Find Academic Supervisors

• Mechanical Engineering
• Aerospace Engineering
• Systems Engineering
• +3 more

Gerhard Stefan Székely is a Lecturer and Head of the Bachelor's Program in Mechanical Engineering at the Lucerne School of Engineering and Architecture (HSLU). He specializes in mechanical systems for space research, systems engineering, and innovative teaching methods like flipped classrooms and blended learning. His industry experience includes 16 years at RUAG Space Zurich, leading R&D in FEM analysis for satellite mechanisms and systems engineering for projects like GAIA BRM and ALADIN instruments. Education: TU München: Mechanical Engineering (Aerospace focus) PhD from Universität Innsbruck: Stochastic structural mechanics for ESA projects Research Focus: Space mechanical systems, systems engineering methodologies, and educational innovation in STEM. Notable projects include the VenSpec-H spectrometer for ESA's EnVision mission and the COW experiment studying cartilage in microgravity. Professional Contributions: Authored over 20 peer-reviewed publications and technical reports on space mechanisms, structural reliability, and educational methodologies. Leads HSLU's contributions to national MINT education networks.

Marios KogiasView profile

Assistant Professor

• Operating Systems
• Networking
• Distributed Systems
• +4 more

Marios Kogias is an Assistant Professor in the Department of Computing at Imperial College London, conducting research at the intersection of operating systems, networking, and distributed systems with a focus on tail-tolerance, fault-tolerance, and confidentiality using emerging datacenter hardware. He holds a PhD in Computer Science from EPFL (2020) supervised by Ed Bugnion, where his thesis won the Dennis M. Ritchie Award and received an Honourable Mention for the Eurosys Roger Needham PhD Award. His undergraduate degree in Electrical and Computer Engineering is from NTUA. His research spans operating systems, networking, distributed systems, cloud computing, and confidential computing, with significant contributions to microsecond-scale computing, serverless architectures, and eBPF applications. Current work emphasizes tail-latency optimization in datacenter environments and confidential computing security. Recent publications reveal strong trends in microsecond-scale systems, confidential computing, and serverless workflows, demonstrating expertise in leveraging hardware innovations for performance and security guarantees across distributed environments. His scientific awards include: Dennis M. Ritchie Award (2021) Honourable Mention for the Eurosys Roger Needham PhD Award (2021) IBM Fellowship Microsoft Swiss JRC Fellowship Distinguished Artifact Award (ASPLOS 2021) Best Student Paper Award (Eurosys 2020) Dr. Kogias actively mentors PhD students including Konstantinos Prasopoulos (co-advised with Ed Bugnion), Scofield Liu, Farzad Mohammadi, and Adel Sefiane. He teaches Networked Systems, Computer Networks and Distributed Systems, and Object-Oriented Design at Imperial, supported by fellowships from IBM and Microsoft during his doctoral research. His collaborative work spans EPFL, Microsoft Research Cambridge, and Imperial College London, focusing on systems architecture for next-generation datacenter applications.

Christos NomikosView profile

Associate Professor

• Algorithm Design and Analysis
• Approximation Algorithms
• Computational Complexity
• +3 more

Christos Nomikos is an Associate Professor at the Department of Computer Science and Engineering, University of Ioannina. He holds a PhD from the National Technical University of Athens (NTUA) and has been a faculty member since 2003. His roles include teaching and research supervision in computer science disciplines. Education: Diploma in Electrical Engineering, NTUA, 1992 PhD in Computer Science, NTUA, 1997 Research Interests: Christos Nomikos focuses on theoretical computer science and logic, including algorithm design and analysis, computational complexity, formal languages, logic programming, and graph theory. His work explores foundational aspects of programming languages and their semantics, as well as applications in knowledge representation and automated reasoning. Teaching: In the 2024-25 academic year, he teaches Discrete Mathematics II , Principles of Programming Languages , Computational Complexity , and Approximation Algorithms . He emphasizes structured learning approaches and maintains a dedicated ecourse page for students. Advising & Grants: He supervises undergraduate thesis projects in computer science, with a focus on theoretical topics like Turing machine transformations and Boolean grammar algorithms. No explicit grant information is provided in the texts. His advising process involves rigorous academic evaluation and prefers candidates with strong performance in core computer science courses. Labs/Teams: While no specific lab or team affiliations are mentioned, his research collaborations include work with international conferences and journals, indicating active participation in academic networks.

