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Sasha VassarView profile

Senior Lecturer

• Artificial Intelligence in Education
• Cognitive Load Theory
• Educational Technology
• +3 more

Dr. Sasha Vassar is a Senior Lecturer at the School of Computer Science and Engineering, University of New South Wales (UNSW), specializing in the intersection of Artificial Intelligence and Education. She holds a PhD from UNSW and has transitioned from industry roles in engineering process optimization and critical infrastructure software development to academia, driven by her passion for innovative pedagogical approaches. Her research focuses on leveraging Large Language Models (LLMs) to enhance programming education, particularly through compiler-integrated conversational AI systems like the Debugging C Compiler (DCC) . This work, which integrates cognitive load theory principles, has received recognition including the 2024 Australian Financial Review Higher Education Award for Teaching and Learning Excellence. Sasha is a UNSW Nexus Fellow and Associate Fellow of the Higher Education Academy, emphasizing her contributions to educational innovation. Her publications (15 most recent listed) span AI-driven education tools, cognitive load theory applications in VR, and compiler-based error explanation systems. Beyond research, she advocates for scalable support of at-risk students in introductory programming courses and has pioneered adaptive learning resources in engineering design education through earlier work on virtual workshops. Awards: 2024 Australian Financial Review Award, 2022 UNSW Teaching Excellence Award Grants/Research: Focuses on LLM integration in compilers, cognitive load theory, and scalable educational technologies Labs/Teams: Leads the DCC project team and collaborates with UNSW's educational technology initiatives

• Computer Science Education
• Women in Computing
• Student Retention
• +3 more

Lecia Jane Barker is a Professor in the Department of Computer Science at the University of Colorado Boulder's College of Engineering and Applied Science, with additional affiliation with the National Center for Women and Information Technology. With over two decades of research in computer science education, her work has evolved from examining classroom climate issues to comprehensive studies on student retention, service learning, and diversity in computing. Her research interests focus on creating inclusive computing environments through evidence-based approaches. Barker has pioneered work on understanding defensive climates in CS classrooms, developing strategies for recruiting and retaining women in computing, and implementing service learning experiences that connect computer science education with community needs. Recent work examines student belonging in remote learning environments, interdisciplinary transfer in programming education, and data privacy concerns in learning management systems. Analysis of her recent publications reveals a strong emphasis on practical, evidence-based approaches to improving CS education. Her work consistently addresses the intersection of pedagogy, diversity, and technological innovation, with increasing attention to service learning as a mechanism for engaging students with real-world problems. The trend shows growing collaboration with interdisciplinary teams examining both the technical and social dimensions of computing education. Barker has been instrumental in developing tools and frameworks adopted by CS departments nationwide, including evidence-based surveys for identifying causes of student attrition and methodologies for investigating women's course choices in computing programs. Her extensive service to the CS education community includes leadership roles in major conferences like SIGCSE and ITiCSE, where she regularly presents research and facilitates workshops on improving CS education practices. She has secured significant funding for projects addressing critical challenges in computing education, particularly those related to diversity and inclusion.

Jasleen KaurView profile

Associate Professor

• Networking
• Distributed Systems
• Congestion Control
• +6 more

Jasleen Kaur is an Associate Professor and Director of Graduate Studies in the Department of Computer Science at the University of North Carolina at Chapel Hill . She holds a B.Tech from the Indian Institute of Technology, Kanpur (1997), an M.S. (1999), and a Ph.D. (2002) in Computer Sciences from the University of Texas at Austin . Research focus: Networking , Distributed Systems , Congestion Control , and Internet Measurements Education: IIT Kanpur - B.Tech, Computer Science & Engineering (1997) University of Texas at Austin - M.S. (1999), Ph.D. (2002) Her research spans packet-scale congestion control , bandwidth estimation , and network tomography . Key projects include design of ultra-high speed transport protocols , scalable tomographic infrastructures , and K-12 Computer Science Education initiatives . Recent publications highlight her work on: Datacenter network performance analysis HTTPS webpage fingerprinting techniques Machine learning applications in bandwidth estimation Passive TCP performance modeling Web traffic classification using anonymized headers Selected scientific awards : NSF CAREER Award (2004) UNC Junior Faculty Development Award (2004) Google Faculty Award (2020) She has advised: 6 Ph.D. candidates (ABD or graduated 2008-2018) 10 Master's students (2009-2020) 3 Bachelor's students (2009-2015) Dr. Kaur coordinates the Triangle Computer Science Distinguished Lecture Series and has taught courses including: Internet Services and Protocols (Spring 2025) Wireless and Mobile Networks (Spring 2024) Networked and Distributed Systems (Spring 2023)

