Find Academic Supervisors

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +9 more

Ioannis Mitliagkas is an Associate Professor at the University of Montréal's Department of Computer Science and Operations Research, affiliated with the Faculty of Arts and Science. He is a core faculty member at Mila and a staff research scientist at Google DeepMind. His research focuses on machine learning, deep learning, optimization algorithms, and generative models. He holds the Canada CIFAR AI Chair and has led numerous projects on distributed training, causal discovery, and robust generalization. Education: While specific academic degrees are not explicitly listed in the text, his roles imply a PhD in Computer Science. His work bridges theoretical foundations and applied machine learning. Research Interests: Machine learning theory, optimization dynamics, generative models, out-of-distribution generalization, and causal inference. His work emphasizes scalable algorithms for deep learning and distributed systems. Awards: Canada CIFAR AI Chair (2018). Grants & Projects: Lead researcher on over 50 projects funded by CRSNG, MITACS, and others, including grants on optimization dynamics, causal discovery, and industrial applications (e.g., healthcare, energy, robotics). Students: Advised PhD and Master's students on topics like distributional shifts, generative models, and performative prediction.

Romaric DuvignauView profile

Associate Professor

• Distributed Systems
• Network Security
• IoT/CPS Applications
• +7 more

Romaric Duvignau is an Associate Professor at Chalmers University of Technology since 2024, affiliated with the Department of Computer Science and Engineering within the IT Faculty. His primary research focuses on distributed systems, network security, and cyber-physical systems (CPS), with an emphasis on optimizing communication and computational efficiency in distributed applications. Key projects include the READY project for continuous monitoring systems and contributions to P2P energy sharing optimization via the DEEP and HyperP2P initiatives. He has been at Chalmers since 2017 and also holds teaching responsibilities in computer networks and data-driven systems. Research Interests: Distributed Algorithms & Optimization Cybersecurity & Privacy Peer-to-Peer Energy Sharing Vehicular Networks Edge Computing & Streaming Analytics Notable Articles: His recent works include advancements in P2P energy matching algorithms (2025), Byzantine fault-tolerant consensus (2025), and vehicular data localization (2021). Earlier contributions include impactful studies on vehicular networks (DRIVEN framework, 2020) and network traffic analysis (2019). Teaching & Grants: Responsible for courses like Computer Communication (EDA343) and Data-driven Support for CPS. Active in educational innovation, exploring AI impacts on teaching and active learning tools like Kahoot/Mentimeter. Labs/Teams: Member of the Distributed Computing and Systems group at Chalmers Networks and Systems division, collaborating with researchers like Marina Papatriantafilou and Vincenzo Gulisano on energy systems and distributed algorithms.

Zhengmao LiView profile

Assistant Professor

• Multi-energy Systems
• Electric Energy Storage
• Uncertainty Handling
• +7 more

Zhengmao Li is an Assistant Professor at Aalto University's Department of Electrical Engineering and Automation. He holds a Ph.D. in Electrical Engineering from Nanyang Technological University (NTU), Singapore, and has served as a Research Fellow at Stevens Institute of Technology (2019-2021) and NTU/Singapore ETH Center (2021-2023). His research focuses on multi-energy system planning and operation, particularly in integrated power, thermal, and gas networks for microgrids, seaports, and smart buildings. Key areas include resilience enhancement via demand response, robust optimization under uncertainties, and advanced algorithms like deep reinforcement learning. Education: B.E. (Information Engineering), M.E. (Electrical Engineering) from Shandong University, China; Ph.D. (Electrical Engineering) from NTU, Singapore. Research emphasizes handling uncertainties in renewable energy, developing resilient energy systems, and applying optimization techniques such as distributionally robust stochastic methods. His work bridges theoretical advancements with practical applications in smart grids, maritime energy systems, and low-carbon industrial operations. Over 50+ peer-reviewed articles highlight his contributions to energy system resilience, multi-agent coordination, and sustainable energy technologies.

