Find Academic Supervisors

• Robotic Manipulation
• Control Theory
• Differential Geometry
• +5 more

Fares J. Abu-Dakka is an Assistant Professor in the Mechanical Engineering Program at New York University Abu Dhabi, where he joined in September 2024. Previously, he served as a Lecturer and Researcher in the Faculty of Engineering - Electronic and Informatic Department at Mondragon Unibertsitatea (2023-2024) and as a Senior Scientist and Leader for the Robot Learning Group at the Munich Institute of Robotics and Machine Intelligence, Technische Universität München (2022-2023). His research focuses on advancing robotic manipulation through the integration of control theory, differential geometry, and machine learning, with particular emphasis on enhancing robotic systems' performance, adaptability, and safety. Dr. Abu-Dakka explores how advanced mathematical frameworks and intelligent algorithms can empower robots to interact more effectively with complex environments, with applications spanning industrial automation, prosthetics, and human-robot collaboration. His recent publications demonstrate expertise in geometry-aware motion planning, variable impedance control, safe execution of robotic skills, and human-robot skill benchmarking. He has organized multiple workshops at major robotics conferences including IROS and ECC, focusing on formal methods in robotics systems, certified control synthesis, and variable impedance learning and control. IEEE Senior Member (2024) Member of the ELLIS Society e.V (2024) Dr. Abu-Dakka actively contributes to the robotics community through workshop organization and collaborative research, with recent work examining AI-driven intent detection, neural interfaces, sensory feedback systems for prosthetics, and certified control synthesis with rigorous safety guarantees. His research bridges theoretical control methods with practical robotic applications, emphasizing mathematical rigor while addressing real-world implementation challenges.

Atılım Güneş BaydinView profile

Assistant Professor

• Probabilistic Machine Learning
• Generative Modeling
• Probabilistic Programming
• +4 more

Dr. Atılım Güneş Baydin is an Assistant Professor in Machine Learning at the University of Oxford , where he leads the Oxford AI for Science Lab . He holds dual appointments in the Department of Computer Science and Department of Engineering Science , and is affiliated with Jesus College , Kellogg College , and the European Lab for Learning and Intelligent Systems (ELLIS) . Academic Roles : Departmental Lecturer (Assistant Professor) in Machine Learning, Senior Researcher in Engineering Science Research Groups : Torr Vision Group, Oxford Applied and Theoretical Machine Learning Group (OATML) His research focuses on probabilistic programming , differentiable programming , and generative modeling , with applications in scientific discovery and space missions. He develops methods for large-scale simulator inference and deep learning for space weather , collaborating with NASA , ESA , and Lawrence Berkeley National Lab . Notable Contributions : Co-organized 7+ NeurIPS workshops on ML for Physical Sciences Led the development of PyProb and DiffSharp probabilistic programming frameworks Served on editorial board of ACM Transactions on AI for Science

Martina MaggioView profile

Professor

• Control Theory
• Cyber-Physical Systems
• Real-Time Systems
• +4 more

Martina Maggio is a Professor at the Department of Computer Science , Saarland University (since March 2020) and holds a 20% position at the Department of Automatic Control , Lund University (since 2023). Her research bridges control theory with software engineering to address predictability in computing systems. PhD : Politecnico di Milano (completed with work on control-theoretical tools for computing systems) Visiting Graduate Student : MIT CSAIL (collaborated on Self-Aware Computing project) Her research focuses on: Control theory for resource allocation in cloud infrastructures and embedded systems Formal verification of event-triggered systems and deadline misses Robustness of cyber-physical systems under computational faults Automated methodologies for runtime adaptation in software Timing requirements in big data applications Security vulnerabilities in control systems Key article trends include: control-theoretic approaches to computing systems, stochastic modeling of deadline misses, formal verification of timing constraints, and security-aware control algorithms . Scientific recognition: Co-authored paper selected as Most Influential Paper at SEAMS 2025 Best Paper Award at ECRTS 2021 ACM SIGSOFT Distinguished Paper Award (FSE 2020) Outstanding Reviewer (ICPE 2020) Mentorship includes supervising PhD candidates (e.g., Clara Rubeck, Robert Pietsch) and serving as an academic advisor for industry collaborations (e.g., with Bosch Corporate Research). She also contributes to open-source tool development (WeaklyHard.jl) and has held leadership roles like Program Co-Chair for ICCPS 2020.

