Find Academic Supervisors

Yuan TianView profile

Assistant Professor

• Data Mining
• Software Engineering
• Social Network Analysis
• +3 more

Yuan Tian is an Assistant Professor in the School of Computing at Queen's University, Faculty of Arts and Science. She holds a PhD in Information Systems from Singapore Management University (2017) and a B.Sc. in Computer Science from Zhejiang University (2012). Her research focuses on integrating heterogeneous data sources to enhance software engineering practices, including data mining, recommender systems, and social network analysis. Prior to Queen's, she was a data scientist at Living Analytics Research Centre (LARC), SMU. She has held visiting positions at Carnegie Mellon University, INRIA Paris, and SAIL Canada. Research Interests: Data Mining Software Engineering Social Network Analysis Information Retrieval Recommender Systems Computer Security Recent Research Trends: Her work emphasizes AI-driven solutions for software bug management, code translation, vulnerability detection, and developer behavior analysis. Notable contributions include leveraging LLMs for technical debt repayment and enhancing code vulnerability detection via Graph Neural Networks. Awards: SMU Presidential Doctoral Fellowship (2015-2016) Best Paper Award at SANER 2017 Grants & Advising: No formal advisees listed, but active in collaborative projects with industry and academic partners. Labs/Teams: Previously associated with SOAR Group at SMU and currently leads research in Queen's School of Computing.

Pierre FlenerView profile

Professor

• Artificial Intelligence
• Constraint Programming
• Discrete Optimization
• +5 more

Pierre Flener is a Professor at the Department of Information Technology, Division of Computing Science at Uppsala University. He leads the Optimisation Group and is a member of the Centre for Interdisciplinary Mathematics. His work focuses on constraint programming and discrete optimization, addressing complex scheduling, routing, and resource allocation challenges. Flener is an Officer of the Order of Merit of Luxembourg and co-founder of NordConsNet, the Nordic Network for Constraint Programming researchers. Research Interests: Flener’s research spans constraint programming, combinatorial optimization, and algorithm design. He develops models and tools for automated decision-making in domains like air traffic management, sensor networks, and industrial robotics. His work emphasizes practical applications, leveraging constraint satisfaction techniques to solve real-world puzzles such as vehicle routing and personnel allocation. Key Contributions: Flener has authored over 100 publications on constraint solving, symmetry breaking, and CP-based approaches to industrial problems. Notable projects include airspace sectorization optimization, energy-efficient sensor networks, and financial portfolio design. He has led initiatives like Auto-Tabling for MiniZinc and collaborated on CP applications in bioinformatics and image processing. Labs & Teams: He heads the Optimisation Group at Uppsala, fostering research in CP and its applications. NordConsNet, co-founded by Flener, connects Nordic researchers and practitioners in constraint technology.

Riyadh BaghdadiView profile

Assistant Professor

• Compilers
• Programming Languages
• Deep Learning
• +9 more

Riyadh Baghdadi is an Assistant Professor of Computer Science at New York University Abu Dhabi and a Global Network Assistant Professor at the Tandon School of Engineering, NYU. He is also a Research Affiliate at MIT, where he previously completed a postdoctoral fellowship. His academic journey includes a PhD and Master’s from Sorbonne University (INRIA/UPMC) and an engineering degree from Ecole Supérieure d’Informatique in Algiers. Assistant Professor, NYU Abu Dhabi Global Network Assistant Professor, Tandon School of Engineering, NYU Research Affiliate, MIT His research lies at the intersection of compilers, programming languages, and applied machine learning, with a focus on developing advanced compiler techniques for deep learning, high-performance computing, and data-parallel algorithms. He is the lead developer of the Tiramisu compiler , a polyhedral compiler designed to optimize dense and sparse deep learning workloads across diverse architectures including CPUs, GPUs, and FPGAs. Riyadh’s recent publications demonstrate a strong trend toward integrating machine learning into compiler optimization—particularly in cost modeling, loop scheduling, and automatic code generation. His work addresses critical challenges in optimizing sparse neural networks and enabling efficient execution on resource-constrained platforms like smartphones and autonomous vehicles. Outstanding Paper Award, MLSys 2021 He has mentored 18 students and taught core courses such as Computer Systems Organization and Machine Learning at NYUAD. His service to the academic community includes program committee roles at MLSys, IPDPS, ECOOP, and PACT, as well as organizing workshops on polyhedral compilation and machine learning for hardware-software co-design. Riyadh actively contributes to open-source projects and collaborates with industry leaders including Google, Facebook, NVIDIA, and Intel. He leads the development of Tiramisu and collaborates on DSLs like GraphIt and Halide, focusing on performance portability and automation in compiler design.

