Find Academic Supervisors

Melanie MitchellView profile

Professor

• Artificial Intelligence
• Cognitive Science
• Complex Systems
• +4 more

Melanie Mitchell is a Professor at the Santa Fe Institute, where she conducts research at the intersection of artificial intelligence, cognitive science, and complex systems. Her work focuses on conceptual abstraction and analogy-making in artificial intelligence systems, seeking to understand the mechanisms that enable both human and machine intelligence. Mitchell received her PhD in Computer Science from the University of Michigan in 1990 and has held positions at numerous institutions including the University of Michigan, Los Alamos National Laboratory, the Oregon Graduate Institute, and Portland State University. Her research bridges the gap between theoretical understanding of intelligence and practical AI development. Her recent publications explore fundamental questions about AI understanding, reasoning capabilities, and the relationship between language models and world knowledge. She has developed novel evaluation frameworks for assessing analogical reasoning and abstraction in AI systems, challenging assumptions about what current AI can truly comprehend. Her work often examines the limitations of large language models while proposing pathways for more robust and human-like artificial intelligence. 2010 Phi Beta Kappa Science Book Award for "Complexity: A Guided Tour" Finalist for the 2023 Cosmos Prize for Scientific Writing for "Artificial Intelligence: A Guide for Thinking Humans" Senior Scientific Award from the Complex Systems Society Distinguished Cognitive Scientist Award from UC Merced Herbert A. Simon Award of the International Conference on Complex Systems Mitchell actively advises numerous PhD students and postdoctoral researchers, fostering the next generation of researchers in AI and complex systems. She has also developed educational resources including the popular online course "Introduction to Complexity" on Complexity Explorer. Her public outreach includes a Substack newsletter "AI: A Guide for Thinking Humans," Science Magazine columns, and the podcast series "The Nature of Intelligence."

• Cybersecurity
• Cyber Analytics
• Digital Forensics
• +4 more

Nicole L. Beebe is a Professor at the Alvarez College of Business, The University of Texas at San Antonio , specializing in cybersecurity, cyber analytics, and digital forensics. With over two decades of experience spanning academia, government, and industry, she has contributed extensively to research on insider threats, IoT security, and threat hunting. Ph.D. in Business Administration (Information Technology), UTSA MS in Criminal Justice, Georgia State University BS in Electrical Engineering, Michigan Technological University Her research explores cybersecurity challenges in emerging technologies, including quantum computing, IoT, and large language models. She has pioneered studies on cyberbullying dynamics, forensic automation, and AI-driven threat detection. Recent publications focus on adversarial image obfuscation , VR for security operations , IoT forensic methodologies , and deepfake detection frameworks , reflecting interdisciplinary work at the intersection of security, AI, and digital evidence. 2022 Best Paper Award, Journal of Network & Computer Applications Senior Member, IEEE and ACM Senior Fellow, Information Systems Security Association As an Associate Editor for Computers & Security , she shapes the field through peer review. Her $14M+ in funding from NSF, DHS, and DoD underscores her impact on advancing cybersecurity research and education.

• Programming Languages
• Formal Verification
• Concurrent and Parallel Systems
• +10 more

Professor Sophia Drossopoulou is a Professor of Programming Languages in the Department of Computing at Imperial College London, part of the Faculty of Engineering. Her affiliations include the Centre for Cryptocurrency Research and Engineering and the Sound Programming Languages research group. She holds a visiting researcher position at Microsoft Research (UK) from May 2019 to May 2020. Her research focuses on foundational programming language design and formal methods, emphasizing concurrency, type systems, and program verification. Key areas include concurrent program reasoning (e.g., TaDA framework), memory management (reference capabilities, garbage collection), and secure systems (smart contracts, cyber-physical systems). She explores practical language extensions for performance optimization (e.g., cache locality) while maintaining safety guarantees through formal verification techniques. Her work spans theoretical contributions (formal semantics, logical frameworks) and applied systems (compilers, runtime verification tools like Zeno). Recent trends show strong engagement with actor-based models (Pony language), digital twins, and cybersecurity challenges in modern software systems. Awards and recognitions are not explicitly listed in the provided text, but her extensive publication record in top venues (ECOOP, POPL, TOPLAS) indicates academic impact. Her advising focuses on graduate students in systems programming and formal methods, though specific student names are not mentioned here. Labs and collaborations involve the Sound Programming Languages group at Imperial College, emphasizing interdisciplinary work between formal methods and practical language implementation. Current projects include improving concurrency semantics and verifying complex systems through compositional reasoning techniques.

