Find Academic Supervisors

• EDI in Spanish language teaching
• Spanish women's writing
• Translation studies
• +5 more

Dr Mazal Oaknin is an Associate Professor (Teaching) at the School of European Languages, Culture and Society (SELCS) at University College London. With over 15 years of experience in Spanish language and literature education, she specializes in equity, diversity, and inclusion (EDI) in language teaching, Spanish women's writing, and translation pedagogy. Academic appointments: 2013–2100 Associate Professor (Teaching), UCL (SELCS) 2013–2100 Teaching Fellow in Spanish Language and Literature, UCL 2013–2013 Brilliant Club Tutor, Modern Languages 2011–2012 Medical Spanish Teacher, Medipathways/Birkbeck College Research interests: EDI in Spanish language teaching, Spanish women's writing, Translation, Gender studies, Intersectionality. Her work explores feminist narratives in postmodern Spanish literature, mediation strategies in language education, and inclusive curricular design. Publications trend: Focus on translation pedagogy, intersectional feminism in literature, and EDI initiatives in language education. Key themes include media representation, cultural minorities, and innovative teaching methods like collaborative filmmaking. Teaching expertise: Advanced Spanish grammar, translation between Spanish and English, cultural minorities in contemporary Spanish literature, and gender studies. Her pedagogical approach emphasizes critical thinking and cultural mediation. Professional activities: Active in EDI initiatives (2025 workshop), inclusive education (2025 talk), and academic reviewing. Previously taught Spanish at Lycee Racine, Universidad de Malaga, SUNY Geneseo, and Birkbeck College.

Nicole MillsView profile

Lecturer

• Psychology of language learning and teaching
• Virtual environments in language learning
• Curriculum development
• +5 more

Nicole Mills serves as Joint Director of Language Programs (interim), Coordinator of the French language program, and Language Advisor for the Office of International Education at Harvard University, while holding an adjunct lecturer appointment in Education at the Harvard Graduate School of Education. She co-founded the Certificate in Teaching Language & Culture and actively shapes language pedagogy through administrative leadership and innovative curriculum design across multiple Harvard departments. Her educational background includes a Ph.D. in French and Educational Studies and an M.A. in French literature , both from Emory University. These qualifications underpin her interdisciplinary approach to language education, bridging theoretical linguistics with practical pedagogy. Mills’ research pioneers the integration of virtual reality and artificial intelligence in language learning, focusing on psychological aspects of language acquisition, immersive cultural experiences, and curriculum development. Her work demonstrates how experiential learning contexts can transform traditional language instruction, particularly through projects like the La République VR environment that immerses students in Parisian life through diverse perspectives. This research intersects digital humanities, critical theory, and French literary studies to create transformative educational experiences. Her publication trajectory reveals a clear evolution from foundational self-efficacy studies toward cutting-edge explorations of AI and immersive technologies in language education. Recent work emphasizes pedagogical innovation during crises (like pandemic teaching transitions) and the systematic integration of emerging technologies into language program infrastructure, reflecting both theoretical depth and practical applicability. Innovation in Language Program Direction Award (2020) from the American Association of University Supervisors and Coordinators ABL Connect award for Teaching Innovation (2018) from Harvard's Center for Teaching and Learning Mills has secured significant grant funding including Harvard's HILT grants (2017-2020) for VR initiatives and Provost Fund support for AI pedagogy projects. Her collaborative approach is evident in partnerships with Wonda VR, Harvard's Center for Technology in Government, and international institutions for VR/AI development, while her co-founding of the Charlie Archive demonstrates commitment to digital humanities infrastructure for preserving cultural responses to global events. She leads the La République VR project team and the Charlie Archive curation group, fostering interdisciplinary collaboration between technologists, language educators, and digital archivists to create resources that bridge academic research with real-world cultural contexts.

• Science Education
• Educational Technology
• Artificial Intelligence Literacy
• +5 more

Claudia Nerdel is a Professor at the Technical University of Munich (TUM) within the Chair of Life Sciences Didactics . Her work focuses on science education innovation, particularly integrating digital technologies like AI and augmented reality into teacher training and classroom practices. Current research spans AI literacy assessment , modelling competences , and sustainable biotechnology education Active in teacher professional development and digital competency frameworks Her research explores: Mathematical modelling in science education Augmented reality applications for chemistry terminology AI literacy frameworks (e.g., AILIT-S test development) Digital transformation challenges in schools Recent publications analyze AI self-efficacy, LLM feedback quality, and biotech experiment implementation. The TUM-DigiLLab serves as a key lab environment for digital competence development, though specific grants or awards aren't mentioned in the text. Her work emphasizes creating adaptive feedback systems , inclusive digital tools , and evidence-based teacher training .

