Find Academic Supervisors

• Software Reliability
• Software Security
• Symbolic Execution
• +6 more

Cristian Cadar is a Professor in the Department of Computing at Imperial College London, leading the Software Reliability Group. He holds an ERC Consolidator Grant and is an EPSRC Early-Career Fellow. His research focuses on enhancing software reliability and security through techniques like symbolic execution and dynamic analysis. Education: PhD in Computer Science (Stanford University), M.Eng. and B.S. in Computer Science and Mathematics (MIT). Research interests include software engineering, computer systems, and security, with a focus on practical tools for testing and verification. His work has led to advancements in symbolic execution, compiler testing, and dynamic software updates. Key publications span topics like KLEE, symbolic execution optimization, and compiler fuzzing. Awards include the BCS Roger Needham Award, Humboldt Research Award, and SIGOPS Hall of Fame recognition. He advises PhD students and postdocs, focusing on systems programming and compilers. His lab develops tools like KLEE and explores multi-version execution and greybox fuzzing.

Florian TramèrView profile

Assistant Professor

• Computer Security
• Machine Learning
• Privacy-Preserving Technologies
• +2 more

Florian Tramèr is an Assistant Professor of Computer Science at ETH Zurich, leading the SPY Lab. His research focuses on computer security, privacy, and adversarial machine learning, particularly studying vulnerabilities in deep learning systems. He holds a PhD from Stanford University and previously worked at Google Brain. Education: Bachelor's and Master's from EPFL (Switzerland), PhD from Stanford University. Research Interests: Adversarial machine learning, privacy-preserving technologies, cryptography, and evaluating AI safety through frameworks like AgentDojo. His work addresses societal impacts of rapidly deployed machine learning systems. Awards: Two Best Paper Awards at ICML 2024, CAIS SafeBench competition win, Google/Amazon Research Awards. His research has been featured in Nature , Science , and The Economist . Labs/Teams: SPY Lab (ETH Zurich), part of the Information Security Institute and ZISC. Associated with ETHZ AI Center. Advises PhD students on AI safety and security topics.

• Network Security
• Android Security
• Cloud Computing Security
• +7 more

Manoj Singh Gaur is a Professor in the Department of Computer Science and Engineering at Malaviya National Institute of Technology Jaipur (MNIT Jaipur), India. With a publication record spanning over two decades from 2003 to present, he has established himself as a prominent researcher in computer security and architecture. His work primarily focuses on network security, Android security, and Network-on-Chip architectures, with extensive collaborations with researchers from institutions worldwide including University of Padua (Italy), University of Southampton (UK), and other Indian institutions. Dr. Gaur's research interests encompass a broad spectrum of cybersecurity challenges, particularly in mobile and cloud environments. His work addresses critical issues such as Android malware detection, information leakage prevention, DDoS mitigation in cloud environments, and secure deduplication techniques. In computer architecture, he has made significant contributions to Network-on-Chip design, fault tolerance mechanisms, and power-efficient router microarchitectures. His research methodology often combines theoretical analysis with practical implementation, resulting in solutions that address real-world security and performance challenges. Analysis of his recent publications (2020-2023) reveals a continued focus on mobile security, particularly Android application security, with numerous papers on vulnerability detection, permission analysis, and information leakage prevention. Simultaneously, his work in Network-on-Chip architectures demonstrates sustained interest in fault-tolerant routing algorithms and performance optimization for multi-core systems. The interdisciplinary nature of his research, bridging security and architecture concerns, represents a distinctive contribution to the field. Dr. Gaur has mentored numerous students who have co-authored publications with him, indicating an active research group focused on cutting-edge security and architectural challenges. His collaborative approach is evident in the diverse set of co-authors spanning multiple continents, reflecting international recognition of his expertise.

• Machine Learning
• Cybersecurity
• Smart Grid Security
• +5 more

Dr. Abdun Mahmood is an Associate Professor in the Department of Computer Science and IT at La Trobe University, Australia. He holds a PhD from the University of Melbourne and has held academic positions at institutions including RMIT University and UNSW Canberra. His research focuses on Machine Learning and Cybersecurity, particularly in Smart Grid Security, SCADA systems, and Malware Detection. He leads a research group addressing challenges like anomaly detection in big data, SCADA vulnerability analysis, and privacy-preserving techniques. Education: PhD in Computer Science, University of Melbourne (2008) MSc (Research) in Computer Science, University of Dhaka (1999) BSc in Applied Physics and Electronics, University of Dhaka (1997) Research Interests: Machine Learning applications in Cybersecurity Smart Grid Security (e.g., False Data Injection, SCADA vulnerabilities) Malware Detection (Windows, API analysis, memory forensics) Federated Learning for privacy-preserving systems Data analytics for anomaly detection in big data Funded Research: Secured over $1M in grants, including ARC Linkage Projects Recent project: 'Detection of data integrity cyber attacks in smart grid' (2020–2023) Availability: Supervision of Masters/PhD students, collaborative industry projects, and advisory roles. Labs/Teams: Leads a research team focusing on Machine Learning and Cybersecurity, with active projects in SCADA security, malware analysis, and federated learning frameworks.

