Find Academic Supervisors

• Software Engineering Education
• Software Process Simulation
• Educational Games
• +3 more

Emily Oh Navarro is a Continuing Lecturer in the Department of Informatics at the Donald Bren School of Information and Computer Sciences, University of California, Irvine. She has been actively involved in software engineering education, focusing on innovative teaching methods and simulation-based learning environments. Dr. Navarro earned her Ph.D. in Information and Computer Sciences from UC Irvine in 2006, with her dissertation titled "SimSE: A Software Engineering Simulation Environment for Software Process Education." She also holds an M.S. in Information and Computer Sciences and a B.S. in Biological Sciences, both from UC Irvine. Dr. Navarro's research primarily focuses on software engineering education, particularly using simulation and game-based approaches to teach software processes. Her work centers around SimSE, an educational software engineering simulation environment designed to help students learn and practice software engineering processes in an interactive, graphical setting. She has extensively explored how learning theories can be applied to improve software engineering education, investigating various educational approaches and their effectiveness. Her research demonstrates how simulation environments can overcome the limitations of traditional lectures and small-scale class projects by allowing students to experience complex software engineering processes that would be infeasible to practice in real-world academic settings. Dr. Navarro's publications reveal a consistent focus on simulation-based learning tools for software engineering education. Her work spans from theoretical explorations of learning theories to practical implementations of educational tools like SimSE and Problems and Programmers. She has conducted multi-site evaluations of her educational tools, demonstrating their effectiveness across different institutions and student populations. Her research shows progression from conceptual frameworks to practical implementations and rigorous evaluations, establishing her as a significant contributor to the field of software engineering education. Dr. Navarro teaches numerous courses in software engineering and design, including: Informatics 43: Introduction to Software Engineering Informatics 113: Requirements Analysis and Engineering Informatics 117: Project in Software System Design Informatics 121: Software Design I Informatics 122: Software Design II Informatics 191: Senior Design Project ICS 45J: Programming in Java ICS 139W: Critical Writing on Information Technology SWE 241P: Applied Data Structures and Algorithms SWE 245P: GUI Programming SWE 246P: Mobile Programming SWE 272P: Project Management Her teaching methodology reflects her research interests, emphasizing practical experience with software engineering processes through simulation-based learning. While specific information about her advising and grants is limited in available materials, her extensive research on educational methods suggests involvement in educational research projects and likely mentorship of students in software engineering projects. Dr. Navarro's primary research contribution is the SimSE project, which has evolved through multiple iterations and evaluations. This simulation environment represents a significant innovation in software engineering education, addressing the critical gap between theoretical knowledge and practical application in software process management. Her work continues to influence how software engineering is taught at UC Irvine and potentially at other institutions through her multi-site evaluations.

Giorgia RamponiView profile

Assistant Professor

• Machine Learning
• Mathematical Modelling
• Reinforcement Learning
• +3 more

Giorgia Ramponi is an Assistant Professor with Tenure Track at the Faculty of Business, Economics and Informatics at the University of Zurich. She is also an affiliated professor at the ETH AI Center and the Data Science and AI, Computer Science and Engineering department at Chalmers University of Technology. Her educational background includes a Ph.D. in Information Technology from Politecnico di Milano (completed June 2021 with honors), advised by Marcello Restelli, and a Master of Science in Computer Science with Honours Programme (110/110 cum laude) from la Sapienza (July 2017), advised by Flavio Chierichetti and Alessandro Panconesi. Dr. Ramponi's research focuses on machine learning and mathematical modeling, with particular emphasis on reinforcement learning and multiagent learning. Her work bridges theoretical foundations with practical applications, exploring how learning algorithms can make optimal decisions in complex environments. She has made significant contributions to areas including inverse reinforcement learning, multi-agent systems, constrained Markov decision processes, and human-AI interaction through preference learning. Her recent publications demonstrate a strong trend toward addressing fundamental challenges in reinforcement learning, particularly in multi-agent settings, constrained optimization, and learning from human feedback. Her work combines theoretical rigor with practical applications across robotics, economics, and decision-making systems. Hassler Research Grant for "Unified Feedback Integration Framework for Reinforcement Learning" Dr. Ramponi actively contributes to the academic community through conference participation, invited lectures (including at the Mediterranean Machine Learning Summer School), and teaching. She designed and taught the "Data Science and Machine Learning" course for the ETH-Ashesi Master program. She is also a member of the ELLIS community, which connects excellence in AI research across Europe. Her research group focuses on developing frameworks for reinforcement learning with various feedback types, including preferences, rewards, and demonstrations. The group aims to advance the theoretical understanding of learning algorithms while addressing practical challenges in real-world applications.