• Astronomy
• Astrophysics
• Machine Learning
• +5 more

Robert Brunner serves as Professor of Astronomy at the University of Illinois at Urbana-Champaign, where he bridges astrophysical research with computational innovation. His work focuses on extracting knowledge from massive astronomical datasets through advanced statistical and machine learning techniques, while also extending methodologies to finance and agricultural applications. Research interests center on developing machine learning algorithms (random forests, deep neural networks, Bayesian estimation) for astronomical data analysis, cosmological parameter constraints via n-point clustering measurements, and hardware acceleration using GPUs/cloud systems. His interdisciplinary approach spans source classification, transient phenomena detection in surveys like SDSS and DES, and applications in financial time-series analysis and agricultural remote sensing. Recent publications (2019-2025) reveal strong cross-domain expertise: astronomical catalogs for Rubin Observatory and Spitzer surveys coexist with financial market analysis using community detection methods and agricultural computer vision systems. Key methodological threads include spatio-temporal forecasting, multimodal learning for earnings calls, and anomaly detection via extended isolation forests, demonstrating consistent innovation in handling petascale datasets across scientific boundaries.

• Machine Learning
• Artificial Intelligence
• Bayesian Statistics
• +5 more

Dr Cuong Nguyen is a Lecturer in the Department of Mathematical Sciences at Durham University, specializing in Machine Learning, Artificial Intelligence, and Statistics. His research bridges theoretical foundations with practical applications, with particular expertise in Bayesian methods, transfer learning, and multimodal systems. His educational background includes a PhD in Computer Science or a related field (specific institution not mentioned in provided data), with research focusing on machine learning theory and applications. Nguyen has established himself as a researcher with publications spanning top conferences including NeurIPS, UAI, and ACM Web Conference. Research Interests: Nguyen's work centers on lifelong learning systems that overcome catastrophic forgetting, transferability estimation between tasks, and multimodal learning applications. His research integrates Bayesian principles with deep learning to create more robust and adaptable AI systems. Recent Trends: Analysis of his 15 most recent publications reveals a strong focus on practical applications of theoretical machine learning concepts, particularly in security (CAPTCHA systems), real-world problem solving (fake advertisement detection), and fundamental learning theory (transferability metrics). Dr Nguyen has made significant contributions to understanding the theoretical underpinnings of transfer learning and continual learning, with his work on LEEP providing a practical metric for transferability estimation. His research on CAPTCHA systems demonstrates both theoretical rigor and practical security implications. Advising: While specific students aren't listed in the provided data, his publications show collaborations with researchers across institutions, suggesting active supervision of PhD and Master's students. Research Groups: He is affiliated with the Statistics research center within Durham's Department of Mathematical Sciences, contributing to the university's strength in mathematical and computational research.

Johan PauwelsView profile

Lecturer

• Audio Signal Processing
• Music Information Retrieval
• Machine Learning for Audio
• +3 more

Dr. Johan Pauwels is a Lecturer in Audio Signal Processing at Queen Mary University of London's School of Electronic Engineering and Computer Science, where he is affiliated with the Centre for Digital Music and the Centre for Multimodal AI. His educational background includes: Master of Science in Electrical/Electronics Engineering from KU Leuven (2006) Master of Science in Artificial Intelligence from KU Leuven (2007) PhD from Ghent University (2016) on automatic harmony recognition from audio Johan's research focuses on making machines understand audio to the level of a trained professional. His work combines machine learning, signal processing, data science, and music theory to develop tools for musicians, listeners, and music learners. He has been working on narrowing the gap between academic research and user-centric applications, web-based music services, and the personalization of spatial and immersive audio. His specific interests include machine learning for audio, audio signal processing, music information retrieval, and binaural audio. His recent publications show a strong focus on music representation learning, with particular attention to limited data scenarios, multimodal approaches, and spatial audio processing. His work bridges theoretical music concepts with practical machine learning applications, especially in chord recognition, beat detection, and instrument recognition. He has made significant contributions to HRTF (Head-Related Transfer Function) research and development of tools for spatial audio processing. Dr. Pauwels is actively involved in research funding, with current grants including the AIM CDT Internship with Sofilab (2025), AIM CDT Studentship - Stem (2024), and AIM CDT Internship with stem.tech (2024). He currently supervises multiple PhD students, primarily through the UKRI Doctoral School in AI and Music, with research topics spanning intelligent audio editing, neural drum synthesis, source separation, graph neural networks for music recommendation, and more. In addition to PhD supervision, he typically guides 8-10 undergraduate and 8-10 master's students through their final year projects. His teaching responsibilities include ECS7013P Deep Learning for Audio and Music (MSc/PhD level) and ECS411U Signals and Information (first-year undergraduate).

