Find Academic Supervisors

David McAllesterView profile

Professor

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +7 more

David McAllester is a Professor at the Toyota Technological Institute at Chicago (TTIC) and holds a part-time Professor position at the University of Chicago's Department of Computer Science. He earned his B.S., M.S., and Ph.D. from MIT (1978, 1979, 1987). His research spans Artificial Intelligence, Machine Learning, and Theoretical Computer Science , with notable contributions to automated theorem proving (Ontic system), reinforcement learning, probabilistic programming, and computer vision. He is a Fellow of AAAI (since 1997) and has received multiple test-of-time awards for seminal papers in AI planning, constraint solving, and computer vision. Key Contributions: Developed the Ontic verification system for mathematical proofs. Pioneered conspiracy numbers in game tree search (influenced Deep Blue). Co-authored foundational work on policy gradient methods in reinforcement learning. Advanced PAC-Bayesian learning theory and co-training methods. Teaching: Teaches TTIC31230 (Fundamentals of Deep Learning), emphasizing mathematical rigor and research skills in computer vision, NLP, and reinforcement learning. Labs/Teams: Co-founded TTIC's research initiatives in AI and machine learning. Collaborates with industry and academia on foundational AI challenges. Awards: AAAI Fellow (1997) Test-of-Time Awards (AAAI, ICLP, CVPR)

• Reinforcement Learning
• Multi-Objective Optimization
• Automated Design
• +3 more

Florian Felten is a postdoctoral researcher at ETH Zürich's Department of Mechanical and Process Engineering, affiliated with the Artificial Intelligence in Engineering Design group. His research focuses on developing algorithms for automated design using reinforcement learning and multi-objective optimization. He holds a PhD from the University of Luxembourg (excellent thesis award) and an M.Sc. in Computer Science from Université Catholique de Louvain. Education: PhD in Computer Science, University of Luxembourg (2021) M.Sc. in Computer Science, Université Catholique de Louvain, Belgium (2018) His research interests include multi-objective reinforcement learning (MORL), decomposition-based methods, and software frameworks for benchmarking. He contributed to the development of tools like MO-Gym, MORL-Baselines, and a comprehensive taxonomy for MORL/D frameworks. His work emphasizes practical applications in engineering design and optimization. Key achievements include a 2023 NeurIPS publication on benchmarking tools and a 2024 Journal of AI Research paper establishing a MORL taxonomy. He has also explored metaheuristics for exploration strategies in MORL and presented work at OLA and ICAART conferences. Awards: Excellent Thesis Award for PhD work on Multi-Objective Reinforcement Learning Felten's research bridges reinforcement learning and optimization, with applications in automated design and algorithm development. He collaborates on open-source libraries and benchmarks to advance the field.

• Hardware Security
• Machine Learning for Security
• VLSI Design
• +7 more

Sai Manoj Pudukotai Dinakarrao is a Professor in the Department of Computer Engineering at Rochester Institute of Technology's College of Engineering. He is an active researcher with numerous publications spanning hardware security, machine learning applications, VLSI design, and IoT security. His work focuses on developing innovative solutions for security challenges in hardware and embedded systems. Dr. Dinakarrao's research interests center around hardware security, machine learning for security applications, VLSI design methodologies, and IoT security frameworks. His work has pioneered approaches to hardware Trojan detection, processing-in-memory architectures, and energy-efficient security mechanisms for resource-constrained devices. He has made significant contributions to the understanding of hardware vulnerabilities and the development of robust countermeasures. His recent publications demonstrate a clear trend toward integrating machine learning techniques with hardware security solutions, particularly focusing on processing-in-memory architectures for efficient security implementations. His work spans both theoretical foundations and practical implementations, with applications in IoT security, malware detection, and hardware verification. The research shows increasing sophistication in addressing security challenges through novel hardware-software co-design approaches. Dr. Dinakarrao has mentored numerous graduate students who have co-authored publications with him, indicating an active research group focused on cutting-edge security and hardware design topics. His collaborations span multiple institutions and research groups, demonstrating his integration within the broader academic community.