Shabnam Sadeghi EsfahlaniView profile

Associate Professor

• Robotics
• Mechatronics
• Artificial Intelligence
• +3 more

Dr. Shabnam Sadeghi Esfahlani is an Associate Professor in Robotics at the School of Engineering and the Built Environment, Anglia Ruskin University , where she serves as Deputy Leader of the BORI research group and leads the Automation & Robotics MSc program. Her interdisciplinary expertise spans mechatronics, artificial intelligence, virtual reality, and serious games , with a focus on applications for rehabilitation, medical training, and autonomous systems . As a Chartered Engineer and Senior Fellow of the Higher Education Academy , she has secured significant funding from Innovate UK, Horizon 2020, and GCRF , with grants exceeding £3 million. Education PhD in Mechanical Engineering, Anglia Ruskin University BSc (First Class) in Statistics & Mathematical Science, Shahid Beheshty University Her research integrates AI with robotics for societal impact, exemplified by the open-source SROBO ground robot and projects like Rehabgame and the Assistive Feeding Robot . She has published over 45 peer-reviewed articles and contributes to academic communities as a journal guest editor and conference organizer . Key collaborations include IET, IMechE, and the Nuffield Foundation as a mentor for young students. Scientific Awards & Recognitions: Chartered Engineer (CEng), Engineering Council UK Senior Fellow (SFHEA), Higher Education Academy Student-Voted 'Made a Difference Award' (2018) Post-Graduate Certificate in Higher Education

Molly CrockettView profile

Professor

• Cognitive Science
• Ethics
• Social Epistemology
• +6 more

Molly Crockett is a Professor at Princeton University's Department of Psychology and affiliated with the University Center for Human Values . Her research integrates cognitive science, social psychology, philosophy, and data science to examine how systems of power shape knowledge production, and how technologies like artificial intelligence reinforce social inequalities. Education: Ph.D. from the University of Cambridge Her lab focuses on epistemic injustice , using behavioral experiments, computational modeling, and machine learning to study moral cognition, narrative testimony, and the cultural evolution of ethics. Recent work highlights AI's role in distorting scientific understanding and explores how social norms constrain self-knowledge. Key article trends include analyses of algorithmic bias in social networks, moral outrage dynamics, and the interplay between self-perception and societal expectations. She actively mentors postdoctoral, PhD, and undergraduate researchers, emphasizing diversity and inclusion in scientific practice. Publications span topics from neurobiological markers of guilt to positive illusions in relationships , reflecting her interdisciplinary approach. Her lab avoids tech industry funding to maintain research autonomy.

Sadegh TalebiView profile

Assistant Professor

• Reinforcement Learning
• Markov Decision Processes
• Online Learning
• +2 more

Sadegh Talebi is a Tenure Track Assistant Professor in the Machine Learning Section at the Department of Computer Science, University of Copenhagen . His research focuses on theoretical aspects of reinforcement learning, Markov decision processes, online learning, stochastic multi-armed bandit problems, and resource allocation in networks. Education BSc in Electrical Engineering (minor: Electronics) from Iran University of Science and Technology (IUST) (2004) MSc in Electrical Engineering (minor: Communication Systems) from Sharif University of Technology (2006) PhD in Electrical Engineering from the Department of Automatic Control at KTH Royal Institute of Technology (supervised by Alexandre Proutiere and Mikael Johansson) Research Specializes in theoretical foundations of reinforcement learning and online learning Key contributions in stochastic optimization, MDPs, and bandit algorithms Collaborates on applications in resource allocation and quantum computing Publications include high-impact work on offline RL, differentially private exploration, and scalable MDP solutions in journals like Neural Processing Letters and conferences such as NeurIPS and UAI.