Danijela EfnushevaView profile

Assistant Professor

• Computer Engineering
• Network Security
• Machine Learning
• +5 more

Danijela Efnusheva is an Assistant Professor at the Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University in Skopje, Macedonia. She leads the Computer Technologies and Engineering Lab since 2020 and has held academic positions since 2009. Her expertise spans computer engineering, network security, FPGA design, and machine learning applications. Education: Ph.D. in Technical Sciences (2017), Master of Electrical Engineering (2010), and Bachelor's in Informatics (2008), all from FEEIT, Skopje. Research focuses on FPGA-based hardware architectures, network security solutions, IoT systems, and machine learning applications in healthcare and finance. Key projects include the UbiLAB remote laboratory framework and FPGA implementations for network packet processing. Publications emphasize network security, embedded systems, and machine learning, with contributions to conferences like ETAI and Applied Innovations in IT. She coordinates projects such as the OBD cloud platform and has expertise in remote lab infrastructure and IoT monitoring systems.

Harshvardhan TakawaleView profile

Researcher

• Wearable Sensing
• Acoustics
• Multimodal Imaging
• +7 more

Harshvardhan Takawale is a third-year Computer Science PhD student at the University of Maryland College Park, affiliated with the iCoSMoS Lab under Prof. Nirupam Roy. He has previously worked as a Lead Researcher at Silence Laboratories Singapore and interned at Nokia Bell Labs UK and Cambridge. Education: B.E. in Computer Science from BITS Pilani (2020), currently pursuing PhD at UMD. Research Interests: Focus on enabling acoustic and RF sensing on low-power wearable platforms, gesture/micro-motion detection for smart environments, and physics-informed ML for context-aware systems. His work bridges hardware constraints with advanced inference for ubiquitous healthcare and mobility services. Recent Publications: Contributions to wearable interfaces (Scribe), auditory attention detection on earables, and low-power acoustic event sensing highlight his expertise in multimodal systems and signal processing. Earlier works span cybersecurity (malware detection), NoC fault tolerance, and continuous authentication. Scientific Awards: Dean's Fellowship at UMD Computer Science (2022, 2023). Roles & Projects: Interned at Nokia Bell Labs on earable-based attention detection; led Silence Laboratories' projects on co-location authentication (>92% accuracy) and landmark extraction for proof-of-visit. Also served as Publicity Chair for HumanSys 2024.

• Drug Discovery
• Small Molecule Therapeutics
• Pharmaceutical Development
• +3 more

Stephen Munk is a Professor of Practice and Deputy Director of the Biodesign Institute at Arizona State University. He holds affiliations with the School of Molecular Sciences and has extensive industry experience in drug discovery and manufacturing. Previously served as CEO of Ash Stevens Inc., securing numerous FDA approvals for oncology drugs including Velcade and Vidaza. Elected Senior Member of the National Academy of Inventors (2022). Education: B.S. Chemistry, Arizona State University (1979) Ph.D. Organic Chemistry, UC Berkeley (1985) Postdoctoral Fellow under Dale Boger at Purdue University (1985-1988) Research Interests: Focuses on discovery and development of small molecule therapeutics, particularly anticancer drugs. Expertise in process development for safe synthesis of pharmaceuticals and regulatory compliance. Active in advancing drug discovery efficiency through interdisciplinary approaches. Awards: Senior Member, National Academy of Inventors (2022) Six NIH research contracts secured during Ash Stevens leadership Leadership & Boards: Former Chairman, AACR Chemistry in Cancer Research Working Group (2014) Chairman, MichBio Board of Directors (2009-2014) Member, Henry Ford Community College Biotechnology Advisory Board (2009-2016) Teaching: Teaches special topics courses in biomedicine and chemistry at ASU including BIO 598 and BCH 598.

• Software Engineering
• Machine Learning
• Precision Agriculture
• +7 more

Önder Babur is an Assistant Professor in the Department of Information Technology at Eindhoven University of Technology. His research focuses on software engineering, machine learning applications, precision agriculture, and digital twin technologies. He has contributed to projects involving business process modeling, drone analytics, and energy market methodologies. Babur collaborates with institutions like Wageningen University & Research and has supervised PhD candidates in generative AI approaches and digital twin systems. His research interests span model analytics, clone detection, API usage analysis, and low-code platforms. Notable projects include an empirical study of business process models on GitHub and foundational work in digital twins for energy markets. Babur has co-developed datasets for drone imagery analysis and systematic reviews of food recommender systems. His work bridges theoretical software engineering advancements with practical applications in agriculture and energy systems. Advising PhD candidates include Gürkan Soykan (digital twins in energy markets) and Jeroen Doornbos (generative AI in drone analytics). Projects emphasize collaborative frameworks like SAMOS for model management and Apache Spark-based distributed analytics. Babur’s contributions span 45+ peer-reviewed publications and two active PhD supervisions.