Yonghwi KwonView profile

Visiting Assistant Professor

• Software Systems Security
• Cyber Forensics
• Software Engineering
• +3 more

Yonghwi Kwon is a Visiting Assistant Professor in the Department of Computer Science at the University of Virginia. His research focuses on software systems security, cyber forensics, and software engineering. He received the CAREER Award for developing dynamic defenses against cyber threats. His work emphasizes securing software from cyber attacks, recovering forensic evidence, and improving software testing and reverse engineering techniques. Key research areas include memory safety mechanisms, automated vulnerability detection in web applications and mobile systems, and forensic analysis of phishing campaigns. He has pioneered frameworks like CMASan for memory allocator-aware sanitization and Racedb for detecting race conditions in database-backed systems. His contributions span cloud security automation, kernel exploitation analysis, and embedded system fuzzing. Notable achievements include the 2025 CAREER Award supporting his dynamic defense research, and impactful publications in areas like Android information leakage detection (DryJIN), Bluetooth protocol fuzzing (BTFuzzer), and autonomous driving bug discovery (Drivefuzz). His work bridges theoretical computer science with practical cybersecurity solutions.

Andrea ContinellaView profile

Associate Professor

• Systems Security
• Malware Detection
• Privacy Leak Detection
• +7 more

Andrea Continella is an Associate Professor at the Faculty of Electrical Engineering, Mathematics and Computer Science of the University of Twente, where he contributes to the International Secure Systems Lab (iSecLab). His research focuses on systems security, particularly embedded firmware security, Android app security, malware detection, and program analysis techniques for vulnerability discovery. Ph.D. in Computer Science and Engineering, Politecnico di Milano (cum laude) Postdoctoral Researcher, Computer Science Department, UC Santa Barbara Visiting Researcher, School of Computer Science, University of Sydney Key research contributions include: Developing automated analysis techniques for embedded firmware (KARONTE, ShieldFS) Creating privacy leak detection mechanisms for mobile applications Designing ransomware defense systems using self-healing filesystems Advancing IoT security through misconfiguration detection (S3 buckets) and protocol analysis Pioneering semi-supervised methods for network traffic fingerprinting (FlowPrint) Scientific awards: Dutch Cyber Security Best Research Paper Award 2024 Runner-up USENIX Security Distinguished Reviewer Award 2024 Professional activities: Keynote speaker on firmware security (2024) Member of IPN Cyber Security Special Interest Group Oral presentations on automated vulnerability research (2023)

• Compilers
• Computer Architecture
• High-level Synthesis
• +5 more

Scott Mahlke is a Professor and Associate Chair in the Electrical Engineering and Computer Science Department at the University of Michigan. He is affiliated with the Advanced Computer Architecture Laboratory and the Software Systems Laboratory, where he leads the Compilers Creating Custom Processors (CCCP) research group. His research focuses on compilers, computer architecture, and high-level synthesis, with particular emphasis on designing next-generation computer systems that overcome challenges in performance, power consumption, and reliability. His work bridges the gap between hardware and software through innovative compiler technology that enables customized processors and accelerators. Mahlke's publications demonstrate a strong focus on compiler techniques for exploiting instruction-level parallelism, memory system optimization, and application-specific processor design. His research spans from fundamental compiler algorithms to practical implementations in both general-purpose and embedded systems. National Science Foundation CAREER Award (2003) Morris Wellman Faculty Development Assistant Professor (2004) 2006 ISCA Most Influential Paper Award Multiple best paper awards at major architecture conferences As an advisor, Mahlke has chaired numerous Ph.D. dissertations and actively mentors graduate students in the CCCP group. His research is generously funded by the National Science Foundation, Gigascale Systems Research Center, ARM Ltd., Samsung Advanced Institute of Technology, and other major organizations. The CCCP group maintains strong industry partnerships that facilitate the transfer of research innovations to practical applications.