Eni MustafarajView profile

Assistant Professor

• Socio-technical systems
• Web credibility assessment
• Misinformation detection
• +7 more

Eni Mustafaraj is an Assistant Professor of Computer Science at Wellesley College , a women's liberal arts institution in Massachusetts. She studies web-based socio-technical systems with a focus on algorithmic transparency and credibility assessment of online sources. PhD in Computer Science (2007) from Philipps University of Marburg, Germany Previously a visiting scholar and Norma Wilentz Hess Fellow (2010-2013) Her research explores intersections of human behavior and algorithmic systems, particularly misinformation detection , web literacy tools , and data science education . She pioneered the use of MIT App Inventor in undergraduate programming education and developed TwitterTrails for tracking online misinformation. Notable awards include: NSF CAREER Grant (2018-2023) for credibility signals research Best Paper prize at Web Science Conference (2010) Mellon grant for evidence-based teaching (2017) Dr. Mustafaraj mentors undergraduate researchers in the Cred Lab and DAV Lab , focusing on web literacy, data visualization, and computational thinking. Her work has been featured in media outlets like The New York Times and The Spoke, and she actively contributes to academic discourse through public writing on platforms like Medium.

• Game Studies
• Computer Science Education
• Video Game Ethics
• +6 more

Pawel Grabarczyk serves as Associate Professor at IT University of Copenhagen, leading the Play, Culture, and AI research group within the Games Research Group. He maintains dual affiliations with the Center for Computing Education Research and the Center for Digital Play, reflecting his interdisciplinary work spanning game studies and computing education. His research interests form a unique intersection of game studies, critical theory, and computing education. Grabarczyk applies frameworks like Actor-Network Theory (ANT) and Science & Technology Studies (STS) to analyze video game design, particularly focusing on loot boxes and gambling-like mechanics. His work on gender diversity in computing education has demonstrated that programs emphasizing people rather than technical aspects appeal more to women, with significant implications for recruitment and curriculum design. Analysis of his 41 publications reveals consistent thematic threads across his career. Early work focused on game design and critical theory, while more recent publications address sustainability in gaming through the Nordic Alliance for Sustainability in Gaming projects. His computing education research shows increasing attention to onboarding strategies for students lacking programming confidence, with practical implications for reducing dropout rates. Grabarczyk has secured substantial research funding through multiple projects, including the European Commission-funded MSG (Making Sense of Games) project (2016-2022) and the Nordic Alliance for Sustainability in Gaming series (2021-2024). His work has generated significant media attention in Denmark, with coverage on topics ranging from addressing fake news through games to improving gender balance in the IT industry. As Head of the Play, Culture, and AI research group, he directs a team examining how artificial intelligence intersects with play and cultural practices. His research bridges academic inquiry with practical applications, evidenced by collaborations with game developers and educators seeking to implement his findings on inclusive computing education and ethical game design.

• Computer Science Education
• Programming Education
• Educational Technology
• +3 more

Naaz Sibia is an active faculty member in the Department of Computer Science at the University of Toronto, with a robust publication record spanning from 2021 to 2025. Sibia has established themselves as a prominent researcher in computer science education, particularly focusing on student learning experiences in programming contexts. Their work appears consistently in top-tier computer science education venues including ITiCSE, SIGCSE, Koli Calling, and DataEd. Sibia's research interests center around programming education, with particular emphasis on self-explanation techniques, multiple representations in introductory programming, student interaction in Q&A forums, and the integration of educational technology. Their recent work has increasingly explored the application of large language models in educational contexts, reflecting emerging trends in the field. A significant portion of their research investigates how students learn programming concepts, with attention to factors like student discomfort, isolation, and the impact of different instructional approaches. Analysis of Sibia's publication trends shows a clear progression from foundational studies on student learning behaviors to more sophisticated investigations incorporating AI technologies. Their 2023-2025 work demonstrates growing interest in large language models for educational applications, voice interfaces for self-explanation, and the impact of multiple representations on novice programmers. The research consistently emphasizes practical applications for improving computer science education at both introductory and advanced levels. Sibia maintains a strong collaborative network, frequently working with researchers including Angela M. Zavaleta Bernuy, Michael Liut, and Andrew Petersen. These collaborations span multiple institutions and have resulted in numerous joint publications across various computer science education venues. Their work appears to focus primarily on empirical studies of student learning, often employing mixed-methods approaches to investigate educational interventions. While specific laboratory affiliations aren't detailed in the publication record, Sibia's research suggests involvement with educational technology initiatives at the University of Toronto, particularly those focused on enhancing programming education through innovative pedagogical approaches and technological interventions. Their recent work on large language models indicates engagement with emerging AI technologies in educational contexts.

