جست‌وجوی استاد و پژوهشگر

• Computer Science
• Quantum Computing
• Medical Imaging
• +۳ مورد دیگر

Lauri Kettunen is a Professor and Vice Dean at the Faculty of Information Technology, University of Jyväskylä. His research spans quantum computing, medical imaging AI, and IT education innovations. He leads the Quantum Information and Computation Team, exploring quantum computational boundaries and practical applications. Recent work focuses on deep learning frameworks for medical image analysis, predictive learning analytics in computer science education, and quantum cellular automata gate design. Research interests include: Quantum Computing: Developing scalable quantum logic gates and QCA systems Medical AI: Annotation-efficient deep learning for medical imaging challenges Education Technology: Addressing first-year IT student challenges and predictive learning analytics Industry Collaboration: Frameworks for successful academia-industry partnerships in engineering Publications reflect interdisciplinary focus with 2024 highlights in European Journal of Engineering Education (education analytics), International Journal of Computer Assisted Radiology and Surgery (medical imaging), and IEEE Transactions on Instrumentation and Measurement (sleep analysis). Active in organizing events like CM3 Transport 2023 conference.

Vesa Lappalainenمشاهده پروفایل

مدرس ارشد

• Computer Science
• Quantum Computing
• Educational Technology
• +۳ مورد دیگر

Vesa Lappalainen is a Senior University Lecturer at the University of Jyväskylä's Faculty of Information Technology. His work bridges quantum computing education, learning analytics, and programming pedagogy. He leads the Quantum Information and Computation Team, focusing on advancing quantum computational methods and scalable educational frameworks. Position: Senior University Lecturer University: University of Jyväskylä Research Group: Quantum Information and Computation Team Research interests include quantum computing pedagogy, predictive learning analytics for student retention in CS1, and innovative online learning environments. Notable contributions include developing interactive quantum circuit simulators and multi-functional digital course materials. His work emphasizes equity in education and scalable solutions for competency-based admissions. Publications span quantum education tools, learning analytics models, and comparative studies on programming education methods (e.g., game-based learning, TDD in CS1). Collaborations include international teams on projects like DEFA and KATTI, addressing challenges in educational technology and student identity development. Lappalainen has advised numerous collaborative projects and contributed to curriculum design in programming education. Current research trends focus on integrating multimodal emotion tracking in learning systems and generalizable predictive models for student success.

Amanda Fernandezمشاهده پروفایل

استادیار

• Artificial Intelligence
• Deep Learning
• Computer Vision
• +۸ مورد دیگر

Amanda Fernandez is an Assistant Professor in the Department of Computer Science at The University of Texas at San Antonio (UTSA), where she contributes significantly to research, teaching, and interdisciplinary collaboration. She will be a faculty member in the newly launching College of AI, Cyber and Computing in Fall 2025, reflecting her central role in advancing UTSA's strategic focus on emerging technologies. Her research lies at the intersection of artificial intelligence, deep learning, computer vision, and computer science education . She directs the UTSA Vision and AI Lab (VAIL) and serves as Machine Learning & Deployment Thrust Lead for MATRIX: The AI Consortium for Human Well-Being , and as a Principal Investigator in the Consortium on Nuclear Security Technologies (CONNECT) . Her work emphasizes explainable and robust AI, with applications in nuclear materials, VR/AR, and biomedical signals like ECG. The 15 most recent publications reflect a strong trend in explainable AI, adversarial robustness, and AI-enhanced education . Her work spans technical AI research (e.g., ICML, CVPR workshops) and educational innovation (e.g., SIGCSE, RESPECT), demonstrating a dual commitment to advancing both the science and pedagogy of computing. Themes include model attribution, training optimization, semantic segmentation, and the integration of LLMs in introductory CS courses. Senior Member of the IEEE Senior Member of the National Academy of Inventors (NAI) Funded by the National Science Foundation (NSF) Supported by the U.S. Department of Energy (DOE) Supported by the U.S. Department of Defense (DOD) Fernandez is deeply committed to mentoring undergraduate researchers through her CS-CURE course, which provides hands-on research experience. She advises student organizations including ACM-W and GDSC , and leads initiatives such as CS4SA to increase Latinx participation in computer science. Her grants support both research and educational outreach, emphasizing inclusivity and workforce development. She leads the Vision and AI Lab (VAIL) at UTSA and is an integral member of two major research consortia: MATRIX and CONNECT , fostering collaboration across institutions including Los Alamos National Lab, Argonne National Lab, and UNLV.