• Smart Products
• Service Systems
• Digital Platforms
• +5 more

Prof. Dr. Stefan Eicker is a Professor and Chairholder of Business Information Systems and Software Engineering at the Faculty of Computer Science, University of Duisburg-Essen, Germany. He has held this position since April 2004, following previous academic appointments at the Technical University of Clausthal, the University of Essen, and other German institutions. His research spans multiple domains within information systems and software engineering, with a particular focus on digital transformation and emerging technologies. Prof. Eicker's research interests center around Smart Products , Service Systems , Internet of Things , and Platform Economics . His work explores how digital technologies transform traditional business models and create new value propositions. He has developed taxonomies for smart services and investigated quality factors in self-tracking solutions, demonstrating his interdisciplinary approach that bridges technical and business perspectives. His research particularly emphasizes the integration of physical and digital components in modern products and services. His recent publications (2019-2024) reveal a strong focus on digital platform economies, smart services, and IoT applications. The research shows a clear trajectory toward understanding value creation mechanisms in digital ecosystems, with increasing attention to practical applications in energy systems, self-tracking technologies, and business model innovation. His work often involves collaboration with colleagues like Gero Strobel and Tobias Brogt, indicating an active research group focused on digital transformation. Prof. Eicker has contributed significantly to the academic community through his extensive publication record spanning nearly two decades, with work appearing in journals, conference proceedings, and edited volumes. His research bridges theoretical frameworks with practical applications in business contexts. He maintains an active role in academic administration and education at the University of Duisburg-Essen, where he has contributed to curriculum development and the implementation of systems for managing academic information. His work on the bolognaT3 system demonstrates his commitment to improving academic processes through technology.

Emmanuel FonsecaView profile

Assistant Professor

• Radio Astronomy
• Astrophysics
• Neutron Stars
• +3 more

Emmanuel Fonseca is an Assistant Professor in the Department of Physics and Astronomy at West Virginia University (WVU), joining in Fall 2021. Previously, he was a postdoctoral researcher at McGill University (2016–2021) and completed his Ph.D. in Astronomy at the University of British Columbia (2016). His research focuses on radio astronomy, particularly pulsars and fast radio bursts (FRBs), leveraging facilities like CHIME, the Green Bank Telescope, and NANOGrav. He specializes in using pulsars as laboratories for testing fundamental physics and detecting gravitational waves via pulsar timing arrays. Education: Ph.D. in Astronomy, University of British Columbia (2016) M.Sc. in Astronomy, University of British Columbia (2012) B.Sc. in Physics and Astronomy, Pennsylvania State University (2010) Research Interests: Emmanuel’s work spans three key areas: Compact Objects: Investigating neutron stars and extreme environments using pulsar binaries and relativistic dynamics. CHIME Pulsar/FRB Science: Developing instrumentation and analyzing data from the Canadian Hydrogen Intensity Mapping Experiment to study FRBs and pulsars. Gravitational Waves: Contributing to NANOGrav’s efforts to detect nanohertz gravitational waves via millisecond pulsar timing arrays. Collaborations: He is a core member of NANOGrav and instrumental in maintaining CHIME’s pulsar and FRB backend systems. His work bridges hardware/software development with observational astronomy. Labs/Teams: Involved with the CHIME/FRB Collaboration and the NANOGrav Collaboration, advancing both observational infrastructure and theoretical astrophysics.

John RegehrView profile

Professor

• Compilers
• Software Testing
• Formal Verification
• +3 more

John Regehr is a Professor at the School of Computing, University of Utah, specializing in compilers, software testing, and formal verification. His research develops tools to improve software correctness and efficiency, including Csmith (random C program generator) and C-Reduce (test-case reducer). His group focuses on compiler validation, fuzzing techniques, and superoptimization, primarily targeting the LLVM infrastructure. Research interests span compilers, testing methodologies, formal verification, embedded systems, and program analysis. Recent work emphasizes practical tools backed by formal methods to detect and prevent software errors. Publications demonstrate strong trends in compiler verification and testing, with consistent focus on LLVM optimization correctness, translation validation, and automated bug detection through fuzzing and synthesis techniques. Scientific awards include: PLDI 2015 Distinguished Paper Award ICST 2014 Best Paper Award ACM SIGSOFT Distinguished Paper Award Leads a research group developing tools like Souper (superoptimizer) and Alive2 (translation validator). Maintains active academic service through program committees (PLDI, CGO, OOPSLA) and contributes to open-source compiler infrastructure.