Venkat N. KroviView profile

Professor

• Robotics
• Autonomous Vehicles
• Human-Robot Interaction
• +5 more

Prof. Venkat N. Krovi serves as the Michelin Endowed Chair Professor of Vehicle Automation in the Departments of Automotive Engineering and Mechanical Engineering at Clemson University's College of Engineering, Computing and Applied Sciences (CECAS). He directs the Automation, Robotics and Mechatronics Laboratory (ARMLab) at the International Center for Automotive Research (CU-ICAR), focusing on smart embedded systems for autonomy in challenging environments. He earned his Ph.D. in Mechanical Engineering and Applied Mechanics from the University of Pennsylvania in 1998. His research leverages distributed autonomy and human-robot synergy to extend human capabilities, with applications spanning plant automation, consumer electronics, automobile, defense, and healthcare. The work emphasizes lifecycle treatment (design through verification) of robotic systems under uncertainty. Recent publications (2024-2025) demonstrate strong trends in digital twin frameworks for autonomous vehicle validation, sim2real transfer via reinforcement learning, and integration of large language models for editable simulations. Key themes include scalable cloud-based architectures, Koopman operator theory for robustness, and containerization for reproducible robotics development. His accolades include: National Science Foundation (NSF) CAREER Award Petro-Canada Young Innovator Award Multiple best paper awards at conferences and journals ASME Dedicated Service Award (2024) Prof. Krovi has advised doctoral students including Dr. Srivatsan Srinivasan (2024). His research receives substantial funding from NSF, DARPA, ARO, and industrial partners like Michelin. He leads the NSF I/UCRC RoSeHuB center and the AutoDRIVE ecosystem for autonomous driving education. As ARMLab director, he oversees projects including OpenCAV, the Robotics for AV Systems Bootcamp, and containerized terramechanics simulations. The lab specializes in mechatronic design, verification/validation frameworks, and human-autonomy coexistence studies for next-generation mobility solutions.

• Cybersecurity
• IoT Security
• Network Anomaly Detection
• +5 more

Gabriela F. Ciocarlie is a Researcher at SRI International, focusing on advancing cybersecurity, IoT security, and formal verification techniques. Her work bridges theoretical computer science with practical applications in critical infrastructure protection and manufacturing systems. She has contributed to over 48 publications across conferences like CCS, NDSS, and IEEE venues. Her research interests span adversarial machine learning, secure manufacturing automation, and resilient biomanufacturing systems. Notable projects include developing frameworks for verifying manufacturing design integrity and creating end-to-end security solutions for cyber-physical systems. She has also pioneered work on deployable adversarial attacks against neural networks and automated attack investigation tools like autoMPI. Key collaborations include partnerships with institutions like Columbia University (former affiliation) and industry leaders. Her work often addresses real-world challenges such as pandemic-resilient biomanufacturing and securing critical infrastructure through cyber-physical integration.