Maggie ChenView profile

Professor

• Financial Mathematics
• Fintech
• Market Microstructure
• +5 more

Professor Maggie Chen is a Personal Chair (Professor) in Financial Mathematics at Cardiff University, affiliated with the School of Mathematics and the Operational Research Group. She holds adjunct/visiting professorships at Columbia University and University College London. Her research focuses on interdisciplinary financial modeling using Hawkes processes and network approaches to address modern finance challenges, particularly in Fintech, market microstructure, and regulatory impacts. She co-leads the EPSRC UKFin+ Network for Financial Services and advises organizations such as the Royal Statistical Society and the Center for Research toward Advancing Financial Technologies (CRAFT). Education: BSc in Computer Science from Lanzhou Jiaotong University (China), MSc in Finance & Investment Management and PhD in Finance from the University of Aberdeen (UK). Prior to academia, she worked in software engineering and management at China Telecom and Dell (China). Research Interests: Liquidity provision, information dynamics in financial markets, Hawkes processes for modeling financial jumps, cryptocurrency analysis, ESG applications, and policy impacts. She collaborates with governments, companies, and start-ups to develop deployable solutions in Fintech. Grants & Projects: Principal Investigator/Co-Investigator on projects funded by Wales Data Nation Acceleration Institute, Turing Institute, and Data Transformation Innovation Institute. Her work on jump detection has been adopted by U.S. regulators including the Commodity Futures Trading Commission (CFTC) and IMF. Labs/Teams: Leads the Fintech theme at the Data Transformation Innovation Institute, Cardiff. Engages in collaborative networks like the UKFin+ Network and CRAFT.

Zoran StajicView profile

Professor

• Electrical Power Engineering
• Power Systems
• Synchronous Machines
• +5 more

Zoran Stajic is a Full Professor at the Faculty of Electronics, University of Niš, Serbia, where he has been an academic since earning his PhD in 2001. He was promoted to Associate Professor in 2007 and Full Professor in 2012, both in the Department of Electrical Power Engineering. He holds a Master’s degree (1996) and a Bachelor’s degree (1993) from the same institution, all in Electrical Power Engineering. His research focuses on electrical power systems, with particular emphasis on synchronous and induction machines, mathematical modeling, and control systems. His work includes modeling of synchronous machine starting, rotor voltage effects in double-fed induction motors, and measurement systems for industrial applications such as pump station control. He also investigates power transformer protection and associated professional risks in engineering environments. The available publications span from 1997 to 2006 and reflect a strong focus on power engineering dynamics, modeling, and system protection. They appear in journals such as Facta Universitatis and Buletinul Stiintific al Universitatii "Politehnica" din Timisoara, covering topics in power systems, control, and safety. The research demonstrates consistent application of computational and mathematical tools to real-world electrical engineering problems. Currently, he is managing three research and development projects for the multinational company RADIUS Group, and is involved in two national research projects. His work bridges academic research and industrial application, particularly in power system design and engineering safety. He is affiliated with the Department of Electrical Power Engineering at the Faculty of Electronics, contributing to both education and applied research. While no formal advising of students is mentioned, his active project leadership suggests involvement in training researchers and engineers.

Heba El-FiqiView profile

Lecturer

• Artificial Intelligence
• Machine Learning
• Swarm Intelligence
• +7 more

Dr. Heba El-Fiqi is a Lecturer at the School of Engineering and Information Technology (SEIT), UNSW Canberra. Her research focuses on artificial intelligence, swarm intelligence, machine learning, and computational linguistics. Notable contributions include a novel AI-based solution for air traffic control using swarm robotics and the development of the Weighted Gate Layer Autoencoders (WGLAE) for EEG signal recovery. She holds an IEEE active membership, co-chaired Women in Artificial Intelligence (WAI) activities, and served on conference program committees (AAAI, IJCAI, ECAI). Education: PhD in Computer Science (2013): UNSW, Thesis: "Detection of Translator Stylometry using Pair-wise Comparative Classification and Network Motif Mining" Master of Computer Science (2009): Cairo University, Thesis: "An Intelligent System for Tracking Network Attacks" Research Interests: Swarm robotics, autonomous systems, and AI for air traffic management Machine learning applications in signal recovery and medical diagnostics Computational linguistics and translator stylometry Awards: Dell EMC Award of Distinction (2017) IBM Big Data Developer - Instructor Award for Educators (2017) Teaching & Supervision: Course Coordinator for ZEIT4150 (Artificial Intelligence) and ZEIT4151 (Machine Learning) Supervised over 10 undergraduate engineering projects, including work on deep reinforcement learning for autonomous combat and humanoid robot navigation Labs & Teams: Active in UNSW's AI research community, contributing to initiatives like Canberra AI Week and Women in Engineering (WIE) leadership roles.