Daniel W. BarowyView profile

Associate Professor

• Programming Languages
• End-User Programming
• Spreadsheet Debugging
• +5 more

Daniel W. Barowy is an Associate Professor in the Department of Computer Science at Williams College . His research focuses on programming languages , particularly end-user programming , crowdsourcing , and spreadsheet debugging . He integrates program analysis with statistical techniques to improve software usability and robustness. Current affiliations: Williams College (2017-present) Education: University of Massachusetts Amherst (PhD, 2017) His research explores language abstractions for human-computer integration , with notable projects like ExceLint (spreadsheet error detection), FlashRelate (spreadsheet data extraction), and Riker (incremental build systems). He emphasizes artifact verification , securing PLDI 2015 Distinguished Artifact Award and USENIX ATC 2022 Best Paper . Recent publications demonstrate trends in spreadsheet reliability , crowdsourcing frameworks , and scalable educational tools . He has received "Artifact Verified" badges for multiple projects and actively contributes to software tool development , including AutoMan , ExceLint , and CheckCell . USENIX ATC 2022 Best Paper PLDI 2015 Distinguished Artifact Award Multiple "Artifact Verified" badges As an educator, he teaches CSCI 334: Principles of Programming Languages and CSCI 331: Computer Security . His work bridges theoretical PL research with practical applications in spreadsheet programming and crowdsourcing platforms .

• Hardware Design
• FPGA Programming
• Parallel Computing
• +5 more

Satnam Singh is a Professor at Newcastle University's School of Electrical and Electronic Engineering, UK. With a research career spanning over three decades from 1989 to present, Singh has established himself as a leading expert in hardware design, FPGA programming, and parallel computing systems. His research interests focus on hardware-software co-design , reconfigurable computing , and functional programming applications for hardware design. Singh has pioneered work in using functional languages like Haskell for hardware description and verification, particularly through his contributions to the Lava hardware description language. His work bridges theoretical computer science with practical hardware implementation challenges. Analysis of his publication history reveals a clear evolution from early work on formal verification and FPGA design in the 1990s, through substantial contributions to parallel programming models in the 2000s, to more recent applications of machine learning techniques in diverse domains including cheminformatics and sensory systems. His 2022-2025 publications demonstrate continued innovation in specialized processor programming, AI applications for olfactory systems, and health hazard classification using deep learning. Singh has maintained extensive collaborations throughout his career, notably with Krishna R. Pattipati (15 joint publications), Anuradha Kodali (8 publications), and David J. Greaves (5 publications), reflecting his ability to bridge theoretical computer science with practical engineering applications. His work spans multiple prestigious venues including FPGA, FCCM, ICFP, and IEEE Transactions on Systems, Man, and Cybernetics, demonstrating both theoretical depth and practical impact across computer architecture, programming languages, and applied machine learning domains.

• Operations Research
• Discrete Event Simulation
• Deterministic Scheduling
• +4 more

John Fowler is a Professor in the Department of Supply Chain Management at Arizona State University's W. P. Carey School of Business, holding the Motorola Professorship in International Management. He previously served as Department Chair (2011-2016) and Avnet Professor of Industrial Engineering at ASU. Education: Ph.D. in Industrial Engineering, Texas A&M University (1990) M.S. in Industrial Engineering, Texas A&M University (1986) B.S. in Industrial Engineering, Texas A&M University (1982) Research Interests focus on discrete event simulation , deterministic scheduling , and multi-criteria decision making , particularly in semiconductor manufacturing systems and healthcare operations. His work bridges operations research with applications in pandemic preparedness, supply chain design, and energy-efficient scheduling. Recent Publications demonstrate expertise in simulation modeling for pandemic response, semiconductor supply chain optimization, and healthcare systems analysis. He has developed decision support frameworks for push-pull supply chains and hybrid scheduling approaches for batch processing in semiconductor manufacturing. Scientific Awards Fellow of the Institute of Industrial and Systems Engineers Outstanding Paper Award, International Journal of Lean Six Sigma (2013) Editorial Service includes Editor-in-Chief of IIE Transactions on Healthcare Systems Engineering (2011-2016) and ongoing roles at Journal of Simulation and IEEE Transactions on Semiconductor Manufacturing . He has held leadership positions in academic societies and served on editorial boards for multiple journals. Grants and Industry Collaboration include NSF funding for healthcare delivery systems, Intel-sponsored semiconductor research, and Mayo Foundation partnerships. His work has addressed pandemic influenza preparedness through simulation models and developed capacity allocation solvers for high-tech industries. Laboratory Leadership encompassed directing the Modeling and Analysis of Semiconductor Manufacturing Laboratory at ASU from 1996 to 2009, where he pioneered simulation frameworks and scheduling methodologies for complex manufacturing systems.

