Find Academic Supervisors

John SousView profile

Assistant Professor

• Condensed Matter Physics
• Quantum Materials
• Ultracold Atoms/Molecules
• +4 more

John Sous is an Assistant Professor of Applied Physics at Yale University. His research focuses on complex quantum systems with strong correlations, exploring novel functionalities for future technologies such as energy materials. Key areas include correlated quantum matter (quantum materials, ultracold atoms/molecules) and dynamical nonlinear systems (optically driven quantum systems, neural learning models). He has been awarded the AFOSR Young Investigator Program Award (2024-2027) and the Gordon and Betty Moore Foundation Postdoctoral Fellowship (2022-2023). His work bridges theoretical physics and computational methods, with recent contributions to superconductivity theory, polaron dynamics, and topological protection mechanisms. Notable research trends include investigation of bipolaronic superconductivity mechanisms, electron-phonon coupling effects, and quantum many-body systems under optical excitations. His studies often leverage advanced computational tools like the Generalized Green's function Cluster Expansion Python package. Collaborations with experimental groups and interdisciplinary efforts in AI applications (e.g., benchmarking foundation models) highlight his cross-cutting research approach. Key Awards: Nevill Mott Prize (2024), AFOSR YIP (2024), Moore Fellowship (2022) Grant activities include Yale Engineering seed funding for AI-driven physics research. His advisory work focuses on graduate students in condensed matter and quantum physics domains.

• Distributed Computing
• Blockchain
• Fault Tolerance
• +3 more

Rida Bazzi is an Associate Professor at the School of Computing and Augmented Intelligence (SCAI) at Arizona State University (ASU), affiliated with the Center for Cybersecurity and Trusted Foundations. He joined ASU in 1996 after roles as an Assistant Professor at Florida International University and a senior consultant at I-Cube. His expertise spans distributed computing, blockchain, fault tolerance, and cybersecurity. Education: Ph.D. and M.Sc. in Computer Science from Georgia Institute of Technology (1994), B.E. in Computer and Communications Engineering from American University of Beirut (1989). Research focuses on distributed systems, blockchain mechanisms, secure protocols, and fault-tolerant designs. Notable work includes fractional spending in blockchain transactions, secure routing, and anti-phishing strategies. He has authored over 50 publications and holds patents in image matching and federated learning. Teaching includes courses like Principles of Programming Languages (CSE 340) and contributes to curriculum development. His grants include NSF-funded projects on scientific workflow updates and cybersecurity tools. He has served on committees for conferences like PODC and IEEE Distributed Computing Systems. Active in service roles, including the ACM Symposium on Distributed Computing (General Chair, 2008) and Fulton School Sabbatical Committee (2006–2007).

Abraham WesterbaanView profile

Lecturer

• Category Theory
• Von Neumann Algebras
• Operator Algebras
• +4 more

Dr. Abraham Westerbaan is a Lecturer at Radboud University's ICIS (Digital Security Department) and a Scientific Programmer at Radboud's iHub. He earned his Ph.D. in 2019 under the supervision of Bart Jacobs, defending a thesis titled *The Category of Von Neumann Algebras*, which explores foundational aspects of operator algebras and category theory. His research spans quantum computing, mathematical logic, and cryptographic systems, with notable contributions to quantum programming languages and mathematical structures in theoretical physics. Westerbaan's work integrates advanced algebraic concepts with computational security, reflecting his dual role in academic research and applied scientific programming. His publications emphasize categorical frameworks for quantum mechanics, operational quantum logic, and cryptographic protocols for network privacy. He maintains an active GitHub repository documenting his thesis and related projects, showcasing collaborations in operator algebra theory and computational mathematics. While no specific grants or awards are explicitly listed, his extensive publication record and academic roles indicate sustained engagement with cutting-edge research in theoretical computer science and mathematical physics. His affiliations with ICIS and the iHub highlight a commitment to both foundational inquiry and applied technological development.

