Find Academic Supervisors

Vanessa CesárioView profile

Professor

• Human-Computer Interaction (HCI)
• Cultural Heritage
• Participatory Design
• +5 more

Professor Vanessa Cesário is a leading academic in Digital Media and Human-Computer Interaction (HCI) , currently affiliated with the Faculty of Arts and Humanities at the University of Madeira . She is an active researcher at the Interactive Technologies Institute (ITI) , focusing on Cultural Heritage, Participatory Design , and Interactive Storytelling across international collaborations. Ph.D. in Digital Media (Communication Sciences and Technology) Master’s in Cultural Management Bachelor’s in Communication Her research integrates technology-mediated cultural preservation through methodologies like collaborative autoethnography and inclusive design . She develops gamified museum experiences and serious games for educational purposes, notably creating the OWL CLUB application for student engagement. Recent publications emphasize AR for marine conservation , LLM-driven sustainability games , and empathic design for marginalized communities . Her work addresses ethical AI implementation and emotional dynamics in gaming , with practical applications in no-code exhibition tools and retro-inspired interactive systems . Key projects include eGames Lab and MEMEX , with fieldwork spanning teenager engagement in museums , elderly participatory design , and migrant community integration . She advocates for cross-disciplinary research and modular communication frameworks to enhance cultural and educational technologies.

• Operating Systems
• Embedded Systems
• Networking
• +7 more

Matthew Lentz is an Assistant Professor of Computer Science at Duke University, where he has been employed since Fall 2021. He is affiliated with the Duke Systems Group within the Department of Computer Science under Trinity College of Arts & Sciences. Prior to joining Duke, he served as a Postdoctoral Researcher at VMware Research Group and maintains an affiliation as an Affiliated Researcher . Education: Ph.D. in Computer Science from University of Maryland, College Park (2020), advised by Bobby Bhattacharjee External Relationships: Collaborations with Broadcom and ongoing postdoctoral work with VMware Dr. Lentz's research operates at the intersection of systems, networking, and security. His work includes: Developing abstractions and tools for secure, trustworthy software systems Improving performance in modern networking and machine learning applications Exploring network performance measurement and security threat analytics Advancing GPU-as-a-Service infrastructure (via NSF CC* Compute grant 2024-2026) Research trends show consistent focus on: System security (endpoint security, verification, privacy-preserving communication) Networking optimization (service mesh, collective communication, mobile networks) Machine learning systems (inference optimization, MoE training, heterogeneous computing) Mobile computing (Bluetooth beacons, peripheral control, contact tracing) Scientific Awards: NSF CC* Compute grant for GPUs-as-a-Service infrastructure (2024) Advising & Grants: Advises current PhD students Alexander Du, Chenyang Liu, and Luka Duranovic while serving as co-advisor for Yongji Wu (now at UC Berkeley). Collaborates with Danyang Zhuo and Kartik Nayak.

Zhen TaoView profile

Researcher

• Usable Privacy and Security
• Privacy Policy Analysis
• Artificial Intelligence
• +5 more

Zhen Tao is a Research Associate at the Chair of Software Engineering & AI , part of the TUM School of Computation, Information and Technology at the Technical University of Munich. They hold an academic affiliation in Heilbronn, Germany, and their email address is zhen.tao@tum.de . Education M.Sc. in Machine Learning and Computer Vision from The Australian National University Bachelor of Advanced Computing (Honours) from The Australian National University Bachelor of Computer Science and Technology from Shandong University (Weihai) Zhen Tao's research focuses on Usable Privacy and Security and Privacy Policy Analysis , with contributions to topics such as GUI testing, code vulnerability detection, and LLM-based agents for personalized app reviews. Their work intersects software engineering, human-computer interaction, and AI ethics, reflecting a multidisciplinary approach to improving user interface design and software security. They are part of a research team led by Prof. Chunyang Chen, collaborating on projects related to large language models, code optimization, and compliance with AI regulations like the EU AI Act.