Ron DaboraView profile

Associate Professor

• Wireless Communications
• Cooperation and Relaying in Wireless Networks
• Secure Communications
• +7 more

Ron Dabora is an Associate Professor at the Department of Electrical and Computer Engineering, Ben-Gurion University, Israel. He is currently a Visiting Research Collaborator at Princeton University. His research focuses on wireless communications, secure communications, channels with memory, signal processing for communications, and machine learning for communications. Education : B.Sc. and M.Sc. from Tel-Aviv University (1994, 2000), Ph.D. from Cornell University (2007). Past Roles : Postdoctoral researcher at Stanford University (2007–2009), R&D engineer at Communications Research Institute (1994–2000), Algorithms Group at Millimetrix Broadband Networks (2000–2003). Research Interests include wireless communications, cooperation and relaying in networks, secure communications, feedback and channel coding, machine learning for communications, and power line communications. His current projects are Resilient UAV Communications (2025–2027) and Inherent Limitations and Signal Processing for Communications with Asynchronous Sampling (2020–2025). Past projects span 5G algorithm design, wireless network optimization, and power line communications. Advising : Currently supervising Zikun Tan (Ph.D., Signal Processing and Machine Learning for Communications), Itai Zino (Ph.D., Machine Learning for Communications), and others. Former students include Nir Shlezinger (Weizmann Institute), Daniel Zahavi (LEVL Technologies), and Yoel Bokobza (Samsung Research). Honors : IEEE Senior Member. Teaching : Courses in digital wireless communications, network information theory, signal processing, and modern communications.

• Educational Technology
• Digital Humanities
• Interaction Design
• +5 more

Dr. John G. Keating is an Associate Professor in the Department of Computer Science at Maynooth University and serves as the Associate Dean for Teaching and Learning in the Faculty of Science & Engineering. He holds a PhD in Atmospheric Physics (1990) and an EdD in Science Learning and Applied Linguistics (2014). His research interests span Educational Technology, Digital Humanities, Interaction Design, and Linguistic Analytical Methods, with a focus on Collaborative Learning and Science Learning in online environments. He has led numerous funded projects, including work on collaborative annotation tools, digital humanities archives, and adaptive learning environments. Notable awards include the Maynooth University Teaching Fellowship and IBM Faculty Fellow recognition. Education: PhD in Atmospheric Physics (Maynooth University, 1990), EdD in Science Learning (Open University, 2014), MEd (2006). Key Projects: Development of collaborative annotation software, digital humanities platforms (e.g., Irish in Europe Project), and tools for systemic functional linguistic analysis. Grants: Over €6 million in funding from bodies like IRCHSS, Intel Ireland, and the EU for projects in digital humanities and educational technology. Awards: Maynooth University Teaching Fellowship (2019-20), IBM Faculty Fellow (2004-6), EPSRC Visiting Fellow (1994-6). Labs/Teams: Co-founder of Maynooth University's An Foras Feasa Institute, contributing to interdisciplinary research in digital humanities and education.

Jacek StefańskiView profile

Professor

• Digital Radio Communication Systems
• Adaptive Reception Techniques
• Error Protection in Radio Channels
• +6 more

Prof. Jacek Stefański is a distinguished academic serving as Dean of the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology since 2020. He holds a professorship (awarded in 2020) and has held various academic roles, including Vice-Dean for Organizational Studies (2016–2020) and Head of the Department of Radio Communication Systems and Networks (2018–2020). His research focuses on digital radio communication systems, adaptive reception techniques, radiolocation, and asynchronous localization methods. Education: He earned his Ph.D. (2000) and habilitation (2012) at Gdańsk University of Technology, and became a full professor in 2020. His academic career spans roles from research assistant (1993–2001) to his current dean position. Research Interests: His work emphasizes asynchronous wireless localization (e.g., ATDOA method), radiolocation systems, and wireless monitoring technologies. He has contributed to projects like the AEGIR system for ground-based radiolocation and the ARPOL mobile measuring station. Publications: Over 230 publications, with recent work focusing on hybrid localization systems, IoT sensor networks, and SDR-based solutions. His research bridges theoretical advancements and practical applications in radio communication and navigation. Awards: Golden Cyborg Award (2023) Silver Medal for Long Service (2018) Gold Medal in Poznań International Fair (2010) Advising & Grants: Extensive supervision of academic projects and involvement in funded research on radiolocation, environmental monitoring, and wireless systems. Labs/Teams: Leads research teams in radio communication systems, radiolocation, and IoT applications, leveraging Gdańsk Tech’s infrastructure for hands-on experimentation and simulation.