Karen RaffertyView profile

Professor

• Virtual Reality
• Mixed Reality
• Machine Intelligence
• +3 more

Professor Karen Rafferty is a prominent academic and Head of the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast. She holds the rank of Professor and specializes in Virtual Reality (VR), Mixed Reality (MR), and Multi-sensory Interaction technologies, with a focus on their applications in Health & Training and Industry & Automation. Her research emphasizes disruptive technological practices, leveraging software engineering, sensor fusion, and real-time systems. Prof. Rafferty has led or contributed to numerous national and EU-funded projects, including roles as Principal Investigator (PI) and Co-Investigator (CoI) in grants such as the ARISE project on steel erection robotics and the PWC Research and Development Centre. She is actively involved with professional bodies like the IET, serving as a registration interviewer and evaluator for EU research programs (H2020, FP7, FP6). Her awards include the Best Paper Award at the 33rd International Manufacturing Conference and recognition for contributions to Digital Enterprise Technology. She teaches Engineering Entrepreneurship and VR/AR technologies, emphasizing interdisciplinary innovation. Key research themes span AI-driven systems, blockchain for sustainable decision-making, and haptic feedback integration in immersive environments. Her work often bridges academia and industry, with practical applications in manufacturing automation and healthcare training.

Vesa LappalainenView profile

Senior Lecturer

• Computer Science
• Quantum Computing
• Educational Technology
• +3 more

Vesa Lappalainen is a Senior University Lecturer at the University of Jyväskylä's Faculty of Information Technology. His work bridges quantum computing education, learning analytics, and programming pedagogy. He leads the Quantum Information and Computation Team, focusing on advancing quantum computational methods and scalable educational frameworks. Position: Senior University Lecturer University: University of Jyväskylä Research Group: Quantum Information and Computation Team Research interests include quantum computing pedagogy, predictive learning analytics for student retention in CS1, and innovative online learning environments. Notable contributions include developing interactive quantum circuit simulators and multi-functional digital course materials. His work emphasizes equity in education and scalable solutions for competency-based admissions. Publications span quantum education tools, learning analytics models, and comparative studies on programming education methods (e.g., game-based learning, TDD in CS1). Collaborations include international teams on projects like DEFA and KATTI, addressing challenges in educational technology and student identity development. Lappalainen has advised numerous collaborative projects and contributed to curriculum design in programming education. Current research trends focus on integrating multimodal emotion tracking in learning systems and generalizable predictive models for student success.

• Mathematical Modelling
• Solar Physics
• Helioseismology
• +12 more

Dr Andrew Gascoyne is a Senior Lecturer in Mathematics and Artificial Intelligence at the School of Mathematics and Computer Science, Faculty of Science and Engineering, University of Wolverhampton. He earned his PhD in Applied Mathematics and MMath from the University of Sheffield, and a PGCert in Higher Education from the University of Wolverhampton. He has been with the university since 2015, progressing from Visiting Lecturer to Senior Lecturer in 2019, and also served as an External Examiner at Griffith College, Australia. PhD in Applied Mathematics, University of Sheffield MMath in Mathematics, University of Sheffield PGCert in Higher Education and Professional Practice, University of Wolverhampton His research spans two main domains: Solar Physics and Deep Machine Learning . In solar physics, he focuses on helioseismology, magnetohydrodynamics (MHD), and the interaction of solar acoustic waves with magnetic structures like sunspots. More recently, his work has expanded into AI, where he develops energy-efficient deep learning models applied to bioacoustics, aquaponics, natural language processing, and optimization. He is actively involved in the Wolverhampton Light & Matter Research Group (WLM), Wolverhampton Cyber Research Institute (WCRI), and the Digital Innovations and Solution Centre (DISC). His recent publications reflect a shift toward interdisciplinary applications of machine learning and sustainable technologies, with a focus on IoT, aquaponics, and neural network optimization. Earlier works centered on solar wave dynamics and magnetic field interactions, demonstrating a strong foundation in mathematical and computational modeling. Andrew is passionate about mathematics education and is a member of the Higher Education Academy (HEA). He is currently involved in a £2.2 million WMCA-funded project aimed at improving basic math skills for working adults in the West Midlands, addressing the 'Maths GCSE barrier' in STEMM careers. He supervises postgraduate research and coordinates the MSc Data Science and MSc Artificial Intelligence programs. He has delivered professional presentations, such as 'Sunspots, Sunquakes and Solar Storms,' to promote public understanding of solar physics.

