Find Academic Supervisors

• STEM Education
• Graduate Student Education
• Self-Efficacy
• +3 more

Seema Rivera is an Associate Professor in the Department of Education at Clarkson University, where she also serves as Associate Director of the Institute for STEM Education. She holds a BA in Psychology from Binghamton University, an MS in Chemistry for Adolescent Education from The College of Saint Rose, and a Ph.D. in Curriculum and Instruction from the University at Albany. Prior to her academic career, she taught chemistry in New York public schools. Her research focuses on: STEM graduate education development Self-efficacy and identity formation in graduate students Innovative STEM teaching methodologies for secondary education Science pedagogy through multimodal approaches Rivera's publications demonstrate strong emphasis on science education techniques, particularly examining how read-aloud strategies and visual models enhance science comprehension. Her work frequently explores elementary and secondary education contexts with consistent focus on teacher training and curriculum design. She has secured research funding including: Principal Investigator for 'Watershed Education through STEM Enrichment' ($20,000 NYS DEC grant) Co-director of David A Walsh Mini-Conference Grant ($2,000 Clarkson University award) Rivera supervises chemistry student interns and maintains active conference participation, presenting regularly at major education conferences including Association for Science Teacher Education (ASTE) and National Association for Research in Science Teaching (NARST).

James C. GeeView profile

Professor

• Biomedical Image Analysis
• Medical Imaging Computing
• Image Segmentation
• +5 more

James C. Gee is a Professor of Radiologic Science in Radiology at the University of Pennsylvania's Perelman School of Medicine. He serves as Director of the Penn Image Computing and Science Laboratory and Co-Director of the Translational Biomedical Imaging Center , with affiliations in Bioengineering and Applied Mathematics graduate groups. His research focuses on biomedical image analysis, specialization in segmentation, registration, and morphometry applied to neurodegenerative diseases and multi-organ systems. Education : B.S. in Computer Science/Electrical Engineering (University of Washington, 1987), Ph.D. in Computer and Information Science (University of Pennsylvania, 1996) Research : Quantitative medical imaging methods, brain connectomics, neurodegeneration mapping, and translational imaging technologies Publications : 15+ recent works on AI-driven image analysis for Alzheimer's disease, cardiac amyloidosis, and radiomics applications Leadership : Directs MSE-DS Online Degree Program, co-chairs Radiology DCOAP Committee, and founded RISE (Radiology Initiative to Support Inclusive Excellence) His laboratory develops advanced computational tools like ITK-SNAP for biomedical imaging, with applications in both in vivo clinical imaging and ex vivo histology . The work spans cross-disciplinary collaborations in computer science, neuroscience, and clinical medicine.

Qi LongView profile

Professor

• Genomics
• Computational Biology
• Biostatistics
• +10 more

Qi Long is a Professor at the University of Pennsylvania, holding joint appointments in the Department of Biostatistics, Epidemiology and Informatics (Perelman School of Medicine), Department of Computer and Information Science (School of Engineering and Applied Science), and Department of Statistics and Data Science (The Wharton School). He serves as Founding Director of the Center for Cancer Data Science, Associate Director of the Penn Institute for Biomedical Informatics, and Associate Director for Quantitative Data Science at the Abramson Cancer Center. His research bridges statistical and machine learning (ML/AI) method development with biomedical applications, focusing on precision medicine and population health. Education : Ph.D. (2005) and M.S. (2003) in Biostatistics from University of Michigan; B.S. (1998) in Computer Science from University of Science and Technology of China. Research interests include: Robust statistical and ML/AI methods for big health data (-omics, EHRs, imaging, mHealth) Multimodal data integration and subgroup heterogeneity analysis Missing data, causal inference, Bayesian methods, and clinical trials Data privacy, algorithmic fairness, and responsible AI in healthcare Foundation models and agentic AI for biomedicine His publications focus on privacy-preserving AI, fairness-aware ML, and integrative models for multi-omics and EHRs. Recent work explores LLMs and watermark detection in hybrid human-AI settings. Scientific Awards : Elected fellow: AAAS, ASA, IMS, ISI, AMIA He leads large NIH- and ARPA-H-funded initiatives, directing statistical coordinating centers for national clinical trials. His lab trains numerous PhD/Master’s students and postdocs, many of whom hold prestigious academic or industry positions.

