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Ali GhorbaniView profile

Professor

• Cybersecurity
• Machine Learning
• Network Security
• +3 more

Dr. Ali Ghorbani is a Professor and Tier 1 Canada Research Chair in Cybersecurity at the University of New Brunswick's Faculty of Computer Science, where he served as Dean from 2008-2017. He is the founding Director of the Canadian Institute for Cybersecurity (CIC), established in 2016. Ghorbani holds BSc (Tehran), MSc (George Washington University), and PhD (UNB) degrees. With 42+ years in academia, his research spans cybersecurity, machine learning, adaptive systems, and critical infrastructure protection. Ghorbani has co-founded three cybersecurity startups (Sentrant Security, EyesOver Technologies, Cydarien Security) and co-invented four awarded patents. He has published 300+ peer-reviewed papers and supervised 250+ researchers. Professional roles include co-founding the National Cybersecurity Consortium (NCC) and serving as co-editor-in-chief of Computational Intelligence journal. Awards & Honors: Canada Research Chair in Cybersecurity Startup Canada Senior Entrepreneur Award (2017) RBC Top 25 Canadian Immigrants (2019) CAIAC Lifetime Achievement Award (2024)

George T. HeinemanView profile

Associate Professor

• Software Engineering
• Component-Based Software Engineering
• Software Architecture
• +3 more

George T. Heineman is an Associate Professor of Computer Science at Worcester Polytechnic Institute (WPI). He holds a BS from Dartmouth College (1989), an MS (1990), and a PhD (1996) from Columbia University. His research focuses on software engineering, component-based systems, and modularity, with notable contributions to type-safe modular software evolution through the CoCo design pattern. Heineman emphasizes professional software engineering practices in teaching, challenging students with industry-relevant projects to foster best practices. His work has been published in leading venues like ECOOP, with a 2021 paper on CoCo gaining attention for its impact on Java language design. He received the WPI Trustees' Award for Outstanding Teaching in 2022, reflecting his dedication to education. His research spans algorithm design, system architecture, and cybersecurity, with publications ranging from foundational theory to practical applications in automated assessment and network security. Education: BS in Computer Science, Dartmouth College, 1989 MS in Computer Science, Columbia University, 1990 PhD in Computer Science, Columbia University, 1996 Research interests include software evolution, design patterns, and modular software systems. His recent work addresses challenges in maintaining stable APIs and enabling cohesive extensions in object-oriented systems. Collaborations with institutions like the University of Copenhagen and TU Dortmund highlight his international academic engagement. Beyond research, Heineman contributes to curriculum development, including WPI's new graduate programs in computing and workforce development initiatives.

R. SekarView profile

Professor

• Computer Security
• Network Security
• Software Security
• +3 more

R. Sekar serves as SUNY Empire Innovation Professor and Associate Chair in the Department of Computer Science at Stony Brook University, actively contributing to academic leadership and research initiatives. Education Ph.D. in Computer Science from Stony Brook University (1991) B. Tech in Electrical Engineering from Indian Institute of Technology, Madras (1986) Research Interests Professor Sekar's research focuses on practical software and systems security solutions, integrating principles from programming languages, compilers, operating systems, algorithms, networks, and artificial intelligence. His work addresses critical challenges including software vulnerability mitigation (buffer overflows, SQL injection, XSS), malware defense, high-performance intrusion detection (network and host-based), attack isolation/recovery mechanisms, self-healing systems, and distributed system monitoring. This interdisciplinary approach emphasizes building real-world systems to solve tangible security problems. Scientific Awards Chancellor's award for Excellence in Scholarship and Creative Activities (2011) SUNY Research and Scholarship Award (2006) Faculty Service award, Department of Computer Science (2002-2004) Promising Inventor Award, Research Foundation of SUNY (2003) Department Research Excellence award (2000-2002) Advising and Grants While the source text does not specify doctoral advisees or individual grant projects, Professor Sekar's sustained research productivity and award history indicate substantial external funding and mentorship activities. His leadership as Associate Chair further demonstrates institutional commitment to academic guidance. Labs and Teams Professor Sekar directs the Security Lab (SECLab) at Stony Brook University, as evidenced by his research website https://seclab.cs.sunysb.edu/sekar/ , which serves as the operational hub for his security research initiatives and team collaborations.

• Computer Science
• Software Engineering
• Systems Software
• +3 more

Dr. Constantinos Constantinides is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University. His contact information includes an email at constantinos.constantinides@concordia.ca and a personal website at http://users.encs.concordia.ca/~cc/ . While no specific research interests, awards, students, or publications are detailed in the provided text, his affiliation suggests expertise in computer science and software engineering disciplines. The linked website may contain additional information about his research, teaching, and professional activities.