Lisa ZhangView profile

Assistant Professor

• Computer Science Education
• Machine Learning Education
• Programming Languages Education
• +3 more

Lisa Zhang is an Assistant Professor, Teaching Stream in the Department of Mathematical and Computational Sciences (MCS) at the University of Toronto Mississauga (UTM). She holds cross-appointments at the Institute for the Study of University Pedagogy (ISUP) and the Institute for Management and Innovation (IMI). Her teaching focuses on machine learning, programming languages, and writing across the curriculum (WAC) for computer science students. She coordinates the UTM CS Education Research Group and is known for research in machine learning education and pedagogical strategies for diverse learners. She has taught courses such as CSC311 (Machine Learning), CSC324 (Programming Languages), and CSC338 (Numerical Methods). Notably, she is on sabbatical for the 2025-2026 academic year, during which she will focus on grant-funded work and limited undergraduate collaboration. Zhang's research emphasizes undergraduate research projects, curriculum design, and AI education. She has advised numerous undergraduate students on CS education-related projects, contributing to over two dozen publications in venues like ITiCSE, SIGCSE, and Koli Calling. Her awards include the University of Toronto Cheryl Regehr Early Career Teaching Award (2025) and SIGCSE Best Paper (2023). Her work spans educational technology, mastery learning, and addressing challenges in AI curricula across institutions. She has collaborated with industry and academic partners to develop model AI assignments and pedagogical tools for STEM education.

Daniel ZingaroView profile

Professor

• Computer Science Education
• Educational Technology
• Programming Pedagogy
• +3 more

Daniel Zingaro is a Professor, Teaching Stream and Associate Chair (CSC) in the Department of Computer Science at the University of Toronto Mississauga. He holds a PhD from the Ontario Institute for Studies in Education (OISE) at the University of Toronto, focusing on Computer Science Education. His research emphasizes measuring and improving student learning in CS, particularly through concept inventories and Peer Instruction methodologies. Education: PhD in Computer Science Education, OISE, University of Toronto Research Interests: Zingaro's work centers on pedagogy in introductory CS courses, including the design of effective teaching strategies, the impact of Peer Instruction, and the development of assessment tools like the Basic Data Structures Inventory (BDSI). He also explores the role of generative AI in programming education and competitive programming pedagogy. Publications: His work spans textbooks (e.g., Algorithmic Thinking , Learn to Code by Solving Problems ) and peer-reviewed articles on topics like student performance prediction and educational technology. Recent trends focus on integrating AI tools like GitHub Copilot into teaching and assessing foundational CS concepts. Awards: Best Paper Award (Journal of Online Learning and Teaching, 2012) Teaching & Advising: Teaches courses ranging from introductory programming to advanced topics like Operating Systems and Algorithms. He actively develops curriculum materials and advocates for student-centered learning approaches. Current initiatives include exploring LLMs' impact on CS1 pedagogy and expanding accessible educational resources in multiple languages. Labs/Teams: Leads projects on AI-assisted programming education and collaborates with institutions globally to refine concept inventories and Peer Instruction techniques.

