Find Academic Supervisors

Zohreh ShamsView profile

Researcher

• ML Interpretability
• Explainable AI
• Knowledge Discovery
• +5 more

Zohreh Shams is a Visiting Fellow at the Computer Laboratory, University of Cambridge, and Chief Scientific Officer at Leap Labs. Previously, she served as a Senior Research Associate at the University of Cambridge and held roles at Babylon Health as a Senior ML Scientist. Her research focuses on ML interpretability, explainable AI, knowledge discovery, and automated reasoning with applications in healthcare and safety-critical systems. Dr. Shams completed her PhD in Artificial Intelligence at the University of Bath, specializing in explanatory decision-making in multi-agent systems using Argumentation Theory. Her work bridges cognitive science and AI, collaborating with institutions like the University of Brighton on projects such as Accessible Reasoning with Diagrams , which explores explainable ontology reasoning systems. Her research interests include generative modeling, concept-based representations, and the integration of domain knowledge into AI systems. Notable contributions include developing frameworks like CGXplain for neural network explanations and REM for healthcare data analysis. Her publications span venues such as ECCV, AAAI, and TMLR. Shams has contributed to interdisciplinary projects, including the Integrated Cancer Medicine initiative, and maintains affiliations with Wolfson College as a former Junior Research Fellow. Her work emphasizes ethical AI practices, clinician collaboration, and the societal impact of explainable AI systems.

• Moral Psychology
• Cognitive Development
• Artificial Intelligence Ethics
• +3 more

Sydney Levine is a former Postdoctoral Associate at the MIT Media Lab, where she collaborated with Iyad Rahwan (MIT Media Lab), Joshua Tenenbaum (MIT Brain and Cognitive Sciences), and Fiery Cushman (Harvard). Her research focuses on moral learning in adults, children, and machines, integrating cognitive development, moral psychology, and AI ethics. She holds a PhD in Psychology from Rutgers University, advised by Alan Leslie, with a dissertation on moral rules and representations. Her work bridges philosophy, cognitive science, and artificial intelligence, addressing questions like how moral norms are acquired and applied across contexts. She has collaborated with scholars from Georgetown Law, Rutgers Philosophy, and the University of Pittsburgh’s History and Philosophy of Science department. Key contributions include studies on AI-generated art attribution (iScience, 2020) and public perceptions of blame in automated systems (Nature Human Behavior, 2019). Her research emphasizes translating human moral reasoning into computational frameworks for ethical AI.

Mahsa GhasemiView profile

Assistant Professor

• Reinforcement Learning
• Control Theory
• Active Perception
• +3 more

Mahsa Ghasemi is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, leading the AKADEMI Group. Her research focuses on theoretical advancements in trustworthy sequential decision-making for autonomous systems, emphasizing human-aware collaboration and adaptation to dynamic environments. She is affiliated with the Institute for Control, Optimization and Networks (ICON). Education: PhD in Electrical and Computer Engineering from The University of Texas at Austin (2021), MSE in Mechanical Engineering (2017), and BSc in Mechanical Engineering from Sharif University of Technology (2014). Research Interests: Reinforcement learning, control theory, active perception, multi-agent systems, robotics, and online learning. Applications span disaster response, healthcare, and autonomous systems design. Key methodological directions include compositional learning, human-AI collaboration, and adaptive decision-making under uncertainty. Teaching: Courses include Reinforcement Learning Theory (ECE 59500), Introduction to Reinforcement Learning (ECE 49595), and Python for Data Science (ECE 20875). Awards: Finalist for Student Best Paper Award at the 2018 American Control Conference (ACC). Students: Current advisees include Maheed H. Ahmed, Jayanth Bhargav, and Somtochukwu Oguchienti. Past members include Lai Wei and Juan Sebastian Mateo Ruiz Bulla. Service: Editorial roles at ICRA, ICCPS, and IFAC workshops. Reviewer for top conferences (NeurIPS, ICML) and journals (Automatica, IEEE TAC). Labs/Teams: Leads the AKADEMI Group, focusing on algorithmic and theoretical research in autonomous decision-making systems.