Pavan PoudelView profile

Assistant Professor

• Parallel and Distributing Computing Algorithms
• Robotics
• Internet of Things
• +3 more

Dr. Pavan Poudel is an Assistant Professor of Computer Science at the University of Houston-Clear Lake (UHCL), part of the College of Science and Engineering. He joined UHCL in Fall 2024 after working as a Research Engineer in the software industry and completing a postdoctoral research position at Augusta University's Department of Computer and Cyber Sciences. He holds a Ph.D. in Computer Science from Kent State University (2021) and a Bachelor's in Computer Engineering from Tribhuvan University, Nepal. His research focuses on Parallel and Distributed Computing, Robotics, and Fault-Tolerance, with expertise in optimizing scheduling algorithms and designing scalable systems for distributed environments. Dr. Poudel's research includes transactional memory systems, distributed robotics algorithms (e.g., gathering, scattering, and coverage), and fault-tolerant solutions for real-time applications like medical and disaster response. His work bridges theoretical computer science with practical applications, emphasizing efficiency and scalability. He has published extensively in venues like Theoretical Computer Science (TCS) and Information, addressing topics such as time-optimal robot navigation and adaptive versioning in transactional memory. His academic awards include the Best Presentation Award at Kent State’s Graduate Research Symposium (2021) and scholarships like the John Sechrist and Hine Scholarships (2019–2020). He teaches courses such as Computer Organization and Assembly Language and Computer Game Programming. His publications demonstrate a strong focus on distributed systems, robotics algorithms, and concurrency control, reflecting his commitment to advancing scalable and reliable computing solutions.

Bill ChuView profile

Professor

• Cybersecurity
• Cyber Threat Intelligence
• AI in Cybersecurity
• +2 more

Bill Chu is a Professor and Managing Director of CyberDNA at the University of North Carolina at Charlotte's College of Computing and Informatics. His research focuses on cybersecurity automation, cyber threat intelligence, application security, and AI-driven cybersecurity solutions. He has contributed to numerous publications on threat detection, vulnerability analysis, and secure software development practices. Chu's work emphasizes practical applications of cybersecurity, including static analysis tools, interactive code annotation for vulnerability detection, and educational tools for secure programming. His research spans collaborations with institutions and industry to bridge gaps in cybersecurity education and practice. Notable contributions include developing frameworks for extracting actionable threat intelligence from social media, automated unit testing for XSS vulnerabilities, and leveraging large language models for vulnerability detection. Chu actively participates in workshops and conferences on cybersecurity and software engineering, contributing to advancements in both theory and application.

Djamel E. KhelladiView profile

Researcher

• Software Engineering
• Model-Driven Engineering
• Software Evolution
• +6 more

Djamel E. Khelladi is a CNRS researcher affiliated with the IRISA research lab and the DIVERSE team at University of Rennes 1 , France. Previously, he held postdoctoral and PhD positions at Johannes Kepler University (JKU) Linz, Austria, and Université Pierre et Marie Curie (UPMC), France. Research interests include: Model-Driven Engineering Software Evolution & Co-evolution AI and Generative AI Applications Polyglot Programming Digital Twins Recent article trends focus on integrating Large Language Models (LLMs) for code-metamodel co-evolution, polyglot programming challenges, incremental build optimization in configurable systems, and empirical studies on software evolution. His work often bridges theoretical modeling with practical implementation in industrial contexts. Academic service roles include: Proceedings Co-Chair @MODELS 2025 Co-Organizer of Models and Evolution (ME) workshops (2023-2025) Co-Editor for special issue on Model Driven Engineering for Digital Twins (SoSym 2024/25) PC member in top venues: ICSE , ASE , MODELS , ECMFA , MSR , FSE

• Human-Computer Interaction (HCI)
• Learning Sciences
• Artificial Intelligence
• +5 more

Hariharan Subramonyam is an Assistant Professor (Research) at Stanford University's Graduate School of Education and Computer Science (by courtesy) . He is also a Faculty Fellow at the Institute for Human-Centered AI (HAI) and a core member of the Stanford HCI Group . His research focuses on augmenting human learning, creativity, and sensemaking with AI, informed by cognitive psychology and collaborative design practices. Research Interests: Human-Computer Interaction (HCI) Learning Sciences Generative AI Educational Technology Assistive Technology Cognitive Psychology Recent Trends: Designing AI tools for education and creative coding Co-developing AI experiences with multidisciplinary teams Ethical and responsible AI frameworks Accessible interfaces for visually impaired users Scientific Awards: Best Paper Award, CHI 2025 Best Paper Honorable Mention, CHI 2025 Best Paper Award, ASSETS 2024 Best Paper Award, UIST 2023 Best Paper Award, IUI 2021 Best Paper Award, CHI 2020 Advising & Grants: Co-advising PhD student Neha Rajagopalan Mentoring 23+ student researchers Principal Investigator on HAI Hoffman Yee Grant (2024) Labs & Teams: Stanford Human-Computer Interaction Group Stanford Institute for Human-Centered AI Collaborative projects with University of Michigan, Georgia Tech, and National University of Singapore

