Find Academic Supervisors

• Computer Science Education
• Broadening Participation in Computing
• Equity and Inclusion in CS Education
• +7 more

Colleen M. Lewis is a Professor at the University of Illinois Urbana-Champaign specializing in computer science education research with a strong focus on equity, inclusion, and pedagogical innovation. Her work spans K-12 through higher education computing contexts, examining systemic barriers and developing evidence-based interventions to broaden participation in computing. Her research interests include: Equity and inclusion in computer science education Microaggressions and bias in computing environments Physical and tangible models for teaching abstract programming concepts Goal-congruity theory applications in computing education Organizational change strategies for diversity initiatives Dr. Lewis's recent work demonstrates a sophisticated multi-institutional approach to studying critical issues in computing education. Her publications reveal a consistent focus on intersectional analyses of student experiences, with particular attention to how gender, race, and other identity factors interact with learning environments. She has developed innovative teaching approaches including physical Java models and syntax exercises for Python, while simultaneously addressing systemic issues like the 'diversity-hire narrative' that can undermine inclusion efforts. Her scientific contributions include: Pioneering research on goal-congruity interventions in computing education In-depth analysis of microaggressions in CS classrooms Development of the Teaching Practices Game for TA training Multi-institutional studies of computing self-efficacy across identity groups Dr. Lewis actively shapes the field through leadership roles in SIGCSE, ICER, and RESPECT conferences, often organizing panels and workshops on broadening participation in computing. Her collaborative work across numerous institutions demonstrates her commitment to generating generalizable knowledge that can transform computing education nationwide.

Muhammad Ali GulzarView profile

Assistant Professor

• Software Engineering
• Automated Debugging
• Testing
• +6 more

Muhammad Ali Gulzar is an Assistant Professor in the Department of Computer Science at Virginia Tech and an Amazon Scholar at Amazon Web Services. He holds a Ph.D. (2020) and B.S. (2014) in Computer Science from UCLA and Lahore University of Management Sciences, respectively. His research focuses on improving developer productivity through automated debugging and testing for emerging software domains, including data-intensive systems, machine learning applications, and computational notebooks. Education Ph.D., Computer Science, University of California, Los Angeles (2020) B.S., Computer Science, Lahore University of Management Sciences, Pakistan (2014) Research Interests Dr. Gulzar redesigns software productivity tools for emerging applications across three areas: (1) automated tracking-code localization in web applications, (2) re-engineering testing/debugging for data-intensive systems (e.g., Apache Spark), and (3) advancing testing/debugging in federated learning. His work includes projects like TraceFL for federated learning interpretability, NoT.JS for JavaScript tracking blocking, and MeanCache for LLM caching. Recent Contributions Recent papers address accessibility issues in ad-driven web pages (ICSE 2025), interpretability in federated learning (ICSE 2025), and notebook executability (MSR 2025). His team received the ACM CCS 2024 Distinguished Artifact Award for tracking JavaScript function analysis and a 2024-25 Amazon-VT Award for semantic caching research. Grants & Projects CCI-funded project on web ad transparency/accessibility engineering Amazon Scholar collaboration on productivity tools Student Contributions Recent advisees include Abdul Haddi Amjad (ad accessibility), Waris Gill (Federated Learning), and Tien Nguyen (notebook analysis). Students have contributed to industry-relevant tools like GitMeter for evidence-based hiring and DeSQL for SQL debugging.

• Computer architecture
• Computing with novel technologies
• Brain-inspired computing

