Find Academic Supervisors

• Transportation Engineering
• Intelligent Transportation Systems
• Infrastructure Resilience
• +7 more

Sean Qian is a Professor at Carnegie Mellon University (CMU), jointly appointed in the Department of Civil and Environmental Engineering, Heinz College of Information Systems and Public Policy, and the Department of Electrical and Computer Engineering. He directs the Mobility Data Analytics Center (MAC) and co-founded TraffiQure Technologies to commercialize AI-driven infrastructure solutions. His research focuses on dynamic network modeling, intelligent transportation systems, climate resilience, and infrastructure interdependency. Supported by NSF, U.S. DOT, and industry partners, his work integrates AI, big data, and policy analysis to address urban mobility challenges. Education: PhD in Civil Engineering (UC Davis, 2011), MS in Statistics (Stanford, 2012), and dual MS/BS in Civil Engineering (Tsinghua University, 2006/2004). Research emphasizes smart cities, EV integration, cybersecurity for infrastructure, and equity in mobility policies. He serves on editorial boards for Transportation Research journals and TRB committees. Awards include the NSF CAREER Award (2018) and Greenshields Prize (2017). Grants and collaborations include projects with Fujitsu, IBM, and state agencies, addressing curbside management, climate adaptation, and rural mobility. His lab develops tools like the Rural Access Mobility Platform and social digital twin technologies for infrastructure resilience.

Scott MillerView profile

Lecturer

• Physical Layer Security
• Visible Light Communication (VLC)
• Acoustic Telemetry Systems
• +5 more

Scott Miller is a Lecturer in the Department of Industrial & Systems Engineering at Texas A&M University. His research focuses on physical layer security, wireless communication systems, and signal processing in harsh environments. He specializes in visible light communication (VLC), acoustic telemetry, and hybrid FSO-mmWave systems. Key contributions include secure IM-OFDMA system design, IQ imbalance analysis, and medium-specific communication protocols for downhole monitoring. Research interests span secure communication protocols, network coding, cognitive radio networks, and system reliability in fading channels. He has pioneered work on VLC-based downhole gas pipeline monitoring using hydrogen/nitrogen mediums and developed learning-based link selection approaches for hybrid wireless systems. His work emphasizes practical applications in industrial telemetry and secure wireless transmission. Notable trends in his publications include: Advancements in physical layer security mechanisms for uplink systems Innovative use of non-traditional mediums (e.g., CO₂, hydrogen) for VLC Integration of machine learning for signal detection and link optimization Robustness analysis of hybrid FSO-mmWave networks under various impairments His research bridges theoretical communication frameworks with real-world industrial applications, addressing challenges in energy, oil/gas sectors, and secure IoT deployments.

• Artificial Intelligence
• Neural Networks
• Quantization
• +3 more

Hang Li is a Researcher in the Department of Molecular Biophysics and Biochemistry at Yale University’s Yale School of Medicine. Their work focuses on advancing neural network architectures, quantization techniques, and spiking neural networks (SNNs). They are affiliated with the Molecular Biophysics and Biochemistry department and contribute to interdisciplinary research in artificial intelligence and computational neuroscience. Research interests include optimizing neural networks for efficiency through quantization, exploring spiking neural networks for low-power computing, and developing methods like hybrid SNN designs, post-training calibration, and neuromorphic architectures. Their recent work addresses challenges in extreme low-bit quantization, data augmentation for object detection, and temporal coding in SNNs. Publications highlight innovations in quantization methods (e.g., TesseraQ, GenQ), spiking transformer architectures, and workload-balanced pruning strategies. While no awards are explicitly listed, their contributions to model efficiency and neuromorphic computing are notable in the field. Hang Li collaborates on projects involving neuromorphic hardware, system inconsistency benchmarking (SysNoise), and data-driven spatio-temporal analysis. Their research bridges theoretical advancements and practical applications in AI and biomedical informatics.