Damon McCoyView profile

Professor

• Cybersecurity and Privacy
• Online Payment Systems
• Economics of Cybercrime
• +6 more

Damon McCoy is a Professor of Computer Science and Engineering at NYU Tandon School of Engineering and Co-Director of the NYU Center for Cybersecurity (CCS). He holds a Ph.D. in Computer Science from the University of Colorado, Boulder (2009) and a B.S. in the same field (1999). His research focuses on empirically measuring the security and privacy of technology systems, with a current emphasis on online payment systems, cybercrime economics, automotive security, privacy-enhancing technologies, and censorship resistance. He co-directs Cybersecurity for Democracy , a multi-university initiative addressing online threats to democratic processes. His research has examined topics such as the monetization of YouTube conspiracy content, toxic online discourse targeting election officials, and vulnerabilities in automotive systems. He has collaborated with institutions like the International Computer Science Institute and led studies on social media platforms' content moderation practices, including Facebook's handling of political ads and YouTube's ecosystem of deceptive advertising. McCoy's work often bridges cybersecurity with societal impacts, analyzing how technology intersects with democracy, misinformation, and economic exploitation. His contributions include developing frameworks for securing automotive software updates (Uptane) and methodologies to combat cybercrime supply chains. He has received funding from the National Science Foundation and other agencies to pursue projects addressing emerging cyber threats. Key affiliations include the NYU Center for Cybersecurity and the Center for Automotive Embedded Systems Security (CAESS), where he contributed to early automotive security analysis. He advises on cybersecurity policy and has published extensively in top-tier journals and conferences, focusing on real-world applications of security research.

Ioana JivetView profile

Research Professor

• Learning Analytics
• Educational Technology
• Artificial Intelligence in Education
• +5 more

Ioana Jivet is a Research Professor and Head of the Learning Analytics Research Professorship at FernUniversität in Hagen, Germany. She leads interdisciplinary research focused on leveraging learning analytics to enhance educational practices through data-driven insights. Her role in the CATALPA Graduate School involves collaboration across disciplines to translate research into actionable educational strategies. Educational Background: PhD in Learning Analytics from Open Universiteit Nederland (2021) Postdoc at TU Delft (2019–2021) Research roles at DIPF (2021–2024) and studiumdigitale (2021–2024) Research Interests: Self-regulated learning mechanisms and feedback systems Cultural and ethical dimensions of learning analytics adoption Design of human-centered learning dashboards Privacy concerns in educational technology AI-driven educational decision support systems Her work emphasizes practical applications, such as developing adaptive feedback tools and investigating cross-cultural usage patterns of learning analytics platforms. Professional Contributions: Secretary of the Society for Learning Analytics Research (SoLAR) General Chair for the 2024 European Conference on Technology Enhanced Learning (EC-TEL) Co-editor of Springer volumes on technology-enhanced learning Lab & Teams: Active in the Learning Analytics research group at FernUniversität, focusing on translational research between academic theory and classroom practice.