Xiaofeng LiuView profile

Assistant Professor

• Artificial Intelligence
• Deep Learning
• Medical Imaging
• +5 more

Xiaofeng Liu is an Assistant Professor at Yale University School of Medicine in the Departments of Radiology & Biomedical Imaging and Biomedical Informatics & Data Science. He is also an Associate Member at the Broad Institute of MIT and Harvard. Previously, he held faculty positions at Harvard Medical School and research roles at Massachusetts General Hospital and Beth Israel Deaconess Medical Center. PhD in Mechatronics from University of Chinese Academy of Sciences Dual Bachelor's degrees in Automation (Wang-Daheng Elite Class) and Communication from University of Science and Technology of China His research integrates trustworthy AI, medical imaging, and data science to improve diagnosis, prognosis, and treatment monitoring for neurological disorders, cancer, and cardiovascular diseases. Key focus areas include domain adaptation techniques, diffusion models, and interpretable AI systems. Led special issues in IEEE Transactions on Pattern Analysis and Medical Image Analysis Developed novel frameworks like Ordinal UDA and Memory-Consistent Adaptation Scientific accolades include the Trailblazer R21 Award (NIBIB), OpenAI Research Award, and National Artificial Intelligence Research Resource Pilot Award. He serves as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and actively contributes to MICCAI and NIH review panels. His lab at Yale (XLiu Lab) investigates neural basis of intelligence to inspire AI development, with applications in brain tumor segmentation, cardiac imaging, and cross-modal medical diagnostics.

• Artificial Intelligence
• Embodied AI
• Robotics
• +4 more

Roozbeh Mottaghi is a Senior AI Research Scientist Manager at Meta's Fundamental AI Research (FAIR) division and an Affiliate Associate Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington. His career spans roles as Research Manager at the Allen Institute for AI and Postdoctoral Researcher at Stanford University. He holds a Ph.D. in Computer Science from UCLA, advised by Alan Yuille, and advanced degrees from Simon Fraser University, Georgia Tech, and a B.Sc. from Sharif University of Technology. Ph.D., Computer Science, UCLA (2016) M.Sc., Computer Science, Simon Fraser University M.Sc., Electrical Engineering, Georgia Institute of Technology B.Sc., Computer Engineering, Sharif University of Technology His research focuses on Embodied AI, Robotics, Computer Vision, and Multimodal Learning, with key contributions to human-robot collaboration benchmarks (PARTNR), 3D scene understanding, and adaptive navigation systems. Recent work explores zero-shot manipulation via video tracking (Track2Act) and lifelong navigation benchmarks (GOAT-Bench). Publications span 3D vision, reinforcement learning, and visual reasoning, including NeurIPS 2022 Outstanding Paper Award for "Ask4Help" and leadership in organizing challenges at CVPR and ICCV workshops. He advises students and interns who have transitioned to leading institutions like AI2, Stanford, and Brown University.

Kilian Q. WeinbergerView profile

Professor

• Machine Learning
• Computer Vision
• Deep Learning
• +4 more

Kilian Q. Weinberger is a Professor of Computer Science at Cornell University's College of Engineering, focusing on Machine Learning, Deep Learning, and AI applications. He has held previous roles as Associate Professor at Washington University in St. Louis and Research Scientist at Yahoo! Research. His research spans metric learning, resource-constrained learning, Gaussian Processes, and advancements in 3D perception for autonomous systems. Education : Ph.D. in Machine Learning (University of Pennsylvania), BA in Mathematics and Computing (University of Oxford) Key Research Areas : AI in Science, Computer Vision, Autonomous Vehicles, and Neural Network Efficiency His recent work emphasizes interpretable machine learning, large language models, and multimodal applications. Awards include NSF CAREER (2012) Daniel M Lazar '29 Teaching Award (2016) Ann S. Bowers Excellence Award (2024) ACM and AAAI Fellow (2024) He teaches advanced courses like CS6784 (Cornell) and has mentored numerous PhD students across institutions. Current affiliations include the Sloan Research Fellowships Selection Committee since 2024.