Ziad KobtiView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Science
• +6 more

Ziad Kobti is a Professor and Director of the School of Computer Science at the University of Windsor. His work focuses on advancing artificial intelligence, machine learning, and data science with applications in healthcare, cybersecurity, and social network analysis. He leads initiatives such as the Graph Neural Network Lab and collaborates with industry partners like Sterling Information Technologies to foster cybersecurity talent. Kobti has organized programming competitions, co-op education programs, and research workshops, emphasizing experiential learning for students. His research spans recommendation systems, graph neural networks, pandemic modeling, and medical diagnostics, with a focus on impactful real-world solutions. He is involved in academic leadership, program development, and community outreach through events like Computer Science Demo Day. Research interests include: Graph Neural Networks and Dynamic Link Prediction Recommender Systems and Anomaly Detection Social Network Analysis and Team Formation Healthcare Informatics and Medical Imaging Cybersecurity and Privacy Risk Analysis Machine Learning for Public Health and Pandemic Preparedness Recent projects include collaborations on AI-driven crop disease detection, electric vehicle research, and palliative care simulation frameworks. He has developed frameworks like UDIS and BeComE, and contributes to open-source tools for social network analysis and anomaly detection. His work integrates multi-disciplinary approaches, combining computational models with domain-specific expertise in healthcare, agriculture, and social systems.

Sneha Kumar KaseraView profile

Professor

• Networking
• Wireless Systems
• Cybersecurity
• +6 more

Sneha Kumar Kasera is an Associate Dean for Academic Affairs and Professor at the John and Marcia Price College of Engineering, affiliated with the Kahlert School of Computing. She leads the ANSR Lab, founded in 2003, focusing on advanced networked systems research. Her work spans networks and systems, including mobile/pervasive systems, wireless security, IoT, crowdsourcing, and spectrum management. She has held leadership roles in conferences like IEEE WoWMoM, ACM WiSec, and ACM MobiCom. Kasera’s research emphasizes practical applications of networking and security, with contributions to spectrum sharing, dynamic zone protocols, and privacy-preserving technologies. Her professional activities include program co-chair roles for major conferences and editorial positions for IEEE Transactions. She advises numerous PhD and MS students, many of whom hold prominent roles in academia and industry. Kasera’s lab develops open platforms like POWDER for experimental wireless research, addressing real-world challenges in network design and security. Key research themes include spectrum monitoring, deep reinforcement learning for network optimization, and privacy-aware systems. Her publications highlight advancements in 5G/6G networks, wireless localization, and IoT ecosystems. Her work bridges theoretical contributions with scalable, deployable solutions for modern communication challenges.

Lena Maier-HeinView profile

Professor

• Machine Learning
• Biomedical Image Analysis
• Surgical Data Science
• +3 more

Prof. Lena Maier-Hein is a full professor at Heidelberg University and managing director of the National Center for Tumor Diseases (NCT) Heidelberg. She leads the division of Intelligent Medical Systems (IMSY) at the German Cancer Research Center (DKFZ) and oversees the cross-topic program 'Data Science and Digital Oncology'. Her research focuses on machine learning in biomedical imaging, particularly surgical data science and computational biophotonics. She chairs the Surgical Data Science initiative and serves on editorial boards for journals like Nature Scientific Data and IEEE TPAMI. Her awards include the 2024 German Cancer Award, 2013 Heinz Maier-Leibnitz Prize, and European Research Council grants. She advocates for trustworthy AI in healthcare, co-developing frameworks like Metrics Reloaded and TRIPOD+ AI. Her work bridges academic, clinical, and industrial sectors through initiatives like the FeTS challenge. Key contributions include advancing photoacoustic imaging, surgical AI systems, and validation methodologies. She emphasizes ethical AI deployment and interdisciplinary collaboration to address clinical challenges.