Georgios Tzimpragos is a Professor in the Department of Electrical & Computer Engineering at the University of Wisconsin-Madison, with a focus on computer architecture and novel computing technologies. Previously, he was an Assistant Professor at the University of Michigan (2022–2025) and earned his PhD from UC Santa Barbara (2022), focusing on neuro-inspired temporal paradigms for superconducting computing. His research explores overcoming technology limitations in superconductors, quantum computing, and biological computing systems, aiming to bridge computation and verification for improved correctness and security. Education: PhD in Computer Science, University of California-Santa Barbara (2022) MS in Computer Science, University of California-Davis (2016) BS in Computer Science, National Technical University of Athens (2012) Research Interests: His work spans computer architecture, novel computing technologies (e.g., superconductors, flexible electronics), and brain-inspired computing. Recent projects include developing energy-efficient superconducting circuits, quantum computing hardware, and neuromorphic systems that integrate biological principles. His group emphasizes formal verification and semantic gap reduction in computing systems. Recent Publications Trends: His articles address topics like superconducting logic design, quantum circuit simulation, and neuromorphic hardware acceleration. Recent work highlights include clockless superconducting logic, evolutionary neural network optimization, and formal methods for circuit validation. Awards & Honors: James E. Smith Professorship (UW-Madison, 2025) ACM SIGARCH & IEEE CS TCCA Outstanding Dissertation Award (2023) Quantum Research Institute Fellowship (U-Michigan, 2024) Grants & Teaching: Teaches E C E 554 (Digital Engineering Laboratory) and has received grants supporting superconducting and quantum computing research. His lab collaborates with industry partners on low-power and scalable architectures.

• Advanced Manufacturing
• Robotics
• Automation Systems
• +4 more

Dr. Laura Justham is a Reader in Human Centric Inclusive Engineering at Loughborough University, serving as Co-Director of Undergraduate Admissions and Academic Liaison Officer for the Women’s Engineering Society (WES). She holds a PhD from Loughborough University (2007) and a MPhys DIS (2003). Her career includes postdoctoral research and a lectureship since 2012. She leads the Intelligent Automation Centre and focuses on advanced manufacturing, robotics, and human-centric engineering solutions. Research interests span advanced manufacturing processes, tactile sensor integration, human-robot interaction, and automation systems. Her work emphasizes precision engineering, metrology, and biomimetic sensor design. Recent publications address robotic calibration, hardness prediction, and friction estimation. No scientific awards are listed. She actively contributes to undergraduate admissions strategy and promotes women in engineering through WES collaborations. Her research is supported by grants (details unspecified), with a focus on practical applications in manufacturing and robotics. Laura leads interdisciplinary teams in the Intelligent Automation Centre, integrating machine vision, control systems, and instrumentation. Her labs explore electroadhesives for robotic material handling and 3D printing precision. Future work includes sustainable manufacturing and human-centric technology design.

Pak Ho ChungView profile

Researcher

• Computer Security
• Privacy
• Malware Analysis
• +2 more

Pak Ho Chung is a Researcher at the School of Computer Science within the College of Computing at the Georgia Institute of Technology. His research interests include computer security, privacy, malware analysis, authentication, and software security. His recent publications focus on code injection defenses, privacy-preserving authentication methods, and real-time security systems, demonstrating a consistent focus on improving software security frameworks and protecting against emerging threats.

Guanhua YanView profile

Associate Professor

• Cybersecurity
• Big Data
• Networking
• +1 more

Guanhua Yan is an Associate Professor in the School of Computing at Binghamton University. He holds a PhD in Computer Science from Dartmouth College (2005) and previously worked at Los Alamos National Laboratory for nine years as a Research Associate and Research Scientist. His research focuses on cybersecurity, big data, networking, and modeling/simulation, with notable contributions to 5G network security, malware classification using AI, and adversarial machine learning. Education: PhD in Computer Science, Dartmouth College, 2005 Research Interests: Cybersecurity: Including 5G security, malware detection, and adversarial attacks Big Data: Anomaly detection and large-scale workflow analysis Networking: IoT protocols, SDN/NFV, and telecom infrastructure Modeling & Simulation: Network behavior modeling and vulnerability testing Recent work emphasizes proactive defenses against evolving cyber threats, such as 5G core network hardening and developing explainable AI for malware classification. His publications span over two decades, addressing topics ranging from Bluetooth worm propagation to modern 5G vulnerability exploitation. No scientific awards or grants are explicitly mentioned in the provided text. He advises no listed students, though his research likely involves graduate-level collaboration.