Jake Renzellaمشاهده پروفایل

مدرس ارشد

• Computer Science Education
• Educational Technology
• Learning Analytics
• +۳ مورد دیگر

Dr Jake Renzella is a Senior Lecturer at the University of New South Wales' School of Computer Science and Engineering (CSE), where he serves as Co-Head of the Computing and Education research group and Director of Studies for Computer Science. His work focuses on Pedagogical Generative AI and education technology, with a particular emphasis on compiler tools like the Debugging C Compiler (DCC) and its AI debugger, as well as the Formatif teaching platform. Research: Premier computer science education conferences (SIGCSE TS, ICSE), AI-driven educational tools, learning analytics Collaboration: Key role in Day of AI Australia initiative Scientific Awards: 2024 Australian Financial Review Higher Education Innovation Award 2023 UNSW Engineering Faculty Education Award 2022 UNSW Teaching Excellence Award Associate Fellow of the Higher Education Academy Grants: $2,165,000 total including Google.org Generative AI Accelerator ($1.5M), NVIDIA Academic Grant (cloud GPU hours), and multiple Google/UNSW grants for inclusion research and teaching innovation.

• Programming Languages
• Computer Security and Privacy
• Blockchain
• +۲ مورد دیگر

John Mitchell is the Mary and Gordon Crary Family Professor in Stanford’s School of Engineering and holds courtesy professorships in Electrical Engineering and Education. He chairs the Computer Science Department and directs the d.school. His roles include former Vice Provost for Teaching and Learning, leading over 1,000 online education projects and co-founding Stanford CourseWare, foundational to MOOCs. He co-directs the Lytics Lab, Carta Lab, and Pathways Lab, focusing on data-driven educational improvements. Education: BS in Mathematics from Stanford (1978), PhD in Computer Science from MIT (1984). Research focuses on programming languages, cybersecurity, blockchain, machine learning, and edtech. Key projects include Reinforcement Learning for educational scheduling, cryptographic analysis in generic groups, and privacy in telephony metadata. He authored textbooks like Foundations for Programming Languages and has over 250 publications with 30,000+ citations. Publications emphasize edtech innovations and security (e.g., Bitcoin consensus protocols, generative AI ethics). Awards include ACM Fellow and American Academy of Arts and Sciences election. Advises doctoral students (e.g., Jeongyeon Kim) and oversees grants on learning technologies. Leads teams exploring Generative AI in education, trustworthy ML systems, and hybrid pedagogy post-pandemic. Active in labs advancing educational outcomes through iterative design and data science.

Austin Cory Bartمشاهده پروفایل

دانشیار

• Computer Science Education
• Learning Sciences
• Educational Technology
• +۳ مورد دیگر

Austin Cory Bart is an Associate Professor at the University of Delaware specializing in Computer Science Education and Learning Sciences. With expertise spanning both software development and educational theory, he creates technological tools to support diverse learning experiences across multiple educational levels. Education: PhD in Computer Science (2017), Virginia Tech Certification in Learning Sciences (2015), Virginia Tech Honors Bachelor with Distinction (2012), University of Delaware Bart's research centers on Digital Education, which integrates Computer Science Education and Learning Sciences. In Computer Science Education, he develops methods to improve student performance through better feedback systems and classroom tools, with special attention to increasing diversity in computing fields. His Learning Sciences work investigates how people learn and how to design effective educational software. He explores structuring learning experiences with clear expectations, providing targeted feedback to address misconceptions, and designing materials that support students' agency, self-efficacy, and motivation. He has created numerous educational technologies including BlockPy (a dual block/text Python environment), Pedal (a Python feedback analysis toolset), Bakery (Python curriculum for CS1), and the CORGIS project (educational datasets). His work emphasizes addressing challenges of teaching introductory computer science to students with diverse backgrounds and skill levels. Bart is committed to supporting education and diversity in computer science. He recognizes that effective digital education requires integrating knowledge from software engineering, learning sciences, data science, and cognitive science. His goal is to create technical scaffolding that supports educational experiences from kindergarten through undergraduate levels.

• Software Engineering
• Artificial Intelligence
• Cybersecurity in AI
• +۵ مورد دیگر

Amin Alipour is an Associate Professor in the Department of Computer Science at the University of Houston, where he directs the Software Engineering Research Group. He holds a Ph.D. from Oregon State University and multiple advanced degrees from institutions in Iran and the U.S. His work focuses on improving safety and user experience of AI-assisted software engineering tools, with funding from NSF and IARPA. Prior to joining the U.S., he taught at the University of Kashan and Shiraz University in Iran. Education: Ph.D. in Computer Science, Oregon State University (Advisor: Alex Groce) M.S. in Computer Science, Michigan Technological University M.S. in Computer Engineering, Tarbiat Modares University B.S. in Computer Engineering, Petroleum University of Technology (Iran) Research interests include: Secure AI systems, particularly in code LLMs Trojan detection and mitigation in neural models Educational impacts of AI tools in programming education Formal methods for software verification Human-AI collaboration in programming tasks His recent articles explore cybersecurity in AI, student interactions with LLMs, and adversarial attacks on code models. He currently serves on program committees for ICER 2025 and ICSME 2025. His research group develops tools like FeatureExtractor and ProgramTransformer for analyzing neural code intelligence models.