Kevin BaumView profile

Researcher

• Explainable AI
• AI Ethics
• AI Fairness
• +4 more

Kevin Baum is a computer scientist currently serving as the deputy head of the Neuro-Mechanistic Modelling (NMM) department at the German Research Center for Artificial Intelligence (DFKI) since January 2023, and head of the Centre for European Research in Trusted AI (CERTAIN) at DFKI since December 2023. Based at the Saarland Informatics Campus in Saarbrücken, Germany, he completed his doctorate in philosophy in March 2024, combining technical expertise with philosophical depth. His work bridges computer science with ethics, focusing on making AI systems transparent and accountable to human users. Dr. Baum's research program centers on interdisciplinary questions concerning the explainability and transparency of AI systems. His work spans multiple significant projects including the Explainable Intelligent System (EIS) initiative and project E7 of the Transregional Collaborative Research Centre 248 "Foundations of Perspicuous Software Systems" (CPEC). He has developed frameworks for understanding stakeholder perspectives on explainable AI and investigated how different information types about automated systems affect user perceptions of fairness and justice. His approach consistently combines theoretical foundations with practical implementations across diverse contexts. Analysis of his publication trends reveals a clear trajectory from theoretical foundations in machine ethics toward practical implementations of explainability requirements in real-world contexts. His work demonstrates increasing focus on human oversight effectiveness, fairness monitoring, and ethical considerations across various AI applications. He has made significant contributions to both academic discourse and practical AI development guidelines, with publications spanning computer science, philosophy, psychology, and human-computer interaction venues. Award for Ethics for Nerds lecture series As a research leader, Dr. Baum contributes to shaping AI development practices through his departmental leadership and interdisciplinary collaborations. His current work with CERTAIN focuses on establishing European research standards for trusted AI development and deployment, emphasizing the practical implementation of ethical requirements in AI systems. He maintains active collaborations across multiple institutions and disciplines, reflecting his commitment to bridging technical and philosophical considerations in AI development. At DFKI, he leads research that combines neuroscientific insights with AI development to create more interpretable systems. The NMM department focuses on both theoretical research on explainable AI foundations and practical applications in various domains, with particular attention to how different stakeholders understand and require explanations from AI systems.

Rita BorgoView profile

Professor

• Data Visualization
• Computer Science
• Human-Computer Interaction
• +4 more

Dr Rita Borgo is a Professor in Data Visualization and Head of the Human Centred Computing Group at King's College London's Department of Informatics. She holds a leadership role within the Faculty of Natural, Mathematical & Engineering Sciences and is affiliated with the Centre for Urban Science and Progress (CUSP) London. Her research focuses on interdisciplinary visualization challenges, including human-computer interaction, AI trust calibration, and epidemiological modeling. Education details are not explicitly stated in the provided text. Her work spans over 58 publications, emphasizing visualization techniques for large datasets, trust in AI systems, and urban science applications. Key projects include RAMPVIS (visual analytics for pandemic response) and Trusted Autonomous Systems Hub (AI ethics and human-machine partnerships). Research interests include data visualization, human factors, generative AI, and policy simulation. Recent articles explore trust calibration in AI, time-series visualization, and ethical clinical decision support systems. She has led grants totaling £multi-million, including EPSRC-funded initiatives. Collaborations with organizations like ContactEngine Limited highlight her industry engagement. Labs/Teams: Leads the Human Centred Computing Group and contributes to CUSP's urban data initiatives. Supervised student work includes a notable BSc thesis by Munkhtulga Battogtokh. Current projects address visualization in nuclear policy, social media mental health correlations, and trustworthy autonomous systems.

• Hepatology
• Gastroenterology
• Cirrhosis
• +4 more

Guadalupe Garcia-Tsao is a Professor of Medicine at Yale University's Yale School of Medicine and past Chief of Digestive Diseases at the VA-Connecticut Healthcare System. She is also an Associate Editor of the New England Journal of Medicine and former President of the American Association for the Study of Liver Diseases (AASLD). Her work focuses on the complications of cirrhosis, particularly portal hypertension, variceal hemorrhage, ascites, and spontaneous bacterial peritonitis. She has authored over 200 peer-reviewed publications and co-edited major textbooks in gastroenterology and hepatology. Education: M.D. from Universidad Nacional Autónoma de México (1977) Internal Medicine Residency at Instituto Nacional de la Nutrición (1980) Gastroenterology Fellowship at Instituto Nacional de la Nutrición (1982) Hepatology Training at Yale University (1985) Research Interests: Her research emphasizes patient-oriented studies in cirrhosis complications, including portal hypertension management, variceal bleeding prevention, and ascites treatment. She explores innovative approaches like AI-driven diagnostic tools and biomarker discovery for alcohol-associated hepatitis. Her work bridges clinical practice and translational science to improve outcomes for liver disease patients. Awards: EASL International Recognition Award (2014) AASLD Clinician Educator and Mentor Achievement Award (2015) 2023 ALEH Mentor Award Advising & Grants: Dr. Garcia-Tsao mentors junior faculty and trainees, emphasizing clinical and translational research. She leads grants focused on cirrhosis pathophysiology, including studies on AI in liver disease and global cohort analyses for alcohol-associated hepatitis. Notable collaborations include work with the International Club of Ascites and ADQI initiatives. Labs & Teams: She directs the Clinical and Translational Core of the Yale Liver Center and collaborates with the VA-CT Healthcare System on translational projects. Her team investigates liver dysfunction mechanisms and develops novel therapeutic strategies for advanced liver disease.