• Optimization
• Convex Analysis
• Nonlinear Analysis
• +3 more

Radu Ioan Bot is a Professor and Dean of the Faculty of Mathematics at the University of Vienna, where he also serves as Head of the Department of Mathematics. His primary affiliations include the Department of Mathematics (Oskar-Morgenstern-Platz 1, 1090 Wien) and the Research Network Data Science (Währinger Straße 29, 1090 Wien). Bot's research centers on optimization theory with emphasis on convex/nonconvex optimization, monotone operators, and dynamical systems. He develops fast algorithms for variational inequalities and monotone inclusions by bridging continuous-time dynamics with discrete optimization methods. His work frequently addresses bilevel optimization, Tikhonov regularization, and second-order dynamics, yielding accelerated convergence rates for complex problems. Analysis of his 15 most recent publications (2023-2025) reveals dominant trends in time-scaling techniques, vanishing damping dynamics, and structured splitting methods. Key contributions include unifying Nesterov acceleration with Heavy Ball dynamics, developing reflected forward-backward algorithms for constrained optimization, and establishing strong convergence guarantees for monotone operator flows. These advances demonstrate consistent innovation in accelerating optimization while maintaining theoretical rigor. No scientific awards were mentioned in the provided source material. Details regarding student advising and research grants were not specified in the available information, though his leadership roles as Dean and Department Head indicate significant administrative responsibilities alongside active research. Bot participates in the University of Vienna's Research Network Data Science, suggesting interdisciplinary engagement in data-driven methodologies with potential applications in machine learning and computational mathematics.

• Systems Theory
• Machine Learning
• Artificial Intelligence
• +4 more

Tyler Cody is an Associate Professor of Data Science at the University of Virginia School of Data Science and a member of the National Security Data and Policy Institute. His research bridges systems engineering and artificial intelligence through abstract systems theory. Education: Ph.D. in Systems Engineering, University of Virginia B.S. in Systems Engineering, University of Virginia (minors: Computer Science, Applied Mathematics) Dr. Cody's research centers on systems theory as a meta-theory for learning, with applications in machine prognostics, telecommunications, computer networks, fraud detection, and computer vision. He investigates phenomena in learning processes, focusing on change and reuse, lifecycles, and iterated games. His recent publications (2024-2025) reveal a strong trend in applying systems theory to machine learning assurance and cyber security. Key areas include reinforcement learning for cyber operations, combinatorial methods for testing ML systems, and outcome-based engineering for AI. His work also addresses ethical implications and architectural design of learning systems. Scientific Awards: No awards listed in the provided information. No details were provided regarding student advising or research grants. Dr. Cody contributes to the National Security Data and Policy Institute, where his expertise supports data-driven approaches to national security challenges through systems-theoretic frameworks.

Ming JinView profile

Assistant Professor

• Trustworthy AI
• Cyber-physical systems
• Machine Learning
• +4 more

Ming Jin is an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. He holds a PhD from UC Berkeley and a B.Eng. from Hong Kong University of Science and Technology. His research focuses on trustworthy AI, CPS security, and energy systems, with affiliations to the Power and Energy Center and Autonomy and Robotics @ VT. Education: PhD in Electrical Engineering and Computer Science (UC Berkeley, 2017), B.Eng. (Honors) in Electronic and Computer Engineering (HKUST, 2012). Postdoc in Industrial Engineering and Operations Research at UC Berkeley. Research interests include safe reinforcement learning, foundation models, cybersecurity, and power systems. Awards include the Siebel Scholarship (2018) and first place in the 2021 CityLearn Challenge. Active in conference organization (e.g., ICML, AAAI) and tutorial development on topics like Safe RL and CPS security. Grants include NSF support for embodied optimization (2025), Amazon-VT Initiative (2023), and Commonwealth Cyber Initiative projects. Involved in labs focused on AI, robotics, and energy systems. Publications span AI safety, RL frameworks, and CPS resilience, with over 50 peer-reviewed articles since 2015.

• Experimental Economics
• Behavioral Economics
• Neuro-Economics
• +7 more

Rosemarie Nagel is an ICREA Research Professor in the Department of Economics at Universitat Pompeu Fabra (UPF). She holds a PhD from the University of Bonn (1994) under Reinhard Selten. Her research bridges economic theory with behavioral insights, focusing on experimental economics, neuroeconomics, and game theory. She has pioneered the level-k reasoning model in Keynesian beauty-contest experiments and explored bounded rationality through lab and neuroscientific methods. Her work spans macroeconomic experiments, industrial organization, and strategic behavior analysis. She has been a full professor at UPF since 2006 and joined ICREA in 2007. Education: PhD in Economics, University of Bonn, 1994 (Advisor: Reinhard Selten) Postdoctoral work with Al Roth at University of Pittsburgh, 1994-1995 Research Interests: Experimental Economics (macro and micro), Neuro-Economics, Geno-Economics, Game Theory, Industrial Organization, Negotiation. Her methods integrate cognitive models, laboratory experiments, and neuroscience tools to study human decision-making. Key Contributions: Her work on strategic uncertainty in coordination games and neural correlates of strategic thinking has been published in top journals like American Economic Review , Econometrica , and Review of Economic Studies . She co-organizes macroeconomics and computational economics workshops. Professional Activities: Editor for Games and Economic Behavior , frequent speaker at international conferences, and advisor to policy institutions. Her lab focuses on experimental design and neuroeconomic applications.