Joel MillerView profile

Associate Professor

• Infectious Diseases
• Network Theory
• Stochastic Analysis and Modelling
• +7 more

Joel Miller is an Associate Professor in Mathematics & Statistics at La Trobe University, School of Computing Engineering and Mathematical Sciences. His research focuses on infectious disease modelling, network theory, and ethical considerations in public health policy. He has held academic positions at Monash University, Penn State University, and the University of British Columbia Centre for Disease Control. Miller earned his PhD from the University of Cambridge and has contributed to significant projects, including the Australian Centre for AI in Medical Innovation and federal studies on pandemic response. His teaching includes courses on Vector Calculus, Infectious Disease Modelling, and Network Epidemiology. Notable awards include the 2021 La Trobe honourable mention for research impact and the RAPIDD Postdoctoral Fellowship. Education: PhD (University of Cambridge), MMath (University of Cambridge), BS (Harvard Mudd College). Research interests span epidemic dynamics, network structures, stochastic simulation algorithms, and ethical frameworks in computational modelling. He has led grants including a 2020 federally-funded study on pandemic testing capacity and contributed to the National COVID plan for Remote/Indigenous communities. Scientific Awards: Larkins Fellowship, RAPIDD Postdoctoral Fellowship, and multiple fellowships. His work has been featured in journals like PLOS Computational Biology and Science Advances. Teaching and Advising: Supervised numerous PhD/Masters students and developed courses like Dynamics of Spreading Processes on Networks. His mentorship includes projects on hepatitis C epidemiology, hypergraph contagion, and AI-driven network structure analysis. Professional Activities: Editorial roles in journals like PNAS and Science Advances, and media collaborations with outlets like Reuters and the New York Times.

FİDEL ÇAKMAKView profile

Associate Professor

• Mobile-assisted language learning
• Technology-based language education
• Artificial intelligence in language learning
• +7 more

Assoc. Prof. FİDEL ÇAKMAK is an academic at the Department of Foreign Language Education, Faculty of Education, Alanya Alaaddin Keykubat University. His academic journey includes roles as a lecturer and associate professor at institutions like Mersin University and Alanya Alaaddin Keykubat University. He holds a Doctorate from Boğaziçi University (2014) and has extensive experience in teacher training and curriculum development. His research focuses on mobile-assisted language learning (MALL), artificial intelligence (AI) in education, affective factors in language learning, and digital pedagogy. Notable projects include developing in-service training programs for educators and investigating the impact of AI on teacher well-being. He has also contributed to international journals like ReCALL and Computers in Human Behavior Reports . Prof. Çakmak has served on editorial boards, including CALL-EJ and Language and Technology , and is a member of organizations like EUROCALL and TESOL. His work emphasizes integrating technology with traditional teaching methods to enhance language acquisition and teacher effectiveness.

Charles ClarkeView profile

Professor

• Search
• Information Retrieval
• Question Answering
• +3 more

Charles Clarke is a Professor in the Department of Computer Science at the University of Waterloo. His research focuses on information retrieval, search, question answering, data science, and software tools. He holds a PhD in Computer Science from the University of Waterloo (1996), an MMath in Computer Science (1990), and a BSc (Honours) in Mathematics/Computer Science from Memorial University of Newfoundland (1986). Clarke has organized and contributed to major workshops, including LLM4Eval and Search Futures, which explore the role of large language models (LLMs) in evaluation and future search technologies. His work emphasizes human-AI collaboration, adversarial testing of models, and novel evaluation metrics like Normalized Residual Gain. Key contributions include frameworks for benchmarking LLM-based relevance judgments and analyzing prompt sensitivity in AI systems. He has published extensively on topics such as adversarial attacks on neural ranking models (EMPRA) and generative approaches to information retrieval evaluation. Clarke actively participates in conferences like SIGIR and WSDM, contributing to testbeds and community-building initiatives in IR research. His research bridges theoretical advancements in AI with practical applications, addressing challenges in search effectiveness, security, and human-AI synergy.