Kristian SandahlView profile

Professor

• Software Engineering
• Test Automation
• Machine Learning Applications in Testing
• +3 more

Kristian Sandahl is a Professor at Linköping University's Department of Computer and Information Science (IDA), affiliated with the Software and Systems (SAS) division. His research focuses on software testing methodologies, machine learning applications in software engineering, and continuous integration/delivery (CI/CD) processes. He leads projects addressing flaky test detection, test prioritization, and requirements engineering challenges in large-scale systems. Recent work includes a Vinnova-funded project (SEK 5.6M) advancing AI-generated software quality assurance. He collaborates extensively with industry partners, emphasizing practical solutions for modern software development challenges. Notable contributions include the 'Testing Hopscotch Model' redefining tester roles and machine learning-driven approaches to test case selection. His publications span IEEE journals and conferences, addressing topics like flakiness classification in C++ projects, industrial CI/CD challenges, and security risk integration in requirements engineering.

Alessandro BozzonView profile

Professor

• Human-Centered Artificial Intelligence (HCAI)
• Human-Computer Interaction
• AI-driven Systems Design
• +3 more

Prof. Alessandro Bozzon is a Full Professor of Human-Centered Artificial Intelligence (HCAI) at Delft University of Technology, holding dual appointments in the Faculty of Industrial Design Engineering and the Faculty of Electrical Engineering, Mathematics, and Computer Science. He leads the Department of Sustainable Design Engineering and chairs the Delft AI Labs and Talents initiative. His research focuses on developing AI systems that augment human capabilities while ensuring ethical and societal benefits, with contributions spanning HCAI, human-AI interaction, and algorithmic fairness. Awarded the 2023 Fellow of the Netherlands Academy of Engineering and recipient of the IBM Faculty Award (2017), Bozzon has pioneered projects like SocialGlass (urban analytics) and TROMPA (music digitization). He teaches courses on Machine Learning for Design and Crowd Computing, and has mentored over 70 graduate students. His work addresses challenges in healthcare AI, automated decision-making, and sustainable energy systems through interdisciplinary collaborations with industry and policymakers. Key projects include the Equal-Life initiative (children’s greenspace access) and FAIR digital product passports for ICT. He co-directs the Future Libraries Lab, exploring AI’s role in cultural heritage. His research has been featured in top venues like CHI, ACM conferences, and interdisciplinary journals.

Gidon ErnstView profile

Assistant Professor

• Formal Methods
• Software Verification
• Software Testing
• +5 more

Gidon Ernst is a Junior Professor (Assistant Professor) for Software Verification at the Department of Computer Science, LMU Munich, where he leads research in the Software and Computational Systems Lab. His work focuses on formal methods for software engineering, with particular emphasis on verification, testing, and security analysis of complex systems. His research interests span formal methods for software engineering, software testing, interactive and automated proofs, hybrid systems, and falsification. Ernst's work addresses challenges in relational proofs for substitution principle, software testing with Legion (a tool combining concolic execution and fuzzing), concurrent information flow security with SecCSL and SecC, and falsification of temporal logic requirements for hybrid systems. His approach often combines theoretical foundations with practical tool development. Ernst's publication record shows a strong focus on verification of security properties, particularly information flow security in concurrent systems, and falsification of hybrid systems. His recent work has advanced techniques for adaptive probabilistic search in falsification, compositional vulnerability detection, and contract-based software specification. He actively contributes to the verification community through competitions like VerifyThis and ARCH. Best Master Lecture 2022 in Computer Science (awarded by Gruppe Aktiver Fachschaftika) Ernst has supervised PhD student Dongge Liu on software fuzzing with machine learning (2018-2022), co-supervised with Toby Murray and Ben Rubinstein from the University of Melbourne. His teaching includes courses on formal specification and verification, methods in software engineering, and various seminars on software quality assurance and deductive verification. He has also served on numerous program committees and organized major verification events including SPIN 2025 and the VerifyThis challenge series. Ernst maintains active collaborations with researchers worldwide, particularly in the areas of hybrid systems verification (with Ichiro Hasuo, Sean Sedwards, and Zhenya Zhang) and information flow security (with Toby Murray). His work bridges theoretical computer science with practical software engineering challenges, developing tools that have seen adoption in both academic and industrial settings.