David CastañónView profile

Professor

• Stochastic Control
• Estimation Theory
• Game Theory
• +6 more

David Castañón is a Professor of Electrical and Computer Engineering (ECE) and Systems Engineering (SE) at Boston University. He holds a PhD from MIT (1976) and has held leadership roles including Department Chair of BU ECE (2010-2014) and President of the IEEE Control Systems Society (2008). His research focuses on stochastic control, optimization, game theory, and distributed computing, with applications in sensor management, inverse problems, and autonomous systems. Education: PhD, Massachusetts Institute of Technology (1976). Key affiliations include the Center for Information and Systems Engineering, the Rafik B. Hariri Institute for Computing, and the ALERT Department of Homeland Security Center of Excellence. He teaches courses such as EC702 Recursive Estimation and EC719 Statistical Learning Theory. Research interests span stochastic control, estimation theory, optimization algorithms, and multi-agent systems. Notable contributions include work on sensor management, cooperative operations, and inverse problem solutions for medical and security imaging. His work often integrates theoretical frameworks with practical applications in autonomous systems and distributed computing. Scientific achievements include IEEE Fellow status (2006), CSS Distinguished Member Award, and leadership roles in major conferences like the IEEE Conference on Decision and Control (2007 as General Chair). He has also served on the Air Force Advisory Board and the IEEE Society Review Committee. Grants and lab affiliations include the NSF Engineering Research Center for Subsurface Sensing (2001-2013) and the SENTRY DHS Center of Excellence (2021-present). His interdisciplinary collaborations bridge robotics, medical imaging, and security systems.

Damian Nale DailisanView profile

Lecturer

• Computational Social Science
• Machine Learning
• Multi-Agent Systems
• +3 more

Dr. Damian Nale Dailisan is a Lecturer in the Department of Humanities, Social and Political Sciences at ETH Zürich, affiliated with the Computational Social Science group. He holds a Ph.D. in Physics from the University of the Philippines, specializing in traffic modeling and machine learning applications. His research focuses on multi-agent systems, particularly in transportation and urban systems. He has held postdoctoral roles and contributed to projects like the ACCeSs@AIM lab. His work bridges computational methods with real-world challenges, including traffic control optimization, AI-driven decision-making, and smart city infrastructure. Notable projects include FAIRLANE for priority lane management and studies on democratizing traffic control systems. Dailisan’s publications span journals like Transportation Research Part C and IEEE Access, addressing topics such as reinforcement learning in traffic signals and ethical AI frameworks. He has presented at workshops like 'Back to the Future' at ETH Zurich and collaborates with interdisciplinary teams to enhance urban mobility solutions. His technical expertise includes Python, network analysis, and agent-based modeling, with contributions to open-source tools for earthquake networks and social systems analysis.

• Immersive Media
• Extended Reality (XR)
• Virtual Reality (VR)
• +6 more

Prof. Jan-Niklas Voigt-Antons is a Professor of Applied Computer Science specializing in Immersive Media at Hamm-Lippstadt University of Applied Sciences. His work focuses on Extended Reality (XR), Virtual Reality (VR), Augmented Reality (AR), and their applications in healthcare, education, and human-machine interaction. He holds a Dr.-Ing (Engineering Doctorate) and has extensive industry experience with organizations like Daimler AG, Telekom Innovation Laboratories, and the German Research Center for Artificial Intelligence (DFKI). Research interests include user experience (UX) design, emotion analysis in virtual environments, and accessibility for aging populations. He has led projects on wearable health tech, AR/VR usability, and immersive training systems. Notable areas of impact include developing guidelines for inclusive XR interfaces and advancing telemedicine through immersive technologies. Prof. Voigt-Antons has collaborated with Charité – Universitätsmedizin Berlin on healthcare applications and has contributed to standardization efforts in telecommunications. His technical competencies include agile project management, statistical analysis, and mobile application development for iOS/Android. He advises on UX evaluations, usability testing, and physiological data analysis for industry partners. Key achievements include creating the Story Time Dataset for video quality research and developing the ColorTable system for flavor perception studies. His work emphasizes real-world deployment, with projects addressing mobility challenges for seniors in rural areas and improving dementia care through tablet-based interventions.