Matthias AlthoffView profile

Associate Professor

• Cyber-Physical Systems
• Formal Verification
• Autonomous Vehicles
• +5 more

Prof. Dr. Matthias Althoff is an Associate Professor of Cyber-Physical Systems at the Technical University of Munich (TUM), leading the Chair of Cyber-Physical Systems within the TUM School of Computation, Information and Technology. His research focuses on formal safety verification, model-based design, and reachability analysis for systems such as autonomous vehicles, robotics, and power grids. Education: He earned his diploma in Mechatronics and Information Technology (2005) and PhD (2010, summa cum laude) from TUM. He held postdoctoral positions at Carnegie Mellon University (2010–2012) and served as a junior professor at TU Ilmenau (2012–2013) before joining TUM as a full professor in 2013, becoming an associate professor in 2019. Research Interests: His work spans cyber-physical systems, formal methods for safety assurance, autonomous vehicles, modular robotics, and smart grid control. He develops tools like CommonRoad and CORA for scenario-based testing and reachability analysis. Awards: He has received the IEEE/ACM William J. McCalla ICCAD Best Paper Award (2012) and the Best Poster Award at the IEEE Intelligent Vehicles Symposium (2009). Labs/Projects: Leads the Cyber-Physical Systems group, collaborating on projects such as the Scenario Factory for automated vehicle testing and CommonPower for safe smart grid control. He also co-founded startups RobCo and aiina .

• Computer Science
• Artificial Intelligence
• Reinforcement Learning
• +6 more

Eric Eaton is a prominent researcher in Computer Science, specializing in Artificial Intelligence, Reinforcement Learning, and Federated Learning. His work bridges theoretical advancements with practical applications in healthcare, robotics, and educational technology, as evidenced by his collaborations with institutions like the Vector Institute and co-authors such as Marcel Hussing and Amir-massoud Farahmand. Research Focus: Lifelong Learning, Object-Centric Representation, and Algorithmic Fairness Key Contributions: ELLA algorithm, Distributed Continual Learning frameworks, and AI integration in surgical video analysis His recent publications address critical challenges in high update ratio reinforcement learning, federated learning for surgical data, and ethical considerations in algorithmic fairness. These works highlight his interdisciplinary approach, combining AI with healthcare and education. Eaton's leadership in projects like FORLA and Slot-BERT demonstrates innovation in unsupervised learning and temporal coherence. His involvement in the CS2023 curriculum design underscores his commitment to advancing computer science education.

David JacksonView profile

Senior Lecturer

• Artificial Intelligence
• Digital Narrative
• Human-Computer Interaction
• +5 more

Dr David Jackson is a Senior Lecturer in Digital Visualisation at Manchester Metropolitan University's School of Digital Arts (SODA). As SODA Doctoral Student Lead and Research Degrees Coordinator, he explores the creative application of AI in narrative contexts, focusing on audience engagement, prosody in conversational agents, and parasocial dynamics. His work includes the Audience with a Hero project (Innovate UK-funded) and co-founding Algorithms for Marginalised People (AMP) , addressing AI biases through projects like Mood/Music . Research interests: His practice-led work bridges AI, digital storytelling, and immersive technologies. Key areas include conversational agent design, reparative approaches to algorithmic bias, transdisciplinary methodologies, and the psychological impact of voice-based interactions in VR environments. He investigates how prosodic features (tone, pitch, pace) affect audience perception of synthetic voices and the role of parasocial relationships in shaping interactions with digital personas. Scientific contributions: Notable works include the Storybox conversational agent framework and the D-PAF conversational storytelling methodology. His AHRC post-doctoral fellowship (2018) advanced techniques for integrating audience psychology into AI-driven narrative systems. Selected publications: Recent articles examine generative AI's cultural biases in creative education ( Brazilian Creative Industries Journal , 2023), speech-led VR documentaries ( XR-Metaverse Cases , 2023), and chatbot development for Shakespearean narratives ( Expert Systems with Applications , 2021). His 2026 book Listening In: How Audio Surveillance became Artificial Intelligence will explore surveillance technology's evolution.

Friso BostoenView profile

Assistant Professor

• Competition Law
• Digital Regulation
• Antitrust Law
• +5 more

Friso Bostoen is an Assistant Professor of Competition Law & Digital Regulation at Tilburg University , affiliated with TILT and TILEC . He holds a Ph.D. from KU Leuven and was a Max Weber Fellow at the European University Institute. Research Focus: Regulation of online platforms, AI, and generative AI under antitrust law, with a global perspective (Africa, Asia, Latin America). Teaching: Competition law at KU Leuven, London School of Economics, Erasmus University, Waseda University, and University of Trento. Publications: 15+ articles on AI, DMA, platform power, and antitrust enforcement in top journals. Side Activities: Editor of the CoRe Blog and host of the Monopoly Attack podcast. Research Trends: His work bridges antitrust law with digital innovation, focusing on AI, metaverse, and global platform regulation. Key themes include contestability in tech markets, gatekeeper designation frameworks, and adapting abuse-of-dominance assessments for free/digital products. Collaborations: Regularly co-authors with Giorgio Monti, Nicolas Petit, and Anouk van der Veer, addressing challenges in tech mergers, platform neutrality, and regulatory design.