Matthias DanglView profile

Researcher

• Software Verification
• Model Checking
• SMT-Based Verification
• +3 more

Matthias Dangl is a researcher at the Department of Computer Science, Ludwig-Maximilians-Universität München (LMU Munich), within the Software and Computational Systems Lab. Previously, he was affiliated with the University of Passau, where he contributed to software engineering education and research. His research focuses on software verification, model checking, and formal methods, with a particular emphasis on witness-based validation and SMT-based techniques. He has developed tools like CPAchecker, which are widely recognized in academic and industrial verification communities. Dangl has won multiple awards, including categories in SV-COMP'15 and medals for his contributions to software verification. His work bridges theoretical advancements with practical implementations, enhancing the reliability of software systems through automated reasoning and verification. Education: PhD in Computer Science (2022), LMU Munich Master's in Computer Science (2013), University of Passau Research Interests: Software Verification Model Checking Witness-Based Validation SMT Solvers Formal Methods in Software Engineering Grants & Collaborations: His research is supported by collaborations with academic and industrial partners, focusing on advancing verification frameworks and tools. Labs/Teams: He is a core member of the Software Systems Lab at LMU Munich, contributing to projects like the VerifierCloud and CPAchecker.

Kexin RongView profile

Assistant Professor

• Database Systems
• Machine Learning
• Data Management
• +6 more

Kexin Rong is an Assistant Professor in the School of Computer Science at Georgia Tech, leading the D2I Lab as part of the Georgia Tech Database Group . She also holds an affiliation with VMware Research Group by Broadcom. Education: Ph.D. in Computer Science (2021) from Stanford University, advised by Peter Bailis and Philip Levis B.S. in Computer Science (2015) from California Institute of Technology Research Interests focus on democratizing data science through systems that integrate database management , machine learning , and human-computer interaction . Key projects include SketchQL for sketch-based video retrieval, Inshrinkerator for deep learning checkpoint compression, and Lotus for ML preprocessing pipeline characterization. Scientific Awards include Amazon Research Award (2024) NSF Awards #2335881 (2024) and #2333737 (2023) Catherine M. and James E. Allchin Early Career Professorship (2022) SIGMOD Distinguished PC Award (2024) SIGMOD Jim Gray Doctoral Dissertation Award Honorable Mention (2022) Advising includes Ph.D. students: Rajveer Bachkaniwala, Hongbin Zhong Co-advised students: Hantian Zhang (with Xu Chu), Renzhi Wu (with Xu Chu) Former Ph.D. advisee: Peng Li (2023 graduate) Lab & Collaborations : Leads the D2I Lab at Georgia Tech, which has received funding from NSF and Bosch Research. The lab actively collaborates with institutions like Stanford University and contributes to open-source projects including MacroBase and ASAP .

• Cloud Computing
• Edge Computing
• Reinforcement Learning
• +3 more

Hongyun Liu is a researcher involved in the HiCONNECTS project (2023-2025), focusing on heterogeneous integration for connectivity and sustainability. His research interests span cloud computing, edge computing, reinforcement learning, IoT data analysis, performance anomaly detection, and workflow offloading. Recent work emphasizes robust frameworks for cloud applications, meta-learning in scheduling, and anomaly detection using ensemble learning and GAN-based models. His publications address time-critical systems, edge-cloud continuum optimization, and statistical modeling of cloud resource performance. Liu contributes to projects like HiCONNECTS as a core member, exploring sustainable and efficient resource management strategies. No awards or grants are explicitly listed in the provided information.