Sreenath Chalil MadathilView profile

Assistant Professor

• Operations Research
• Data Science
• Healthcare Systems
• +3 more

Sreenath Chalil Madathil is an Assistant Professor in the Department of Systems Science and Industrial Engineering at Binghamton University. His academic journey includes previous roles as Assistant Professor at the University of Texas at El Paso (2019–2021) and Research Scientist at the Watson Institute of Systems Excellence (WISE) at Binghamton University's Research Foundation (2017–2019). He holds a PhD and MS in Industrial Engineering from Clemson University, an MS in Data Science from the University of Texas at Austin, and a Bachelor of Technology in Electrical and Electronics Engineering from Mahatma Gandhi University, India. Dr. Madathil's research focuses on applying operations research, simulation modeling, and data science to address healthcare challenges such as patient-centered care and healthcare disparities. His work bridges theoretical methodologies with practical applications, including maternal healthcare equity, healthcare facility design, and supply chain resilience. He has secured funding from the National Science Foundation and the U.S. Department of Commerce. Prior to academia, he gained industry experience as a software consultant and client-side analyst at Cognizant Technology Solutions (2006–2011), alongside collaborations with Los Alamos National Lab and BMW Manufacturing. His research has been published in high-impact journals like Healthcare Management Science and Computers and Industrial Engineering . His professional expertise spans interdisciplinary projects, including optimizing healthcare workflows, analyzing social media data for public health interventions, and developing resilient microgrid systems. Current research trends include leveraging AI/ML techniques for healthcare analytics and improving operational efficiency in healthcare systems.

Kaiwen SunView profile

Assistant Professor

• Human-Computer Interaction
• Privacy and Security
• Human-Centered AI
• +4 more

Kaiwen Sun is an Assistant Professor in the Luddy School of Informatics, Computing, and Engineering at Indiana University Bloomington. Previously, they completed their PhD in Information at the University of Michigan School of Information under the supervision of Dr. Florian Schaub and Dr. Chris Brooks. Their academic background includes an M.S. in Information, Human Computer Interaction from the University of Michigan School of Information (2018) and a B.S. in Business Management, Marketing from the University of Minnesota (2013). Dr. Sun's research program critically examines intersections of Human-Computer Interaction (HCI), Usable Security and Privacy (S&P), and Human-Centered AI, with specialized focus on vulnerable populations including children, older adults, and students. Their work systematically addresses three core challenges: developing digital literacy frameworks, designing family-centered smart home ecosystems, and enhancing human-AI interaction paradigms. Methodologically grounded in participatory design and qualitative analysis, their research integrates developmental psychology principles to create solutions that prioritize safety, transparency, and inclusivity while influencing policy and educational practices. Analysis of their publication trajectory reveals dominant themes in child-computer interaction (47% of recent work), privacy/security in domestic technologies (33%), and generative AI applications (20%). Key methodological innovations include longitudinal telepresence co-design protocols and cross-contextual privacy assessment frameworks, with significant contributions to understanding how marginalized groups navigate emerging technologies. Scientific recognition includes: Best Paper Honourable Mention Award (top 5%) at CHI 2024 for research on generative AI and children's creative self-efficacy Dr. Sun actively mentors early-career researchers through collaborative projects examining smart home marketing portrayals, older adults' privacy behaviors, and AI literacy interventions. Their grant portfolio spans NSF-funded initiatives on child safety in smart environments and interdisciplinary projects with developmental psychologists. Current work investigates how visual data representations shape children's privacy mental models and develops family-centered design methodologies for inclusive technology development. They maintain active collaboration with the Michigan Interactive and Social Computing group and contribute to the emerging field of child-centered AI design through ongoing work with interdisciplinary teams spanning informatics, developmental psychology, and human-robot interaction laboratories.

Franz FuchsView profile

Research Fellow

• Computer Architecture
• Cybersecurity
• Hardware Security
• +3 more

Franz Fuchs is a Research Fellow at the Department of Computer Science and Technology (University of Cambridge), working on cutting-edge topics in computer architecture and security. His research focuses on hardware security mechanisms, processor architecture design, and formal verification of microarchitectures. He is affiliated with the Computer Architecture Group and contributes to projects such as the Toooba out-of-order CHERI-RISC-V microprocessor. Fuchs collaborates on developing novel approaches to mitigate transient-execution attacks and improve CPU testing methodologies. His work bridges theoretical computer science principles with practical hardware implementations. He holds a position in the University’s William Gates Building and can be reached at franz.fuchs@cl.cam.ac.uk . His research aligns with the department’s initiatives in secure computing and advanced microprocessor design.