• Cloud Computing
• Edge Computing
• Artificial Intelligence
• +7 more

Dong Yuan is an Associate Professor at the School of Electrical and Computer Engineering at The University of Sydney. He holds a B.Eng and M.Eng from Shandong University (SDU) and a PhD from University of Technology Sydney (SUT). His research spans multiple cutting-edge domains including cloud and edge computing, artificial intelligence, blockchain, and smart energy systems. Professor Yuan's research interests focus on the intersection of computing systems and intelligent applications. His work bridges theoretical foundations with practical implementations in distributed systems, with particular emphasis on optimizing resource allocation, enhancing security, and improving efficiency in next-generation computing environments. His research has significant applications in energy systems, cybersecurity, and AI deployment in constrained environments. His recent publications demonstrate a strong trend toward federated learning, privacy-preserving techniques, blockchain applications for security, and edge-based AI deployment. The research shows increasing integration of multiple domains, with papers combining AI techniques with energy systems, blockchain with privacy preservation, and edge computing with distributed learning frameworks. His work consistently addresses practical challenges in real-world deployment of intelligent systems. Professor Yuan actively supervises a substantial cohort of 18 research students working on diverse projects spanning AI security, blockchain applications, energy systems optimization, and advanced machine learning techniques. His students' research projects reflect his broad expertise and the interdisciplinary nature of modern computing research. His laboratory work appears to focus on distributed computing systems with applications in energy, security, and AI. The research environment supports both theoretical algorithm development and practical implementation testing across multiple domains including smart grids, cybersecurity frameworks, and edge computing infrastructure.

Douglas DensmoreView profile

Professor

• Synthetic Biology
• Microfluidics
• Computer Architecture
• +4 more

Roles and Affiliations: Douglas Densmore is a Professor of Electrical and Computer Engineering at Boston University, holding the Tegan Family Distinguished Faculty Fellowship. He leads the CIDAR Group and directs the Biological Design Center. His primary appointment is in the College of Engineering, with affiliations in Biomedical Engineering, Bioinformatics, and Materials Science & Engineering. He also serves as an affiliated PI in the Engineering Biology Research Center and NSF CELL-MET ERC. Education: He earned a PhD in Electrical Engineering from UC Berkeley (2007), following MS (2004) and BS (2001) degrees from UC Berkeley and the University of Michigan, respectively. His postdoctoral research included work at UC Berkeley and the Synthetic Biology Engineering Research Center (SynBERC). Research Interests: Densmore focuses on synthetic biology tools, microfluidics, and design automation. His work integrates electronic design automation (EDA) principles with biological systems to create high-level abstraction frameworks. Key areas include: Development of domain-specific languages for genetic circuit design Standardization of biological parts and data exchange Hardware-software-wetware co-design for synthetic systems Microfluidic platforms for high-throughput experimentation Awards and Recognition: He has received prestigious honors such as the AIMBE Fellowship (2021), Inaugural DAC Under-40 Innovators Award (2017), and NSF CAREER Award (2013). His contributions span academic, entrepreneurial, and editorial roles in synthetic biology. Grants and Labs: His research is supported by grants from NSF, NIH, DARPA, and industry partnerships. He leads the CIDAR lab, which combines computational and experimental approaches. He co-founded companies like Lattice Automation and Asimov, advancing bio-design automation commercially.

Ning YangView profile

Assistant Professor

• Information Systems
• Technology Uses and Adoptions
• AI Awareness
• +7 more

Ning Yang is an Assistant Professor of Information Systems at the Quinlan School of Business, Loyola University Chicago. She holds a PhD in Information Systems from the University of Alabama and contributes to both teaching and research in business analytics, system requirements, and data mining. Her academic work focuses on emerging technologies and their integration into organizational and educational contexts. Education: PhD, Information Systems, University of Alabama MS, Marketing, University of Alabama MA, Educational Psychology, University of Alabama MA, TESOL, University of Alabama BS, English, Anhui University Her research interests include technology adoption, AI awareness, Internet of Things, cybersecurity, business analytics, and pedagogy . She explores how individuals and organizations interact with digital systems, with a focus on user behavior, educational effectiveness, and security awareness. Her work combines behavioral models with technical implementation, often applying replication and empirical validation methods. The 15 most recent publications reflect a strong interdisciplinary focus, bridging information systems, education, health informatics, and cybersecurity . Trends include the role of trust and perception in technology adoption, AI literacy in business education, and data-driven methods in learning and decision-making. Her research frequently applies frameworks like the Technology Acceptance Model (TAM) and extends them to new domains such as IoT and mHealth. Scientific Awards and Recognitions: Selected for AMCIS Doctoral Consortium (2020) Selected for ICIS Doctoral Consortium (2020) Dr. Yang actively contributes to academic advising and curriculum development, particularly in business analytics and information systems education. She teaches core courses including Business Analytics, Requirements Analysis, and Data Mining. While specific grant details are not listed, her participation in doctoral consortia and publication record suggest active research funding and scholarly engagement. She is a member of the Association of Information Systems and maintains a presence through Google Scholar. She is affiliated with the Quinlan School of Business and contributes to its academic mission through teaching, research, and professional service. No dedicated lab or research team is mentioned, but her work aligns with initiatives in applied artificial intelligence and business analytics at Loyola.

