Find Academic Supervisors

• Transportation Systems Analysis
• Traffic Engineering
• Sustainable Transportation
• +3 more

Yu Nie is a Professor in the Department of Civil and Environmental Engineering at Northwestern University, affiliated with the NU-TREND research group within the McCormick School of Engineering. His work focuses on optimizing transportation networks, integrating human behavior, infrastructure design, and network topology to enhance mobility, reliability, and sustainability. He holds a Ph.D. from the University of California, Davis, an M.S. from the National University of Singapore, and a B.S. (cum laude) from Tsinghua University. His research interests span interdisciplinary approaches combining optimization, network science, traffic flow theory, economics, and statistics. Key areas include congestion pricing strategies, ride-hailing market dynamics, autonomous vehicle integration, and transit system design. He has contributed to studies on dockless bike-sharing systems, ethics-aware transit design, and traffic management in autonomous vehicle zones. Nie’s recent publications (2024–2025) highlight advancements in modular autonomous vehicle systems, co-modal freight solutions, and policy frameworks for sustainable urban mobility. His work often bridges theoretical insights with practical applications, addressing challenges like EV charging chaos and ride-pooling impacts. He received the 2021 Transportation Science Meritorious Service Award for his editorial contributions. His research also explores freight exchange platforms, taxi market resilience during pandemics, and the role of route choice models in transit design. Labs/Teams: Yu Nie is associated with the NU-TREND research group, specializing in innovative transportation solutions through interdisciplinary collaboration.

• Robotics
• Artificial Intelligence
• Machine Learning
• +3 more

Dr. Danesh Tarapore is an Associate Professor at the University of Southampton specializing in robotics and AI. He focuses on human-robot interaction, swarm intelligence, and autonomous systems. His current research involves developing resilient robotic teams and optimizing learning algorithms for constrained environments. He supervises 6 PhD students in the iPhD MINDS and Computer Science programs. Dr. Tarapore's work bridges theoretical advancements with practical applications in autonomous navigation, multimodal dataset creation, and quality-diversity optimization. His publications span conferences like HRI and journals in robotics and AI. He collaborates with institutions like the University Hospital Southampton and the Boldrewood Innovation Campus. Research Interests: Human-robot collaboration, swarm systems, machine learning, and adaptive control Key Contributions: HRI-SENSE dataset, evolutionary subset selection algorithms, forest navigation frameworks Grants and Funding: Active projects in multi-agent systems and resilient robotics Dr. Tarapore maintains active roles in the robotics community through conference participation and interdisciplinary collaborations.

Joaquin VanschorenView profile

Associate Professor

• Machine Learning
• Automated Machine Learning
• Algorithm Selection
• +4 more

Joaquin Vanschoren is an Associate Professor of Machine Learning at Eindhoven University of Technology (TU/e), affiliated with the Faculty of Mathematics and Computer Science. He leads the Automated Machine Learning group and serves as Education Director for the Data Science program. His research focuses on democratizing AI, algorithm selection, and open science platforms like OpenML. He has received awards including the Dutch Data Prize and Amazon Research Award. Education: PhD in Engineering (KU Leuven, Belgium), MSc in Computer Science (KU Leuven). Research visits included IBM, Amazon Research, and universities globally. Research Interests: Machine Learning, Automated ML, Meta-learning, AI Safety, Data-centric AI. He co-founded OpenML and chairs MLCommons' AI Safety working group. Key Projects: NeurIPS Datasets and Benchmarks track, MLCommons initiatives, OpenML platform. Supervised 78 research works and authored 200+ papers. Awards: Dutch Data Prize (2016), Amazon Research Award (2019), Microsoft Azure Research Awards (2016–2017). Labs/Teams: OpenML open source team, MLCommons collaborations, Automated Machine Learning group at TU/e.