Christophe HauserView profile

Assistant Professor

• Software Security
• Binary Program Analysis
• Reverse Engineering
• +6 more

Christophe Hauser is an Assistant Professor at Dartmouth College and a member of the Institute for Security, Technology and Society (ISTS). His research focuses on software security, binary program analysis, reverse engineering, and vulnerability discovery across software and hardware systems. He integrates formal methods, machine learning, and program analysis to enhance cybersecurity defenses. Education: Joint PhD from CentraleSupélec (France) and Queensland University of Technology (Australia). Prior roles include Research Lead at USC Information Sciences Institute (USC-ISI) where he founded the Binary Analysis and Systems Security (BASS) group, and postdoctoral researcher at UC Santa Barbara’s Seclab. Research Interests: Binary code and firmware analysis, intrusion detection, usable security, privacy-preserving systems. Notable projects include BinPool (vulnerability dataset), BinHunter (vulnerability localization), and Harm-DoS (DoS mitigation). His work emphasizes practical applications such as automated fuzzing, decompilation analysis, and neuro-symbolic code search systems. He has contributed to open-source tools like angr and developed frameworks for firmware integrity validation (BootKeeper).

Michael GodfreyView profile

Professor

• Software Engineering
• Software Evolution
• Empirical Studies
• +5 more

Michael Godfrey is a Professor at the University of Waterloo's David R. Cheriton School of Computer Science, affiliated with the Department of Electrical and Computer Engineering. His work focuses on software engineering, empirical studies of software systems, code review practices, and open-source software ecosystems. He holds a Ph.D., M.Sc., and B.Sc. from the University of Toronto (1997, 1988, 1986). Research interests include software evolution, mining software repositories, provenance tracking, code duplication analysis, and program comprehension. He actively explores how developers interact with code reviews, documentation systems, and modern CI/CD pipelines. Recent work investigates app store software ecosystems, Bash scripting vulnerabilities, and deep learning API testing. Publications span empirical studies of developer communities (e.g., Stack Overflow analysis), architectural teaching methods, and automated testing techniques like documentation-guided fuzzing. His work bridges theory and practice, addressing challenges in large-scale systems maintenance and developer productivity. Leverages tools like Elasticsearch for repository mining and explores anomaly detection in software development. Engaged in curriculum development for software architecture education and has contributed to conferences like MSR and ICSE.

Hong ZhangView profile

Assistant Professor

• Large-scale data analytics
• Distributed ML training and serving systems
• Application and network scheduling
• +5 more

Hong Zhang is an Assistant Professor at the University of Waterloo, specializing in large-scale data analytics, distributed machine learning systems, and cloud computing. His research focuses on advancing robust and efficient methods for modern AI systems, including topics like adversarial robustness, zero-knowledge proofs for deep learning, and scalable inference techniques for large language models. He holds a Ph.D. in Computer Science from the Hong Kong University of Science and Technology and dual bachelor's and master's degrees in Electronics and Communications Engineering from Huazhong University of Science and Technology. Education: Ph.D., Computer Science and Engineering, Hong Kong University of Science and Technology (2019) B.Sc. and M.Sc., Electronics and Communications Engineering, Huazhong University of Science and Technology, China (2013) Research Interests: Hong's work bridges theoretical foundations and practical applications in distributed ML systems, data center networking, and secure AI. Key areas include optimizing large-scale training/serve pipelines, mitigating adversarial vulnerabilities, and developing privacy-preserving techniques like zero-knowledge proofs for deep learning. His contributions span both algorithmic innovations and real-world system implementations. Publications Trends: Recent work emphasizes scalable inference acceleration (e.g., Eagle series), trustworthy AI (e.g., generative model guidelines), and cryptographic safeguards for ML (e.g., zkdl/zkllm frameworks). He frequently explores intersections between security, efficiency, and scalability in modern AI systems. Awards/Grants: No specific awards or grants mentioned in available texts. Advising & Labs: While student names aren't listed, his research group likely focuses on distributed systems and secure ML. Active collaborations likely involve cloud computing and adversarial machine learning domains.