Lance E. ChampagneView profile

Associate Professor

• Operations Research
• Military Simulation and Modeling
• Machine Learning
• +3 more

Lance E. Champagne serves as an Associate Professor in the Graduate School of Engineering and Management at the Air Force Institute of Technology (AFIT), where he applies operations research and computational modeling to defense systems optimization. His work bridges military applications with advanced analytics, focusing on simulation-driven solutions for complex operational challenges. His academic foundation includes: Doctor of Philosophy (Ph.D.) in Operations Research from AFIT (2004) Master of Science (M.S.) in Operations Research from AFIT (1999) Bachelor of Science (B.S.) in Biomedical Engineering & Mathematics from Tulane University (1991) Champagne's research centers on military simulation, machine learning, and logistics optimization. He develops agent-based models for combat scenarios, UAV operations, and personnel training systems, while pioneering applications of deep learning in defense contexts. His work integrates Bayesian methods, digital twin frameworks, and quaternion neural networks to solve problems in threat avoidance, supply chain resilience, and real-time decision-making under cyber constraints. Recent publications reveal a pronounced shift toward AI-enhanced defense analytics, particularly in video/text classification for situational awareness and multi-objective optimization for wartime logistics. His 2022-2025 output shows consistent innovation in adapting commercial AI techniques (IndoBERT, GANs) to military-specific challenges while maintaining rigorous validation standards through probabilistic agreement metrics. His scholarly recognition includes: 2022-2023 Graduate School of Engineering and Management Dean's Distinguished Teaching Professor 2014 Winter AFIT/ENC Mathematics and Statistics Instructor of the Quarter As an advisor, Champagne mentors numerous graduate students evidenced by asterisk-marked co-authorships across 15+ publications annually. His research is supported by defense contracts focused on simulation validation, autonomous systems, and logistics optimization. He actively contributes to military operations research through book chapters on simulation history and validation methodologies, while maintaining operational relevance through direct collaboration with Air Force training and maintenance units.

• Software Engineering
• Value-Based Software Engineering
• Software Quality Assurance
• +7 more

LiGuo Huang is an accomplished researcher and academic in the field of software engineering with a publication record spanning over two decades from 2003 to 2025. With 95 publications documented in the dblp database, Huang has established a significant presence in both traditional software engineering domains and emerging areas where machine learning intersects with software development practices. Huang's research has evolved from foundational work in value-based software engineering to cutting-edge applications of artificial intelligence in software analysis and maintenance. Huang's research interests encompass a broad spectrum of software engineering topics, with particular emphasis on value-based software engineering, software quality assurance, defect classification, and software process modeling. More recently, Huang has focused on applying machine learning and deep learning techniques to software engineering problems, including code summarization, vulnerability detection, and software maintenance. This evolution reflects the broader shift in the field toward data-driven approaches for software development and analysis. The publication trends reveal a consistent research trajectory with increasing publication rates in recent years, particularly in the application of machine learning to software engineering problems. Huang's work shows a strategic progression from theoretical foundations in software quality to practical applications of AI in software development. The research spans empirical studies, systematic literature reviews, and novel technical approaches to longstanding software engineering challenges, demonstrating both theoretical depth and practical relevance. Huang has collaborated extensively with researchers across multiple institutions, forming particularly strong partnerships with Jidong Ge, Bin Luo, Chuanyi Li, and Barry W. Boehm. The collaboration with Boehm in early career publications suggests mentorship that evolved into peer collaboration, while more recent work shows Huang mentoring newer researchers who now serve as primary authors on joint publications. Huang's research has practical implications for software development practices, particularly in improving software quality, enhancing developer productivity through AI-assisted tools, and providing empirical evidence for software engineering decision-making. The work bridges theoretical computer science with practical software engineering concerns, making significant contributions to both academic research and industry practice.

Pascal PoizatView profile

Professor

• Software Engineering
• Formal Methods
• Business Processes
• +7 more

Pascal Poizat is a Professor at Université Paris Nanterre, affiliated with the LIP6 laboratory (Sorbonne Université, CNRS). His research focuses on software engineering, formal methods, and business process modeling, particularly in the areas of component composition, distribution, and behavioral verification using techniques like model checking and conformance testing. His recent work analyzes dependency management in package ecosystems (e.g., Maven Central) and BPMN verification frameworks like sBPMN and VBPMN. He has received a best paper award at BPM 2019 and leads tools such as Goblin and VerChor for automated verification. Research trends : Dependency update quality optimization, BPMN formalization, and choreography verification. Key subfields : Symbolic testing, process algebra, temporal logic, adaptive composition, and data-aware service modeling. Poizat has held leadership roles including Deputy Director of the Mathematics and Computer Science Department at Université Paris Nanterre and served on national recruitment committees (CNRS, Université Paris Sud). He has coordinated research projects like ANR PARDI and ANR PIMI.