Michael HicksView profile

Professor

• Programming Languages
• Computer Security
• Quantum Computing
• +7 more

Michael Hicks is a Professor in the Department of Computer and Information Science at the University of Pennsylvania and an Amazon Scholar. Previously, he was a Professor at the University of Maryland (2002-2021) and Senior Principal Scientist at Amazon Web Services (2022-2025). He co-founded the Programming Languages research lab (PLUM) at Maryland and was its director, as well as Director of the Maryland Cybersecurity Center (MC2). He is a Fellow of the Association of Computing Machinery (ACM), former Chair of ACM SIGPLAN, and Editor-in-Chief of Proceedings of the ACM on Programming Languages (PACMPL). His research focuses on programming languages and their application to software security , quantum programming , and formal verification . He has pioneered dynamic software updating techniques, developed tools like Checked C for memory safety, and co-designed the Cedar authorization policy language at AWS. In quantum computing, he works on verified compilers like VOQC and Qunity to ensure reliability and correctness. His recent work trends include quantum programming languages , secure multi-party computation (e.g., Wysteria , Symphony ), and fuzz testing methodologies . He has contributed to formal verification for quantum programs and language-based security frameworks. Scientific Awards include: ACM Fellow He has advised numerous PhD and Master’s students , including James Parker, Andrew Ruef, Kesha Hietala, and Aseem Rastogi, who now hold academic and industry positions. His lab, PLUM , has been instrumental in advancing programming languages research.

Oded PadonView profile

Assistant Professor

• Programming Languages
• Verification
• Formal Methods
• +4 more

Oded Padon is a Senior Scientist (≈Assistant Professor) at the Weizmann Institute of Science , affiliated with the Faculty of Mathematics and Computer Science . He joined Weizmann in September 2024 after prior roles as a researcher at VMware Research Group , a postdoc in Alex Aiken's group at Stanford University , and a PhD student at Tel Aviv University under Mooly Sagiv . Research Interests : Oded's work focuses on developing principled algorithms for automated verification of complex systems, particularly distributed protocols, storage systems, and cluster management. He emphasizes decidable logics, primal-dual methods, and decomposition techniques. His recent interests include verification of deep neural networks , quantum computing , and leveraging large language models for verification tasks. Key projects: Ivy (safety/liveness verification), mypyvy (invariant inference), Verus (Rust verification), TASO (deep learning optimization), Quartz (quantum circuit superoptimizer). Scientific Awards : Azrieli Early Career Faculty Fellowship 2020 ETAPS Doctoral Dissertation Award 2017 Google PhD Fellowship in Programming Languages Radhia Cousot Young Researcher Best Paper Award (SAS 2017) Publications span decidable verification, invariant inference, quantum optimization, and machine learning for verification. His work has received Jay Lepreau Best Paper Award at SOSP 2024 (Anvil) and Distinguished Paper Awards at POPL 2024 (An Infinite Needle) and OOPSLA 2023 (Leaf).

• Software Testing
• Software Verification
• Programming Languages
• +7 more

Karine Even-Mendoza is a Lecturer at King's College London specializing in software reliability, with active contributions to SPLASH, ASE, and ISSTA conferences. Her research bridges traditional software engineering with emerging domains like quantum computing and green software. Her research interests focus on: Advanced testing methodologies (Fuzzing, Model Checking) Machine Learning integration for test generation Quantum software validation Energy-efficient computing solutions Recent publications (2020-2025) reveal a strategic evolution from compiler testing foundations to ML-augmented quantum and green computing research. This progression demonstrates consistent innovation in software reliability while addressing industry-critical challenges in emerging technologies. As a Lecturer, she actively supervises student projects and conference submissions. Her involvement in conference organization (SPLASH, ICTSS) and industry collaborations (eBay, IBM legacy) provide students with real-world exposure to software engineering challenges. Her research group develops practical tools like CsmithEdge and SearchGEM5, maintaining strong GitHub engagement for open-source contributions to compiler and simulator testing.

