Find Academic Supervisors

Els LefeverView profile

Associate Professor

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +5 more

Els Lefever is an Associate Professor at Ghent University, where she works with the LT3 (Language and Translation Technology) research team. Her position focuses on computational linguistics and natural language processing research, with strong ties to both theoretical and applied aspects of language technology. Dr. Lefever earned her PhD in Computer Science from Ghent University in 2012 with her dissertation titled "ParaSense: Parallel Corpora for Word Sense Disambiguation." Her academic journey began as a computational linguist at the R&D department of Lernout & Hauspie Speech Products before transitioning to academia. Els Lefever's research spans multiple areas within computational linguistics with particular expertise in multilingual natural language processing. Her work focuses on computational semantics, cross-lingual word sense disambiguation, and multilingual terminology extraction. Recent research directions include automatic detection of irony in online text, argumentation mining in social media, sentiment analysis of financial news, language modeling for low-resourced languages, and computational approaches to Byzantine Greek epigrams. Her research demonstrates a consistent pattern of bridging theoretical computational linguistics with practical applications across diverse language domains and historical periods. Professor Lefever actively supervises PhD research on several cutting-edge topics including terminology extraction from comparable corpora, event extraction and sentiment mining of financial news, language modeling for low-resourced languages, argumentation mining in social media, and the automatic detection of links between Byzantine Greek epigrams. Her supervision portfolio demonstrates her commitment to advancing multiple frontiers of computational linguistics simultaneously. As an educator, Professor Lefever teaches courses in Terminology and Translation Technology, Language Technology, Localisation, Digital Text Analysis, and Digital Humanities. Her teaching reflects her research interests, providing students with both theoretical foundations and practical skills in language technology applications. The LT3 research group, where Professor Lefever is a key member, maintains strong connections with both academic and industry partners. The group has participated in numerous international conferences and shared tasks including SemEval competitions across multiple years, demonstrating consistent contributions to benchmark datasets and evaluation methodologies in natural language processing.

Stephen MitroffView profile

Professor

• Cognitive Neuroscience
• Visual Cognition
• Attention
• +5 more

Stephen Mitroff is a Professor of Cognitive Neuroscience at George Washington University’s Department of Psychological and Brain Sciences. He leads the Visual Cognition Lab, focusing on mechanisms of visual memory, perception, and attention. Prior to GW, he held faculty positions at Duke University (2005–2015) after completing postdoctoral research at Yale and a Ph.D. in Cognitive Psychology from Harvard University. His research emphasizes individual differences in visual search performance, leveraging big data from smartphone apps and professional populations like radiologists and TSA officers. Education: Ph.D. in Cognitive Psychology (Harvard, 2002), B.A. in Cognitive Science (UC Berkeley). Research explores how individual traits, training, and environmental factors influence visual cognition. Recent work includes pandemic-related mental health impacts, error dynamics in decision-making, and optimizing visual search for real-world applications. The lab employs innovative methodologies such as consistency-based training protocols and drift-diffusion modeling to dissect cognitive processes. Advising and grants: While specific grant details are not provided, his work has been supported by industry-academic partnerships, particularly in aviation security and medical diagnostics. The Visual Cognition Lab collaborates with professional groups to translate research into practical tools like sports vision training and baggage screening assessments. Labs/Teams: Principal Investigator of the GW Visual Cognition Lab, which integrates computational modeling with behavioral experiments to address applied cognitive science challenges.

