Find Academic Supervisors

• Artificial Intelligence
• Planning and Scheduling
• Reinforcement Learning
• +5 more

Sheila A. McIlraith is a Professor in the Department of Computer Science at the University of Toronto, where she leads research at the intersection of artificial intelligence, knowledge representation, and formal methods. With an extensive publication record spanning over three decades, she has made significant contributions to planning, reinforcement learning, and epistemic reasoning in AI systems. Her research interests focus on developing formal frameworks for AI planning and decision-making, with particular emphasis on interpretable AI, reward specification in reinforcement learning, and multi-agent systems. She has pioneered work in reward machines for reinforcement learning, epistemic planning, and the application of formal methods to ensure safety and fairness in AI systems. Her recent work bridges symbolic AI with deep learning approaches to create more transparent and controllable intelligent agents. Analysis of her recent publications reveals a strong trend toward integrating formal specification languages with machine learning, particularly in reinforcement learning where she develops methods for specifying complex tasks using linear temporal logic and related formalisms. Her work increasingly addresses ethical considerations in AI, including fairness in sequential decision making and the impact of ethics education in computer science curricula. McIlraith has mentored numerous PhD students who have become prominent researchers in AI, including Rodrigo Toro Icarte, Toryn Q. Klassen, and Andrew C. Li. Her collaborative network spans major AI research institutions worldwide, with frequent collaborations with researchers at institutions including the Vector Institute and international universities. Her research group explores the theoretical foundations of AI planning while developing practical applications in areas including robotic assistance, ethics-aware AI, and interpretable decision systems. Current projects focus on language-guided reinforcement learning, multi-agent verification, and the development of tools for responsible AI development and deployment.

Koulik KhamaruView profile

Assistant Professor

• Statistics
• Machine Learning
• Optimization
• +7 more

Koulik Khamaru is an Assistant Professor of Statistics at Rutgers University , located at Hill Center 403. His research focuses on the theory and application of statistics, machine learning, and optimization, with specific interests in EM algorithms, Gaussian mixture models, reinforcement learning, and non-convex optimization. Education : PhD in Statistics from University of California, Berkeley (advisors: Martin J. Wainwright and Michael I. Jordan) Undergraduate and Master's degrees in Statistics from Indian Statistical Institute, Kolkata Research Interests : Statistical theory of adaptive models and algorithms Reinforcement learning and sequential decision-making Optimization in high-dimensional and non-convex settings Algorithmic convergence and statistical guarantees Recent Work : Recent papers include advancements in adaptive linear estimating equations (NeurIPS 2023), instance-dependent reinforcement learning analysis (JMLR), and variance-reduced stochastic approximation techniques. His work is supported by NSF grant DMS-2311304. Teaching : Taught a course on Reinforcement Learning in Spring 2024.

Adriana RicklinView profile

Researcher

• Data Science
• Reinforcement Learning
• Machine Learning
• +3 more

Adriana Ricklin is a Research Associate at Lucerne University of Applied Sciences and Arts, affiliated with the Lucerne School of Business and the Institute of Communication and Marketing. Concurrently, she is a PhD candidate at Eindhoven University of Technology's Department of Mathematics and Computer Science, researching sequential decision-making methods for e-commerce optimization under Professor Maurits Kaptein. Education: PhD candidate at Eindhoven University of Technology (2024-present) MSc Applied Information & Data Science, Hochschule Luzern (2019-2022) BSc Business Administration, Hochschule Luzern (2015-2018) Research Focus: Her expertise spans reinforcement learning, predictive analytics, and machine learning applications in sustainability-focused e-commerce. Key projects include Algorithmic Nudging for Sustainability, Sharing Potential Accelerator, and real-time sentiment analysis for brand reputation. Publication Trends: Recent works demonstrate consistent focus on machine learning adaptations for consumer behavior prediction and sharing economy optimization, utilizing advanced methods like LSTMs and graph neural networks across e-commerce and peer-to-peer platforms. Professional Engagements: Contributes to multiple HSLU research initiatives including Sustainable Consumer Advisor and AI-assisted talent acquisition projects, collaborating with teams at the Teaching & Research Institute for Data Science.