Miryung KimView profile

Professor

• Software Engineering
• Data-Intensive Computing
• Heterogeneous Computing
• +7 more

Miryung Kim is a Professor and Vice Chair of Graduate Studies in the Computer Science Department at the University of California, Los Angeles . She directs the Software Engineering and Analysis Laboratory and has pioneered research in Software Engineering for Data Intensive Computing (SE4DA) and Software Engineering for Heterogeneous Computing (SE4ML) . Her work spans code clone detection, automated testing/debugging for Apache Spark, and developer tools for heterogeneous systems. Education: BS in Computer Science, Korea Advanced Institute of Science and Technology (KAIST), 2001 MS and PhD in Computer Science and Engineering, University of Washington (2003 & 2008) Research Interests: Her research focuses on software evolution , data-intensive systems , and heterogeneous computing . Key contributions include foundational work on code clone analysis , large-scale studies of data scientists' roles , and fuzz testing frameworks for big data and AI accelerators. She established SE4DA and SE4ML research directions. Article Trends: Recent publications emphasize fuzz testing for MLIR compilers , Apache Spark systems, and quantum software stacks . The work spans automated test generation , heterogeneous platform validation , and code optimization in dataflow environments. Scientific Awards: ACM SIGSOFT Influential Educator Award (2022) ICSME Most Influential Paper Awards (2023 & 2020) NSF CAREER Google Faculty Research Award Humboldt Research Fellowship ACM Distinguished Member Advising & Grants: She has mentored 8 tenure-track faculty members (Columbia, Purdue, Virginia Tech) and leads a $4.9M Office of Naval Research project on synergistic software customization . Her former students (Tianyi Zhang, Muhammad Ali Gulzar) have received NSF CAREER awards.

• Software Testing
• Compiler Testing
• Software Engineering
• +3 more

Yongqiang Tian is a Lecturer in the Department of Software Systems & Cybersecurity at Monash University. His research focuses on software testing, compiler testing, and software engineering. He holds a Ph.D. in Computer Science from the University of Waterloo (Canada) and another Ph.D. in Computer Science from the Hong Kong University of Science and Technology, alongside a BEng in Information Engineering from City University of Hong Kong. His research interests emphasize compiler testing methodologies, program reduction techniques, and deep learning system reliability. Recent work includes leveraging large language models (LLMs) for enhancing compiler testing and program analysis. He has explored probabilistic delta debugging, program reduction algorithms, and evaluating DNN model behaviors under compression. Key contributions include frameworks like T-Rec for language-agnostic program reduction, Lampr for LLM-aided optimization, and tools such as EvalDNN for deep learning model evaluation. His articles highlight trends in compiler bug analysis, testing deep learning libraries, and improving fault localization through algorithmic innovations. No scientific awards are explicitly mentioned. Advising and grants details are not provided in the text. His work often intersects with program analysis, compiler validation, and applying AI techniques to software engineering challenges.

• Security Testing
• Software Testing
• Computer Security
• +5 more

Dr. Ishbel Mary Macdonald Duncan is an Honorary Senior Lecturer at the University of St Andrews' School of Computer Science. She leads interdisciplinary projects such as the Cyber Nexus initiative (funded at £420K by the Scottish Government) and the Virtual Dive Experience, collaborating with industry and cultural heritage organizations. Her research focuses on security evaluation, software testing, educational technology, and virtual worlds applications in heritage preservation and student orientation. Education: PhD in Strong Mutation Testing from the University of Durham (1989–1993), BSc from the University of St Andrews. Professional affiliations include IEEE, ACM, and ISSA. Research Highlights: Security Testing: Network security, intrusion detection, and vulnerability detection. Virtual Worlds: Developing OpenVirtualWorlds.org for cultural heritage (e.g., St Andrews Castle, Linlithgow Palace), ViStA for international student orientation, and the Virtual Dive Experience with the Sea Bird Centre. Education Technology: Agent-based learning systems for Java programming education and metrics for software testing visualization. Grants & Projects: EU-funded CINE (€200K) and CUPIDO (€20K) for cultural heritage SMEs. Abertay-led Tay Cities Cyber Security initiative (£20M). Scottish Funding Council grants for heritage response to COVID-19 and digital infrastructure. Expertise: Security systems, software metrics, system failure analysis, and large-scale distributed testing methodologies.