Shahin ShahrampourView profile

Assistant Professor

• Machine Learning
• Optimization and Control
• Distributed Learning
• +5 more

Shahin Shahrampour is an Assistant Professor in the Department of Mechanical & Industrial Engineering at Northeastern University, with a courtesy appointment in Electrical & Computer Engineering. He holds a Ph.D. in Electrical & Systems Engineering from the University of Pennsylvania, an M.A. in Statistics from The Wharton School, and a B.S. in Electrical Engineering from Sharif University of Technology. Prior to Northeastern, he was at Texas A&M University and held a postdoctoral fellowship at Harvard University. Research Interests: Machine learning, optimization and control, distributed and sequential learning, with a focus on computationally efficient methods for data analytics. Specific areas include manifold optimization, online and reinforcement learning, non-convex optimization, and statistical signal processing. His work bridges theory and applications in networked systems and dynamic environments. Awards & Grants: Received a $500,000 NSF grant (2023) for distributed optimization in non-convex environments. Best Paper Award at IEEE ICASSP 2022 for contributions to signal processing. TEES Engineering Genesis Award (2020) for multidisciplinary research at Texas A&M. Principal Investigator on NSF-funded projects in collaborative online optimization and decentralized learning. Labs & Teams: Leads research in machine learning and control systems at Northeastern University, focusing on interdisciplinary challenges in distributed optimization and real-time learning. His lab emphasizes theoretical foundations and practical implementations in networked and dynamic systems.

Yun FuView profile

Professor

• Artificial Intelligence
• Computer Vision
• Pattern Recognition
• +2 more

Yun Fu is a tenured Professor in the Department of Electrical and Computer Engineering at Northeastern University, with a joint appointment in the Khoury College of Computer Science. He has established himself as a leading researcher in Artificial Intelligence, with over 500 publications in top-tier venues including IEEE/ACM transactions and major AI conferences. His work spans both theoretical foundations and practical applications, with significant impact in computer vision and machine learning. Professor Fu earned his Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. His academic career progressed from Assistant Professor at SUNY Buffalo to his current position as tenured Professor at Northeastern University, where he has held appointments since 2012. His educational background includes a Beckman Graduate Fellowship at UIUC (2007-2008). His research focuses on advancing Artificial Intelligence with particular emphasis on Computer Vision, Pattern Recognition, and Machine Learning. His seminal work includes the "Residual Dense Network for Image Super-Resolution" presented at CVPR 2018, which was ranked among the Top 10 Most Influential CVPR papers. His research interests span image processing, anomaly detection, multimodal learning, and trajectory prediction, with applications ranging from healthcare to consumer technology. Analysis of his recent publications reveals a strong trend toward developing efficient and robust AI systems that bridge computer vision with language understanding. His work increasingly focuses on multimodal learning, trajectory prediction for multi-agent systems, anomaly detection in complex environments, and model validation techniques for black-box systems, while maintaining practical applications in real-world scenarios. Professor Fu's extensive recognition includes: Fellow of IEEE (2018), OSA (2019), SPIE (2018), IAPR (2016), AAIA (2021), and AAAI (2025) Member of Academia Europaea (2022) and European Academy of Sciences and Arts (2023) Fellow of National Academy of Inventors (2023) Multiple Young Investigator Awards from NAE, ONR, ARO, IEEE, ACM, and INNS 12 Best Paper Awards from major conferences Industrial Research Awards from Google, Amazon, Samsung, JPMorgan, and others Professor Fu has successfully mentored numerous Ph.D. students who now hold prominent positions in academia and industry at institutions including Amazon, Microsoft, Meta, Adobe, and major universities. His entrepreneurial ventures include founding Giaran (acquired by Shiseido in 2017) and co-founding TVision Insights, demonstrating his commitment to translating research into real-world impact. He has secured significant research funding from both government agencies and industry partners. As the PI and Founding Director of the SmiLe Lab at Northeastern University, Professor Fu leads a dynamic research group focused on advancing the state-of-the-art in AI and Computer Vision. The lab fosters interdisciplinary collaboration across computer science, electrical engineering, and applied mathematics, with ongoing projects in efficient deep learning, multimodal understanding, and practical AI applications.

Naeha SharifView profile

Lecturer

• Computer Vision
• Natural Language Processing
• Medical Image Analysis
• +5 more