• Programming Languages
• Functional Programming
• Type Systems
• +4 more

Ningning Xie is an Assistant Professor in the Department of Computer Science at the University of Toronto, with a part-time role as a Research Scientist at Google DeepMind and a Faculty Affiliate at the Schwartz Reisman Institute. Her research focuses on programming languages, particularly functional programming, type systems, and logics, with applications in broader domains. Ph.D. from the University of Hong Kong B.S. from Zhejiang University Her work explores foundational and applied aspects of programming languages, including: Multi-stage programming and staging techniques Type systems and type inference Algebraic effect handlers and concurrency Compiler design and optimization Recent publications highlight advancements in type inference, effect handlers, and parallel computing. She actively contributes to academic service as a program chair and committee member for conferences like PEPM, Haskell, and POPL. ACM SIGPLAN Distinguished Paper Award (ICFP'25, PLDI'25, PLDI'21) Cambridge Hall of Fame Awards: Publication of the Year As a mentor, she seeks motivated students for her research group. Her affiliations with Google DeepMind and Schwartz Reisman Institute facilitate interdisciplinary collaboration.

• Global Optimization
• Blockchain Technologies
• Quantum Computing
• +2 more

Prof. Dr. Remigijus Paulavičius is a Senior Researcher, Professor, and Group Leader at the Blockchain and Quantum Technologies Group of Vilnius University. He earned his Doctor of Science degree in Computer Science in 2010 from Vilnius University's Institute of Mathematics and Informatics, focusing on global optimization with simplex subdomains under the supervision of Dr. J. Žilinskas. Research Interests : His work bridges global optimization methods with blockchain and quantum computing. Key areas include non-convex bilevel programming, derivative-free optimization algorithms (e.g., DIRECT), and applications in blockchain scalability, consensus protocols, and quantum circuit design. Scientific Awards : Best Paper Award, Journal of Global Optimization (2014) Funding from Lithuanian State Science and Studies Foundation for doctoral students (2007–2009) Member, Young Academy of the Lithuanian Academy of Sciences (since 2019) Projects & Leadership : Leads research on blockchain simulators, quantum machine learning frameworks, and optimization toolboxes (e.g., DGO, DIRECTGO). Involved in international collaborations and conference committees, including AIChE and EUROPT. Publications : Over 70 works on global optimization algorithms, blockchain applications, and quantum computing, with recent focus on generative AI, energy consumption in blockchains, and reproducibility in machine learning.