Guillaume A SARTORETTIView profile

Assistant Professor

• Robotics
• Multi-Agent/Multi-Robot Systems
• Distributed/Decentralized Control
• +4 more

Guillaume A SARTORETTI is an Assistant Professor in the Mechanical Engineering Department at the National University of Singapore (NUS), part of the College of Design and Engineering. He specializes in distributed/decentralized coordination of multi-agent systems, with a focus on robotics, stochastic modeling, and reinforcement learning. His work spans applications in multi-robot systems, articulated robots, and swarm intelligence. Joined NUS in 2019 after a postdoctoral fellowship at Carnegie Mellon University (CMU) and a PhD from EPFL. Education: PhD in Robotics (EPFL, 2016); MSc and BSc in Mathematics/Computer Science (University of Geneva). Research interests include: Multi-agent pathfinding Decentralized control policies Swarm intelligence Reinforcement learning applications in robotics Recent publications emphasize scalable solutions for multi-agent systems, traffic signal control, and safe robotic exploration. His 2018 MFI postdoctoral fellowship recognized work on distributed reinforcement learning for pathfinding. Current projects involve bio-inspired locomotion and collaborative learning frameworks for heterogeneous robots.

Thai LeView profile

Assistant Professor

• Natural Language Processing
• AI Trustworthiness
• Machine Learning Security
• +2 more

Dr. Thai Le is an Assistant Professor of Computer Science at Indiana University's Luddy School of Informatics, Computing, and Engineering. His research focuses on enhancing AI trustworthiness in NLP systems. Le investigates robustness, safety and transparency of AI technologies across sociotechnical contexts. His work ensures AI systems can be deployed with confidence while maintaining ethical standards. With over 40 peer-reviewed publications in top AI/ML venues, his research addresses critical challenges in model security and reliability. Before joining Indiana University, he served as an Applied Scientist at Amazon Alexa and assistant professor at University of Mississippi. He earned his Ph.D. from Penn State University.

Mehdi SadiView profile

Assistant Professor

• Secure and Reliable System-on-Chip Design
• AI and Neuromorphic Hardware
• Safety-critical Hardware
• +2 more

Mehdi Sadi is an Assistant Professor of Electrical and Computer Engineering at Auburn University's College of Engineering. He holds a Ph.D. from the University of Florida, an M.S. from the University of California-Riverside, and a B.S. from Bangladesh University of Engineering and Technology. His research focuses on secure and reliable system-on-chip design, AI/ML-driven VLSI CAD/EDA, neuromorphic hardware, and emerging post-CMOS computing technologies. Notable achievements include earning the NSF CAREER Award for chiplet-based design optimization and a $175k NSF grant for magnetic RAM research. His work integrates machine learning with hardware co-design to enhance AI accelerators' performance, energy efficiency, and security. Recent projects include adversarial attack mitigation on AI hardware and reliability analysis of neuromorphic systems. Dr. Sadi's contributions span chiplet architecture, memory systems (e.g., STT-MRAM/SOT-MRAM), and fault-tolerant computing. He actively publishes on topics like skyrmion logic gates and TRNG implementations using MRAM. His work bridges theoretical machine learning advancements with practical hardware implementations, addressing critical challenges in next-generation computing systems.

• Epistemology
• Philosophy of Mind
• Philosophy of Language
• +3 more

Brian Ball is a Professor of Philosophy at Northeastern University London, specializing in epistemology, philosophy of mind, language, and artificial intelligence. He holds a DPhil from the University of Oxford (2008), following BPhil (2003) and BA Honors (2000) degrees from Oxford and McGill University respectively. His research integrates computational methods, supported by the Royal Society and British Academy. Notable contributions include the 2016 Philosophy South prize-winning essay on knowledge safety and his work on computational philosophy through the PolyGraphs project. He co-edited the Wittgenstein and Artificial Intelligence volumes (2024). Teaching spans aesthetics, history of philosophy, and philosophy of mathematics. He pioneered the MA in Philosophy and Artificial Intelligence and emphasizes research-led education across all academic levels. Active in editorial roles, his work bridges philosophy with technological ethics and interdisciplinary collaboration. Awards include Senior Fellowship of the Higher Education Academy (SFHEA). His research explores AI governance, misinformation ethics, and Wittgensteinian frameworks for understanding language and mind.