• Conceptual Modelling of Information Systems
• Data Modelling
• IT for Development
• +2 more

Dr. Sietse Overbeek is an Associate Professor at the Faculty of Science, Utrecht University , where he has served as Director of the Artificial Intelligence, Computer Science, and Information Science Master's programs since 2022. He holds a Ph.D. in Computer Science (2009) and an M.Sc. in Information Science (2005) from Radboud University Nijmegen. 2008-2009: Postdoc at Delft University of Technology 2009-2012: Assistant Professor at Delft University of Technology 2013-2015: Assistant Professor at University of Duisburg-Essen 2015-2017: Lecturer at Utrecht University Dr. Overbeek's research focuses on conceptual modeling , data modeling , method engineering , and software development , with particular emphasis on agile practices , tool support , and sustainability in software development . His recent work explores LLM applications in requirements engineering and data-centric infographics . Key publication trends include: 2025: LLM-assisted agile development and AI team integration 2024: Environmental impact modeling for cyber-physical systems 2023: Work practice mining, CRM quality frameworks, and e-government architecture 2022: Data-centric infographics, impact assessment tools, and fairness in method engineering 2021: Human sustainability and blockchain governance

Mayur NaikView profile

Professor

• Programming Languages
• Artificial Intelligence
• Neurosymbolic Programming
• +2 more

Mayur Naik is the Misra Family Professor in the Department of Computer and Information Science at the University of Pennsylvania. His research specializes in programming languages and artificial intelligence, with current focus areas including neurosymbolic programming, trustworthy AI for healthcare, and AI-enhanced development tools. He leads the development of the Scallop neurosymbolic programming language and maintains active collaborations in bioinformatics and clinical research. Education: Ph.D. in Computer Science, Stanford University (2008) M.S. in Computer Science, Purdue University (2003) B.S. from BITS Pilani (1999) Research Focus: His work bridges symbolic AI with neural approaches through neurosymbolic programming, enabling interpretable and domain-aware solutions. Current projects include Dolphin (scalable neurosymbolic learning), IRIS (LLM-assisted static analysis), and applications in computer vision and healthcare. Research emphasizes both theoretical foundations and practical system implementations. Publication Trends (Recent Focus): Recent work demonstrates strong emphasis on neurosymbolic learning frameworks (55%), AI-enabled programming tools (30%), and healthcare applications (15%). Key technical threads include LLM integration with program analysis, GPU acceleration for symbolic reasoning, and weakly-supervised learning paradigms. Awards & Honors: Test of Time Award, Foundations of SW Engineering (2023) Distinguished Paper Awards (PLDI 2019, SIGSOFT 2015, FSE 2015, SIGPLAN 2014) NSF CAREER Award (2013) Lockheed-Martin Teaching Excellence Award (2015) Outstanding Junior Faculty Award, Georgia Tech (2016) Academic Leadership: Advises 8 PhD students with research spanning neurosymbolic systems, program analysis, and AI security. Teaches core courses CIS 5470 (Software Analysis) and CIS 5500 (Database Systems). Research supported by NSF and industry partnerships. Labs & Teams: Leads the neurosymbolic programming research group at UPenn, collaborating with Intel Labs, Google AI, and healthcare institutions. Group develops open-source tools including Scallop compiler and LLM-assisted analysis frameworks.

Arash AminiView profile

Professor

• Theoretical Signal Processing
• Statistical Signal Processing
• Graph Signal Processing
• +5 more

Arash Amini is a Professor at the Electrical Engineering Department of Sharif University of Technology, Tehran, Iran. He received dual B.Sc. degrees in Electrical Engineering (Communications) and Petroleum Engineering (Reservoir) in 2005, followed by M.Sc. and Ph.D. degrees in Electrical Engineering (Signal Processing) in 2007 and 2011, respectively, all from Sharif University of Technology. During his doctoral studies, he spent a year (2009–2010) as a visiting scholar at the Biomedical Imaging Group (BIG), EPFL, Switzerland. Current Position: Professor (since May 2025) Previous Roles: Assistant Professor (2013–2018), Associate Professor (2018–2025), Researcher at BIG, EPFL (2011–2013) Editorial Role: Associate Editor for IEEE Signal Processing Letters (2014–2018) Research Interests : Theoretical and Statistical Signal Processing Graph Signal Processing Large Language Models Signal Processing for Communications Compressed Sensing Recent Publication Trends focus on graph signal processing, compressed sensing, biomedical imaging, and mathematical optimization. Key subfields include harmonic retrieval, subspace-informed matrix completion, sparsity-driven algorithms, and AI alignment benchmarks. Scientific Awards : Silver medal at the International Mathematical Olympiad (IMO2000) Advising includes 15+ Ph.D. and Master’s students, with collaborative projects involving institutions like EPFL and researchers such as Prof. F. Marvasti and Prof. S. Rini.