Tineke BrunfautView profile

Professor

• Language Testing
• Second Language Reading
• Second Language Listening
• +5 more

Professor Tineke Brunfaut is a leading academic in Applied Linguistics at Lancaster University 's School of Social Sciences. Her work focuses on language testing , second language reading , listening , and integrated/multimodal skills . She coordinates the Language Testing Research Group and has received prestigious awards including the ILTA Best Article Award , e-Assessment Award , and TOEFL Outstanding Young Scholar Award . Key research areas: Cognitive and affective factors in testing, methodological innovations (eye-tracking, discourse analysis), and language test development. Recipient of multiple grants from the British Council, Trinity College London, and British Academy. PhD supervision interests: Language testing, integrated skills, assessment literacy, and validation. Recent publications explore technology-enhanced assessment , multimodal viewing-to-write tasks , and the impact of delivery modes on test performance . She has consulted for global institutions on test design and evaluation. Scientific Awards : ILTA Best Article Award e-Assessment Award for Best Research TOEFL Outstanding Young Scholar Award Her teaching includes MA programs in Language Testing, TESOL, and Applied Linguistics, covering Test Construction , Research Methods , and Statistical Analyses .

Roop Aparajita Subhra PurushottamView profile

Associate Professor

• Machine Learning
• Extreme Classification
• Optimization Techniques
• +5 more

Roop Aparajita Subhra Purushottam is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. His research focuses on machine learning foundations and applications, particularly in extreme classification, optimization techniques, robust learning, and educational technology. He has developed scalable algorithms for web-scale applications and innovative teaching tools for programming education. His research interests span: Design and analysis of machine learning algorithms Statistical learning theory and online optimization Non-convex optimization for large-scale problems Robust learning against adversarial corruptions Applications in information retrieval, education, and environmental monitoring Recent publications demonstrate a strong focus on extreme classification techniques, efficient deep learning architectures, and educational technologies. His work consistently appears in top-tier conferences including KDD, ICML, NeurIPS, and CVPR, with innovations in scaling machine learning systems to handle millions of labels and users. Significant Awards: Gopal Das Bhandari Distinguished Teacher Award (2024) PK Kelkar Faculty Fellowship (2024-2027) Microsoft Bing Ads Greatness Award (2021) Computer Society of India Faculty Award (2018) Multiple best paper awards and nominations at major conferences He leads several research grants and consults for industry partners including Microsoft Research and Tower Research. His team develops open-source tools like Prutor for programming education and DEFRAG for efficient feature agglomeration in extreme classification. He has advised numerous PhD and Master's students who have received prestigious awards for their research contributions.

Nicole NovielliView profile

Associate Professor

• Affective Computing
• Software Engineering
• Natural Language Processing
• +5 more