Wei NingView profile

Professor

• Change Point Analysis
• Causal Inference
• High Dimensional Data
• +4 more

Wei Ning is a Professor and Professor of Research Excellence in the Department of Mathematics and Statistics at Bowling Green State University (BGSU), within the College of Arts & Sciences. His research expertise focuses on statistical methodologies, particularly in change point analysis, empirical likelihood methods, and sequential analysis. He actively contributes to high-impact academic journals and collaborates on projects addressing challenges in reliability engineering, biostatistics, and financial data analysis. Education: Ph.D. in Statistics, Syracuse University (2006), advised by Professor Hyune-Ju Kim M.A. in Statistics, Syracuse University (2003) B.A. in Mathematics and B.S. in Finance, University of Science and Technology of China (USTC) (1999) Research Interests: Wei Ning’s work centers on cutting-edge statistical techniques, including: Change Point Analysis : Detecting abrupt shifts in distributional parameters across various models Empirical Likelihood Method : Developing robust inferential procedures for complex datasets Time Series Analysis : Modeling temporal dependencies with applications in environmental and financial domains Survival Analysis : Analyzing time-to-event data under censoring mechanisms Sequential Analysis : Optimizing decision-making processes in real-time data scenarios Recent Research Trends: His recent publications emphasize advancements in detecting structural changes in distributions (e.g., Weibull, skew-t, Kumaraswamy) through empirical likelihood-based approaches and sequential testing frameworks. Key applications span reliability engineering, financial modeling, and biomedical research, reflecting his versatility in both theoretical and applied statistics. Awards: No scientific awards explicitly mentioned. Advising & Grants: No formal advisee list or grant details provided in the text. His academic contributions are primarily through collaborative research with co-authors like Said K.K., Tian Y.B., and others. Labs/Teams: No specific labs or research teams explicitly listed, though he participates in cross-disciplinary projects within the department’s research groups.

• Reinforcement Learning
• Collaborative AI
• Differential Privacy
• +4 more

Christos Dimitrakakis is a Professor affiliated with the University of Neuchâtel, University of Oslo, and Chalmers University of Technology. He leads research groups across these institutions, advising PhD students and postdoctoral researchers in areas such as reinforcement learning, collaborative AI, and fairness in AI. His teaching includes advanced machine learning courses at the University of Neuchâtel, covering topics like privacy, fairness, Bayesian inference, and decision-making under uncertainty. His research focuses on foundational challenges in AI, including safety, risk, differential privacy, and algorithmic fairness. He has contributed to methodologies for reinforcement learning, multi-agent systems, and Bayesian inference. Notable awards include the Student Paper Award at EAAMO'22 and the Best Paper Award at Cooperative-AI@NeurIPS 2021. Recent articles highlight advancements in matching markets under uncertainty, ad-hoc teamwork strategies, and minimax-bayes reinforcement learning. His work bridges theoretical insights with practical applications, emphasizing ethical AI and robust decision-making. He advises a diverse cohort of students and postdocs, including those exploring fairness, reinforcement learning, and computational social choice. His collaborations span academia and industry, with past members contributing to roles at institutions like the Turing Institute, Volvo, and EPFL.

Fateme JamshidiView profile

Researcher

• Causal Inference
• Bayesian Networks
• Multi-Armed Bandits
• +3 more

Fateme Jamshidi is a doctoral researcher at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the College of Management of Technology and the Institute for Management, Technology and Economics. Her research focuses on: Causal inference and discovery Bayesian network modeling Multi-armed bandit algorithms Context-specific independence analysis Hidden confounding in causal networks Nonparametric statistical testing Recent work trends demonstrate expertise in causal modeling under uncertainty, with applications to machine learning and decision systems. Her publications explore graph-dependent regret bounds, causal imitability, and structural side information integration.