Craig ZillesView profile

Professor

• Computer Science Education
• Learning Analytics
• Computer Architecture
• +7 more

Craig Zilles is a Professor and Severns Faculty Scholar in the Department of Computer Science at the University of Illinois at Urbana-Champaign. His primary affiliation is within the College of Engineering. He holds a Ph.D. from the University of Wisconsin-Madison (2002), where his research focused on compiler-architecture interactions and speculation techniques. Research Interests: Zilles' work spans computing education (e.g., computer-based testing, learning analytics, concept inventories) and computer architecture (e.g., compilers, dynamic optimization, managed languages). He pioneered the Computer-based Testing Facility (CBTF), a platform revolutionizing assessment practices in STEM education. Awards & Recognition: Recipient of the NSF CAREER Award, IEEE Micro Top Picks, ASPLOS Best Paper Awards (2010, 2013), and numerous teaching accolades including the Excellence in Undergraduate Teaching Award (2018) and the IEEE Education Society's Mac Van Valkenburg Early Career Teaching Award (2010). Teaching: Instructs courses such as CS 105 (Intro Computing for Non-Tech), CS 233 (Computer Architecture), and advanced topics in computer science education. Known for innovative pedagogical approaches, including mastery-based grading and automated assessment systems. Labs & Projects: Leads the CBTF initiative and contributes to PrairieLearn, an open-source platform enabling adaptive, randomized problem generation. His research also explores AI-driven autograding and anti-plagiarism strategies in the age of generative AI.

Gursimran Singh WaliaView profile

Professor

• Empirical Software Engineering
• Security Vulnerability Analysis
• Software Quality Improvement
• +6 more

Gursimran Singh Walia is an Associate Dean and Director of Graduate Studies at Augusta University's School of Computer and Cyber Sciences (SCCS), and an affiliated Professor of Computer Science at North Dakota State University. His 16-year academic career focuses on advancing computer science education and research, emphasizing software engineering, cybersecurity, and pedagogical innovation. He leads SCCS's graduate programs and academic policies while fostering industry collaboration and inclusivity in STEM education. Research & Education Dr. Walia’s research spans empirical software engineering , security vulnerability analysis , software quality improvement , and computer science pedagogy . He pioneered tools like the Non-intrusive Classroom Attention Tracking System (NiCATS) and Testing Tutor , and developed frameworks for error classification in software requirements. His work bridges cognitive psychology and software development, addressing human factors in requirements engineering. Awards & Recognition Excellence in Teaching Award (NDSU) Nominated for Peltier Award (Teaching Innovation) Best Industry Paper Runner-Up (IEEE ISSRE 2018) Exemplary CS Educational Research Paper (SIGCSE 2017) Leadership & Impact He spearheaded Augusta University’s new Bachelor of Science in Data Science and strengthened cybersecurity pathways via articulation agreements with 22 Georgia technical colleges. His initiatives include AI-driven STEM education strategies and multi-institutional studies on programming misconceptions. Teaching Teaches courses like Software Project Planning , Empirical Software Engineering , and System Analysis & Design . Integrates active learning and technology (e.g., NiCATS, Testing Tutor) to enhance student engagement.

Sarah ZelikovitzView profile

Associate Professor

• Machine Learning
• Data Mining
• Educational Technology
• +3 more

Sarah Zelikovitz is an Associate Professor in the Department of Computer Science at the College of Staten Island, CUNY. She specializes in Machine Learning, Data Mining, and Educational Technology, with a focus on integrating research projects into undergraduate curricula. Her work spans interdisciplinary areas such as bioinformatics, health informatics, and robotics education. Dr. Zelikovitz teaches courses like Data Science, Introduction to Computer Science, and Seminar in Computer Science. She actively contributes to curriculum design, emphasizing project-based learning and computational thinking. Her research explores topics including query classification, tandem repeats in genomic data, and educational assessment tools. Her articles reflect expertise in algorithms for string processing, algorithm optimization, and predictive analytics in clinical contexts. Recent work includes studies on side-sharing tandems in arrays and depression detection via social media analysis. Despite no listed awards, her contributions to educational technology and interdisciplinary research are notable. Office Hours: Spring 2025 (Building 1N, Room 212) are Tues/Thurs 12:20–2:20 PM. She advises students on degree requirements using Degree Works and offers virtual meetings via Zoom.