Nicole Novielli, Ph.D., is Associate Professor at the University of Bari “A. Moro” , Italy, where she conducts research on affective computing applied to software engineering and human-computer interaction. She leads the Collaborative Development Group and coordinates national projects investigating emotions in software teams, AI quality and IoT ecosystems. Education: Ph.D. in Computer Science, University of Bari, 2010 – thesis on “Lexical Semantics of Dialogue Acts” M.Sc. in Computer Science (Knowledge & Software Engineering), University of Bari, 2006 – summa cum laude B.Sc. in Computer Science, University of Bari, 2004 – summa cum laude Visiting researcher at USC-ICT, University of Aberdeen, FBK-irst (Trento) Research interests revolve around recognizing and exploiting affective and cognitive states in computer-mediated cooperative work. She studies sentiment and emotion mining in developers’ textual communication, multimodal emotion recognition via low-cost biometric sensors, and natural-language dialogue simulation for intelligent interfaces. Her work couples software engineering with natural language processing , social media analytics and human-computer interaction . Recent articles (2021-2025) reveal a clear trend: integrating deep learning and large language models into software engineering tasks—automated issue labelling, sentiment classification, technical-debt detection—while validating these techniques through rigorous empirical studies and biometric experiments . A parallel stream explores developer experience , measuring how emotions and cognitive load influence productivity, code quality and collaboration. Scientific awards include the 2020 Apex Award for Publication Excellence , multiple Distinguished Reviewer Awards at flagship venues (ESEC/FSE, ICSME, MSR), the Best Paper Award SANER 2019 and the Best Student Paper Award ACII 2009 . She currently teaches “Sentiment Analysis” in the Data-Science MSc and “Computer Networks” in the ITPS programme. She has advised numerous B.Sc., M.Sc. and PhD projects and is PI or Co-PI of four ongoing grants: EmoQuest (SIR), EMPATHY (PRIN), FAIR-Spoke 6 (PnRR), and QualAI (PRIN 2022). Dr. Novielli serves on the editorial boards of Empirical Software Engineering and Journal of Systems and Software , has guest-edited special issues on affect awareness in SE, and has chaired tracks at ICSE, SANER, MSR, ICSME and SSBSE. She co-leads the Collaborative Development Group and actively releases datasets and open-source tools for the community.

Charles DenrocheView profile

Senior Lecturer

• Cognitive linguistics
• Metaphor and metonymy
• Discourse analysis
• +5 more

Charles Denroche is Senior Lecturer in Linguistics and Applied Language Studies at the University of Westminster, based in the School of Humanities within the College of Liberal Arts and Sciences. His interdisciplinary career combines bioscience (Magdalen College, Oxford) with advanced linguistics (PhD UCL-IOE, PGDip Westminster, PGCE TESOL IOE), and extensive professional experience as translator, lexicographer and language educator. Education & Training MA in Bioscience – Magdalen College, University of Oxford PhD in Education (Linguistics) – UCL Institute of Education, University of London Post-Graduate Diploma in Linguistics – University of Westminster PGCE TESOL – UCL Institute of Education Certificate in English Phonology – International Phonetic Association Studies in Italian Language & Literature – University of Florence Studies in Germanistik & Anglistik – Heinrich Heine University of Düsseldorf Research Interests Denroche’s work centres on how meaning is made through language. His research integrates cognitive linguistics , semiotics and translation studies to explore the roles of metaphor and metonymy in discourse, multimodal communication and pedagogy. He is particularly interested in the cognitive processes that underlie figurative language, the interface between grammar and semantics, and how these insights can inform translator training and language education. Current projects extend his theory of metonymic processing to literary translation collaboration and the analysis of visual and multimodal texts, aiming to bridge theoretical linguistics with practical applications in education and professional practice. Recent Publications & Trends Over the past decade Denroche has produced a coherent body of work that moves from foundational theory-building—most notably the 2015 monograph Metonymy and Language —to innovative methodological applications such as using drawing as a research tool for metaphor-led discourse analysis. Across 15 key outputs (2011-2024), the research trajectory shows a consistent focus on metaphtonymy (the interplay of metaphor and metonymy) at text level and on developing practical frameworks for translators, educators and discourse analysts. Honours & Awards Award of Excellence in Teaching and Learning (2008) – Westminster Exchange, University of Westminster Doctoral Supervision & Grants Denroche currently supervises one active doctoral researcher (MPhil/PhD) investigating metaphor in Arabic urban literature. He welcomes applications from prospective postgraduate students in the areas of figurative language, semiotics/multimodality, discourse analysis and translation/interpreting studies. Professional Memberships & Service International Association for Cognitive Semiotics (IACS) Researching and Applying Metaphor (RaAM) External examiner for Birkbeck, University of Hertfordshire, UCL Institute of Education Reviewer for American Journal of Semiotics , Journal of Pragmatics , Language and Cognition , Cognitive Linguistics Studies , Metaphor and the Social World