David KargerView profile

Professor

• Algorithms
• Information Retrieval
• Human-Computer Interaction
• +3 more

David Karger is a Professor of Computer Science at MIT, affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL). He holds a B.A. from Harvard University and a Ph.D. from Stanford University. His research spans algorithms, information retrieval, human-computer interaction, and theory of computation. He leads the Haystack group, focusing on information management systems and collaborative tools. Notable contributions include the Scatter/Gather browsing system, the Mavo web application framework, and projects like Wikum and Squadbox for online collaboration and harassment prevention. Education: A.B. Summa cum Laude in Computer Science, Harvard University (1989) Ph.D. in Computer Science, Stanford University (1994) Research Interests: Karger’s work integrates algorithmic theory with practical systems, emphasizing human-centered design. Current projects address misinformation detection, social interaction systems, and educational tools. His research bridges theoretical computer science and applied domains such as web technologies and healthcare informatics. Awards: ACM Doctoral Dissertation Award (1994) Mathematical Programming Society Tucker Prize (1997) National Academy of Sciences Award for Initiative in Research (2004) Advising & Grants: Karger has advised over 30 students, many of whom have gone on to leadership roles in academia and industry. His work has been supported by grants from the MIT Schwarzman College of Computing and collaborations with companies like Akamai and Google. Labs & Teams: He leads the Haystack Group within CSAIL, collaborating with interdisciplinary teams on projects such as Mavo, Wikum, and Eyebrowse. His research also intersects with the Theory of Computation and Algorithms groups at MIT.

• Electronic Design Automation (EDA)
• Computer Architecture
• Embedded Systems
• +5 more

Prof. Wolfgang Ecker is a Professor at the Technical University of Munich (TUM), affiliated with the Chair of Design Automation within the TUM School of Computation, Information and Technology . His research focuses on Electronic Design Automation (EDA), RISC-V processor architectures, and hardware-software co-design. He leads projects advancing EDA tools for embedded systems, neural network acceleration, and formal verification methodologies. Ecker's work bridges machine learning techniques with traditional EDA challenges, addressing topics like energy-efficient AI inference and automated documentation generation. His contributions span compiler optimization, FPGA implementations, and fault analysis in digital systems. Recent research highlights include contributions to the TRISTAN project for RISC-V ecosystem development, model-driven architecture frameworks, and AI-driven timing analysis. He actively collaborates on open-source EDA tools and explores Rust-based embedded systems development. Ecker’s lab emphasizes practical applications in edge computing and automotive microcontroller safety, with a strong emphasis on interdisciplinary collaboration across TUM’s CIT School. His publications (15 most recent listed) reflect a focus on EDA tool innovation, processor design, and leveraging machine learning for hardware optimization. While no specific awards are mentioned, his involvement in ERC-funded projects and leadership in international collaborations underscores his academic impact.

Elisa BaniassadView profile

Teaching Professor

• Software Engineering
• Educational Technology
• Aspect-Oriented Programming
• +3 more

Elisa Baniassad is a Teaching Professor in the Department of Computer Science at the University of British Columbia (UBC), within the Faculty of Science. She specializes in software engineering education and has received numerous teaching accolades including the UBC Killam Teaching Prize and CS-Can/INFO-CAN Excellence in Teaching Award. Her courses focus on software construction, engineering principles, and advanced software design. Dr. Baniassad has taught CPSC 310 (Introduction to Software Engineering), CPSC 210 (Software Construction), and CPSC 410 (Advanced Software Engineering) across multiple terms since 2000. Her research interests span educational methodologies in software engineering, aspect-oriented programming, and the design of effective learning tools. Notable contributions include studies on team dynamics in software development, automated assessment techniques, and pedagogical frameworks for large-scale programming courses. She has authored over 50 peer-reviewed articles on topics ranging from mutation analysis in student tests to the efficacy of online learning environments. Awards include recognition for teaching excellence at UBC and contributions to computer science education. Her work emphasizes practical applications of software engineering principles in academic settings, with a focus on fostering student mastery through innovative assessment strategies and feedback mechanisms.