Stephen Lee-UrbanView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Game AI
• +3 more

Stephen Lee-Urban is a Teaching Associate Professor in the Department of Computer Science & Engineering at Lehigh University, affiliated with the Rossin College of Engineering. He holds a Ph.D., M.S., and B.S. in Computer Science and Engineering from Lehigh University, all completed with summa cum laude distinction. His research focuses on fundamental and applied artificial intelligence, machine learning, game AI, cognitive systems, and automated planning. He has contributed to innovative projects such as HuManIC (human-machine interpretive control), CORA (cognitive systems framework), and crowdsourced narrative generation systems. His academic career includes significant work in cybersecurity through intelligent agent modeling of malware, as well as contributions to game AI for strategy games and military training simulations. Notable awards include summa cum laude honors for all three of his university degrees. Lee-Urban's scholarly output spans over 20 publications since 2000, with recent emphasis on AI applications in collaborative storytelling, adaptive planning systems, and human-computer interaction. His research integrates machine learning techniques with sociocultural analysis, crowd-powered content creation, and hierarchical task networks. Current work explores autonomous systems capable of leveraging crowd intelligence for generating interactive narratives and optimizing military training scenarios. While no specific grants or advising roles are listed, his interdisciplinary approach bridges computer science with game design, cybersecurity, and cognitive modeling.

• Computer Science
• Machine Learning
• Data Science
• +4 more

Yong Zhang is affiliated with Tsinghua University's Research Institute of Information Technology in Beijing, China. His research focuses on machine learning, optimization algorithms, edge computing, and their applications in areas like time series analysis, federated learning, and sensor networks. He has collaborated on projects involving neural networks, scheduling problems, and privacy-preserving techniques. Education: Yong Zhang earned a PhD in Computer Science and Engineering from Fudan University in 2007. His academic career includes roles at institutions like the Chinese Academy of Sciences and the University of Hong Kong, reflecting a strong interdisciplinary background. Research Contributions: His work spans theoretical computer science, algorithm design, and applied machine learning. Notable areas include developing efficient scheduling algorithms for energy systems, creating robust federated learning frameworks for industrial demand forecasting, and advancing methods for sentiment analysis using multimodal data. He has also contributed to biomedical engineering through smartphone-based health monitoring systems. Collaborations: He frequently collaborates with researchers at institutions like the University of Electronic Science and Technology of China, Nanyang Technological University, and The Hong Kong Polytechnic University. Key projects involve data caching optimization in edge computing, distributed algorithms for dynamic networks, and combinatorial optimization problems. Labs & Future Work: His team explores cutting-edge topics in AI-driven systems, including trust-aware machine learning, distributed resource allocation, and real-time data processing for IoT applications. Current research emphasizes scalable solutions for complex optimization challenges in both academic and industrial settings.

• Human Aspects of Cyber Security
• Cyber Awareness and Hygiene
• Serious Games (Cyber Defence Exercises)
• +5 more

Dr. Kaie Maennel is a Lecturer at the School of Computer and Mathematical Sciences, part of the Faculty of Sciences, Engineering and Technology at the University of Adelaide. Her research focuses on Human Aspects of Cyber Security, including Cyber Awareness and Hygiene, Serious Games (Cyber Defence Exercises), and Learning Analytics in Cyber Training. She also investigates Usable Security, Information Security Culture, and Cybersecurity Risk Management. With over 20 years of corporate experience in Audit and Assurance at Deloitte, she holds professional certifications: ACCA, CIA, and Estonian CPA. Her research interests emphasize bridging cybersecurity education with practical applications, leveraging experiential learning and advanced analytics. She actively participates in high-profile cybersecurity initiatives like the NATO CCDCOE’s Locked Shields workshop, contributing to exercise design and assessment frameworks. Dr. Maennel is eligible to supervise Masters and PhD students as a Co-Supervisor, focusing on cybersecurity education, exercise methodologies, and human-centric security strategies. She prioritizes interdisciplinary approaches and real-world impact through collaborations with industry and academic partners. Her work spans cyber defense exercise ontology development, cultural adaptation of cybersecurity programs, and leveraging behavioral genetics insights. Recent trends in her publications highlight the integration of AI into cybersecurity training and the critical role of human factors in mitigating cyber risks.