Dr. Naeha Sharif is a Lecturer in Computer Science & Software Engineering at The University of Western Australia (UWA), specializing in Computer Vision (CV) and Natural Language Processing (NLP). She holds a PhD from UWA (2021) focused on AI-driven image captioning and received the prestigious ACS 1962 Medal for outstanding IT/CS research. Her work bridges AI with healthcare, including automated medical assessment via spine scans (DXA) for abdominal aortic calcification analysis and AI-driven medical report generation for fundus imaging. Education: PhD in Computer Science (UWA 2021), Master of Engineering in Biomedical Engineering (Kyung Hee University, 2013). Previous roles include Postdoctoral Fellow at Edith Cowan University (2021–2022). Research Interests: Vision-language models, medical image analysis, captioning metrics, multimodal learning, and AI applications in healthcare. Active in UN SDGs related to health and innovation. Teaching: Unit Coordinator for Computer Graphics & Animation (CITS3003, 2022–2023) Computational Thinking with Python (CITS1401, 2023) Grants: Lead investigator on the 'Generative AI for Screening of Depressive Disorders' project (2024). Awards: Recognized for student learning contributions (2025), best paper awards (2024/2020), and academic excellence honors (2021/2020). Labs/Teams: Member of UWA Natural & Technical Language Processing Group and IEEE Signal Processing Society.

• Reinforcement Learning
• Robotics
• Deep Learning
• +3 more

Glen Berseth is an Associate Professor in the Department of Computer Science and Operations Research at the University of Montreal and a Senior Academic Fellow at Mila – Quebec Institute for Artificial Intelligence. He is also a Canada CIFAR Chair in AI and Co-Director of the Montreal Robotics and Integrative AI Laboratory (REAL). His work focuses on reinforcement learning, robotics, and deep learning applied to autonomous systems. He holds a postdoctoral background from Berkeley Artificial Intelligence Research (BAIR), working under Sergey Levine. His research emphasizes real-world applications, including human-robot collaboration, continual learning, and multi-agent systems. He teaches courses on robot learning at the University of Montreal and Mila, covering cutting-edge techniques for general-purpose robots. Key research interests include reinforcement learning for robotics, adaptive interfaces, and sim-to-real transfer. His recent work addresses challenges in autonomous learning systems, such as robust locomotion control and efficient exploration strategies. Notable awards include the Canada CIFAR AI Chair. He has supervised numerous students, including PhD candidates Ozgur Aslan and Siddarth Venkatraman, and Master’s students like Roger Creus-Castanyer and Léa Demeule, focusing on topics like reinforcement learning and robotic control. Berseth leads research projects funded by organizations like the CRSNG, FCI, and MITACS, addressing topics such as modular lifelong learning and generalization in robotics. His lab, REAL, explores embodied AI and robotics integration.

• Artificial Intelligence
• Data Science
• Machine Learning
• +7 more

Hady W. LAUW is an Associate Professor in the School of Computing and Information Systems (SCIS) at Singapore Management University (SMU), serving as Director of the BSc (Computer Science) Programme and a Lee Kong Chian Fellow. He holds a PhD from Nanyang Technological University (2008). His research focuses on artificial intelligence, data science, machine learning, and recommender systems, with notable contributions to multimodal recommendation frameworks like Cornac and collaborative filtering techniques. He teaches advanced courses including IS712 Machine Learning for postgraduate students and CS608 Recommender Systems for MITB programme participants. His work emphasizes practical applications, such as designing explainable recommendation systems and integrating A/B testing into frameworks. Key research interests include web mining, preference learning, representation learning, and decision support systems. He has advised multiple students on topics like neural networks, collaborative filtering, and comparative analysis of reviews. Scientific achievements include the Lee Kong Chian Fellowship and over 100 publications in top venues like ACM WWW and IEEE TKDE. He actively contributes to conferences as a PC member and chairs events like PAKDD. His Cornac framework supports reproducible research in multimodal recommendations. He leads the Preferred.AI research group, fostering undergraduate and postgraduate research in computing. His work bridges theoretical advancements with industry applications, particularly in data-driven decision making and automated evaluation metrics.