Hsin-Yi LinView profile

Assistant Professor

• Quantum Machine Learning
• Speech Enhancement
• Data Assimilation
• +3 more

Hsin-Yi Lin is an Assistant Professor in the Department of Mathematics and Computer Science at Seton Hall University. Her research focuses on quantum machine learning, speech enhancement, and data assimilation with applications in computational physics and neural network interpretability. She explores interdisciplinary topics including quantum neural networks, adversarial robustness in speech models, and multi-scale geophysical dynamics modeling. Recent work emphasizes quantum measurement optimization, transfer learning in quantum circuits, and novel diffusion models. Her publications address challenges in speech quality assessment, domain adaptation, and scalable state estimation using recurrent networks. She has contributed to foundational mathematical analysis of transport equations and developed extensions to signal processing frameworks like SEMamba. Key research themes include hybrid quantum-classical systems, explainable AI for quantum models, and robust machine learning under adversarial conditions. Collaborations bridge theoretical mathematics with applied computational methods in earth system prediction and periodic migration modeling.

• Compiler Design
• Programming Languages
• Static Analysis
• +5 more

Elisabeth Sommer is a Lecturer at the Chair of Languages and Description Structures in Computer Science at Technische Universität München (TUM). She has been actively involved in teaching since at least 2018, specializing in compiler design, programming languages, and static analysis. Her courses include Compiler Construction , Virtual Machines , and Functional Programming and Verification . She has consistently taught advanced topics such as program optimization, language-based security, and quantum computing in compilers. Research interests focus on compiler design principles, static analysis techniques, and the integration of quantum computing with compiler optimization. She collaborates on projects like Goblint (a static analyzer) and contributes to initiatives such as SpAGAT and Teleteaching . Elisabeth has organized events like the Summer School Marktoberdorf 2019 and contributed to academic workshops. Her teaching spans multiple semesters, including recent offerings like Static Analysis - Strengthen Equalities by Relaxing Polyhedra (Summer 2025) and Quantum Computing at Compile Time (Winter 2024/25). She advises student theses and practical courses, though specific student names are not listed.

• Compiler Design
• Static Analysis
• Functional Programming
• +3 more

Alexandra Graß, M.Sc., is a member of the TUM Institute of Computer Science I2 , specifically within the Computer Science 2 - Chair of Languages and Description Structures in Computer Science led by Prof. Dr. Helmut Seidl. She has been actively involved in teaching and research since at least 2019. Her teaching responsibilities include courses such as Compiler Construction , Virtual Machines , Functional Programming and Verification , and Program Optimization . She has also supervised internships and seminars focused on static analysis, quantum computing at compile time, and next-gen programming interfaces. Her research interests span compiler design, static analysis techniques, and their application to software verification and optimization. Projects she contributes to include tools like Goblint and Teleteaching . She has organized and participated in academic events such as the Summer School Marktoberdorf and Ferienakademie conferences. Alexandra has consistently taught core computer science courses across multiple semesters, demonstrating expertise in both theoretical and applied aspects of programming languages and compiler technologies. While no specific grants or awards are listed, her active involvement in teaching and research indicates significant contributions to the field.