Paul HübnerView profile

Researcher

• Source Code and Requirements Traceability
• Software Analytics and Data Mining in Software Engineering Repositories
• Knowledge Management Requirements Engineering

Paul Hübner is a Researcher affiliated with the Institute of Computer Science at the University of Heidelberg . His work focuses on source code and requirements traceability , software analytics , and knowledge management in requirements engineering . Education : Master's degree in Computer Science from the University of Ulm Key Research Areas : Traceability, Software Repository Analysis, Interaction Data Utilization His 14 publications (2012–2025) examine trace link creation using interaction logs, commits, and issue tracking systems. He has taught advanced software engineering courses at Heidelberg and collaborated extensively with Barbara Paech and others. Teaching Roles : Contributed to courses like "Introduction to Software Engineering" and "Software Practicum: Advanced Software Engineering" (2012–2018).

Sasa MisailovicView profile

Associate Professor

• Programming Languages
• Compilers
• Software Engineering
• +8 more

Sasa Misailovic is an Associate Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign. He earned his PhD from MIT in Summer 2015 and was promoted to tenured Associate Professor in 2022. His research focuses on programming languages, compilers, software engineering, and formal verification, particularly for improving performance, energy efficiency, and resilience in systems with approximation or probabilistic components. PhD: MIT, Summer 2015 His work spans probabilistic programming, LLMs, and compiler design for approximate/hardware-aware systems. Recent publications highlight trends in structured LLM generation (Syncode, IterGen), static analysis for automatic differentiation, and verification of neural networks. He has received the NSF CAREER Award and multiple Best Paper Awards (OOPSLA '13, '14). Notable graduates include Jacob Laurel (Georgia Tech), Zixin Huang (NVIDIA), Keyur Joshi (Google), Saikat Dutta (Cornell), and Vimuth Fernando (Amazon AWS). Current projects include the StructuredLLM initiative with papers accepted at ICLR'25, ICML'25, and TMLR'25. NSF CAREER Award Best Paper OOPSLA '14, '13 SIGPLAN Research Highlight Test-of-Time SEAMS '15 Distinguished Paper ICSE NIER '20 He leads tools like AxProf, ProbFuzz, HPVM, and Syncode. His teaching includes advanced compiler courses and topics in approximate/probabilistic programming.

• Data Mining
• Large Language Models (LLMs)
• Knowledge Graphs
• +7 more

Jing Gao is an Associate Professor at the Elmore Family School of Electrical and Computer Engineering , Purdue University , West Lafayette, Indiana. She serves as the Director of Graduate Admissions and focuses on data mining, machine learning, and their applications in healthcare, biology, and societal systems. Research Interests Data veracity analysis and multi-source data integration Knowledge graphs and large language models Fairness, interpretability, and efficiency in AI Federated learning and transfer learning Applications in healthcare, education, and criminal justice Recent Article Trends Her work emphasizes large language models (LLMs) with innovations in knowledge editing, federated learning, and fairness-aware systems. Key themes include: Improving LLM efficiency via sparse adaptation and quantization Advancing privacy-preserving federated learning architectures Developing fairness-aware algorithms through counterfactual reasoning Integrating knowledge graphs for factuality validation Optimizing multi-agent reasoning and self-reflective confidence Technical Areas Her research spans computer engineering domains including: Signal & Image Processing Communications & Networking AI/ML Model Efficiency

• Synthetic Biology
• Cyber-Physical Systems
• Autonomous Robotics
• +6 more

Ayush Pandey is an Assistant Teaching Professor in the Department of Electrical Engineering at the University of California Merced. His work bridges synthetic biology, cyber-physical systems, and robotics, with a focus on modeling, formal verification, and education. Academic Rank: Assistant Teaching Professor Department: Electrical Engineering Email: ayushpandey@ucmerced.edu His research spans three primary domains: Synthetic Biology: Developing modeling frameworks (e.g., BioCRNpyler) for modular biological circuit design, analyzing gene circuits subject to context effects, and characterizing biomolecular components in cell-free systems. Cyber-Physical Systems: Advancing assume-guarantee contracts (Pacti) for scalable system verification and robustness guarantees in model reduction of dynamical systems. Autonomous Robotics: Designing autonomous two-wheeler locomotion models, three-wheeled mobile robots, and low-cost bicycle navigation systems. Recent trends in his publications highlight interdisciplinary integration of AI/ML with biological systems, including tools for high school students to engage with neural networks and methods for classifying large language model hallucinations.