Mark Guzdialمشاهده پروفایل

استاد

• Computer Science Education
• Media Computation
• Introductory Programming
• +۳ مورد دیگر

Mark Guzdial is a Professor at Georgia Institute of Technology's College of Computing, specializing in Computer Science Education research. His work focuses on media computation for teaching programming, learner-centered design, and collaborative learning environments. Key contributions include developing project-based learning frameworks and language-independent CS1 assessments. 2020: Strongly-themed CS1 curriculum design 2016: CS1 knowledge assessment replication studies 2006: CoWeb collaborative learning platform integration 2003: Media computation for non-majors 2001: CS1 programming skills assessment Research trends show sustained focus on making computing accessible through domain-specific pedagogy, with applications in Python programming, MATLAB instruction, and Squeak system design. His work bridges education theory with practical implementation across multiple computing subfields.

Igor dos Santos Montagnerمشاهده پروفایل

استاد آموزشی

• Computer Science
• Computing Education
• Free Software
• +۳ مورد دیگر

Igor dos Santos Montagner is a Teaching Professor at Insper in the School of Computer Science and Engineering . He holds a PhD in Computer Science from the University of São Paulo (2017) and a bachelor's degree from the same institution (2010). His research focuses on computing education , particularly student-centered teaching methodologies and their impact on professional skill acquisition beyond classroom content. He advocates for the use of Free Software in academic environments, exploring its benefits for education and open technology access. His projects include collaborations with Craig Zilles (University of Illinois at Urbana-Champaign) and Rafael Corsi (Insper) on frequent testing and feedback systems in CS1 courses, as well as studies on C programming language pedagogy to improve low-level programming education. Research Interests : Computing education, student-centered learning, Free Software integration in academia, programming language pedagogy, and professional skill development. Key Projects : Frequent Testing in CS1 (2020–2024), C Language Teaching Methodologies (2021–2023), Open Development elective design (2020). Collaborations : University of Illinois at Urbana-Champaign (Craig Zilles, Mariana Silva), Rafael Corsi (Insper), and open-source communities like ElementaryOS, Nim, and Gleam. His publications analyze mastery-oriented grading, active learning in programming courses, and Open Source contribution frameworks. He emphasizes technology accessibility and digital wellbeing , reflecting on smartphone usage habits and minimalist tech practices in his personal blog.

Lindsay Jamiesonمشاهده پروفایل

استاد آموزشی

• Computer Science Education
• Cybersecurity
• Computational Thinking
• +۳ مورد دیگر

Lindsay Jamieson is a Teaching Professor and Associate Dean of Teaching Faculty at Northeastern University's Portland campus. She is affiliated with the Khoury College of Computer Sciences and specializes in Computer Science Education , with additional expertise in Graph Theory and Cybersecurity . Her research focuses on pedagogical innovations, including computational thinking development, Treisman-style workshops, and student engagement strategies in introductory computer science courses. She has contributed to algorithm complexity studies in graph alliances. Recent publications highlight her work in CS education pathways (2020), workshop implementation for CS1 courses (2015), and non-programming interventions (2012). Earlier works (2007-2009) explore theoretical aspects of alliance detection in graphs and generalized series-parallel graph analysis. She leads initiatives like the cybersecurity camp for middle/high school students, emphasizing practical skill-building and outreach in Portland.

David W Reedمشاهده پروفایل

استاد

• Programming Languages
• Computer Science Education
• Curriculum Development
• +۳ مورد دیگر

David W Reed is a Professor and Chair of the Department of Computer Science, Design & Journalism at Creighton University, Omaha, Nebraska. He holds a PhD in Computer Science and has been a faculty member since 2000, after positions at Bennett College, Duke University, and Dickinson College. He earned a B.S. in Computer Science and Mathematics from Vanderbilt University, and M.S. and Ph.D. in Computer Science from Duke University. Affiliations : Liberal Arts Computer Science Consortium (since 1999), ACM/IEEE Computer Science 2013 Steering Committee, Computer Science Teachers Association (Board Chair 2015-2017). Research Focus : Programming languages, computer science education, and curriculum development, with a focus on apprentice-based learning, Web-based programming, and innovative instructional methods. His publications and presentations often address pedagogical frameworks like CS2013 knowledge units, nifty programming assignments, and empirical investigation techniques. He has contributed extensively to the AP Computer Science program, serving as Chief Reader (2004-2008), and advocates for K-12 computer science integration. His work emphasizes problem-solving patterns, curriculum adaptability, and interdisciplinary approaches to computer science education.