• Mechanical Engineering
• Process Engineering
• Transport Systems
• +3 more

Adrian Müller is a Lecturer at the Department of Mechanical and Process Engineering within the ZHAW School of Management & Law at Zurich University of Applied Sciences (ZHAW). His contact details include the email mueladri@ethz.ch and a phone number. He is based in Winterthur, Switzerland. His research interests focus on Mechanical Engineering , Process Engineering , and Transport Systems , with recent work exploring regret minimization , constrained Markov decision processes , and optimization algorithms within artificial intelligence and operations research . His recent publications highlight advancements in regret minimization and constrained MDPs, emphasizing decision-making algorithms and their applications in machine learning and operations research. Adrian Müller has not been listed with any scientific awards in the provided information. No information is available regarding his advising of students or grants received. Details about his involvement in specific labs or research teams are not provided in the available data.

• Control Theory
• Switched Systems
• H∞ Control
• +5 more

Ruihua Wang is a researcher with a focus on Control Theory , Switched Systems , and H∞ Control , as evidenced by her extensive publication record in journals like Information Sciences and Trans. Inst. Meas. Control . Her work spans applications in engineering systems, biomedical devices, and educational technology. Ruihua Wang’s research interests include: Control Theory and Lyapunov Function approaches Switched and Markovian Jump Systems Machine Learning for predictive maintenance and signal processing Biomedical engineering for wearable health monitoring Educational technology in multimedia learning Her recent publications highlight trends in finite-time control strategies, resilient control under cyberattacks , and adaptive algorithms for systems like bearings and rotating machinery. She also explores interdisciplinary applications, such as instructor voice enthusiasm in educational tools.

Ricky Roet-GreenView profile

Associate Professor

• Operations Research
• Operations Management
• Service Systems
• +3 more

Ricky Roet-Green is an Associate Professor of Operations Management at Simon Business School, University of Rochester. Her research focuses on modeling strategic customer behavior in congestion-prone service systems using queueing theory, game theory, and mechanism design. She holds a PhD in Operations Research from Tel-Aviv University and conducted postdoctoral research at the University of Toronto's Rotman School of Management and Department of Mechanical and Industrial Engineering. Education: PhD in Operations Research, School of Mathematical Sciences, Tel-Aviv University (2014) Postdoctoral Fellowships: Rotman School of Management, University of Toronto (2014-2016) Department of Mechanical and Industrial Engineering, University of Toronto (2014-2016) Research interests include optimizing service systems through mechanism design, analyzing customer strategies in queueing environments, and integrating game theory with stochastic modeling. Her work emphasizes revenue and social welfare maximization in settings like ride-sharing platforms, healthcare systems, and omnichannel retail. Her publications demonstrate expertise in topics such as shared waiting room design, expedited service mechanisms, and information disclosure policies. Notable awards include the 2021 M&SOM Meritorious Service Award. Ricky collaborates with researchers across disciplines, including Prof. Philipp Afeche, Prof. Opher Baron, and Prof. Michael Jong Kim. Her work contributes to both theoretical advancements in operations research and practical solutions for service system optimization.