• Rare event simulation
• Formal methods
• Monte Carlo methods
• +5 more

Carlos Esteban Budde is a Professor in both the Department of Economics and Management and the Department of Information Engineering and Computer Science at the University of Trento, Italy. His research focuses on rare event simulation, formal methods, and cybersecurity, with particular emphasis on developing automated algorithms for importance splitting techniques applicable to high-reliability systems and fault trees. Education: PhD in Computer Science (2012-2017) from National University of Córdoba, Argentina. Thesis: "Automation of Importance Splitting Techniques for Rare Event Simulation." MSc in Computer Science (2010-2012) from National University of Córdoba, Argentina. Final project: "Fully measurable non-determinism in continuous probabilistic processes." BSc in Computer Analyst (2007-2010) from National University of Córdoba, Argentina. Professor Budde's research centers on rare event simulation and formal methods for analyzing stochastic systems. His work develops theoretical frameworks for automated importance splitting algorithms that can handle general stochastic models beyond Markovian assumptions. He implements these techniques in practical tools like FIG (Finite Improbability Generator) for reliability analysis of high-criticality systems, with applications spanning cybersecurity, infrastructure maintenance, and safety-critical systems. His interdisciplinary approach bridges theoretical computer science with practical engineering challenges in system reliability. His recent publications demonstrate a strong focus on quantitative security analysis using attack and fault trees, with increasing integration of machine learning techniques for predictive maintenance. Budde has made significant contributions to rare event simulation methods applicable to non-Markovian systems, extending formal verification techniques to hybrid systems where traditional model checking approaches fail. His work consistently emphasizes practical implementation and empirical validation through diverse case studies. Scientific Awards: MSCA Postdoctoral Fellowship (2022) for "Projection of Security Vulnerabilities caused by Exploits in Dependencies" Best short-paper award at QEST 2020 for "The Dynamic Fault Tree Rare Event Simulator" Best MSc grade point average award from National University of Córdoba (2012) Flag bearer of the Faculty of Mathematics, Astronomy, Physics, and Computer Science (2011) Professor Budde has supervised numerous MSc and BSc theses at University of Twente and National University of Córdoba, with students working on topics including Bayesian network applications, predictive maintenance, fault tree analysis, and statistical model checking. His research is supported by multiple international projects including CS4E (Cyber Security for Europe, H2020), SEQUOIA (Smart maintenance optimization), and SUCCESS (SecUre aCCESSibility for IoT). As an active member of the research community, Budde serves on program committees for major conferences including TACAS, QEST, and CSF, and has organized workshops such as PM 2021. His work bridges theoretical computer science with practical applications in system reliability and cybersecurity.

Fausto MarcantoniView profile

Assistant Professor

• Network Security
• Cybersecurity
• Penetration Testing
• +5 more

Fausto Marcantoni is an Assistant Professor at the University of Camerino , specifically within the School of Science and Technology - Computer Science Section . His academic work focuses on network security, penetration testing, and cybersecurity education. He actively supervises student theses and leads the Laboratorio di Reti di Elaboratori (Computer Networks Laboratory) at the university. Contact: fausto.marcantoni@unicam.it | Tel. +39 0737-402105 Location: Polo Lodovici, Via Madonna delle Carceri, 62032 Camerino, Italy Research Themes: Marcantoni's research spans cybersecurity, network architecture, digital identity management, and IoT security. He has explored applications in cloud computing, RFID technology, and security-awareness gamification through projects like SEC-GAME. His Google Scholar publications reflect a dual expertise in both computer science and animal nutrition chemistry, though the latter may represent a coincidental namesake. Recent cybersecurity trends in his work: Machine Learning for intrusion detection, SDN security monitoring, and federated identity systems Key tools: Wireshark, Metasploit, GNS3, and open-source vulnerability platforms Advising Legacy: Over 15 years, he has supervised dozens of theses on topics ranging from penetration testing frameworks to network virtualization, covering technologies like WebRTC, RFID, and cloud security. Students include Giacomo Belli, Simone Squadroni, Lorenzo Brancaleoni , and many others working on topics like malware analysis, OSINT, and smart contract security.