George MagoulasView profile

Professor

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +7 more

Prof. George Magoulas is a Professor of Computer Science at the University of London's School of Computing and Mathematical Sciences and Director of the Birkbeck Knowledge Lab. He specializes in machine intelligence, machine learning algorithms, and AI system architectures, with applications in healthcare (e.g., neurodegenerative disease diagnosis) and educational technologies. His research has received awards from IEEE, ACM, and others. He holds a PhD in Nonlinear Optimization for Neural Networks and a PGCE in Higher Education. Education: BEng/MEng (Integrated Master's in Systems & Control Engineering), University of Patras, Greece PhD in Nonlinear Optimization for Neural Networks Learning, University of Patras, Greece PGCE in Teaching and Learning (Higher Education) Research & Leadership: He leads the Birkbeck Knowledge Lab, focusing on AI's impact on learning and communication. His work includes designing learning algorithms for psychophysiological data modeling and developing the cloudUPDRS app for Parkinson's disease assessment. He has supervised over 12 PhD students and contributed to 200+ publications. Awards & Recognition: Stanford’s “World’s top 2% of Scientists” (2024) Best Paper Awards at IEEE, ACM, and EUNITE Keynote speaker at major AI and e-learning conferences Honorary membership in the Hellenic Artificial Intelligence Society Administrative Roles: Director of Teaching & Learning Quality (2016–2023) Chair of Postgraduate Programmes Exam Board (2010–2022) Editor-in-Chief, International Journal on Artificial Intelligence Tools Teaching: He teaches courses on Artificial Intelligence, Neural Networks, and Project Management at both undergraduate and postgraduate levels. Labs & Collaborations: He directs the Birkbeck Knowledge Lab and is a member of the Data Science and AI Research Group. His projects include analyzing violent cycles using AI and collaborating on EU-funded initiatives.

Ahmad HaririView profile

Professor

• Psychology
• Neuroscience
• Molecular Genetics
• +3 more

Professor Ahmad Hariri is a faculty member in the Department of Psychology & Neuroscience at Duke University, part of the Trinity College of Arts & Sciences. He holds affiliations with the Duke-UNC Brain Imaging and Analysis Center, the Duke Institute for Brain Sciences, and the Duke Initiative for Science & Society. His research integrates neuroimaging, pharmacology, and molecular genetics to study biological pathways underlying individual differences in behavior and psychopathology risk. Education: Ph.D. in Psychology from UCLA (2000), M.S. and B.S. in Psychology from the University of Maryland, College Park (1997, 1994). Research interests include understanding how genetic and environmental factors influence brain function and behavior, with a focus on psychopathology, stress, and resilience. His work explores neural correlates of traits like psychopathy, childhood adversity effects on brain structure, and biomarkers of aging. Recent studies highlight links between lead exposure and neurodegeneration, neighborhood disadvantage and dementia risk, and the role of brain connectivity in self-regulation. Key achievements include over 200 publications, including high-impact papers in Nature Aging , Neuron , and Biological Psychiatry . Honors include the APA Distinguished Scientific Award for Early Career Contribution (2009) and being named a Highly Cited Researcher (2014). Grants include leadership roles in the Duke-NCCU Postdoctoral Training Program in Child Psychiatric Conditions and the Duke Psychiatry Physician-Scientist Residency Program. He has contributed to editorial boards of journals like Cortex and Biology of Mood and Anxiety Disorders . Professional activities include mentoring in the Summer Neuroscience Program and serving as a Bass Connections Faculty Team Leader.