Yash Vardhan PantView profile

Assistant Professor

• Control Theory
• Formal Methods
• Machine Learning
• +5 more

Yash Vardhan Pant is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, Canada. He leads the Control, Learning and Logic (CL²) group, focusing on integrating Control Theory, Formal Methods, Machine Learning, and Optimization to ensure safe autonomy in robotic and cyber-physical systems. Prior to this, he was a Postdoctoral Fellow at UC Berkeley under Professors Sanjit Seshia and Rahul Mangharam, and earned his PhD in Electrical Engineering from the University of Pennsylvania under Prof. Rahul Mangharam. His research emphasizes computationally efficient solutions for real-world problems, with implementations on platforms such as quadrotors, autonomous vehicles, and hybrid powertrains. Key themes include safe autonomy, decentralized control, and human-autonomy interaction. He serves as Publicity Chair for IEEE/ACM ICCPS 2022 and has advised students including Leroy, Zhao Feng, and Soroush. His work addresses challenges in urban air mobility, mixed traffic safety, and distributed traffic management. Pant's recent publications explore topics like model predictive control for autonomous systems, learning-based collision avoidance, and formal scenario testing for autonomous vehicles. He has developed tools such as Fly-by-Logic for multi-drone fleet planning and contributed to open-source platforms like the F1/10 autonomous vehicle platform. Education: PhD in Electrical Engineering, University of Pennsylvania (2021) Postdoctoral Fellowship, UC Berkeley (2019–2021) Research Interests: Safe Autonomy, Cyber-Physical Systems, Formal Methods, Reinforcement Learning, Autonomous Vehicle Control. Labs/Teams: CL² Group, VeHICaL Project (UC Berkeley), Real-Time and Embedded Systems Lab (UPenn).

Lale AkarunView profile

Professor

• Biometrics
• 3-D Face Recognition
• Hand Vein Recognition
• +8 more

Lale Akarun is Professor of Computer Engineering at Boğaziçi University, Istanbul, where she also serves as Director of the TETAM research center. She leads the Media Laboratory (MEDIALAB) and the Perceptual Intelligence Laboratory (PILAB), focusing on biometrics, sign-language recognition, computer vision and image processing. Research Interests: Biometrics: 3-D Face & Hand-vein Recognition Gesture & Sign-Language Recognition for HCI Computer Vision, Image Processing & Graphics Her recent work (2023-2025) targets robust sign-language translation via self-supervised learning, multi-cue skeleton fusion, cross-dataset transfer and attention modeling, while also contributing new benchmarks for 3-D object reconstruction and occlusion-aware human-pose estimation. Laboratories & Teams: Media Laboratory (MEDIALAB) Perceptual Intelligence Laboratory (PILAB) She advises graduate researchers and has released influential datasets such as BosphorusSign22k, fostering advances in Turkish sign-language recognition and low-resource transfer learning.

• Digital Image Processing
• Signal Processing
• Soft Computing
• +5 more

Prof. Annamária Várkonyiné Kóczy is a Professor at the Department of Informatics within the Faculty of Economics and Informatics at J. Selye University. She holds a joint appointment as a professor at Óbuda University's Institute of Software Design and Software Development. Her academic journey includes: Electrical Engineering studies (Measurement and Control) at Technical University of Budapest (1975-1981) Mechanical Engineering degree (Mechanical Engineer-Teacher) from the same institution (1978-1983) PhD in Computer Science from Technical University of Budapest (1996) DSc (Doctor of Sciences) from Hungarian Academy of Sciences (2010) Habilitation from Széchenyi István University (2017) Her research spans digital signal processing, soft computing, and hybrid techniques for complex systems, with applications in medical diagnostics, intelligent control, and sustainable technologies. She leads projects in computational intelligence and has developed novel algorithms for real-time processing in resource-constrained environments. Recent publications demonstrate strong focus on AI-driven solutions: machine learning for bioacoustic analysis (beehive swarming), fuzzy logic optimizations for edge computing, medical imaging diagnostics (COVID-19 detection), and sustainable energy control systems. Her work consistently bridges theoretical models with practical implementations in healthcare, environmental monitoring, and industrial diagnostics. She has secured multiple research grants including: OTKA K 105846: Intelligent Space and robotics (2012-2017) OTKA K 78576: Intelligent techniques in signal/image processing (2009-2013) GVOP-3.3.1: Intelligent 3D modeling systems (2006-2007) EU Structural Funds projects on vehicle industry collaborations She mentors graduate researchers in applied informatics and leads international collaborations across Europe and Asia, focusing on intelligent systems development.