Ada DiaconescuView profile

Associate Professor

• Autonomic Computing
• Complex Systems
• Software Engineering
• +12 more

Ada Diaconescu is a Tenured Associate Professor at Télécom Paris (Institut Polytechnique de Paris) since October 2009. She is a member of the Autonomous Critical Embedded Systems (ACES) team within the Information Processing and Communication Laboratory (LTCI) , affiliated with the Computer Science and Networks (Infres) department. Research Focus: Autonomic Computing, Organic Computing, Complex Adaptive Systems, Self-Organising Systems, Smart Grids/Cities/Homes, Nature-Inspired Computing, Socio-Technical Systems Key Methodologies: Decentralised Control, Multi-Agent Systems, Component/Service-Oriented Architectures, Multi-Scale Feedback Loops Her recent publications (2021-2023) explore topics like holonic architectures for self-integration, multi-scale feedback systems in smart environments, and decentralised explanatory frameworks for AI transparency. She co-authored the textbook "Autonomic Computing: Principles, Design and Implementation" (Springer, 2013) and contributed chapters to books on Organic Computing (Springer, 2018) and Self-Aware Systems (Springer, 2017). Scientific Leadership: General Chair of IEEE SASO 2017 Steering Committee Co-Chair of IEEE ACSOS (2019-2021) Co-Organizer of Dagstuhl Seminars (2015, 2018) Doctoral Symposium Co-Chair at FAS* 2019 Teaching: Delivered courses on software engineering, autonomic systems, and complex system modeling at Télécom Paris and other institutions. Collaborations: Active in interdisciplinary research with institutions including Leibniz University, ETH Zurich, Arizona State University, and Lancaster University.

Kousha EtessamiView profile

Professor

• Theoretical Computer Science
• Automated Verification
• Logic
• +11 more

Kousha Etessami is a Professor in the Laboratory for Foundations of Computer Science within the School of Informatics at the University of Edinburgh . His research spans theoretical computer science with a focus on automated verification, logic, algorithms and computational complexity, algorithmic game theory, stochastic systems, automata theory and model checking. Research Interests: Automated verification and model checking Logic and computational complexity Algorithmic game theory and equilibrium computation Analysis of probabilistic systems, Markov decision processes and stochastic games Automata theory and infinite-state systems Finite model theory and descriptive complexity His publications reveal a strong profile in algorithmic game theory, stochastic modelling, and verification of complex systems. Recent work focuses on equilibrium computation complexity, reachability in stochastic games, and fixed-point methods for probabilistic models. He has contributed extensively to both foundational theory and practical algorithms, with journal publications in top venues including SIAM Journal on Computing , Journal of the ACM , Information and Computation , and Games and Economic Behavior . Students & Collaborators: PhD graduates: Thomas Webster (2024), Emanuel Martinov (2021), Alistair Stewart (2014), Dominik Wojtczak (2009) Postdoc: Václav Brožek (Newton Fellow, now at Google-Munich) Teaching Contributions: Professor Etessami teaches and has taught courses on Algorithmic Game Theory, Randomized Algorithms, Discrete Mathematics, Stochastic Modelling and Simulation, and Compiling Techniques at the University of Edinburgh. Contact: Email: kousha@inf.ed.ac.uk Office: Informatics Forum, Room 5.20, 10 Crichton Street, Edinburgh EH8 9AB, Scotland, UK Telephone: +44 (0)131 650 5197

• Programming Languages
• Compiler Technology
• Reversible Computation
• +5 more

Torben Ægidius Mogensen is an Associate Professor at the Department of Computer Science, University of Copenhagen, where he leads research in the Programming Languages and Theory of Computation section. His office is located at Universitetsparken 5, Copenhagen. His primary research focuses on: Automatic program analysis and transformation (especially partial evaluation and semi-inversion) Compiler technology for functional languages Domain-specific language design Reversible computing systems and languages Algorithms, complexity theory, and automata theory Applications in graphics and fractal generation His recent publications demonstrate a strong focus on reversible computation systems, including specialized programming languages like Hermes for encryption, reversible processor architectures, and functional programming extensions. His textbook publications on compiler design (2024) and programming language implementation (2022) indicate significant contributions to computer science education and foundational knowledge. He teaches courses on compilers, programming language technology, and game development, and maintains active research collaborations internationally. He is fluent in Danish and English, with working knowledge of German and Romanian.