Wenwu ZhuView profile

Professor

• Big data analysis
• Multimedia communication and networking
• Multimedia computing
• +5 more

Wenwu Zhu is a Professor and Vice Chair of the Department of Computer Science and Technology at Tsinghua University. He has held prominent positions at Microsoft Research Asia, Intel Research China, and Bell Labs, establishing himself as a leading figure in multimedia computing and networking with international recognition as a FOREIGN member of the Academy of Europe (elected 2018). His educational background includes: Ph.D. in Electrical and Computer Engineering from New York University (1996) Professor Zhu's research focuses on the intersection of multimedia systems, networking, and big data. His work has pioneered advancements in internet video streaming, multimedia cloud computing, and social-aware content distribution. He has made significant contributions to understanding how multimedia content can be efficiently delivered across diverse network environments, from traditional wired networks to modern mobile and social platforms. His research bridges theoretical computer science with practical applications, with his work on social-aware video content distribution being transferred to Tencent company. His publication record shows a clear evolution from foundational work on internet video streaming in the early 2000s, through multimedia cloud computing in the early 2010s, to more recent work on social-aware multimedia and network embedding using deep learning approaches. This progression reflects the changing landscape of multimedia computing from infrastructure-focused to socially-aware and AI-driven systems. Professor Zhu has received numerous prestigious honors: AAAS Fellow (2016) SPIE Fellow (2013) IEEE Fellow (2010) Minister of Education's Natural Science Award, 1st prize (2017) Chinese Institution of Electronics's Natural Science Award, 1st prize (2015, 2012) National Natural Science Award, 2nd prize (2012) Chief Scientist for NSFC Major Project (2016) Chief Scientist for Ministry of Science and Technology's 973 Project (2014) Multiple Best Paper Awards including ACM Multimedia 2012 As Editor-in-Chief of IEEE Transactions on Multimedia since 2017 and through leadership roles as General Co-Chair for ACM CIKM 2019 and ACM Multimedia 2018, Professor Zhu has significantly shaped the multimedia research community. His research has been supported by major grants including NSFC Major Projects and Ministry of Science and Technology's 973 Projects, demonstrating both academic and national strategic importance. He has published over 300 referred papers with an H-Index of 55, including 6 Best Paper Awards and 7 books or book chapters. Professor Zhu leads a research group at Tsinghua University focused on multimedia big data computing, with strong industry connections. His team has made pioneering contributions to structural network embedding using deep learning and social contextual recommendation systems, bridging theoretical advances with practical applications in social media platforms.

Kemal TepeView profile

Professor

• Vehicular Networks
• Cognitive Radio
• Wireless Communication
• +6 more

Professor Kemal Tepe is a faculty member in the Faculty of Engineering at the University of Windsor, specializing in wireless communication and information processing. His research focuses on vehicular networks, cognitive radio systems, and smart grid technologies. He leads the Wireless Communication and Information Processing Lab , where he develops solutions for autonomous driving systems, cybersecurity in vehicle-to-infrastructure communication, and spectrum sensing techniques. In 2016, he was awarded the Medal of Excellence by the Faculty of Engineering for his dedication and service. Tepe’s work bridges theoretical advancements and practical applications, addressing challenges in autonomous systems, machine learning for anomaly detection, and IoT security. His contributions include pioneering methods for detecting adversarial behavior in vehicular networks and improving spectrum utilization through probabilistic modeling. Collaborations with industry partners like Ford Motor Company and involvement in initiatives such as the Perspective Magazine automotive research highlight his industry-relevant research. His research interests span a wide range of domains including: Autonomous vehicle safety and communication protocols Cognitive radio networks and spectrum management Machine learning for network security and anomaly detection Wireless sensor networks and energy-efficient protocols Smart grid integration and communication architectures Tepe’s publications emphasize practical implementations, such as real-time routing protocols for wireless sensor networks and hardware designs for cognitive radio systems. His lab’s innovations have been showcased in industry-relevant platforms, demonstrating the real-world impact of his work.

