEE Fellow, IS&T; Honorary Member (2022); Service Award (2023) Fellow, SPIE and AIMBE IEEE Signal Processing Society Claude Shannon-Harry Nyquist Award (2021) Electronic Imaging Scientist of the Year (2014) SIAM Imaging Science Best Paper Prize (2020) Founder, IS&T Computational Imaging Conference (2003) Co-Founder, IEEE Transactions on Computational Imaging Vice President, IS&T; Former VP of Publications (2000–2004) Bouman has advised numerous graduate students and leads a vibrant research group developing open-source tools like MBIRJAX , SVMBIR , and OpenMBIR . His research has been supported by the National Science Foundation, General Electric, Intel, Xerox, Hewlett-Packard, and the State of Indiana 21st Century Fund. He maintains an active presence through tutorials, conference leadership, and educational resources including video lectures and a textbook on Foundations of Computational Imaging. Labs and Research Teams: His group develops cutting-edge software for tomographic reconstruction, clustering, segmentation, and dynamic sampling. Projects include Gaussian Mixture modeling (GMCluster), Plug-and-Play implementations, Sparse Matrix Transforms, and UAV sensing datasets. The research is highly interdisciplinary, bridging engineering, mathematics, and biomedical applications.

Jennifer JensonView profile

Professor

• Digital Games
• Digital Literacies
• Multiliteracy and Multimodality
• +5 more

Dr. Jennifer Jenson serves as Professor of Digital Languages, Literacies, and Cultures within the Department of Language and Literacy Education at the University of British Columbia's Faculty of Education. She joined UBC in January 2019 after 18 years at York University, where she directed the Institute for Research on Digital Learning. Her academic leadership extends to co-editing Loading: The Journal of the Canadian Game Studies Association and past presidency of the Canadian Game Studies Association. Dr. Jenson holds a Ph.D. from Simon Fraser University. Her research program critically examines digital games, literacies, and pedagogical practices through lenses of gender, equity, and decolonization. She has conducted longitudinal studies on gender and digital gameplay, developed educational games through the Play:CES lab, and authored influential reports for the Ontario Ministry of Education including “21st Century Skills, Technologies and Learning”. Her publication trajectory (2015-2023) reveals consistent focus on regendering game cultures, multimodal learning frameworks, and computational literacy in youth media engagement. Work spans critical analysis of “The Entrepreneurial Gamer” concept to contemporary digital literacies in multimodal contexts, emphasizing how game-based learning can disrupt exclusionary practices while advancing inclusive education. No scientific awards were explicitly documented in the source materials. Dr. Jenson maintains active graduate supervision, currently advising doctoral candidates like Laura Brass researching immigrant women language teachers’ identities. Her sustained research funding includes leadership of the international SSHRC Partnership Grant “Re-Figuring Innovation in Games” (2015-2021) and SSHRC Insight Grant “Think, Design, Play” (2014-2020), with collaborations spanning school boards, technology companies, and global institutions like SRI International and Nottingham University. She co-founded the CFI-funded Play:CES lab where teams developed educational games including “Contagion” and “Epidemic: Self-Care for Crisis”, and currently leads the Re-Figuring Innovation in Games (ReFiG) collective – an international consortium of scholars, community organizers, and industry partners dedicated to transforming game culture through equity-centered innovation.

• Digital Media in Education
• Film Education
• Media Literacy
• +4 more

Dr. Michelle Cannon is a Lecturer in Digital Arts and Media Education at the University College London (UCL) Institute of Education's Culture, Communication & Media department. She co-leads the Digital Storytelling module and serves as a dissertation supervisor for the MA Digital Media: Education program. Co-investigator, ESRC/AHRC-funded Play Observatory project Editorial board member: Film Education Journal, Learning, Media and Technology Executive Committee Member, Media Education Association International collaborations: British Film Institute, EU cultural agencies Her research explores digital media production in educational settings, focusing on film literacy, postdigital theory, and pandemic-related learning adaptations. Utilizing ethnographic and participatory visual methods, she investigates how children's creative practices intersect with social and cultural learning. Recent publications highlight her work on: Digital storytelling and transludic practices in pandemic contexts Ethnographic studies of postdigital play environments Critical media education frameworks Comparative analysis of European film literacy programs Scientific contributions include: ESRC/AHRC grant for children's play research International film literacy framework development Leadership in EU-funded scoping studies She trains teachers in digital media integration and has evaluated school film programs for the BFI. Her longitudinal study on lower-achieving children's learning experiences included documentary film production.