Shuangquan WangView profile

Assistant Professor

• Mobile & Wearable Computing
• Activity Recognition
• Smart Health
• +3 more

Shuangquan (Peter) Wang is an Assistant Professor of Computer Science at Salisbury University. He holds a PhD in Computer Science from the College of William & Mary (2020) and a PhD in Pattern Recognition and Intelligent Systems from Shanghai Jiao Tong University (2008), along with earlier degrees from Wuhan University of Technology and Wuhan Institute of Technology. His research focuses on mobile/wearable computing, activity recognition, smart health, and machine learning. He has over 10 years of experience in academia and industry, including roles at Philips Research East Asia and Nokia Research Center (Beijing). His work emphasizes wearable sensor-based health monitoring, such as fall detection, mastication analysis, and Parkinson’s disease monitoring. He leads the WISH Research Lab and serves as an Associate Editor for Elsevier's Smart Health Journal. Recent contributions include papers on salinity anomaly detection (2024), LLM-based user requirement analysis (2024), and socially acceptable food recognition (2022). His research trends emphasize interdisciplinary applications of machine learning in healthcare and sensor-driven human activity analysis. Professional service roles include coordinating Salisbury University’s Center for Applied Mathematics and Science (2021–2024) and chairing ACM/IEEE CHASE conferences. He has delivered invited talks on artificial intelligence and its societal impacts to diverse audiences, including the Institute of Retired Persons at Salisbury University. His lab, WISH Research Lab, explores innovative solutions in smart health and mobile computing, integrating wearable technologies with machine learning for real-world health applications.

Mark GothamView profile

Senior Lecturer

• Computational Music Theory
• Digital Humanities
• Music Corpus Creation
• +4 more

Mark Gotham is a Senior Lecturer in Cultural Computation at King’s College London’s Department of Digital Humanities. He holds a unique position bridging STEM and the humanities, with prior roles as an Assistant Professor of Computer Science at Durham University and a Professor of Music Theory at Technische Universität Dortmund. His research focuses on computational methods for music theory, corpus creation, and accessibility. Gotham completed a Ph.D. in Music Theory at the University of Cambridge, an MMus in Composition at the Royal Northern College of Music, and a First-Class Bachelor’s in Music from the University of Oxford. His work spans computational musicology, including projects like the OpenScore initiative, which digitizes and opens music scores. He is affiliated with King’s Computational Humanities Research Group and the Centre for Digital Culture. Gotham’s compositions, such as the award-winning CD *Utrumne est Ornatum*, blend theoretical rigor with creative expression. He collaborates with institutions like Deutsche Telekom on projects like *Beethoven X* and leads the Music Computing Lab at King’s. Gotham’s research emphasizes interdisciplinary approaches, using computational tools to explore musical structures and democratize access to music theory. His contributions include frameworks for aligning symbolic music, standards for harmonic analysis, and pedagogical innovations in music education.

Zhumin ChenView profile

Professor

• Natural Language Processing
• Artificial Intelligence
• Big Data Processing
• +7 more

Zhumin Chen is a Professor and Associate Dean at the School of Artificial Intelligence, Shandong University. He serves as a member of multiple committees including the Natural Language Processing Committee of China Computer Federation (CCF) and holds leadership roles in research institutions like the Qingdao Key Laboratory of Big Data Technology Innovation. His research focuses on natural language processing, artificial intelligence, and big data, with a strong emphasis on large language models, healthcare applications, and recommendation systems. He has secured over 30 major grants including National Key Research and Development Programs and has published extensively in top venues like ACL, SIGIR, and NeurIPS. His work has been recognized with awards such as the SIGIR 2024 Best Paper Honorable Mention and the Shandong Science and Technology Progress First Prize. He leads the development of large-scale AI models and platforms, including the Shanda Intelligent Assistant, which supports teaching and research across the university. Dr. Chen collaborates with institutions like Microsoft Research Asia and Purdue University, and has co-founded multiple industry partnerships with companies such as Inspur and Hisense. His academic services include reviewing for top conferences (SIGIR, WWW, ACL) and journals, and he actively mentors students through doctoral and master’s programs.