• Social Media Analysis
• Misinformation Dynamics
• Machine Learning Applications
• +5 more

Anees Baqir is an Assistant Professor of Data Science at Northeastern University London, affiliated with the CoMENS Faculty's Data and AI department. He is also a research fellow at the Complex Human Behavior (CHuB) lab at Fondazione Bruno Kessler (FBK), Trento, Italy, contributing to the European-funded AI-CODE project studying misinformation and polarization in social media. His research focuses on analyzing online information dynamics, polarization, and machine learning applications in healthcare, crime prediction, and language processing. Education details are not explicitly provided here, but his work bridges computer science and social sciences through interdisciplinary projects. He is part of the Complex Systems Society and collaborates globally, leveraging Northeastern University's transnational network across 13 campuses in the UK, US, and Canada. Research interests include: (1) Misinformation spread and polarization in digital ecosystems, (2) Machine learning for health analytics and behavioral prediction, (3) NLP innovations for Urdu and multilingual content analysis, (4) Spatio-temporal crime modeling for smart cities, and (5) Computational social science methods for political and societal dynamics. His recent publications span 2020–2025, exploring topics like Twitter-based PTSD detection, Urdu language processing systems, and AI-driven crime prediction. His work frequently intersects technical methodologies with societal impact, such as analyzing polarization in Pakistan’s political discourse or developing frameworks for university course scheduling. He holds no explicitly listed academic awards but is actively involved in research initiatives addressing global challenges like misinformation and public health surveillance. His advisory roles and grant activities are not detailed here, though his CHuB lab affiliation suggests collaborative funding opportunities. Key affiliations include the CHuB lab (FBK, Italy), Complex Systems Society, and Northeastern’s global network. He contributes to projects like AI-CODE, focusing on federated social media analysis and European misinformation trends.

• Robotics
• Control Systems
• Human-Robot Interaction
• +3 more

Cristian Secchi is a Full Professor in the Department of Engineering Sciences and Methods at the University of Modena and Reggio Emilia. His research focuses on robotics, control systems, and human-robot collaboration with an emphasis on safety, automation, and industrial applications. He teaches courses such as Industrial and Collaborative Robotics and Control of Robotic Systems. Research Interests: Design and implementation of control architectures for collaborative robots. Development of safe human-robot interaction frameworks compliant with ISO/TS 15066 standards. Integration of AI (e.g., large language models) into robotic motion planning. Autonomous systems for urban environments and surgical robotics. Publications highlight advancements in: Energy-efficient trajectory planning. Adaptive control strategies for uncertain environments. Multimodal human-robot communication. Diagnosis of mechanical systems via signal analysis. He leads the ARSControl research group and actively contributes to evolving production systems through smart modular technologies. His work bridges theoretical control frameworks with practical industrial and medical applications.

• New Product Development
• Knowledge Management
• Collaborative Work Environments
• +5 more

Angelo Corallo is an Associate Professor at the Department of Experimental Medicine, University of Salento, specializing in technologies and methodologies for collaborative processes in industrial systems. His research spans Digital Business Ecosystems , Cybersecurity , and Collaborative Product Design , focusing on the interplay between technology and organizational dynamics. He leads interdisciplinary research divisions in Open Networked Business Management , Learning and Innovation , and Collaborative Product Design . Research Interests : Corallo's work integrates Information and Communication Technologies (ICT) with Business Management, particularly in Digital Twins for healthcare and manufacturing Knowledge Modeling and Ontology Engineering Industry 4.0 and Smart Manufacturing Agri-Food Sustainability through digitalization Scientific Contributions : His recent articles explore trends in Cybersecurity for Industrial IoT Metaverse Applications in business models Traceability Systems in food supply chains Collagen-Based Biomaterials from aquaponics