Gabriele CostaView profile

Associate Professor

• Cybersecurity
• Formal Methods
• Mobile Systems Security
• +5 more

Gabriele Costa is an Associate Professor at the SysMA Group of the IMT School for Advanced Studies Lucca. He holds a Laurea and Ph.D. in Computer Science. Previously, he was part of the cybersecurity group at CNR’s Istituto di Informatica e Telematica and served as a visiting researcher at ETH Zurich (2016-2017). He co-founded the Computer Security Laboratory (CSec) at DIBRIS and is CRO of Talos, a cybersecurity spin-off. His research focuses on formal methods for verifying and testing mobile/modular systems, including mobile software, cloud computing, and critical infrastructure security. He has led/co-founded initiatives like the MAVeriC framework for mobile app security and contributed to projects such as SPARTA (EU H2020) and FilieraSicura (CISCO-funded). Costa actively participates in conferences like EuroSP and ITASEC, serving on program committees. His work spans security protocols, penetration testing tools (e.g., MeTeOr), and educational initiatives like cybersecurity games for K-12 students. He emphasizes bridging theory and practice through frameworks like Lidite (digital twins) and tools targeting zero-day malware detection (Z-MDZS).

• Cyber-Physical Production Systems Engineering
• Software Product Line Engineering
• Variability Modeling
• +6 more

Kristof Meixner is a Postdoctoral Researcher at the Institute of Information Systems Engineering (E194) within the Faculty of Informatics at Technische Universität Wien. He completed his PhD in Computer Science in 2024 and holds a Master's degree in Business Informatics from the same institution. His academic journey includes significant teaching responsibilities and substantial research contributions in cyber-physical production systems engineering. Position: Postdoctoral Researcher, Institute of Information Systems Engineering (Dec 2024 - present) Previous Position: Researcher at Christian Doppler Laboratory for Security and Quality Improvement in Production Systems (Mar 2018 – Dec 2024) Teaching: Lecturer for Advanced Software Engineering, Software Engineering Project, and related courses since 2018 Service: Research Unit Representative in the Scientific Staff Council since 2017 Meixner's research focuses on model-based approaches for software product line engineering in cyber-physical production systems. His work addresses the integration of interdependent models for products, processes, and resources, with particular emphasis on variability management, change impact analysis, and digitalization of production systems. His research combines empirical methods with practical tool development to improve software engineering practices in industrial contexts. His publication portfolio shows a strong trend toward multi-disciplinary approaches that integrate software engineering with production systems engineering. The 2024 publications particularly emphasize risk management, multi-domain modeling, and the application of AI techniques to variability management problems. His work frequently appears in top-tier software engineering and production systems conferences including ETFA, SPLC, and CBI. 80+ peer-reviewed publications 754 citations h-index: 16 As an educator, Meixner has supervised numerous bachelor's and master's theses, demonstrating his commitment to mentoring the next generation of software engineers. His teaching spans both theoretical foundations and practical applications of software engineering principles, with a focus on production systems contexts. He has also contributed to diversity initiatives at TU Wien, serving as a representative on the Diversity Team for the Diversitas 2016 Award. Despite experiencing a spinal cord injury in 2000 that resulted in wheelchair dependency, Meixner has maintained an active research and teaching career, demonstrating both personal resilience and professional dedication to advancing software engineering practices.

Thomas RauschView profile

Lecturer

• Edge Computing
• Cloud Engineering
• Artificial Intelligence Systems
• +3 more

Thomas Rausch is an External Researcher and former University Assistant at TU Wien's Distributed Systems Group (Faculty of Informatics). He holds a PhD in Computer Science (2021) and master's degree in Software Engineering from TU Wien (2016). His work focuses on edge computing, cloud engineering, and AI systems, with notable contributions to frameworks like LocalStack and CognitiveXR. Education: M.Sc. in Software Engineering & Internet Computing (2016), Ph.D. in Computer Science (2021 – TU Wien). Research Interests: Edge computing systems, distributed interactive systems, cognitive augmentation, and cloud-edge AI integration. He has pioneered privacy-preserving edge systems and serverless platforms for data-intensive workloads. Publications: Over 20 peer-reviewed articles on edge computing frameworks, privacy-aware systems, and large-scale AI operations. Recent work includes faas-sim (2023) and Mobility-Aware Serverless Adaptions (2022). Awards: SEC 2019 Best Demo Award, 2020 Netidee Grant, Finalist TU Wien Teaching Awards. Grants: Netidee Grant (2020), CPS/IoT Ecosystem collaboration (2019–present). Teams: Founded CognitiveXR (edge-AI platform) and co-founded LocalStack (serverless testing).