Gina R. BaiView profile

Assistant Professor

• Software Engineering
• Computer Science Education
• Unit Testing
• +5 more

Gina R. Bai is an Assistant Professor of the Practice in the Department of Computer Science at Vanderbilt University's School of Engineering. Her research focuses on software engineering and computer science education, particularly addressing student challenges in software testing and programming practices. She holds a B.S. in Computer Science from Wake Forest University (2016) and a Ph.D. from North Carolina State University (2022), advised by Kathryn T. Stolee. Her work explores testing checklists, generative AI's role in education, fairness in machine learning, and DEI in computing communities. Education: B.S. Computer Science (Wake Forest), Ph.D. Computer Science (NC State) Research Themes: Testing pedagogy, AI in education, DEI, code search Notable Projects: Testing checklist interventions, analysis of ChatGPT in CS1, fairness metrics Her publications emphasize student testing practices, the impact of educational tools, and ethical considerations in software development. She teaches courses like CS 1101 and CS 2201 at Vanderbilt, integrating research into curriculum design.

• Educational Technology
• Programming Tutoring Systems
• Game-Based Learning
• +1 more

Johan Jeuring is Professor of Software Technology for Learning and Teaching at Utrecht University's Department of Information and Computing Sciences. His research focuses on intelligent tutoring systems, computational thinking education, and game-based learning environments. Research Interests: Prof. Jeuring's work explores computational thinking pedagogy, automated feedback systems, and educational game design. His research bridges artificial intelligence with educational psychology to develop effective learning tools. Publications: Recent publications focus on augmented reality learning environments, programming education methodologies, automated assessment systems, and AI applications in education. His work combines empirical studies of learning behaviors with technological innovations in educational tools.

David Ryan KoesView profile

Associate Professor

• Computational Drug Discovery
• Deep Learning
• Discrete Algorithms
• +3 more

David Ryan Koes is an Associate Professor in the Department of Computational and Systems Biology at the University of Pittsburgh, affiliated with the Joint CMU-Pitt PhD Program in Computational Biology. His research focuses on computational drug discovery, deep learning, and discrete algorithms, aiming to develop novel methods for rapid drug development and open-source software tools. He holds an office at 3064 Biomedical Science Tower 3 and 748 Murdoch Building. Key roles include Associate Director of the CPCB program and leadership in initiatives like CompBio Academy. His software contributions include libmolgrid, gnina, and 3Dmol.js, which advance molecular modeling and visualization. Research interests emphasize AI-driven drug discovery, including molecular docking, pharmacophore modeling, and generative models for molecule design. Recent work involves deep learning for protein structure prediction and pharmacophore elucidation. He has secured NIH grants (e.g., R35GM140753) and collaborations with institutions like CMU and industry partners. Advising over 25 students in computational biology, biotech, and data science programs, Koes bridges academia and industry through projects like Pharmit and the Teach-Discover-Treat initiative. His lab's work spans from foundational ML research to applied drug discovery, with a focus on open science and reproducibility.

Ayşe BaşarView profile

Professor

• Machine Learning
• Bayesian Inference
• Decision Making under Uncertainty
• +7 more

Ayşe Başar is a Professor in the Department of Mechanical, Industrial, and Mechatronics Engineering at Toronto Metropolitan University. She serves as Director of the Data Science Lab and Program Director of the Data Science Masters Program. Her research integrates machine learning and Bayesian methods to build predictive models and recommender systems, with applications in software engineering and healthcare. BSc, Boğaziçi University MASc, University of Alabama in Huntsville PhD, London School of Economics Her research focuses on advancing machine learning through Bayesian inference, deep reinforcement learning, and explainability. She develops models that simulate expert decision-making, particularly in complex environments like emergency rooms and software development. Her work emphasizes uncertainty quantification , social learning in networks , and evidence-based decision support systems . The selected publications reflect a strong trend in applying machine learning to real-world problems—particularly in software engineering defect prediction, healthcare team dynamics, and social network analysis. Her work consistently uses probabilistic modeling and ensemble methods to improve prediction accuracy and interpretability. Paper among top 3 most cited in Empirical Software Engineering Journal (2009–2014) Best Paper Award, AAAI (2010) Best Paper Award, IFIP/IEEE IM Symposium (2019) Best Paper Award, ESEM (2013) Dr. Başar actively supervises students and leads impactful research collaborations with institutions like St. Michael’s Hospital and IBM. She has held leadership roles including Director of Big Data for the Provost’s Office and Program Director for Data Analytics Certificates. She is an affiliated scientist at St. Michael’s Hospital and a research fellow at IBM CAS. She leads the Data Science Lab , which focuses on developing advanced machine learning models for complex decision-making. The lab works on projects involving large-scale data integration, predictive analytics, and human-in-the-loop AI systems.