Zezhou ChengView profile

Assistant Professor

• Computer Vision
• Machine Learning
• AI for Science
• +6 more

Zezhou Cheng is an Assistant Professor of Computer Science at the University of Virginia, leading the Computer Vision Lab. He holds a Ph.D. from UMass Amherst (2023), a postdoctoral position at Caltech, and a Bachelor's degree from Sichuan University (2015). His research focuses on computer vision, machine learning, and their applications in ecology, materials science, and autonomous systems. Key areas include 3D understanding, self-supervised learning, and AI-driven ecological monitoring. He has received awards such as the Best Synthesis Award (2020) and Outstanding Reviewer (CVPR 2021). His work spans publications in top venues like CVPR, ICCV, and ECCV, addressing challenges in 3D reconstruction, generative models, and ecological data analysis. Education: Ph.D. in Computer Science, UMass Amherst (2023) Bachelor's Degree, Sichuan University (2015) Postdoctoral Researcher, Caltech (advised by Georgia Gkioxari) Research Highlights: Developed LU-NeRF for unposed scene reconstruction and camera pose estimation Contributed to AI for ecology via bird roost detection using weather radar data Advanced 3D representation learning through procedural programs and self-supervised techniques Awards & Recognition: Outstanding Reviewer, CVPR 2021 Best Poster Award, New England Computer Vision Workshop 2019 National Scholarship (China, 2014 and 2016) His lab explores cutting-edge topics in computer vision, with a focus on interdisciplinary applications. Teaching roles include leading Caltech's AI Bootcamp and serving as a Teaching Assistant at UMass Amherst. Industry collaborations include internships at Google Research, Snap, and Amazon.

• Machine Learning
• Biomedical Image Analysis
• Medical Imaging
• +3 more

Professor Jinman Kim is a Professor in the School of Computer Science at the University of Sydney and Director of the Biomedical Data Analysis and Visualisation (BDAV) Lab. He also serves as Research Director of the Telehealth and Technology Centre at Nepean Hospital. His research focuses on machine learning applications in biomedical image analysis, visualization, and multi-modal data processing. Kim holds a PhD in Computer Science from the University of Sydney (2006) and has held roles including Senior Lecturer (2013), Associate Professor (2016), and Professor (2022). He is an Area Editor for Computer Methods and Programs in Biomedicine and actively contributes to AI-driven healthcare initiatives. His academic journey includes a Marie Curie Fellowship at the University of Geneva (2010) and leadership roles in projects like the ARC Training Centre in Innovative Biomedical Engineering. He co-leads the Digital Health Imaging initiative under the Faculty of Engineering’s Digital Science Initiative. Kim has developed teaching programs such as the Master of Digital Health and Data Science, co-taught with the Faculty of Medicine and Health. Research interests span AI in medical imaging, telehealth systems, and interdisciplinary biomedical engineering. His work includes advancements in PET/CT fusion, tumor segmentation, and medical visual analytics. Kim’s lab explores applications like AI in dental education, cutaneous lymphoma detection, and fair AI models for healthcare. Notable collaborations include the Telehealth Remote Monitoring System for chronic patients and contributions to datasets like the HRDC Challenge for hypertension classification. His labs prioritize translating AI innovations into clinical tools for improved healthcare accessibility and precision.

William F. SpeierView profile

Associate Professor

• Medical Informatics
• Biomedical Engineering
• Neurology
• +3 more

William F. Speier is an Associate Professor in the Department of Radiological Sciences at the University of California, Los Angeles (UCLA) School of Medicine . His work spans Medical Informatics , Biomedical Engineering , and Neurology , focusing on applying Artificial Intelligence and Deep Learning to medical imaging and patient monitoring systems. Speier's research emphasizes improving diagnostics for Thyroid Cancer via multimodal ultrasound and molecular testing, advancing Brain-Computer Interfaces (BCIs) for ALS patients, and optimizing Heart Failure remote monitoring through biometric data analysis. He leads the NIH-funded project Predicting Clinically Significant Thyroid Cancer using Ultrasound (R21EB030691), integrating AI into clinical workflows. His recent publications highlight trends in High-Frequency Oscillations for epilepsy, Gleason Grading in prostate cancer, and Language Models for BCI communication. Collaborations with co-authors like Corey Arnold and Hiroki Nariai underscore his interdisciplinary approach. Speier's work also addresses Diagnostic Imaging , Neural Signal Processing , and Health Technology accessibility. Grants and clinical trial integrations further demonstrate his commitment to translating AI into practical healthcare solutions. His methodologies include 3D ConvNets , Federated Learning , and Active Learning frameworks for histopathology and radiology.