Elika BergelsonView profile

Associate Professor

• Infant Language Acquisition
• Cognitive Development
• Word Learning
• +9 more

Elika Bergelson is the John L. Loeb Associate Professor of the Social Sciences in the Department of Psychology at Harvard University, within the Faculty of Arts and Sciences. She leads the Bergelson Lab (BLAB), part of Harvard's Laboratory for Developmental Studies, focusing on infant language acquisition. Her research examines how infants learn words by studying both cognitive mechanisms and environmental input through naturalistic and lab-based methods. Her research interests center on infant language acquisition , cognitive development , and word learning , with a focus on how language input, social interaction, and sensory experience shape early linguistic knowledge. She explores word comprehension, phonological and semantic representations, and the role of variability in talker and wordform input. Her lab has pioneered large-scale cross-linguistic studies and expanded into atypical development, including language acquisition in blind and deaf infants. The 15 most recent publications reflect a strong trend in naturalistic data collection , cross-linguistic comparison , and methodological innovation , particularly in using LENA recordings and open science practices. Topics include acoustic and talker variability, vocabulary development in sensory-impaired children, remote infant testing, and the reliability of speech analysis tools. Dr. Bergelson is committed to diversity, equity, and inclusivity in research and lab recruitment. She actively promotes open science, public outreach, and policy applications of developmental science. She has mentored numerous students and researchers, though specific names are not listed. Her work is supported by collaborative grants and large-scale interdisciplinary projects. The Bergelson Lab employs methods including eye-tracking , EEG , audio discrimination tasks , and corpus analysis . It continues the SEEDLingS project and collects longitudinal data from infants with sensory impairments. The lab maintains open repositories on GitHub and emphasizes reproducibility and transparency in research.

Xianzhi LiView profile

Associate Professor

• 3D Vision
• Computer Graphics
• Point Cloud Processing
• +3 more

Xianzhi Li is an Associate Professor at the School of Computer Science and Technology, Huazhong University of Science and Technology (HUST). Before joining HUST, he was a postdoctoral fellow at The Chinese University of Hong Kong (CUHK) and the Hong Kong Center for Logistics Robotics. He earned his Ph.D. in Computer Science and Engineering from CUHK under Professors Pheng-Ann Heng and Chi-Wing Fu, preceded by an M.Sc. in Biomedical Engineering (CUHK, 2015) and a B.Eng. in Biomedical Engineering from Sichuan University (2014). His research focuses on 3D vision, computer graphics, point cloud processing, and deep learning, with a specific emphasis on designing machine learning algorithms for 3D data analysis. Recent work includes advancements in 3D point cloud analysis, semantic segmentation, and sim-to-real robotics applications. Key research trends include the integration of large language models with 3D data (e.g., MiniGPT-3D), open-world semantic segmentation (PDF), and joint 2D-3D learning frameworks (Joint-MAE). His contributions span robotics, computer vision, and graphics, with notable applications in industrial bin picking and 3D reconstruction. Awards: Teaching Assistant of Merit (2018), Biomedical Engineering Scholarship (2015), and National Scholarship in China (2013). Teaching: Courses include Computer Vision (Spring 2022), Advanced Topics in Computer Graphics and Visualization, and Principles of Computer Graphics.

Tim WelshView profile

Professor

• Perception
• Cognition
• Cognitive Neuroscience
• +6 more

Dr. Timothy Welsh is a full-time Professor in the Action & Attention Lab at the University of Toronto's Faculty of Kinesiology & Physical Education , where he also serves as Associate Dean, Research since 2023. With cross-appointments from the Faculty of Physical Education and Health, his work bridges cognitive neuroscience and motor behavior research. Academic Background Ph.D. in Human Biodynamics (McMaster University, 2002) M.Sc. in Human Biodynamics (McMaster University, 1999) B.P.H.E. in Physical and Health Education (University of Toronto, 1997) Dr. Welsh's research explores neural mechanisms of goal-directed actions across typical and neurodiverse populations. His lab investigates joint action dynamics , self-objectification effects on motor performance , and cross-reality sensorimotor adaptation , with significant focus on autism and developmental coordination disorder. Recent publications (2024-2025) reveal systematic differences in motor imagery between autistic and non-autistic adults , gender-specific learning patterns , and cross-modal sensory integration challenges in mixed reality environments . These studies demonstrate his lab's expertise in combining behavioral experimentation , EEG analysis , and computational tool development like TAT-HUM. Scientific Recognition Franklin Henry Young Scientist Award Early Distinguished Career Award (NASSP) Outstanding Graduate Supervision Award As principal investigator on 15 active grants (2015-2027), including projects on digital health platforms for concussion rehabilitation and AI-assisted VR learning modules , Welsh's work impacts both basic science and applied interventions. His lab trains students at all levels through KPE 362 (undergraduate) and KIN 5514 (graduate) courses.