• Music Recommender Systems
• Machine Learning
• Artificial Intelligence
• +3 more

Shahrzad Shashaani is a PreDoc Researcher at Vienna University of Technology , affiliated with the College of Engineering. Her work spans multiple domains including artificial intelligence, recommender systems, and biomedical applications. Research Focus : Explainability in music recommender systems, sequential recommendation algorithms, and AI-driven diagnostics Projects : HumRec (2021–2025) - Explainability in Music Recommender Systems Technical Expertise : Machine learning, network security, autonomous vehicles, and biomedical sensor analysis Recent publications highlight her contributions to contrastive learning in music recommendation, self-supervised intrusion detection, and Bayesian frameworks for graphene transistor-based enzyme activity analysis. She also explores AI applications in rapid pandemic screening protocols. Her academic career combines engineering rigor with interdisciplinary applications, particularly in health analytics and intelligent systems. Current responsibilities include course instruction in "Experiment Design and Execution" (VU 194.192).

Fatemeh GhoreishiView profile

Assistant Professor

• Machine Learning
• Bayesian Statistics
• Autonomous Systems
• +4 more

Fatemeh Ghoreishi is an Assistant Professor at Northeastern University, holding joint appointments in the Department of Civil and Environmental Engineering and the Khoury College of Computer Sciences . Her research focuses on machine learning, Bayesian statistics, and decision-making under uncertainty, with applications in autonomous systems, transportation, and cyber-physical systems. She leads the Institute for Experiential AI and collaborates across disciplines to advance AI-driven solutions for complex systems. Education: Ph.D./M.Sc., Mechanical Engineering (Minor in Applied Statistics), Texas A&M University (2019/2016) M.Sc., Biomedical Engineering, Iran University of Science & Technology (2014) B.Sc., Mechanical Engineering, University of Tehran (2012) Research Interests: Dr. Ghoreishi develops data-driven methodologies for uncertainty quantification, reinforcement learning for autonomous systems, and human-AI collaboration. Her work integrates Bayesian inference, optimization, and sensor data to solve real-world challenges in transportation, robotics, and infrastructure security. Recent projects include AI for disaster response, privacy-preserving federated learning, and safe control of autonomous vehicles. Awards & Grants: $900,000 U.S. Army grant (2024): 'Interactive Human-Agent Teaming Via Multiple Feedback Types' $60,000 U.S. Army grant (2023): 'BRITE: Bayesian Inference and Preference Learning' IEEE Senior Member (2025) 2025 AIAA SciTech Best Paper Finalist 2024 MECC Conference Best Paper Award Advising & Teams: She mentors Ph.D. students (e.g., Negar Asadi) and undergraduates (e.g., Ise Okhiria, Alyssa Lee). Her lab emphasizes interdisciplinary collaboration, with affiliations at Northeastern’s Institute for Experiential AI and partnerships with the University of Utah and University of Michigan.

• Reinforcement Learning
• Neural Networks
• Sequential Learning
• +11 more

Aidan Scannell is a Visiting Professor at the Department of Electrical Engineering and Automation, Aalto University. His work focuses on Reinforcement Learning (RL), Neural Networks , and Model-Based Reinforcement Learning , particularly for Continuous Control and Sequential Learning tasks. He collaborates with the Finnish Center for Artificial Intelligence (FCAI) and contributes to advancements in Representation Learning , Constrained Models , and Bayesian Learning . Research Interests His research explores core challenges in RL, including sample efficiency , generalization , and model adaptability . Key areas include function-space neural network parameterization , quantized representations , and residual learning for dynamic environments. His work often integrates Gaussian Processes and context encoding to enhance decision-making in complex, multimodal systems. Research Output Trends Aidan’s publications emphasize Reinforcement Learning (100% of works), Representation Learning (85%), and Neural Networks (80%). Recent articles address offline-to-online adaptation , codebook-based world models , and entropy-regularized meta-learning , reflecting his focus on scalable and data-efficient RL frameworks. Projects He is affiliated with the Finnish Center for Artificial Intelligence (FCAI) , funded by the Academy of Finland, which supports interdisciplinary AI research. His collaborations span researchers like Joni Pajarinen, Arno Solin, and Christopher H. Ek.