Natasha FernandesView profile

Senior Lecturer

• Data Privacy
• Differential Privacy
• Privacy-Preserving Machine Learning
• +3 more

Natasha Fernandes is a Senior Lecturer in the School of Computing at Macquarie University. She holds a PhD in Computing from Macquarie University and École Polytechnique (France), and an undergraduate degree in Pure Mathematics and Computer Science from the University of Sydney. Her roles include involvement in the Data Horizons Research Centre and Future Communications Research Centre, alongside professional casual appointments in academic computing. Her research focuses on the mathematical foundations of data privacy, particularly differential privacy and its applications in natural language processing and machine learning. She develops privacy-preserving systems and tools using quantitative information flow techniques rooted in information theory. Key areas include privacy analyses for financial systems (e.g., Open Banking), API privacy, and optimizing utility in privacy pipelines. Fernandes has led or contributed to five research projects, including work on privacy analyses for financial transaction protocols and UAV-based machine learning systems. She received the 2021 John Makpeace Bennett Award for her doctoral research on differential privacy in metric spaces. Her academic journey combines industry experience as a backend software engineer with rigorous academic contributions, spanning over 21 peer-reviewed publications and collaborations across cybersecurity and privacy engineering domains.

Sandrine BlazyView profile

Professor

• Formal verification
• Semantics of programming languages
• Compilers
• +3 more

Sandrine Blazy is a Professor in the Computer Science Department at the University of Rennes, France. She is a member of CELTIQUE (also referred to as Epicure), a joint project-team with Inria Rennes Bretagne Atlantique and the IRISA laboratory. Since 2021, she has served as deputy director of the IRISA CNRS UMR 6074 laboratory and will be the general chair for POPL 2026, which will be held in Rennes. She is also a member of the editorial board of the LMCS journal. Dr. Blazy completed her PhD at CNAM (Conservatoire National des Arts et Métiers) in 1993 with a thesis titled "La spécialisation de programmes pour l'aide à la maintenance du logiciel" (Program Specialization for Software Maintenance Assistance). She later completed her Habilitation à diriger des recherches (HDR) in 2008 at the University of Évry Val d'Essonne with a thesis titled "Sémantiques formelles" (Formal Semantics). Her research focuses on the formal verification of program transformations and semantic properties of programming languages, particularly in the context of the CompCert compiler and Verasco static analyzer. She develops mechanized semantics using the Coq (or Rocq) proof assistant to ensure software correctness and security. A prime application domain of her work is software security, including constant-time programming for cryptographic applications and software obfuscation techniques. Her teaching includes mechanized semantics (in Coq), functional programming (in OCaml), formal methods (using Why3), and software vulnerabilities. Dr. Blazy's publication record from 2019-2025 shows a sustained focus on verified compilation techniques, particularly in preserving security properties during compilation. Her work bridges theoretical formal methods with practical compiler implementation, resulting in tools that have real-world impact in safety-critical systems. She has made significant contributions to the CompCert formally verified compiler project, with particular attention to constant-time preservation for cryptographic applications and JIT compilation verification. Her scientific achievements have been recognized with several major awards: CNRS Silver Medal (2023) Lucas Award from Formal Methods Europe (2023) ACM SIGPLAN Programming Languages Software Award for CompCert (2022) ACM Software System Award for CompCert (2021) Dr. Blazy has been actively involved in the programming languages research community, serving on numerous program committees for major conferences including POPL, ICFP, PLDI, and CPP. She has mentored students and contributed to education through teaching mechanized semantics and formal methods. Her work with the CompCert compiler has led to practical applications in safety-critical systems, with industry collaborations documented in publications like "CompCert: Practical experience on integrating and qualifying a formally verified optimizing compiler" (ERTS 2018). She leads research within the CELTIQUE project team, which focuses on developing trustworthy software using deductive verification. Her team works on advancing the state of the art in formal verification of compilers and static analyzers, with applications in security-critical domains including cryptographic implementations and safety-critical embedded systems.