• Computer Science
• Electrical Engineering
• Signal Processing
• +3 more

Guo Li is affiliated with the Beijing Institute of Technology, School of Management and Economics. Their research spans computer vision, optimization algorithms, signal processing, and machine learning. Collaborations include work on image super-resolution, sensor networks, and energy systems. Publications are distributed across journals like Comput. Electron. Agric. , IEEE Trans. Circuits Syst. , and Entropy . Research interests focus on computational methods for image processing, algorithm design, and interdisciplinary applications in agriculture and energy. Recent work emphasizes lightweight neural network architectures, sparrow search algorithms, and thermodynamic modeling in materials science. Notable contributions include advancements in citrus fruit detection, fatigue life assessment of superalloys, and load forecasting techniques. Active in international conferences such as CVPR, ICC, and NSDI, with a strong publication record since 1998.

Jean-Baptiste AlayracView profile

Researcher

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

Jean-Baptiste Alayrac is a Researcher at DeepMind, focusing on structured learning from video and natural language. His academic background includes a PhD from the Sierra and Willow groups at Ecole Normale Supérieure and Telecom ParisTech, where he explored machine learning and computer vision. He has held teaching roles as a Teaching Assistant at Ecole Normale Supérieure and other universities, contributing to courses in statistical machine learning and mathematics. His research interests span multimodal learning, vision-language models, self-supervised learning, and efficient retrieval systems. Notable projects include the Flamingo model for few-shot learning and the Perceiver IO architecture for structured data processing. He has also contributed to foundational works like HowTo100M, leveraging large-scale video-text embeddings. Alayrac's publications emphasize cross-modal interactions, with key contributions in adversarial robustness, layered video representations, and weakly supervised learning. His work often bridges computer vision and natural language processing, with applications in instructional video analysis and cross-lingual translation.

Sabine Schulte im WaldeView profile

Associate Professor

• Computational Linguistics
• Natural Language Processing
• Lexical Semantics
• +5 more

Prof. Sabine Schulte im Walde is an Associate Professor at the University of Stuttgart's Institute for Natural Language Processing. She holds affiliations with the International Max Planck Research School for Intelligent Systems (IMPRS-IS), ELLIS Society, and CLARIN-D. Her research focuses on computational modeling of lexical-semantic phenomena, including compositionality of noun compounds and particle verbs, abstract/concrete semantics, and diachronic language change. She has contributed to datasets on association norms for German compounds and led projects on multimodal semantic analysis. Research Interests: Her work bridges computational linguistics and cognitive science, emphasizing data-intensive methods. Key areas include semantic classification of verbs, metaphor detection, and evaluating semantic relations using distributional models. She explores how abstractness influences plausibility judgments and investigates semantic shifts in compounds over time. Awards: Nominated for AcademiaNet by the German Research Foundation (DFG). Active in professional societies including ACL, GSCL, and DGfS. Publications span over 50 peer-reviewed articles in venues like Computational Linguistics and ACL. Labs/Teams: Leads research on computational terminology and semantic change analysis at the Stuttgart Research Focus Language and Cognition (SRF). Collaborates internationally on projects like the DURel annotation tool for diachronic semantics.