Aniruddha KembhaviView profile

Associate Professor

• Computer Vision
• Robotics
• Natural Language Processing
• +5 more

Aniruddha Kembhavi is an Affiliate Associate Professor at the University of Washington's Computer Science & Engineering department and currently serves as Director of Science Strategy at Wayve AI in London, UK. Previously, he led computer vision efforts as Senior Director at Allen Institute for AI (AI2) in Seattle and contributed to Microsoft's Image and Video Search division. His research spans 20+ years in Computer Vision , Robotics , and Embodied AI , focusing on open-source frameworks like AI2-THOR and Molmo. His work emphasizes procedural environment generation , vision-language integration , and 3D asset creation , with large-scale datasets such as Objaverse becoming foundational in 3D computer vision. CVPR 2025 Best Paper (Honorable Mention) CVPR 2023 Best Paper Winner Neurips 2022 Outstanding Paper CoRL 2024 Outstanding Paper IROS 2024 Best Mobile Manipulation Paper ICRA 2024 Best Paper Winner Allen Institute Test Of Time Award 2020 NVIDIA Pioneer Award 2018 His recent publications analyze vision-language models , 3D generation evaluation , and diffusion architectures for unified generation. He actively contributes to community-building as Program Chair for ICCV 2025 and Senior Area Chair for CVPR 2024.

Margaret S. LivingstoneView profile

Professor

• Visual Neuroscience
• Neurobiology
• Primate Cognition
• +8 more

Margaret S. Livingstone is the Takeda Professor of Neurobiology at Harvard Medical School , with affiliations to Harvard University and the Livingstone Lab. Her work bridges visual neuroscience , neurodevelopment , and biomedical innovation , focusing on how primate brains process visual information. Research Interests: Visual recognition, face processing in autism, maternal bonding mechanisms, blood-brain barrier disruption via focused ultrasound, and the evolution of primate visual cortex. Grants: Recipient of a Whitehall Foundation grant (2024) for neuroanatomical constraints on visual domain development. Scientific Awards: David Mahoney Prize (past recipient) Roberts Prize (2013) for best paper Collaborations: Member of the Thalamus Conte Center studying thalamocortical networks, and collaborator in clinical trials for glioblastoma and Alzheimer’s treatments.

Aysun BozantaView profile

Assistant Professor

• Reinforcement Learning
• Natural Language Processing
• Data Science
• +4 more

Aysun Bozanta is an Assistant Professor at Bogazici University, specializing in data-driven research with applications in reinforcement learning, NLP, and social network analytics. She holds a Ph.D. in Management Information Systems from Bogazici University and completed postdoctoral research at Toronto Metropolitan University's Data Science Lab. Education Undergraduate: Işık University, Mathematical Engineering, MIS (DM) M.A.: Boğaziçi University, MIS Ph.D.: Boğaziçi University, MIS Postdoc: Toronto Metropolitan University - Data Science Lab Research Focus Her work bridges machine learning and real-world problems, with emphasis on: reinforcement learning for logistics (food delivery, courier routing), NLP (sentiment analysis, LLM evaluation), and social media analytics (misinformation, public policy responses). Epidemiologic modeling of COVID-19 interventions and recommender systems are additional key areas. Publication Trends Recent articles (2021-2025) reveal a strong focus on applied AI: reinforcement learning optimizes delivery systems; NLP research evaluates Turkish language models and classifies software requirements; COVID-19 studies model policy impacts; and social media analyses explore misinformation propagation. Transformer architectures and replication studies feature prominently. Awards & Recognition Best Paper Award at CASCON'21 for transformer-based requirements classification TUBITAK Post-Doctoral Research Fellowship (2019-Present) Projects & Collaboration Toronto Metropolitan University Project: "Intraday Trading Data Analytics with Financial News from Twitter" (2019-Present, TUBITAK-funded) Toronto Metropolitan University Project: "Getir-Courier Assignment by Reinforcement Learning" (2019-2020) Erasmus+ Project: "Online Vocational Education Platform" (2017-2019) Boğaziçi University Project: "Industry 4.0 Standardization Roadmap for Turkey" (2017-2019) Boğaziçi University Project: "Context-Aware Location Recommender System" (2016-2019) Labs & Teams Affiliated with Toronto Metropolitan University's Data Science Lab during postdoctoral work, focusing on reinforcement learning and NLP projects.