• Human-Computer Interaction
• User Experience Design
• Anthropology-Based Computing
• +۷ مورد دیگر

Dr. John N A Brown is a Lecturer in the School of Computing, Engineering & Technology at Robert Gordon University (RGU), where he leads the H.F. G.U.I.L.D. research group (Human Factors of Gameplay, Usability, Interaction, Learning, and Design). With seven university degrees including three PhDs, he is a Senior Fellow of the Higher Education Academy (SFHEA). Before joining RGU, he worked as a consulting researcher for major tech companies including Google, Facebook, LinkedIn, and Amazon. Dr. Brown's research spans Human-Computer Interaction, User Experience Design, Anthropology-Based Computing, and Calm Computing. He has developed new models of human cognition and interaction, using motion capture to study physiological strain of HCI, and both EEG and Anthropological methods to improve interaction with smart environments. His work focuses on how humans and tools co-adapt and co-evolve to shape society, technology, and the world. His recent publications show a strong focus on AI in education, computing pedagogy, and the philosophical aspects of technology education. The research trends indicate growing interest in ethical implications of AI, competency versus understanding in learning, and innovative assessment methods. Senior Fellow of the Higher Education Academy (SFHEA) Author of three books with Springer Contributor to Bloomsbury Encyclopaedia of Design and Salem Encyclopedia of Mathematics and Society Regular presenter at international conferences across four continents Dr. Brown serves as lead supervisor for three PhD students and co-supervisor for one additional student. He has established the S.O.C.R.A.T.E.S. workshop series for early-career researchers and the P.L.A.T.O. seminar series that connects professionals from various fields with RGU. His teaching includes modules on AI, Data and Society; User Centred Design; and Human Computer Interaction, each tracing technology's evolution from pre-historical to modern times. His H.F. G.U.I.L.D. research group focuses on interdisciplinary approaches to understanding and improving human interaction with technology, particularly in gaming, smart environments, and AI systems. The group explores how to make technology more intuitive, less intrusive, and better aligned with human cognitive and physiological processes.

• Computer Science Education
• Work-Based Learning
• Graduate Apprenticeships
• +۵ مورد دیگر

Tiffany Young serves as Lecturer in the School of Computing, Engineering & Technology at Robert Gordon University, specializing in Computer Science Education and Work-Based Learning pedagogy. Her academic profile integrates teaching responsibilities in Database Systems, Project Management, and Professional Development with active research in Graduate Apprenticeships and industry-academia alignment. As First Year Coordinator and Mental Health First Aider, she maintains strong student engagement while pursuing part-time PhD studies on graduate employability. Her research interests center on: Computer Science Education innovation Work-Based Learning frameworks Graduate Apprenticeship program development Ethical implications of Gen-AI in technical education Industry competency alignment strategies Recent publications analyze pass/fail grading models, data systems curriculum design, and meta-skill embedding in apprenticeship programs, reflecting her focus on practical pedagogical solutions. Dr. Young contributes to the Computing & Engineering Education Research Group and participated in the EU-funded Versatile Islands project (2019-2021), developing SME-focused tourism services. Her collaborative approach spans multiple institutions through ACM SIGCSE working groups and IEEE educational initiatives. Current research examines gender diversity in computing apprenticeships and ethical frameworks for AI integration in computing curricula.

Kian Pokornyمشاهده پروفایل

استاد

• Artificial Intelligence
• Soft Computing
• Data Science
• +۱ مورد دیگر

Kian Pokorny serves as Professor of Computing at McKendree University, maintaining an office in Clark Hall 200 with contact phone (618) 537-6440. His academic foundation includes a Ph.D. from Louisiana Tech and dual degrees (M.S., B.S.) from Central Missouri State University. Education background: Ph.D., Louisiana Tech M.S., Central Missouri State University B.S., Central Missouri State University His research spans Artificial Intelligence , Soft Computing , Data Science , and Computer Science Education , driven by the philosophy: "My highest priority as an educator is to produce lifelong learners with strong critical thinking skills. This I hope to accomplish by creating a non-threatening, student-centered environment within my classroom that allows students to achieve the highest possible levels of learning." This commitment manifests in curriculum innovations and educational tool development. Analysis of his 15 most recent publications reveals consistent focus on computer science pedagogy evolution. Key trends include: integration of data science into traditional curricula (2022), machine learning applications for institutional analysis (2021), and sustained development of the Frances tool suite for computer architecture education (2009-2012). His work bridges theoretical computer science with practical educational implementation, demonstrating particular strength in constraint satisfaction problems and fuzzy logic applications. Scientific recognition: ACI Grant for Intelligent Tutoring Systems Design: An Empirical Approach (2005-2006) While specific student advisees aren't documented, his extensive publication record in educational methodology suggests significant mentorship impact. The ACI grant specifically supported research into adaptive learning systems, indicating early recognition of his contributions to educational technology innovation. No laboratory facilities or research teams are referenced in available materials.