Grant Van HornView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Robotics
• +2 more

Grant Van Horn is an Assistant Professor at the University of Massachusetts Amherst, affiliated with the College of Information and Computer Sciences. He specializes in computer vision and machine learning, focusing on applications in biodiversity and conservation. His work underpins popular tools like iNaturalist, Seek, and Merlin Bird ID. Prior to UMass, he held roles at AWS and the Cornell Lab of Ornithology. Education: PhD in Computer Science, California Institute of Technology (2019) MS in Computer Science, University of California San Diego (2014) BS in Computer Science, University of California San Diego (2012) Research Interests: Grant’s research bridges computer vision and machine learning to create systems that integrate human expertise and large datasets for environmental conservation. Key areas include wildlife species identification, acoustic monitoring, and ecological modeling. His work emphasizes leveraging technology for public engagement and scientific impact. Articles & Trends: Recent publications focus on audio geolocation, species range estimation, and satellite imagery analysis, reflecting his commitment to advancing tools for biodiversity conservation. His work often combines citizen science data with machine learning innovations. Awards: Computing Research Association Outstanding Undergraduate Researcher Honorable Mention (2012) Ben P.C. Chou Doctoral Prize (2019) Fast Company’s 2021 Recognition Advising & Labs: Grant advises students in computer science and conservation technology through the Computer Vision Research Laboratory. He has collaborated on projects like the iWildCam dataset and systems for salmonid counting in sonar data.

Myra FernandesView profile

Professor

• Cognitive Neuroscience
• Memory Studies
• Aging and Dementia
• +3 more

Myra Fernandes is a Professor in the Department of Cognitive Neuroscience at the University of Waterloo, serving as the Research Area Head. Her work focuses on memory processes, cognitive aging, and encoding techniques, with a strong emphasis on neuroimaging and experimental psychology. She investigates how actions, drawing, and context affect memory retention, particularly in clinical populations like those with hippocampal damage or dementia. Her research also explores the cognitive and affective consequences of traumatic brain injury and the role of imagery in mental health conditions such as PTSD. Her studies often integrate behavioral experiments with neuroimaging to uncover neural mechanisms underlying memory formation and retrieval. Recent work highlights the 'drawing effect,' demonstrating how sketching information enhances recall across ages and cognitive conditions. She advocates for rigorous methodologies in research publishing, including registered reports to improve scientific transparency. Key research themes include the enactment effect, symbol superiority, and the impact of emotional and motoric engagement on memory. She has contributed to understanding age-related cognitive changes and the application of cognitive strategies to mitigate memory decline. Her lab's findings have implications for educational practices, clinical rehabilitation, and mental health interventions. Publications span topics from involuntary memory's mental health links to the neurobiological basis of mnemonic strategies. She collaborates widely, bridging cognitive neuroscience with clinical and educational applications. Her work underscores the importance of multidisciplinary approaches to advancing memory science.