Mathias FunkView profile

Associate Professor

• Design Theory
• Systems Design
• Data-Driven Design
• +5 more

Mathias Funk is an Associate Professor in the Industrial Design department at Eindhoven University of Technology (TU/e), leading the Things Ecology lab. His research focuses on designing with data and systems behavior, particularly in IoT and medical technology contexts. He co-founded UXsuite GmbH and developed the OOCSI communication framework. With a PhD in Electrical Engineering from TU/e (2011), Funk has held academic roles since 2013, including postdoctoral positions in Electrical Engineering and Industrial Design. He teaches courses like 'Data-enabled design' and supervises projects on AI, data foundry, and OOCSI applications. His work bridges engineering and design, addressing sustainability and human values through systemic design approaches. Education: PhD in Electrical Engineering, TU/e (2011) Computer Science background from RWTH Aachen Postdoc at TU/e's Electrical Engineering and Industrial Design departments Research Interests: Data-driven design methodologies, IoT systems, medical technology innovation, sustainable design practices, and interactive systems for musical expression. Recent work emphasizes telemonitoring efficacy in heart failure care and federated learning applications. Awards: Best Paper Award (2014) Honorable Mention (2021) Grants/Projects: Leading initiatives like MEDICAID (medtech solutions), ACACIA (interpretable AI), and Data-Driven Servitizing in Professional Printing. Active in clinical workflow prototyping and cardiovascular disease detection projects. Lab Activities: Manages the Things Ecology lab, focusing on systemic design challenges in smart ecosystems. Collaborates internationally through spin-offs and visiting lectureships.

Limin JaoView profile

Associate Professor

• STEM Education
• Teacher Education
• Pedagogy
• +6 more

Dr. Limin Jao is an Associate Professor in the Department of Integrated Studies in Education (DISE) at McGill University’s Faculty of Education. Her research focuses on exemplary teaching practices in mathematics and STE(A)M education, emphasizing interdisciplinary approaches, project-based learning, and student engagement. She holds leadership roles including Vice-President of the Canadian Mathematics Education Study Group and Editorial Board member of the Canadian Journal of Science, Mathematics, and Technology Education. Her expertise spans teacher education, pedagogy, and research-practice partnerships. Education: Ph.D., Curriculum Studies and Teacher Development, Ontario Institute for Studies in Education, University of Toronto M.A., Curriculum Studies and Teacher Development, OISE, University of Toronto B.Ed., Secondary Biology and Mathematics Education, Queen's University B.Sc.H., Biology and Mathematics, Queen's University Research Interests: Limin explores how interdisciplinary and inquiry-based methods foster student engagement and teacher development. Her work bridges theory and practice, advocating for inclusive classrooms and educator collaboration. She has pioneered projects linking community artifacts to STEM learning and co-edited volumes on transdisciplinary education and teacher training borders. Publications Trends: Recent work examines gender perspectives in mathematics, pre-service teacher experiences, and professional learning communities in STEM. Her research emphasizes equity, pedagogical innovation, and the intersection of cultural contexts with educational practices. Awards: 2023 Award for Distinguished Teaching, McGill University Faculty of Education Advising & Grants: Limin supervises graduate students in mathematics education and collaborates on research-practice initiatives. She leads the Jao Research Team and contributes to the Chandaria Research Centre’s advisory board at Branksome Hall. Labs/Teams: The Jao Research Team focuses on innovative teaching strategies and STE(A)M education, with projects addressing student agency and interdisciplinary curriculum design.