• Computer Science
• Machine Learning
• Software Engineering
• +3 more

David Wilson is an academic affiliated with the University of South Wales, Severn School of Radiology, and the Severn Deanery. He has extensive research interests in Computer Science, Machine Learning, Medical Imaging, and Human-Computer Interaction. Research Interests: His work spans fields like Software Engineering, Artificial Intelligence, and Data Science, with a focus on applications in healthcare and inclusive technology. Publications: He has contributed to recent research on generative AI for medical imaging, accessible making, and deep learning applications in biomedical contexts. Collaborations: Notable co-authors include Sharon Zhang, Naira Hovakimyan, and Lisa Moseley, reflecting interdisciplinary research in healthcare technology and machine learning.

Navid ZobeiryView profile

Associate Professor

• Composite Materials
• Advanced Manufacturing
• Scientific Machine Learning
• +3 more

Dr. Navid Zobeiry is an Associate Professor in Materials Science & Engineering at the University of Washington, with an adjunct role in Aeronautics & Astronautics. His research integrates materials science, data science, and advanced manufacturing to address aerospace challenges, focusing on smart testing, manufacturing, and engineering methods using machine learning and physics-based models. He holds patents in AI software for aerospace applications and has been recognized with awards such as the Boeing Certificate of Appreciation (2024) and the SAMPE URS 3rd Best Paper Award (2022). Education: PhD from University of British Columbia (UBC), MSc from UBC, BSc from University of Tehran. Prior to UW, he was a Research Associate and Lecturer at UBC. Research interests span composite materials, failure analysis, scientific machine learning, and uncertainty quantification in aerospace. His work emphasizes probabilistic ML frameworks for material characterization and process optimization. Recent articles focus on composite manufacturing optimization, process-induced deformations, and ML-driven thermal modeling. His publications highlight advancements in pyrolysis characterization, tool-part interaction analysis, and outlier detection in manufacturing processes. Awards include Boeing’s D-DRASS recognition (2024, 2019), a teaching award (2020), and UBC’s President’s Award (2009). He advises graduate students like C. Schoenholz and collaborates with aerospace companies on patented technologies. Labs/Teams: Composites Group at UW, leading projects on AI in manufacturing and material certification. Active in industry partnerships for applied research.

Mike GraulView profile

Professor

• Systems Engineering
• Lean Engineering
• Production System Design
• +5 more

Dr. Mike Graul is a Professor in the Department of Industrial & Systems Engineering at Texas A&M University's College of Engineering, affiliated with the Texas A&M Engineering Experiment Station and the EDGE initiative. He earned his Ph.D. in Industrial Engineering from Texas A&M University in 1995. Research Interests: His work focuses on lean engineering and systems theory, with specialized expertise in systems engineering, system simulation, production system design, and MRO (Maintenance, Repair, Overhaul) production systems. He has recently explored AI/ML applications for MRO prediction, crowd-sourced supply chain engineering, and cause/effect analysis for aircraft maintenance. Industry Experience Highlights: Faculty Sponsor, American Society for Engineering Management (2021–present) Director, IISE Society for Engineering Management Systems (2020–present) ISE PE Exam Committee (Immediate Past Chair, 2011–present) Senior Research Scientist, Knowledge Based Systems, Inc. (1992–present) Scientific Awards & Certifications: Professional Engineer (PE) Certification