Giancarlo PellegrinoView profile

Assistant Professor

• Empirical and behavioral security
• Security testing
• Vulnerability analysis
• +3 more

Dr. Giancarlo Pellegrino is a tenure-track faculty member at the CISPA Helmholtz Center for Information Security in St. Ingbert, Germany. Since 2017 he has held this position, while also serving as a Visiting Assistant Professor at Stanford University and previously as a Visiting Scholar (2017–2018) at the same institution within the CISPA-Stanford Center for Cybersecurity. Education PhD in Computer Science, Eurecom (Sophia-Antipolis, France) — advised by Prof. Davide Balzarotti Research Associate, Security & Trust Group, SAP Research Labs (until August 2013) Postdoctoral Researcher, Saarland University (2015–2017) Research Interests Giancarlo’s work sits at the intersection of empirical and behavioral security , web security , and vulnerability analysis . His group investigates how real-world systems fail, combining large-scale measurement studies with novel detection techniques to improve security testing. Core themes include: Security testing automation and coverage Vulnerability detection in web and mobile applications Human factors in security and privacy decision-making Adversarial machine learning for security tasks Publication Trends Between 2021 and 2025 he has published extensively in top-tier venues such as IEEE S&P, USENIX Security, NDSS, ACM CCS, CHI, and DSN. The work spans empirical analyses of CSRF defenses, large-scale studies of client-side request hijacking, innovative uses of LLMs for web-app scanning, and systematic evaluations of web-crawling algorithms for security measurements. Collectively, these papers advance both the science and tooling of modern vulnerability discovery. Scientific Awards Busy Beaver Award 2017 — conferred for the course “Cybersecurity I” Collaboration & Outreach Giancarlo maintains active collaboration networks through his joint affiliation with Stanford and CISPA, and his research group’s open-source efforts can be explored at https://trouge.net/ . While specific funded projects or advisee names are not detailed in the provided texts, his leadership of the “Scientific Group Pellegrino” indicates ongoing supervision of PhD students and postdocs in empirical security research.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +4 more

Guangyao Chen is an Assistant Professor in the Department of Computer Science within Cornell University's College of Engineering, where he leads research at the intersection of computer vision, machine learning, and artificial intelligence. His work focuses on advancing open-world visual understanding systems capable of handling unknown classes and real-world complexity. His primary research interests include: Computer Vision and Open-Set Recognition Few-Shot Learning and Cross-Domain Adaptation LLM-Visual Integration and Symbolic Reasoning Neuromorphic Computing and Spiking Neural Networks Multi-Modal Learning and Real-Time Systems Analysis of his 2021-2025 publications reveals a strategic evolution toward solving open-world perception challenges. His recent work demonstrates how large language models can unlock complex event understanding from object detectors, while his G-OSR benchmark establishes new standards for graph-based open-set recognition. Notable contributions include real-time multimodal anomaly detection frameworks, retina-inspired saliency models, and Autoagents for automatic agent generation - all addressing critical gaps in deploying AI systems in dynamic, uncontrolled environments. Though specific awards and advising details aren't documented in available sources, his prolific publication output (including 9 papers in 2025) indicates an active research program with significant community impact. His work bridges theoretical advances in representation learning with practical applications in robotics, medical imaging, and industrial systems where handling unknown classes is critical.

Bin HuView profile

Assistant Professor

• Machine Learning Control
• Robustness of Deep Neural Networks
• Reinforcement Learning
• +5 more

Bin Hu is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign, affiliated with the Coordinated Science Laboratory. His research bridges control theory and machine learning, focusing on certifiable robustness of AI models, generative AI for control, and control-theoretic analysis of optimization algorithms. PhD, Aerospace Engineering and Mechanics, University of Minnesota (2016) Postdoctoral Research, Wisconsin Institute for Discovery, University of Wisconsin-Madison (2016-2018) B.Sc, Theoretical and Applied Mechanics, University of Science and Technology of China (2008) M.S., Computational Mechanics, Carnegie Mellon University (2010) His work spans machine learning control , robust neural network design , and policy optimization , with recent emphasis on adversarial attacks in LLMs and stability analysis using control tools. His publications highlight integration of Lyapunov theory , semidefinite programming , and reinforcement learning for control problems. NSF CAREER award (2021) Amazon Research Award (2020, 2024) O. Hugo Schuck Best Paper Award (2024) AFOSR grant for control verification (2023) Bin Hu has advised students like Xingang Guo and led research projects on Lipschitz neural networks , policy gradient methods , and LLM-based control engineering , while teaching courses such as Digital Signal Processing , Control Systems , and advanced topics on the interplay between control and machine learning.