Mark GoadrichView profile

Professor

• Artificial Intelligence
• Game Development
• Data Analytics
• +3 more

Mark Goadrich is a Professor with 17+ years of teaching experience in computer science and mathematics at small liberal-arts undergraduate colleges. His research focuses on applying artificial intelligence algorithms to simulate human player experiences in card games, and he emphasizes integrating computer science education with community service to engage students. Goadrich holds a B.A. from Kenyon College (1998), an M.S. (2000), and a Ph.D. (2007) from the University of Wisconsin–Madison. His research interests span artificial intelligence, game development, data analytics, and innovative pedagogical strategies. He explores topics like tangible learning tools for data structures, procedural content generation in games, and civic engagement through computing curricula. Goadrich has contributed to educational frameworks for IPython in data science and molecular dynamics simulations in undergraduate labs. Though not explicitly listed, his work often intersects with interdisciplinary projects combining computational science and ethical considerations. His publications reflect a strong focus on bridging theoretical computer science concepts with hands-on, student-centered applications.

Zachary DoddsView profile

Professor

• Computer Science Education
• Robotics
• Artificial Intelligence
• +3 more

Zachary Dodds is a Professor at the Computer Science Department of Harvey Mudd College. He is actively involved in course development, ACM programming contest mentorship, and interdisciplinary research at the intersection of computer science, biology, and robotics. Email: dodds@cs.hmc.edu Research Interests : Dodds focuses on computer science education innovation, accessible robotics platforms, and interdisciplinary applications of computing. His work bridges AI, robotics, and computational biology, emphasizing hands-on learning and curricular reform. Articles : His recent publications explore computing literacy, AI in education, and robotics for non-majors. Key themes include pedagogy, accessible technology, and cross-disciplinary engagement. Courses : He teaches foundational courses like CS 5: Introduction to Computer Science , SCI50: CS for Discovery , and advanced topics in CS 183/184: Computer Science Clinic . Programming Contests : Dodds organizes Harvey Mudd's ACM programming teams, which have consistently ranked in the top 10 regionally since 2010. He emphasizes collaborative problem-solving and algorithmic thinking. Labs & Collaborations : He contributes to robotics education through initiatives like the Harvey Mudd Robotics Core Lab and partnerships with institutions such as Chatham College and Carnegie Mellon University.

• Digital Privacy
• Smartphone Security (Android)
• Computer Science Education Research

Ed Novak is an Associate Professor and Department Chair of Computer Science at Franklin & Marshall College. His research focuses on digital privacy/security in Android systems and computer science education innovation. He holds a Ph.D. (2016) and M.S. from The College of William and Mary, and a B.A. from Monmouth College (2010). Education: Ph.D./M.S. in Computer Science, The College of William and Mary (2016) B.A. in Computer Science & Mathematics, Monmouth College (2010) Research Interests: Explores Android security mechanisms, develops tools like Stigma for closed-source app analysis, and investigates educational strategies to counter AI-assisted cheating (e.g., LLM-resistant assignments). Also advocates for oral exams in software courses. Teaching: Regularly instructs core CS courses including CS1/CS2, Mobile App Development, Operating Systems, and Computer Organization. Active in curriculum design emphasizing hands-on learning. Technical Contributions: Maintains GitHub repositories for Stigma (Android instrumentation tool) and hush (ultrasound modem project). Published widely on IoT security, privacy-preserving systems, and educational methodologies.

Guoning ChenView profile

Associate Professor

• Data visualization
• Topology-based methods
• Geometric modeling and geometry processing
• +1 more

Guoning Chen is an Associate Professor in the Department of Computer Science at the University of Houston, leading the Data Visualization and Modeling (DaViM) Lab. He holds a Ph.D. in Computer Science from Oregon State University and M.E. and B.E. degrees from Guangxi University and Xi'an Jiaotong University in China. His research focuses on data visualization, topology-based methods, geometric modeling, and physically-based simulations. Key contributions include vortex extraction in turbulent flows, hexahedral mesh optimization, and interactive vector field design systems. His work bridges computational science and engineering applications, with applications in fluid dynamics, medical imaging, and urban modeling. Chen has received numerous awards, including the NSF CAREER Award (2016) and multiple Best Paper Awards at IEEE Visualization conferences. He is actively involved in software development, including tools for hex-mesh quality analysis and web-based visualization frameworks like FCLWebVis. His teaching includes courses on visualization and introductory programming at the University of Houston. He mentors students in research and contributes to educational frameworks like mastery learning in CS1 courses.