Constantin A. RothkopfView profile

Professor

• Perception and Action
• Computational Neuroscience
• Machine Learning
• +3 more

Prof. Constantin A. Rothkopf is a W3 Professor at the Department of Psychology, Technische Universität Darmstadt, and a secondary member of the Department of Computer Science. He serves as Founding Director of the Centre for Cognitive Science and founding member of the Hessisches Zentrum für Künstliche Intelligenz (hessian.ai). He is also part of the European Laboratory for Learning and Intelligent Systems (ELLIS) and the DAAD Konrad Zuse Schools of Excellence in Artificial Intelligence (ELIZA). His research focuses on the interplay between perception and action, using computational models and experimental studies in humans. Current work includes eye-tracking studies in naturalistic environments, inverse optimal control models, and developing algorithms for virtual agents. Education: Ph.D. in Neuroscience and Informatics from the University of Rochester, followed by postdoctoral research at Frankfurt Institute for Advanced Studies (FIAS). He has held visiting professorships at Central European University (2017) and Columbia University (2023). Awards include an ERC Consolidator Grant (2022) and SCENE Project Funding (2025). Research Interests: Active vision, decision-making under uncertainty, sensorimotor control, and computational modeling. Key themes include how humans use sensory input to form beliefs, make decisions, and act in dynamic environments. Grants/Awards: ERC Consolidator Grant (2022), SCENE Funding (2025) Labs/Teams: Centre for Cognitive Science, hessian.ai, ELLIS Unit Darmstadt

• Software Engineering
• Industrial Automation
• Cloud Computing
• +7 more

David Hästbacka is an Associate Professor (tenure track) at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences at Tampere University. His research focuses on software engineering, industrial automation, and energy systems, emphasizing system architecture, interoperability frameworks, and dependable IoT solutions. He leads a research group exploring edge and cloud computing, semantic integration, and smart energy systems. Education & Professional Background : While specific educational details are not provided, his academic career includes roles such as Postdoctoral Researcher in the SEMIS project (2017-2020) and extensive involvement in EU-funded initiatives like COCOP (EU H2020) and Horizon Europe projects. Research Projects : Active in high-impact projects like Hedge-IoT (Horizon Europe, 2024-2027), TwinfFlow (Business Finland), and TRINEFLEX (Horizon Europe), with a focus on industrial automation, distributed systems, and energy grids. Past projects include FEMMa (Business Finland), DisMa (Academy of Finland), and Arrowhead (ECSEL). Teaching & Supervision : Specializes in Web/Cloud architectures, IoT systems, and dependable automation technologies. Supervises students in topics like edge computing frameworks and MLOps pipelines. Technical Contributions : Develops frameworks for industrial interoperability (e.g., OPC UA PubSub integration), edge-cloud toolchains, and MLOps methodologies. His work addresses challenges in microservices, Kubernetes distributions, and semantic data integration. Labs & Teams : Leads a research group advancing automation technologies through interdisciplinary collaboration, with partnerships in industry and academia to bridge theory and practice in smart systems.

Roman KucView profile

Professor

• Robotics
• Biomimetic Sensors
• Digital Signal Processing
• +4 more

Roman Kuc is a Professor of Electrical Engineering at Yale University, affiliated with the School of Engineering & Applied Science. He directs the Intelligent Sensors Laboratory, focusing on biomimetic sensors for robotics and bioengineering. His research explores brain-based devices (BBDs), sonar sensing, and neuromorphic processing inspired by biological systems. He holds a BSEE from Illinois Institute of Technology and a PhD from Columbia University. Dr. Kuc’s work bridges signal processing, robotics, and bioengineering, with applications in autonomous systems and clinical diagnostics. He has published over 200 papers and authored textbooks like Electrical Engineering in Context and The Digital Information Age . Notable honors include an honorary doctorate from the Glushkov Institute of Cybernetics and the Yale Sheffield Distinguished Teaching Award. His research themes include cognitive mapping via sonar echoes, neural network-based classification of environmental features, and biomimetic approaches to echolocation. Recent work emphasizes sensorimotor integration and robust performance in uncertain environments. Scientific awards highlight his contributions to robotics, signal processing, and education. His lab develops systems that emulate biological sensory mechanisms, aiming to advance robotics, medical applications, and assistive technologies.