Adam ChlipalaView profile

Professor

• Programming Languages
• Formal Methods
• Computer Systems
• +5 more

Adam Chlipala is a Professor at the Massachusetts Institute of Technology working at the intersection of programming languages, formal methods, and computer systems. His research focuses on building practical verified systems with end-to-end machine-checked proofs, particularly using the Coq proof assistant. His educational background includes a Computer Science undergraduate degree from Carnegie Mellon University (2003) and a PhD in Computer Science from the University of California, Berkeley (2007). Following a postdoctoral position at Harvard University through 2011, he joined MIT as faculty. Chlipala's research spans multiple domains with strong emphasis on dependent types , verified compilation , and hardware-software co-verification . His work consistently bridges theoretical foundations with practical implementation, as evidenced by his development of the Ur/Web programming language and his focus on creating clean-slate hardware-software stacks with formal guarantees. Key research thrusts include cryptographic constant-time verification, side-channel security, and verified tensor compilation. His recent publications (2020-2025) reveal a clear trajectory toward increasingly complex verified systems, with growing emphasis on hardware-software integration, cryptographic implementations, and performance-critical applications. The work consistently leverages Coq for machine-checked proofs while addressing real-world constraints like timing channels and hardware interfaces. Chlipala is the author of the influential textbook Certified Programming with Dependent Types , which serves as a primary educational resource for Coq at numerous institutions worldwide. His professional activities include significant service to the PL community through program committees for major conferences including PLDI, POPL, ICFP, and CPP. He leads research initiatives connecting hardware and software verification, most notably through the DeepSpec project which aims to build fully verified computing stacks. His current work focuses on practical applications of dependent types for business applications through Ur/Web and verified cryptographic implementations.

• Combinatorial Optimisation
• Explainable AI
• Metaheuristics
• +7 more

Dr. Lee Christie is a Research Fellow at Robert Gordon University's School of Computing, Engineering & Technology, where he conducts research in optimization and artificial intelligence. He is affiliated with the Complex Optimisation Research Group and maintains connections with the National Subsea Centre through his research on net-zero operations. His educational background includes: BSc (Hons) in Computer Science from Robert Gordon University (2003-2007) MSc in Information Engineering (Distinction) from Robert Gordon University (2009-2011, part-time) PhD in Computational Intelligence from Robert Gordon University (2011-2016) Dr. Christie's research primarily focuses on combinatorial optimization, structure learning, and blockchain technologies. He investigates how to make non-deterministic solvers more transparent through trajectory mining and feature extraction. His work bridges theoretical optimization techniques with practical applications in transportation systems (particularly connected autonomous vehicles), renewable energy (wind farm optimization), and supply chain management. He has published extensively on explainable metaheuristics, with a growing emphasis on making optimization algorithms interpretable for end-users while maintaining effectiveness. His recent publications (2021-2025) demonstrate a clear research trajectory toward explainable AI for optimization algorithms, with applications spanning transportation systems, renewable energy infrastructure, and complex supply chains. These works showcase his ability to translate theoretical advances into practical solutions for real-world problems with societal impact. Dr. Christie has secured research funding for projects including the A.R.T. Forum NSR (2019-2022), which developed implementation roadmaps for automated road transport in the North Sea Region. He teaches programming for business analytics courses and actively contributes to the Aberdeen Python User Group as a steering committee member. His academic service includes supervision of PhD students, with Dr. Martin Fyvie recently completing a dissertation on 'Explainability of Non-Deterministic Solvers' under his guidance as second supervisor. Dr. Christie maintains active collaborations with researchers including John McCall, A.-C. Zăvoianu, and A.E.I. Brownlee, resulting in consistent publication output across reputable venues in evolutionary computation and artificial intelligence.

Vincent WeaverView profile

Associate Professor

• Hardware Performance Counters
• Computer Architecture
• High Performance Computing
• +8 more

Vincent Weaver is an Associate Professor in the Electrical and Computer Engineering Department at the University of Maine's College of Engineering. He leads the VMW Research Group, focusing on low-level systems research including hardware performance counters, computer architecture, and operating systems. Weaver received his BS in Electrical Engineering from the University of Maryland College Park in December 2000, followed by MS (January 2009) and PhD (May 2010) degrees in Electrical and Computer Engineering from Cornell University. He joined the University of Maine faculty in July 2012 as an Assistant Professor and earned tenure and promotion to Associate Professor in September 2018. His research centers on hardware performance analysis, architectural simulation, and systems programming with emphasis on Linux kernel development and embedded systems. Weaver's work bridges theoretical computer architecture with practical systems implementation, often resulting in open-source tools that advance the field. His publications reveal a consistent focus on performance analysis techniques, code optimization, and security through low-level system understanding. Weaver maintains an active teaching schedule including courses in embedded systems, operating systems, and network engineering. He values students with strong programming skills and encourages open source contributions as part of the learning process. His research group provides hands-on experience with cutting-edge processor architectures and performance analysis tools.