Ramana VinjamuriView profile

Associate Professor

• Brain-Machine Interfaces
• Neuroprosthetics and Exoskeletons
• Machine Learning
• +4 more

Ramana Vinjamuri is an Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He holds a secondary appointment as Visiting Professor at the Indian Institute of Technology, Hyderabad, India. His academic journey includes a Ph.D. in Electrical Engineering from the University of Pittsburgh (2008), M.S. in Bioinstrumentation from Villanova University (2004), and B.Tech. in Electrical and Electronics Engineering from Kakatiya University (2002). Dr. Vinjamuri's research focuses on Brain-Machine Interfaces (BMIs) for upper-limb prostheses control , neuroprosthetics and exoskeletons , machine learning in motor control , and neurophysiological signal processing . His work extends synergy-based models to control 37-dimensional hand movements, addresses human-robot interaction through emotionally intelligent systems, and develops neurotechnologies for substance use disorder using wearable sensors and AI. NSF CAREER Award (2019) NSF IUCRC BRAIN Center Planning Grant (2020) Harvey N Davis Distinguished Teaching Assistant Professor Award (2018) His publications demonstrate expertise in EEG and EMG signal analysis , deep learning for motor decoding , synergy modeling , and humanoid robot control . The Vinjamuri Lab at UMBC involves graduate, undergraduate, and high school researchers, with international collaborations in India and the US.

Vagelis PapalexakisView profile

Associate Professor

• Data Science
• Machine Learning
• Artificial Intelligence
• +3 more

Dr. Vagelis Papalexakis is an Associate Professor and Ross Family Chair in the Computer Science & Engineering Department at the University of California, Riverside. His research focuses on data science, machine learning, and tensor methods, with applications in multi-aspect/multi-modal data analysis. He holds a Ph.D. from Carnegie Mellon University and a Diploma/M.Sc. from the Technical University of Crete. Affiliations: Ross Family Chair, Bourns College of Engineering, UCR Education: Ph.D. in Computer Science, Carnegie Mellon University M.Sc./Diploma in Electronic & Computer Engineering, Technical University of Crete His work emphasizes interpretable insights from complex datasets, including tensor-based defenses against adversarial attacks, graph representation learning, and scalable algorithms for high-dimensional data. Notable awards include the NSF CAREER Award (2021), IEEE DSAA Next Generation Award (2021), and ICDM Tao Li Award (2022). Grants include NSF funding for railway safety (CISE MSI: RPEP CPS), USDOT transportation research, and NVIDIA GPU grants. He leads projects in AI ethics, misinformation detection, and gravitational wave analysis. His lab collaborates with industry (e.g., Cisco, Instacart) and national labs (e.g., Lawrence Livermore).

• Artificial Intelligence
• Machine Learning
• Data Mining
• +5 more

Ling Zhao is a distinguished Professor at the School of Management, Huazhong University of Science and Technology, China, with extensive research contributions spanning artificial intelligence, machine learning, information systems, and biomedical applications. With over 150 publications since 2008, Dr. Zhao has established herself as a leading researcher in multiple interdisciplinary domains, particularly in applying computational methods to solve complex real-world problems. Dr. Zhao's research interests encompass a broad spectrum of cutting-edge topics including artificial intelligence, machine learning, data mining, control systems, and information systems. Her work demonstrates exceptional versatility, bridging theoretical computer science with practical applications in healthcare, transportation, cybersecurity, and business management. Notably, she has made significant contributions to sentiment analysis, medical image processing, algorithmic management, and privacy-preserving data analysis. Her research methodology often combines deep learning approaches with domain-specific knowledge to develop innovative solutions. Analysis of Dr. Zhao's recent publications (2023-2025) reveals a strong focus on interdisciplinary applications of AI, with particular emphasis on healthcare informatics (medical image analysis, disease diagnosis), human-computer interaction (algorithmic management effects), and advanced machine learning techniques (graph neural networks, multimodal learning). Her work shows a consistent trend toward increasingly complex and integrated systems that address real-world challenges across multiple domains. Dr. Zhao has made substantial contributions to academic advising and research mentorship, though specific student names aren't detailed in the available publications. Her research has been supported by various grants enabling work in AI applications, biomedical engineering, and information systems. Dr. Zhao maintains active collaborations with researchers across China and internationally, as evidenced by her co-authorship patterns. While specific laboratory information isn't explicitly mentioned in the publication records, Dr. Zhao appears to lead or be significantly involved in research groups focusing on AI applications in management and healthcare. Her work on medical imaging, sentiment analysis, and control systems suggests involvement in multiple specialized research teams addressing different application domains through computational approaches.