Daniel S. BergerView profile

Adjunct Assistant Professor

• Cloud Computing
• Sustainability
• System Reliability
• +7 more

Daniel S. Berger is a Principal Researcher at Microsoft's Azure Systems Research Group in Redmond, focusing on the efficiency, sustainability, and reliability of cloud platforms . He is also an Affiliate Assistant Professor at the Paul G. Allen School of Computer Science at the University of Washington, where he teaches graduate classes. His research spans systems stack innovations for sustainability , including work on memory tiering , repair operations , and cooling systems (Zissou). He leverages system prototyping , simulation , and statistical modeling in his work, often collaborating with PhD students and postdocs from institutions like Columbia, University of Toronto, CMU, and Princeton. Recent publications highlight his leadership in CXL-based memory management , carbon-efficient cloud design , and latency-aware caching . His tools, such as Belatedly and FOO , have demonstrated significant improvements in cache performance and latency optimization. Best Paper Awards: USENIX OSDI 2023, HotCarbon 2023, ACM SOSP 2021. Distinguished Paper: ASPLOS 2023. His work has been integrated into Apache Traffic Server and Microsoft production systems , with open-source tools and datasets released for reproducibility. Collaborations include hardware and OS development teams within Azure and academia.

Ana MilanovaView profile

Professor

• Programming Languages
• Compilers
• Software Engineering
• +6 more

Dr. Ana Milanova is a Professor in the Department of Computer Science at Rensselaer Polytechnic Institute, where she has been since 2003. Her research focuses on programming languages, compilers, and software engineering, with emphasis on static program analysis, security, and applications in Android app taint analysis, secure cryptographic protocols, and machine learning library verification. Research Interests: Her work addresses challenges in secure software development, privacy-preserving techniques, and static analysis methodologies. Recent projects include federated learning frameworks, Python-based static analysis tools, and secure computation protocols. She has contributed to tools like Submitty for automated programming assignment grading and frameworks for secure MapReduce applications. Scientific Awards: NSF CAREER Award, Google Faculty Research Award Grants: SaTC: CORE grants for secure computation and multi-party optimization Labs/Teams: Leads research in secure computation, federated learning, and static analysis tool development. Collaborates on open-source platforms for educational grading systems (Submitty).

Stefan RadevView profile

Assistant Professor

• Bayesian Inference
• Deep Learning
• Probabilistic Modeling
• +3 more

Stefan Radev is an Assistant Professor in the Cognitive Science department at Rensselaer Polytechnic Institute . His work focuses on developing Bayesian methods with generative neural networks and computational models for complex systems like cognition and disease outbreaks. He is the core developer of the BayesFlow framework, which enables amortized Bayesian inference using deep learning. Radev's research addresses computational challenges in Bayesian workflows, such as rapid parameter estimation and model validation through neural networks. His primary research interests include Deep Learning , Probabilistic Modeling , and their applications in computational neuroscience and biomedical engineering. He collaborates with the Center for Modeling, Simulation and Imaging in Medicine (CEMSIM) and contributes to open-source projects like BayesFlow, which supports multi-backend frameworks (PyTorch/TensorFlow/JAX). Recent publications highlight advancements in amortized inference, simulation-based calibration, and robust Bayesian workflows. His work spans domains from cognitive modeling to biomedical imaging, emphasizing interdisciplinary applications of Bayesian methods. No scientific awards are explicitly listed, though his contributions to open-source tools and impactful research indicate significant academic recognition.

Shahrear IqbalView profile

Adjunct Associate Professor

• Smart Systems Security and Privacy
• Critical Infrastructure Security
• Secure Software Engineering
• +3 more

Shahrear Iqbal is an Adjunct Associate Professor at Queen's University and a Cyber Security Researcher at the National Research Council (NRC) Canada . He holds a PhD in Cybersecurity (2017) and MSc in Combinatorial Optimization (2011) from Queen's University, along with a BSc in Computer Science and Engineering (2008) from Bangladesh University of Engineering and Technology. Research Focus: Security and Privacy of Smart Systems, including in-vehicle security, self-aware operating systems, IoT-cloud security, and AI-driven cybersecurity. Teaching: Previously taught CISC490: Cybersecurity at Queen's University. Key Projects: Droid Mood Swing (DMS) for context-aware Android security policies Securing ECU Communications in connected vehicles FCFraud for user-side click-fraud detection