Paul HarveyView profile

Lecturer

• Autonomous Systems
• Network Testing
• Federated Learning
• +3 more

Dr. Paul Harvey is a Lecturer in Low Carbon and Sustainable Computing at the School of Computing Science, University of Glasgow. He specializes in autonomous systems, focusing on safe and meaningful fully-autonomous technologies for design and operation. His roles include co-chair of the UN’s ITU-T Focus Group on Autonomous Networks and visiting researcher at the University of Strathclyde. Industrial experience includes co-founding Rakuten Mobile Innovation Studio, emphasizing open and collaborative research. Research interests span autonomous networks, digital twins, federated learning, and edge computing. Recent work includes digital twin generation for network testing and task offloading strategies in edge-based systems. He actively contributes to standards development (e.g., OntoCommons) and blockchain-based trust frameworks for autonomous systems. His advising includes student Shijia Dong on software modularization using LLMs. Grants and collaborations are managed via his institutional profile. His lab focuses on cross-disciplinary projects at the intersection of networking, AI, and sustainability.

• Software Testing
• Robotics
• Process Mining
• +7 more

Cristina Turcanu is an Assistant Professor in the School of Mathematical and Computer Sciences at Heriot-Watt University, Dubai Campus. She is actively involved in research and teaching, supervising postgraduate and undergraduate programs in computer science, robotics, and data science. She is currently accepting PhD students and contributes to academic leadership as Year 1 Coordinator, Induction Coordinator, and Outreach Coordinator. Education: Bachelor’s in Computer Science, University of Pitesti, Romania Master’s in Advanced Techniques in Information Processing, University of Pitesti, Romania PhD in Computer Science, University of Pitesti, Romania Postgraduate Certification in Teaching and Learning (PGCert-TL), Heriot-Watt University Her research focuses on software testing methodologies applied to robotics, process mining, machine learning, robotic process automation, and knowledge management. She explores how data-driven techniques can improve software validation and automation in complex systems, particularly in robotic environments. Her interdisciplinary work bridges software engineering, AI, and industrial applications. The recent publications reflect a strong trend in applying machine learning and formal methods to robotics and software validation. Her work spans predictive analytics in healthcare and finance, as well as process mining and model checking in robotic systems. This indicates a cohesive research trajectory focused on reliability, automation, and intelligence in software and robotic systems. She is a Fellow of Advance HE and an ambassador for Technovation, a global nonprofit that empowers girls in technology. Her outreach and educational roles emphasize inclusive tech education and innovation. She has advised and taught students across multiple programs including BSc Computer Science, BEng Robotics, MSc Data Science, and MSc Artificial Intelligence. While specific grant details are not listed, her research output suggests active engagement in applied computing projects. She is also involved in knowledge transfer through teaching courses such as Software Development, e-Commerce Technologies, and Knowledge Management. Cristina is associated with research initiatives that contribute to UN Sustainable Development Goals, particularly through technology for societal benefit. Her work integrates ethical and sustainable computing practices, especially in education and automation.

• Chemistry Education
• Computational Chemistry
• Biochemical Simulations
• +5 more

Andreas Drolsum Haraldsrud is an Associate Professor at the Department of Chemistry, University of Oslo. His academic affiliations include membership in the CCSE - Center for Computing in Science Education. He specializes in chemistry education, computational methods, and the application of simulations in understanding chemical concepts. His research focuses on cognitive processes in chemistry education, particularly how computer simulations and AI influence learning. Key areas include biochemical simulations of lipids/proteins, soft material surface processes, and integrating lab techniques like confocal microscopy with computational methods (molecular dynamics, machine learning). Teaching responsibilities include courses such as IN-KJM1900 (Programming for Chemists), KJM1140 (Biochemistry 1), and NAT3000 (Scientific Programming in School). He also engages in teacher training through Profag:VGS and general science education courses. Recent publications emphasize educational technology assessment (Nordic Programming Test), feedback loops in simulations for learning, and computational approaches to chemical kinetics. His work bridges theory and practice, advocating for technology-enhanced STEM pedagogy. No scientific awards are listed, but his contributions to curriculum development and educational research are notable. He advises on multiple interdisciplinary projects combining computational chemistry with biochemistry education. His research group affiliation indicates active participation in science education innovation.