• Generative Programming
• Model-Based Development
• Software System Families
• +7 more

Krzysztof Czarnecki is a Professor at the University of Waterloo's Department of Electrical and Computer Engineering, with a cross-appointment to the School of Computer Science. He serves as leader of the Waterloo Intelligent Systems Engineering Lab and holds the title of University Research Chair. His research focuses on generative software development, model-driven engineering, and autonomous systems, particularly in automotive cybersecurity and perception safety. Education: Doctorate in Computer Science, Technical University of Ilmenau (1999) Master of Science in Computer Science, Technical University of Ilmenau (1995) Bachelor of Science in Computer Science, California State University (1994) Research Interests: Dr. Czarnecki's work spans generative programming, software product lines, and safety-critical AI for autonomous vehicles. Recent projects address robust perception systems, uncertainty quantification in neural networks, and strategic driving behavior modeling. He co-authored Generative Programming (Addison-Wesley, 2000), a foundational text in the field. Publications Trends: Recent work emphasizes multimodal AI integration (e.g., LEO-MINI), 3D object detection improvements (OV-SCAN), and safety assurance frameworks for autonomous systems. His research bridges theoretical software engineering with applied robotics challenges. Awards: Premier’s Research Excellence Award (2004) British Computing Society’s Upper Canada Award (2008) University Research Chair, University of Waterloo (2023) Teaching & Leadership: Teaches courses like ECE 495 (Autonomous Vehicles) and ECE 651 (Software Engineering Foundations). Oversees WatCAR initiatives and collaborates on industry projects through the NSERC Bank of Nova Scotia Industrial Research Chair (previous). Labs & Teams: Directs the Waterloo Intelligent Systems Engineering Lab, focusing on AI-driven solutions for autonomous systems and safety-critical software. Active in cross-disciplinary collaborations with automotive and robotics partners.

• Event Cognition
• Human-Robot Interaction
• Visual Working Memory
• +4 more

Frank Papenmeier is a Professor in the Department of Psychology at the University of Tübingen, within the Faculty of Science. His research focuses on event cognition, human-robot interaction, visual working memory, and visual attention. He coordinates the 'Coordination Cognitive Psychology and Research Methods' research group. His work explores how people perceive and interact with dynamic environments, including studies on event segmentation, cognitive offloading, and aesthetic judgments. He has contributed to over 100 peer-reviewed articles, with recent work addressing topics like the impact of framing on art perception and the role of AI in education. Papenmeier's research integrates experimental methods with interdisciplinary approaches, including collaborations on teleoperation systems and AI-based tutoring. He has presented at major conferences such as the European Society for Cognitive Psychology and the Psychonomic Society. His lab emphasizes methodological rigor, evidenced by contributions to replication databases and open science initiatives. Education: Not explicitly stated in the text, but his titles include Dr. rer. nat. (Doctor of Natural Sciences) and Diplom-Psychologe (Psychology Diploma). Research Interests: His primary areas include event cognition, human-robot interaction (e.g., helping behavior toward robots), visual working memory (e.g., spatial configuration processing), and cognitive offloading (e.g., impact on memory and performance). He also investigates aesthetic judgments and narrative comprehension through eye-tracking and experimental paradigms. Articles Trends: Recent work addresses applied topics like cookie consent interfaces, AI in education (e.g., R programming tutors), and perceptual effects in 3D cinema. His studies often bridge cognitive theory with real-world applications, such as usability design and social robotics. Labs/Teams: Leads the research group 'Coordination Cognitive Psychology and Research Methods' at the University of Tübingen. Collaborates with interdisciplinary teams on projects involving robotics, AI, and human-computer interaction.

Russell Weili ChanView profile

Assistant Professor

• Neuroscience
• Cognitive Psychology
• Neuropsychiatry
• +5 more

Russell Weili Chan is an Assistant Professor at the Digital Society Institute and Cognition, Data and Education department. His research focuses on meditation's effects on cognitive control during motor sequence learning, with contributions to Neuroscience, Cognitive Psychology, and Neuropsychiatry. He has published 15 articles since 2012, exploring topics like mindfulness in sports performance, neuroimaging of motor tasks, and humor comprehension in mental disorders. He serves on editorial boards for Behavioural Brain Research and Frontiers in Psychology . His work bridges cognitive science with clinical applications, emphasizing interdisciplinary approaches to motor learning and mental health. Key Research Areas: Meditation neurophysiology, motor sequencing, neuroimaging, mental disorders, and cognitive neuroscience. Editorial Roles: Behavioural Brain Research (Editor), Frontiers in Psychology (Peer Review).