Daniel HershcovichView profile

Assistant Professor

• Natural Language Processing
• Machine Learning
• Cultural Adaptation in NLP
• +4 more

Daniel Hershcovich is a Tenure Track Assistant Professor at the Department of Computer Science (Faculty of Science, University of Copenhagen) specializing in Natural Language Processing and Machine Learning . His research focuses on cross-cultural adaptation of language models, integrating human values into AI, and analyzing food-related cultural narratives for sustainable diets. Education: Ph.D. in Computational Neuroscience from Hebrew University of Jerusalem B.Sc. in Mathematics and Computer Science from Open University of Israel Recent publications highlight his work on multimodal models (haptic captioning, visual assistants for the blind), historical text analysis (Danish/Norwegian literature, euphemism detection), and cross-cultural NLP (recipe adaptation, cultural value alignment, climate awareness). His projects frequently combine AI ethics with domain-specific applications like food studies, historical linguistics, and accessibility research. Key collaborative networks include institutions in Denmark, Israel, and international partnerships through conferences like ACL, EMNLP, and workshops on cross-cultural NLP. The NLP section at DIKU serves as his primary affiliation for these efforts.

Steve TanimotoView profile

Professor

• Novice programming environments
• Collaborative problem-solving environments
• Technology for educational assessment
• +11 more

Steve Tanimoto is a Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, with an adjunct appointment in the Department of Electrical & Computer Engineering. His work focuses on human-centered computing, particularly in educational technology and collaborative problem-solving environments. He has made significant contributions to the understanding of liveness in programming environments and their application to education, including a keynote at the International Conference on Live Coding (2015) that traced historical influences leading to widespread use of liveness in modern software environments. Dr. Tanimoto's research spans several interconnected domains: Novice programming environments and educational technology Collaborative problem-solving environments and tools Technology for educational assessment, particularly using pattern-recognition methods for teaching written language on tablets Liveness in programming environments and its applications Image processing from interdisciplinary perspectives (as detailed in his MIT Press book "An Interdisciplinary Introduction to Image Processing: Pixels, Numbers, and Programs") His recent publications demonstrate a consistent focus on the intersection of computing education, human-computer interaction, and collaborative problem-solving. A notable trend is the exploration of "liveness" in programming environments and how this concept can enhance educational experiences. His work increasingly integrates AI technologies with educational applications, particularly in the areas of writing instruction and collaborative problem-solving, with significant NIH funding support (P50 HD071764 and U54 HD083091). His notable recognition includes: VL/HCC Best Showpiece Award in 2015 for "Solving Problems by Drawing Solution Paths" Dr. Tanimoto has advised several graduate students through to completion, including Robert Thompson (2019), Sandra Fan (2013), and Tyler Robison (2012). He currently advises Emilia Gan (co-advised with B. Mako Hill) and Edward Misback. His research has been supported by NIH grants for work on computerized writing and reading instruction for students with learning disabilities. His CoSolve research group has developed experimental facilities for collaborative problem-solving, exploring tools that support problem formulation, visualization of problem spaces, and team collaboration dynamics, with applications in education, design, and various problem-solving domains.