Vito LatoraView profile

Professor

• Complex Systems
• Network Science
• Applied Mathematics
• +13 more

Vito Latora is a Professor of Applied Mathematics and Chair of Complex Systems at the School of Mathematical Sciences, Queen Mary University of London, and also holds the position of Professor of Theoretical Physics at the University of Catania. He leads the Complex Systems and Networks Group, driving cutting-edge research at the intersection of physics, mathematics, and interdisciplinary sciences. His research focuses on complex systems, particularly the structure and dynamics of networks, including multiplex, temporal, and higher-order networks such as simplicial complexes and hypergraphs. He explores applications in social, biological, financial, and cognitive systems, with recent work on creativity, innovation, and success through network analysis. The 15 most recent publications reveal a strong trend in advancing network theory beyond pairwise interactions, with a focus on higher-order structures, memory effects, synchronization, and epidemic spreading. His work combines rigorous mathematical modeling with real-world applications, often published in high-impact journals like Nature Communications , Physical Review Letters , and Science Advances . Dual communities in spatial and biological networks Modeling epidemics with limited detection resources Synchronization via higher-order and directed interactions AI-driven financial risk management Evolutionary games on hypergraphs Interdisciplinary success and funding dynamics Vito Latora has mentored several researchers who appear as co-authors, including Iacopini, Williams, Di Bona, and Lacasa. While specific grants are not listed, his collaborative projects with neuroscientists and anthropologists, along with frequent publications, suggest active funding. He is involved in major scientific events such as NetSci 2023, indicating leadership in the network science community. He leads the Complex Systems and Networks Group at Queen Mary, fostering a collaborative environment for studying complex systems through theoretical, computational, and data-driven approaches.

Murali ManiView profile

Professor

• Database Systems
• Data Provenance
• Generative AI
• +12 more

Murali Mani is a Professor in the Department of Computer Science, Engineering, and Physics at the College of Innovation and Technology, University of Michigan-Flint. He is actively involved in teaching courses such as Database Design (CSC 384, CSC 584) and Independent Graduate Study in Computer Science (CSC 591), and serves as Principal Investigator on multiple research grants focused on computing education and data science. His research interests span database systems, data provenance, generative AI for data augmentation, computing education, and the societal impact of technology . He has developed educational tools including epidemiology calculators and market basket analysis modules to support interdisciplinary learning. His work emphasizes integrating computing skills across disciplines such as health sciences and management. The 15 most recent scholarly contributions reflect a strong focus on data management, AI-augmented data curation, educational technology, and the cognitive aspects of learning programming. These publications appear in venues such as VLDB, IEEE FIE, and ACM conferences, with several under review or in preparation for top-tier journals like Communications of the ACM and the VLDB Journal. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: Murali Mani actively mentors students through independent graduate studies and collaborative research projects. He has secured funding from the National Science Foundation (SGER grant on provenance metadata) and internal university sources, including the CIT/CHS Joint Grant and the Office of Research at UM-Flint, supporting projects on civic literacy, computational skills integration, and AI for social science data archiving. Labs and Teams: While no formal lab name is mentioned, Murali Mani leads a research group focused on data systems and computing education, collaborating with colleagues across departments and institutions. He contributes to initiatives such as the Michigan Institute for Data & AI in Society (MIDAS) and the Academic Data Science Alliance (ADSA), and has presented at conferences including IASSIST, FIE, and ICCTAC.