Tesca FitzgeraldView profile

Assistant Professor

• Interactive Robot Learning
• Cognitive Robotics
• Human-Robot Interaction
• +4 more

Tesca Fitzgerald is an Assistant Professor in the Department of Computer Science at Yale University. Her research focuses on interactive robot learning, enabling robots to adapt to novel situations through human-guided learning. She holds a Ph.D. from Georgia Institute of Technology (2020) and a B.S. from Portland State University. Her research interests include cognitive robotics, human-robot interaction, transfer learning, and active learning. She emphasizes robots' ability to reason about novel objects, tasks, and interactions by leveraging human teachers' domain knowledge. Key areas include structuring human-robot interactions to meet learning goals and modeling training data derived from these interactions. Notable honors include the National Science Foundation Graduate Research Fellowship (2014-2017), IBM Ph.D. Fellowship (2017), and Georgia Tech GVU Center Foley Scholars Award (2017). Her work has been presented at top conferences like IJCAI, AAMAS, and HRI. Fitzgerald’s research lab, the IQR Lab, explores inquiry-based methods for robot learning. Prior to Yale, she was a postdoc at Carnegie Mellon University, collaborating with Henny Admoni, Reid Simmons, and Aaron Steinfeld. She has organized workshops on learning and interaction at IROS and served on conference committees, including HRI 2021.

• Machine Learning
• Generative Modeling
• Scientific Computing
• +3 more

Tanya Marwah is a Research Fellow at the Simons Foundation , collaborating with Polymathic AI . She earned her PhD from Carnegie Mellon University's Machine Learning Department, co-advised by Prof. Andrej Risteski and Prof. Zachary Lipton, and holds a master's degree from CMU's Robotics Institute. Her research bridges Machine Learning and Scientific Computing , focusing on generative modeling , inverse problems , and building scientific agents . Her work explores theoretical and empirical foundations for applying ML to differential equations, with key contributions in neural operators , memory mechanisms , and edge embeddings in GNNs . Recent publications highlight trends in PDE solvers via LLMs , cross-modal adaptation , and implicit regularization in SGD . She has received the prestigious Siebel Scholar award and actively contributes to top ML venues (NeurIPS, ICML, ICLR, TMLR). Her collaborations span institutions including Carnegie Mellon University, Polymathic AI, and CMU's Robotics Institute.

• Traditional Chinese Medicine (TCM)
• Data Mining
• Medical Informatics
• +4 more

Associate Professor Josiah Poon is affiliated with the School of Computer Science at the University of Sydney. His research focuses on applying data mining and IT techniques to Traditional Chinese Medicine (TCM), particularly analyzing herbal combinations for effective treatments. He collaborates with institutions in China to improve TCM evidence and has contributed to clinical data analysis, medical informatics, and multimodal AI systems. Teaching includes courses such as INFO1003 (Foundations of IT) and INFO9003 (IT for Health Professionals). Current research students are Rina CABRAL (Multimodality Representation), Yan LI (Long Document Comprehension), and Xiaobin LU (Financial Decisions). Research highlights include developing algorithms to quantify TCM efficacy, analyzing complementarity in herbal combinations, and applying machine learning to healthcare data. Notable projects include a randomized controlled trial on pneumococcal vaccination (2021) and a Google-funded multimodal health detection system (2020). Key areas of expertise span TCM informatics, medical data analytics, and AI-driven healthcare solutions. His work bridges Eastern/Western medicine through computational methods, emphasizing evidence-based practices in TCM.

• Natural Language Processing
• Text Generation
• Machine Learning
• +1 more

Zihao Fu is a Researcher at the Oxford Internet Institute (OII), University of Oxford, where he served as a Postdoctoral Researcher from March 2024 to April 2025, focusing on the Trustworthiness Auditing for AI project. Previously, he was a Research Associate (PostDoc) at the University of Cambridge's Language Technology Lab under Prof. Nigel Collier. His research emphasizes Natural Language Processing, Text Generation, Machine Learning, and Biomedical Applications. Education includes a Ph.D. from The Chinese University of Hong Kong (supervised by Prof. Wai Lam) and a visiting student period at Tsinghua University's NLP Lab. He has substantial experience with large-scale distributed algorithms in Alibaba Cloud's PAI platform. Research interests span NLP, biomedical applications, and AI ethics, with notable contributions to datasets like BAND and frameworks like OxonFair. His work addresses challenges in text generation repetition, parameter-efficient fine-tuning, and algorithmic fairness.