Marzieh AminiView profile

Associate Professor

• Computer Vision
• Sensor Fusion
• Autonomous Vehicles
• +7 more

Dr. Marzieh Amini is an Associate Professor at Carleton University, cross-appointed to the School of Information Technology and Department of Systems and Computer Engineering . She coordinates the Optical Systems and Sensors undergraduate program and leads research in computer vision, sensor fusion, and biomedical signal processing . PhD in Electrical and Computer Engineering (2016), Concordia University Postdoctoral Fellow (2020), McGill University Research Interests focus on autonomous vehicle perception systems integrating machine learning and statistical modeling . Her work addresses multi-sensor integration for reliable operation in diverse environments, including biomedical applications and critical infrastructure monitoring . Recent publications emphasize wildfire management , LiDAR-based infrastructure monitoring , and adverse weather adaptation in autonomous systems . She has received grants from NSERC, NRC, and FRQNT . Honors & Awards include: Volunteer Recognition Awards (IEEE Montreal, 2022 & 2019) FRQNT Postdoctoral Fellowship (2018) IEEE ISCAS Travel Support (2016) Professional Service includes leadership roles in IEEE committees and conference organization.

Roberto Martinez-MaldonadoView profile

Associate Professor

• Learning Analytics
• Artificial Intelligence in Education
• Collaborative Learning
• +3 more

Roberto Martinez-Maldonado is an Associate Professor in the Department of Human Centred Computing at Monash University's Faculty of Information Technology. He holds a PhD in Human-Computer Interaction and Educational Data Mining from the University of Sydney. His research focuses on Learning Analytics, Artificial Intelligence in Education, and Collaborative Learning, with applications in healthcare and classroom settings. Prior to Monash, he worked at the Connected Intelligence Centre (CIC) and as a lecturer at the University of Technology, Sydney. His academic journey includes a Master's in Information Technology from Universidad Tecmilenio and a Bachelor's in Computer Systems Engineering from Instituto Tecnológico de Mérida. Key research projects include developing analytics dashboards for healthcare simulations, teamwork analytics for professional education, and AI-driven tools for reflective practice. His innovations include the HuCETA framework and the Data Storytelling editor, which enhance educational data visualization and teacher-student interaction. He has received multiple awards, including the 2022 Best Student Paper and 2020 Best Paper Award. His work contributes to UN Sustainable Development Goals related to quality education. He supervises PhD students in Multimodal Teamwork and Classroom Analytics. Roberto is actively involved in academic conferences, serving on program committees for LAK and AIED. Media engagements include ABC Radio interviews discussing classroom design and AI in education.

Mehrtash Tafazzoli HarandiView profile

Associate Professor

• Machine Learning
• Deep Learning
• Computer Vision
• +3 more

Mehrtash Tafazzoli Harandi is an Associate Professor in the Department of Electrical and Computer Systems Engineering at Monash University, part of the Faculty of Engineering. His research focuses on machine learning and computer vision, particularly visual data analysis, with contributions to geometric deep learning, continual learning, and medical imaging. He holds editorial roles at IET Computer Vision , Frontiers in Imaging , and Journal of Imaging . Education & Previous Affiliations: Prior to Monash, he worked at NICTA (Canberra & Queensland Research Labs) and CSIRO-Data61. His Erdős number is 4 via a collaboration path through Richard Hartley. Research Interests: His work spans geometric learning, diffusion models, medical image analysis, and sustainable AI applications. Key areas include unlearning mechanisms in AI, 3D reconstruction compression, and robust MRI reconstruction using contrastive learning. Grants & Projects: He leads projects funded by ARC, US Air Force, and industry collaborations, including 'Can Machines Unlearn?' (ARC, A$790k) and 'Exploiting Geometries of Learning' (ARC, A$420k). His work addresses challenges in lifelong learning, model adaptation, and trustworthy AI from limited data. Awards: Recipient of Best Recognition Paper (IEEE DICTA 2013), NICTA Impact Award (2015), and multiple outstanding reviewer recognitions at top conferences. Teaching: Teaches courses on neural networks, computer vision, and advanced data analysis at Monash University. Supervises PhD students with a focus on mathematical and computational proficiency. Labs/Teams: Collaborates with the Australian Center for Robotic Vision (ACRV) and contributes to interdisciplinary projects at CSIRO-Data61. His research group explores cutting-edge AI applications in healthcare, manufacturing, and environmental sustainability.