Laura SouthView profile

Visiting Assistant Professor

• Human-Computer Interaction
• Accessibility Technologies
• Data Visualization
• +4 more

Laura South serves as a Visiting Assistant Professor in the Department of Computer Science at Williams College. She joined the institution after completing her PhD at Northeastern University in 2024 under Michelle Borkin's supervision. Her research centers on digital accessibility , with dual specialization in: Photosensitive epilepsy safety in interactive media (VR, data visualizations, social platforms) Assistive technologies for visual impairments (3D-printed braille systems) Notable contributions include seizure-risk mitigation frameworks for dynamic content and empirical evaluations of tactile interfaces. Her 15 most recent publications (2020-2024) reveal three dominant research trajectories: Photosensitivity safety mechanisms across immersive platforms (6 papers) Accessibility extensions for visualization systems (5 papers) Novel evaluation methodologies for inclusive design (4 papers) Key venues include CHI, IEEE VIS, and ACM TOCHI. Laura teaches human-computer interaction and introductory programming courses. Her educational background includes dual B.S. degrees in Computer Science and Statistics from Colorado State University (2018), followed by a Ph.D. in Computer Science from Northeastern University (2024).

Sangita PokhrelView profile

Lecturer

• Machine Learning
• Deep Learning
• Data Science
• +10 more

Sangita Pokhrel is a Lecturer in Computer Science and Data Science at York St John University's London Campus. She holds an MSc in Computer Science (Distinction) from York St John and an undergraduate degree in Electronics and Communication Engineering from Tribhuvan University, Nepal. Her research focuses on machine learning, AI applications in agriculture and healthcare, blockchain, and educational technology. She leads the Paragon Research Group, emphasizing interdisciplinary innovation in data science and Gen AI. Research Interests: Machine Learning, Deep Learning, AI in Agriculture, Mental Health Analysis, Blockchain, and Educational Technology. She actively collaborates with IBM UK and universities like St Mary’s University to bridge academia-industry gaps. Her funded projects include SAAR and QR initiatives, and she has published in IEEE journals and conferences. Awards include the Best Presentation Award at CDSBDA 2022. Teaching: She instructs master’s modules in Big Data, Agile Software Development, AI, and Data Visualization. Pedagogical focus on practical learning and real-world problem-solving. Engages students through initiatives like The Dock and MakerVerse, offering hands-on experience with VR and podcasting tools. Labs/Teams: Paragon Research Group (focusing on blockchain and Gen AI), collaborations with UK universities to enhance teaching practices. Part of diversity initiatives like U Connect Mentoring and YSJ London Women’s Network.

• Computer Security
• Mobile Security
• Network Security
• +6 more

Georg Merzdovnik is a Lecturer at Vienna University of Technology (TU Wien) with a focus on computer security and privacy . His work spans binary rewriting, web privacy, IoT security, and Android application analysis, reflected in publications in top venues like ACM Computing Surveys and IEEE Euro S&P. He actively supervises student research projects in security-related domains. Primary Affiliation: Vienna University of Technology (TU Wien) Research Interests include: Binary rewriting and obfuscation techniques Web privacy mechanisms post-GDPR IoT device security in IPv6 networks Mobile application threat analysis Malware detection in Windows shellcodes Evaluation of tracker-blocking tools Thesis Supervisions (2012–2024) cover topics like IoT testbeds, Android intent firewalls, and secure messaging services.

• Automated Reasoning
• Knowledge Representation and Reasoning
• Computational Logic
• +5 more

Uwe Egly is an Associate Professor at the Department of Knowledge-Based Systems, Faculty of Informatics, Technische Universität Wien (TU Wien). His research focuses on automated reasoning, proof theory, knowledge representation, and computational logic, with a strong emphasis on quantified Boolean formulas (QBFs), argumentation frameworks, and applications of AI in engineering. He leads projects funded by the Austrian Science Fund (FWF) and the Vienna Science and Technology Fund (WWTF), including the Boolean project (2011–2019) and FAME (2011–2014). Egly is known for developing QBF solvers like DepQBF and contributing to SAT-solving techniques. He teaches courses such as Abstract Argumentation , Formal Methods in Computer Science , and Quantum Computing . His research interests span proof complexity, satisfiability checking, and AI-driven algorithms for path planning. He has advised numerous students on theses involving quantum algorithms, QBF solver optimizations, and argumentation frameworks. Egly’s work bridges theoretical computer science with practical applications, including contributions to deformation monitoring systems and circuit synthesis using SAT-based methods. He is involved in international workshops and conferences, such as SAT, FMCAD, and Dagstuhl Seminars, and has edited proceedings for events like SAT 2014 . His collaborations include projects on scenario-based testing of UML diagrams and semantics-aware model versioning. Egly’s interdisciplinary approach integrates logic, artificial intelligence, and computational methods to solve complex theoretical and applied problems.