Susan HolmesView profile

Professor

• British Television History
• Popular Television
• Celebrity Studies
• +7 more

Susan Holmes is Professor at the School of Media, Language and Communication Studies at the University of East Anglia (UEA), where she joined as Reader in Television in 2007 and was appointed to Professor in 2018. She is an active member of several research groups including Media Equality, Film, Television and Media, Gender and Its Intersections Steering Committee, and HealthUEA. Her work spans television studies, celebrity culture, and feminist approaches to eating disorders. Professor Holmes received her academic training at the University of Sussex (BA in English and Media Studies), followed by an MA in Film from the University of Southampton, and completed her PhD on British film and television in the 1950s at the same institution. Prior to joining UEA, she taught Media and Cultural Studies at the Southampton Institute (now Southampton Solent University) and television and film at the University of Kent. Her research focuses on several interconnected areas that challenge traditional disciplinary boundaries. In television studies, she has made significant contributions to understanding British television history, particularly through her books 'British TV and Film in the 1950s' and 'Entertaining TV: the BBC and Popular Programme Culture in the 1950s,' where she challenges canonical ideas about early BBC television. Her work on reality TV examines historical development, generic labeling, and celebrity. Since 2014, she has conducted important empirical research on feminist approaches to eating disorders, analyzing media representations and working directly with people with lived experience. A key focus of this research examines how feminist approaches are neglected in contemporary eating disorder treatment, with implications for both clinical practice and cultural understanding. She co-founded the influential Celebrity Studies journal in 2010 and edited it for the first eight years of its publication. Analysis of Professor Holmes' recent publications reveals a clear trajectory where her expertise in media and television studies converges with her feminist scholarship on eating disorders. Her most recent work (2023-2025) increasingly focuses on cross-cultural applications of feminist approaches to eating disorders (including work in China), the use of innovative methodologies like Photovoice, and examinations of therapeutic alliance in treatment settings. Simultaneously, she continues to analyze contemporary media representations, particularly focusing on motherhood, true crime television, and reality TV through feminist lenses. This dual focus demonstrates how media representations shape cultural understandings of both celebrity and health conditions. Professor Holmes' significant contributions to academia have been recognized with several prestigious awards: Senior Fellowship from Advance HE (2024) UEA engagement award (2017) ALPSP award nomination for Celebrity Studies as best new journal (2011) She currently runs the Feminist Media Studies research cluster at UEA and is actively supervising postgraduate research in areas including popular television, television/film history, British TV history, stardom/celebrity, and feminist approaches to eating disorders. Her current major research project is the 'ALLIANCE study' (2024-2026), funded by the National Institute for Health and Care Research, which aims to develop practice and policy recommendations to support effective therapeutic alliance in inpatient treatment for eating disorders. This builds on her earlier British Academy-funded research on 'Entertaining Television: British TV, the BBC and Popular Programme Culture in the 1950s' (2007-2008). Professor Holmes plays a significant role in the academic community through her editorial work, serving on the boards of journals including Archives of Psychology, Big Data & Society, and Critical Studies in Television, and previously as co-founder and editor of Celebrity Studies. She has also been active in media engagement, contributing expert commentary on topics ranging from Netflix's portrayal of eating disorders to Celebrity Big Brother.

Michael HornView profile

Professor

• Educational Technology
• Computational Literacy
• Interactive Learning Environments
• +3 more

Michael S. Horn is a Professor at Northwestern University with a joint appointment in Computer Science and the Learning Sciences. He directs the Tangible Interaction Design and Learning (TIDAL) Lab and coordinates the Learning Sciences PhD Program. His work focuses on leveraging interactive technology to design innovative learning experiences, such as tangible programming languages (e.g., Tern) and music-coding platforms (e.g., TunePad). He holds a PhD from Tufts University and has collaborated with institutions like the California Academy of Sciences and the Museum of Science, Boston. Education: PhD in Computer Science, Tufts University MS in Computer Science, Tufts University BS in Computer Science, Brown University Research Interests: Dr. Horn explores how emerging technologies can create engaging learning environments. His projects include museum exhibits (e.g., DeepTree, Build-a-Tree), educational tools for computational literacy (e.g., TunePad, Strawbies), and collaborative design with educators. Recent work emphasizes integrating music and coding to foster computational thinking. Publications: His recent work addresses computational thinking in STEM education, AI-driven qualitative analysis, and hybrid music-coding practices. Themes include equitable participation, teacher professional development, and immersive AR/VR tools for learning. Awards: Recipient of the 2018 Edith Ackermann Award for innovative work in child-computer interaction. His research has led to commercial products like Osmo Coding and Kibo Robotics. Grants & Labs: Principal investigator on NSF grants (e.g., #1612619, #1451762) and collaborator with labs like the Center for Connected Learning (CCL) and the Collaborative Technology Laboratory (CollabLab).