• Digital signal processing
• Machine learning
• Sensor network
• +3 more

Stefan Werner is a Professor at the Department of Electronic Systems, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU). Holding degrees in civil engineering from the Royal Institute of Technology (Sweden) and a Dr.Scient from Helsinki University of Technology (Finland), he leads research in digital signal processing, machine learning, and wireless communication. His work focuses on: Distributed learning algorithms Federated learning security Privacy-preserving signal processing Kernels and graph-based learning Robust wireless communication Recent publications address federated learning with non-convex penalties, anomaly detection in hyperspectral data, and resilience against model-poisoning attacks. Collaborators include researchers from IEEE and international institutions. Selected affiliations: Signal Processing research group, NTNU IoT@NTNU initiative Norwegian Open AI Lab

Chuck AndersonView profile

Professor

• Machine Learning
• Deep Learning
• Reinforcement Learning
• +9 more

Chuck Anderson is a Professor in the Department of Computer Science at Colorado State University (CSU), with joint appointments in the Molecular, Cellular, and Integrative Neuroscience Program, the School of Biomedical Engineering, the Graduate Degree Program in Ecology, and the Online Systems Engineering Program. His research focuses on machine learning, deep learning, and reinforcement learning, with applications to brain-computer interfaces, high-dimensional data, health, environment, and energy. Research Interests: His work spans algorithm development for classification, modeling, and control, with a strong emphasis on understanding learned representations. He applies these methods to neuroscience, climate science, programming, and accessibility technologies, demonstrating a highly interdisciplinary approach. His research integrates insights from computer science, neuroscience, and engineering to solve complex real-world problems. Publication Trends: His recent and advised publications reveal a sustained focus on reinforcement learning, deep neural networks, and brain-computer interfaces. There is a growing trend toward diffusion models, ensemble methods, and applications in climate and healthcare, indicating forward-looking and impactful research directions. Scientific Awards: CSU Online Outstanding Distance Educator Award (2024) Best Overall Paper Award, IJCNN 2015 Advising and Grants: He has advised over 15 Ph.D. students, with recent graduates working on reinforcement learning, neuroimaging, climate AI, and program synthesis. While specific grants are not listed, his sustained output and advising suggest consistent research funding. He also leads an AI consulting company, Pattern Exploration, focusing on custom AI solutions for health, environment, and energy. Labs and Teams: Though not explicitly named, his work implies leadership in a machine learning research group at CSU, likely involving students and collaborators across neuroscience, biomedical engineering, and climate science. His involvement in online AI programs and consulting indicates active outreach and applied research teams.

• Causality
• Statistical Machine Learning
• Probabilistic Modeling
• +12 more

Bryon Aragam is an Associate Professor and Topel Faculty Scholar at the Booth School of Business , University of Chicago . His work bridges causality , statistical machine learning , and probabilistic modeling , with applications in AI systems like ChatGPT and DALL-E. Key research themes include: Causal Structure Learning : Extracting latent causal graphs from multimodal data using nonparametric methods. Deep Generative Models : Analyzing overparametrization and variational inference for representation learning. Latent Variable Discovery : Using Markov boundaries and convex subset lattices to uncover hidden dependencies. Algorithm Design : Developing scalable methods like DAGMA for DAG learning and theoretical guarantees for GES/PC algorithms. His paper trends reveal a focus on nonparametric statistics , graphical models , and neural network theory , with recent work on transformer memory dynamics and identifiability in deep latent models . Papers frequently appear in top venues like NeurIPS , JMLR , and AOS , emphasizing theoretical rigor and practical validation.