• Compiler Design
• Static Analysis
• Programming Languages
• +5 more

Michael Petter is a faculty member at the Technische Universität München (TUM), affiliated with the School of Computer Science II (Informatik 2) and the Department of Languages and Description Structures in Computer Science. He specializes in compiler design, static analysis, and formal verification, with a focus on developing tools like the Goblint framework for multi-threaded C programs. His teaching spans courses such as Compiler Construction, Program Optimization, and Functional Programming, reflecting his expertise in programming languages and software engineering. His research interests include static analysis techniques, concurrency control, and abstract interpretation frameworks. He has contributed to advancements in flow-sensitive analysis, context-sensitivity management, and precision recovery in program analysis. Recent work emphasizes improving static analysis tools for industrial benchmarks and enhancing their usability through incremental methods. Petter’s publications highlight innovations in numerical abstract domains (e.g., octagons), non-local jump misuse detection, and thread-modular analysis for multi-threaded systems. His involvement in projects like the DFG Research Training Group ConVeY underscores his commitment to advancing continuous verification in cyber-physical systems. He advises thesis projects on topics like quantum computing at compile time and program synthesis, fostering interdisciplinary collaboration. While actively engaged in academic events and editorial roles, he maintains a lab focused on cutting-edge compiler and static analysis research.

Alec ChristieView profile

Research Fellow

• Biodiversity Conservation
• Evidence-Based Decision-Making
• Artificial Intelligence in Conservation
• +3 more

Alec Christie is an Imperial College Research Fellow at the Centre for Environmental Policy, Imperial College London (2024–2028), and holds concurrent roles as Visiting Researcher at the University of Cambridge's Department of Zoology and Associate Lecturer at the Open University. He is also a Bye Fellow at Downing College, Cambridge. His research focuses on evidence-based conservation, AI applications in conservation decision-making, and addressing biases in conservation evidence. Christie holds a PhD in Zoology from the University of Cambridge (2021), an MSc in Conservation Science and Practice (co-convenor role), and a BSc in Marine Biology from the University of St Andrews (2017). Research Interests: Evaluation of evidence-based practices in conservation Cost-benefit analysis of conservation interventions AI-driven tools for accelerating evidence synthesis Quantifying biases in conservation study designs Global conservation priorities and policy alignment Teaching: Co-teaches the MSc Conservation Science and Practice program at Imperial College, specializing in evidence-based decision-making modules. Previously, he held a Henslow Fellowship (2021–2024) and contributed to the Open University's S397 Ecosystems module. Awards: Vice Chancellor’s Award for Research Impact (2023) and Fellow of the Higher Education Academy (2022). Collaborations: Leads projects with the Conservation Evidence team and develops decision-support tools for practitioners. Active in fieldwork across Antarctica, the Great Barrier Reef, and Türkiye.

• Machine Learning
• Quantitative Finance
• Cryptocurrency
• +7 more

Dr. Paul Bilokon is a Visiting Lecturer in the Department of Mathematics at Imperial College London's Faculty of Natural Sciences. He holds leadership roles as CEO and Founder of Thalesians Ltd, Chief Scientific Advisor to Thalesians Marine Ltd, and Head of Faculty at the Machine Learning Institute (MLI) and Quantitative Developer Certificate (QDC). Previously, he led global credit and core e-trading quant teams at Deutsche Bank and held senior roles at Morgan Stanley, Lehman Brothers, and Citigroup. His research focuses on machine learning in finance, cryptocurrency, high-frequency trading systems, and domain theory. He earned a distinction from the University of Oxford and won the Donald Davis Prize and British Computer Society SET Award for his work on stochastic processes and imaging. His book contributions include 'Machine Learning and Big Data with kdb+/q' and 'Machine Learning in Finance: From Theory to Practice.' He teaches advanced machine learning and C++ programming, emphasizing reinforcement learning and low-latency applications. Education: Christ Church, University of Oxford (MSci with distinction); Imperial College London (Donald Davis Prize-winning MSci thesis). Research interests include algorithmic trading, quantum machine learning, and drug discovery via graph neural networks. Awards: Quant of the Year 2023, multiple industry accolades. Projects span cryptocurrency arbitrage, low-latency systems, and topological approaches to number theory. Advised numerous students on topics like deep reinforcement learning and electronic market making. Labs/Teams: Thalesians Ltd (quantitative research), MLI (machine learning education), QDC (certification programs). Grants and consulting span financial institutions and tech firms. His work bridges academia and industry, driving innovation in finance and computer science.