• Data Science Systems
• Machine Learning Systems
• Database Systems
• +7 more

Yongjoo Park is an Assistant Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign, affiliated with the Siebel School of Computing and Data Science and the Data and Information Systems (DAIS) Research Lab. He co-founded and serves as Chief Scientist at Keebo, Inc., a startup developing automated data warehouse optimization platforms. Ph.D. in Computer Science and Engineering, University of Michigan, Ann Arbor (2017) M.S. in Computer Science, University of Michigan, Ann Arbor (2013) B.S. in Electrical Engineering, Seoul National University (2009) His research focuses on building intelligent data-intensive systems that integrate statistical and AI techniques for improved reliability, scalability, and usability in data science workflows. Key projects include Kishu (undoable Jupyter notebooks), AirIndex (automated index optimization), and CARE (causal-reasoning data systems). His work bridges database systems and machine learning, emphasizing end-to-end optimization for structured/unstructured data processing. Recent publications highlight trends in computational notebook checkpointing, index tuning through data-aware storage, and AI-driven database learning. His projects have been recognized at top venues including SIGMOD, VLDB, and CHI, with a focus on practical implementations for real-world data challenges. Scientific Awards: NSF CAREER Award (2025) Best Demo Award at SIGMOD 2025 ACM SIGMOD Jim Gray Dissertation Award Runner-up (2018) Engineering Council Outstanding Advising Award (2021) Teaching Excellence Awards (2023, 2024) Yongjoo advises multiple PhD and MS students, including Supawit Chockchowwat (joining CMKL University in Thailand) and Nikhil Sheoran (now at Databricks). His research group maintains strong industry collaborations and open-sources systems like Kishu and VerdictDB through their GitHub organization .

Wenlu WangView profile

Assistant Professor

• Artificial Intelligence
• Data Science
• Machine Learning
• +4 more

Dr. Wenlu Wang is an Assistant Professor in the Department of Computer Science at Texas A&M University - Corpus Christi's College of Engineering and Computer Science. Her research bridges artificial intelligence (AI) with scientific domains through interdisciplinary approaches, focusing on translating complex real-world problems into AI-processable formats. PhD in Computer Science and Software Engineering from Auburn University (2020) Research Interests: • Interdisciplinary AI applications across scientific domains • Large Language Model (LLM) adaptation for specialized tasks • AI integration in chemical language processing • Genomic selection accuracy enhancement • Diagnostic AI systems development • AI pipeline security compliance frameworks Recent Research Trends: Her 2024-2023 publications focus on graph neural networks for spatial data, LLM pipeline optimization, medical imaging analysis, and code generation security. The CMolGPT project demonstrates her work in chemical-specific generative AI for drug discovery. Scientific Awards & Grants: • NIH grant for equitable healthcare AI (2023) • NSF grant for interdisciplinary research (2023) Collaborations & Services: Actively collaborates with biomedical institutions (Biogen partnership) and develops tools for scientific AI translation. Maintains the AIII Lab at Texas A&M University - Corpus Christi.

Mauro CaporuscioView profile

Professor

• Self-adaptive systems
• Cyber-Physical Systems
• Decentralized control
• +5 more

Mauro Caporuscio is a Professor of Computer Science at Linnaeus University's Department of Computer Science and Media Technology , affiliated with the Faculty of Technology . He holds a PhD from the University of L'Aquila (2006), with prior academic appointments as Assistant Professor at Politecnico di Milano (2010-2014) and Postdoctoral Researcher at INRIA Paris-Rocquencourt (2006-2009). Research Groups : Cyber-Physical Systems (CPS), Engineering Resilient Systems (EReS), Linnaeus University Centre for Data Intensive Sciences and Applications (DISA), Smart Industry Group (SIG) Ongoing Projects : Digital Twin of Organizations (DTO), Smart Industry Education, Aladino (architecture methodologies), SmartDat (industrial digitalization) His research focuses on applying Model-Based Software Engineering to Self-Adaptive Systems , emphasizing decentralization and resilience . Key areas include Digital Twin for organizational modeling, Cyber-Physical Systems , and IoT-enabled platforms with applications in robotics and physical rehabilitation. Publications span IEEE Transactions , Springer series, and top software engineering conferences. He contributes to program committees and has developed educational programs including a Master's in Computer Science with software engineering specialization.