• Robot Cooperation
• Human-Robot Interaction
• People Motion Behavior
• +5 more

Anaís Garrell Zulueta is an Associate Professor at the Polytechnic University of Catalonia (UPC) and a Robotics Researcher at the Institut de Robòtica i Informàtica Industrial (CSIC-UPC) . She serves as Vice-Director of the Mobile Robotics and Intelligent Systems subline and supervises the RAIG - Mobile Robotics and Artificial Intelligence Group . PhD (2013): European Doctorate with highest honors from UPC B.S. in Mathematics (2006) from University of Barcelona Diploma d'Estudis Avançats (DEA) in Control, Vision, and Robotics from UPC Her research focuses on Human-Robot Interaction (HRI) and robot cooperation , particularly in urban environments . Key themes include: Explainable AI for robot transparency Human motion behavior prediction Socially aware navigation systems Collaborative transport robotics Autonomous last-mile delivery systems Cybernetic avatars and societal implications Recent projects (2023-2025) include: TORNADO: Foundation models for robots handling deformable objects HandIA: AI-based rehabilitation tools LENA: Lifelong navigation learning TRIFFID: First responder assistance robotics SOCIAL PIA: Cybernetic avatar modeling Scientific recognition includes: Second Prize for Best Spanish Robotics Thesis Best Paper Award Nomination (IEEE/RSJ IROS, 2009) As an advisor, she supervises: PhD students: Ferran Gebelli Guinjoan, Lavinia Hriscu, Edison Bejarano Final year projects: 8 students on topics like LLM-enhanced interaction and LiDAR SLAM systems She operates within the Institut de Robòtica i Informàtica Industrial (IRI) and collaborates with Carnegie Mellon University.

Martim BrandãoView profile

Assistant Professor

• Robotics
• Artificial Intelligence
• Responsible AI
• +12 more

Martim Brandão is a Lecturer (Assistant Professor) in Robotics and Autonomous Systems at King’s College London, where he leads the Responsible Robotics and AI (RRAI) Lab and serves as Co-Director of the UKRI Centre for Doctoral Training in Safe and Trusted AI. His research focuses on ethical, explainable, and safe AI and robotics, with applications in human-robot interaction, motion planning, fairness, and societal impact. His research interests include: Explainable AI and Motion Planning Fairness and Bias in AI Systems Human-Robot Interaction and Social Robotics Adversarial Robustness in Robotics Value Alignment and Ethical AI Inclusive and Participatory Robotics Design His recent publications (2023–2025) reflect a strong trend toward socially responsible robotics, focusing on fairness in navigation, explainability of planning failures, worker-centered agricultural robotics, environmental justice in drone delivery, and the dangers of bias in drowsiness detection and LLM-driven robots. His work emphasizes user understanding, societal impact, and ethical safeguards in autonomous systems. He has advised and collaborated with numerous students and researchers across diverse topics in robotics and AI. He is actively involved in shaping responsible robotics through: Leadership in the RRAI Lab Co-directing a national CDT in Safe and Trusted AI Developing fairness-aware algorithms Advocating for inclusive and ethical design practices His lab and research group focus on: Responsible Robotics and AI Explainability in Multi-Agent Planning Fairness in Coverage and Navigation Human-Centered Evaluation of AI Systems