Muriel WaterlotView profile

Assistant Professor

• Translation Didactics
• Intercultural Communication
• Phraseology
• +4 more

Dr. Muriel Waterlot serves as an Assistant Professor at the Department of Dutch Language and Literature within the Faculty of Humanities at the John Paul II Catholic University of Lublin. Her research focuses on translation didactics , intercultural competence , and contrastive linguistic analysis between Polish, Dutch, and Flemish contexts. Supervised 18+ diploma theses on topics like Zeeuws dialect preservation, Dutch-English modal verbs, and Polish-Dutch phraseological units Contributed to translation theory through publications and presentations at international conferences including TransELTE and THERMOS Active in editorial and expert councils for academic journals and PhD programs Her recent work explores terminological variants in Polish-Dutch-French contexts , multilingual translation approaches , and pedagogical models for foreign language teaching . She advocates for task-based translation methodologies and cross-cultural business communication frameworks. Key projects involve EU-funded initiatives like Language in Business – Business in Language (Erasmus+) Developed the Waterlot Model for tourism web-text translation Coordinated international translation education programs

Rishabh DabralView profile

Researcher

• 3D Computer Vision
• Computer Graphics
• Multi-Modal Interactions
• +6 more

Rishabh Dabral is a Research Group Leader at the Max Planck Institute for Informatics since August 2024, leading the "3D Visual Intelligence" group. He is also affiliated with the Research Training Group on Neuro-Explicit Models of Language, Vision, and Action at Saarland University. Expertise: 3D computer vision, computer graphics, human-object interaction modeling, and motion synthesis. Leadership: Conducts cutting-edge research on 3D human performance capture and physical plausibility in motion. His research focuses on: 3D human pose estimation under gravity constraints Multi-modal gesture synthesis using neural architectures Quantum auto-encoding for 3D representations Wearable robotics informed by human behavior Temporal dynamics in human-object interaction Recent publications at top venues like SIGGRAPH , CVPR , and ICCV demonstrate his work on: Music-driven motion synthesis Egocentric motion capture systems Reactive two-person interaction models Diffusion-based gesture generation Object-aware motion prediction Wearable robotic limb design

• Developmental Psychology
• Language Acquisition
• Cognitive Neuroscience
• +5 more

Claudia Friedrich is a Professor of Developmental Psychology at the University of Tübingen, Faculty of Mathematics and Natural Sciences, Department of Psychology, where she has held her position since April 2013. Her research focuses on the cognitive processes underlying language development in children, with particular emphasis on speech processing, word recognition, and the acquisition of linguistic structures. Her educational background includes a doctorate in Psychology from the University of Leipzig (2003) and undergraduate studies in Psychology at the Technical University of Berlin (1993-1999). Professor Friedrich's research examines how children process speech sounds, word stress patterns, and develop the ability to understand language in context. Her work investigates cognitive and neural mechanisms involved in language acquisition across different age groups, with special attention to how first and second language learners process linguistic information. Through eye-tracking, ERP, and behavioral experiments, her research explores how children develop the ability to take perspectives during communication, process prosodic features of speech, and build lexical representations. She has made significant contributions to understanding how literacy acquisition shapes speech processing and how cross-modal influences affect language development. Her recent publications reveal a consistent focus on developmental trajectories in language processing, with increasing attention to cross-linguistic comparisons and multimodal aspects of language acquisition. The research demonstrates sophisticated methodological approaches combining behavioral, eye-tracking, and neurophysiological measures to investigate language processing from infancy through childhood. Professor Friedrich leads several major research projects including Project A6 "The Development of Common Ground in First and Second Language Acquisition" (2025-2029), a DFG-funded project on "Stress processing at the word level in children with different language backgrounds" (2024-2027), and previously led Project B1 on modal and amodal cognition (2020-2023). Her research has been consistently supported by prestigious funding bodies including the European Research Council (ERC) and the German Research Foundation (DFG). She heads the Developmental Psychology workspace at the University of Tübingen, where her research group investigates cognitive prerequisites for perspective-taking in language comprehension and the influence of perspective taking during language processing through memory experiments and eye movement studies.