Emmanuel PapadakisView profile

Senior Lecturer

• Vacuum Technology
• Planning
• Knowledge Engineering
• +3 more

Emmanuel Papadakis , Senior Lecturer in Artificial Intelligence at the University of Huddersfield 's School of Computing and Engineering , focuses on Vacuum Technology , Planning , Knowledge Engineering , and Robotics . His research aligns with UN SDGs through applications in industrial systems and clinical diagnostics. Key Affiliation : Centre for Autonomous and Intelligent Systems Research Output : 12 publications (2020-2025), including 5 articles in 2023-2025 His work explores AI applications in: Vacuum technology optimization ADHD diagnostic modeling Ontology-based transport maintenance Blockchain-enabled supply chain compliance Recent publications demonstrate interdisciplinary impact across computer science, healthcare, and engineering. Co-authorships with researchers in psychiatry and mechanical engineering highlight collaborative breadth. Leadership : Research Degree Supervision at University of Huddersfield Metrics : h-index 2 (Google Scholar), 5 citations (Scopus) as of June 2025

Markus StrohmaierView profile

Professor

• Computational Social Science
• Data Science
• Machine Learning
• +7 more

Markus Strohmaier is Professor and Chair of Data Science in the Economic and Social Sciences at the University of Mannheim, with affiliations as Scientific Coordinator at GESIS – Leibniz Institute for the Social Sciences and External Faculty Member at the Complexity Science Hub Vienna. His interdisciplinary work bridges computer science, economics, and the social sciences. University of Mannheim – Chair for Data Science in the Economic and Social Sciences GESIS – Scientific Coordinator for Digital Behavioral Data Complexity Science Hub Vienna – External Faculty Former Professor at RWTH Aachen University and University of Koblenz-Landau Previous Post-Doc and Visiting Roles at Stanford University, Xerox PARC, University of Toronto, and Graz University of Technology His research focuses on computational social science , algorithmic fairness , network science , and the modeling of human behavior using machine learning and large-scale data. He develops methods to analyze textual, relational, and emerging data types to understand socioeconomic systems and digital societies. The recent articles reflect a strong trend in studying inequality in algorithmic systems , governance in decentralized organizations (DAOs) , and psychological profiling of AI . His work spans high-impact journals like Nature and Scientific Reports , emphasizing fairness, transparency, and societal impact of data-driven technologies. Notable scientific contributions include: Editor-in-Chief of EPJ Data Science (2018–2022) Founding co-chair of the Computational Social Science section of the German Informatics Society He advises students and leads research projects on algorithmic fairness, digital governance, and behavioral modeling. His team engages in both fundamental methodological development and applied studies in real-world digital platforms. He has been involved in significant grants and collaborative initiatives around digital behavioral data and computational social science infrastructure. His lab and projects include the Algorithmic Fairness initiative and the interactive visualization tool Planets of Disparity , which explores how algorithms behave on different network structures. These efforts aim to enhance public understanding and technical scrutiny of algorithmic systems.

Chang-Hee WonView profile

Professor

• Tactile Sensing
• Optimal Control Theory
• Spectral Imaging
• +7 more

Chang-Hee Won is a Professor in the Department of Electrical and Computer Engineering at the College of Engineering, Temple University. He leads the Control, Sensor, Network, and Perception (CSNAP) Laboratory and is actively engaged in research funded by the National Science Foundation, Pennsylvania Department of Health, and the Department of Defense. His research interests span tactile sensing , optimal control theory , spectral imaging , and dynamic sensing systems . His work integrates sensor technologies with machine learning for biomedical applications, particularly in cancer detection and tissue characterization. He has published over 120 peer-reviewed articles and secured multi-million dollar grants as principal investigator. The recent publications highlight a strong trend in applying tactile and imaging sensors with deep learning for medical diagnostics , especially in breast cancer and malignant melanoma classification. His research bridges control theory, sensor design, and AI-driven health technologies. He serves as a frequent reviewer for the National Science Foundation and sits on the National Institute of Health Biomedical Imaging Study Section. Reviewer, National Science Foundation panels Member, NIH Biomedical Imaging Study Section He has advised multiple graduate students including N. Rahman, V. Oleksyuk, and H. Ahn, who have co-authored recent journal publications. His research is supported by significant grants from federal and state agencies, as well as industry partnerships. He directs the Control, Sensor, Network, and Perception (CSNAP) Laboratory , which focuses on developing advanced sensor systems, control algorithms, and perception technologies for biomedical and engineering applications.