جست‌وجوی استاد و پژوهشگر

Pieter J.L. Cuijpersمشاهده پروفایل

دانشیار

• Cyber-Physical Systems
• Real-Time Systems
• Formal Methods
• +۶ مورد دیگر

Pieter J.L. Cuijpers is an Associate Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e), affiliated with the Interconnected Resource-aware Intelligent Systems (IRIS) group and EAISI Mobility. His research focuses on quantitative formal modeling of cyber-physical systems, real-time scheduling, and performance analysis of embedded systems, leveraging tools like max-plus algebra and the Lean proof assistant. He holds an MSc in Electrical Engineering (2000) and a PhD in Computer Science (2004), both from TU/e. Research interests include hybrid systems, energy-aware scheduling, Time-Sensitive Networking (TSN), and formal verification. He teaches courses such as Quantitative Evaluation of Embedded Systems and advocates for innovative educational methods like MOOCs and challenge-based learning (CBL). His work contributes to UN Sustainable Development Goals related to industry, innovation, and infrastructure. Notable recognition includes the Outstanding paper award RTNS 2016 . He coordinates the capstone project CBL Embedded Systems and has mentored 44 students, including master’s thesis advisor for M.P.J. Stevens. His lab, IRIS, explores interconnected systems and resource optimization.

• Software Engineering
• Formal Methods
• Software Testing
• +۳ مورد دیگر

Ali Mili is Professor in the Department of Computer Science at New Jersey Institute of Technology's Ying Wu College of Computing. With an h-index of 20 and over 2,095 citations, he maintains a robust research profile spanning software engineering, formal methods, and cybersecurity. His work focuses on software verification, testing methodologies, and secure computing paradigms. Research interests center on enhancing software reliability through innovative approaches to program correctness, fault detection, and quality assurance. Recent publications demonstrate persistent exploration of formal verification techniques and testing frameworks, with emerging emphases on secure coding practices. Honors include: 3M National Teaching Fellow (2014) Fellow of International Society for the Scholarship of Teaching and Learning (2020) Fellow of the Royal Canadian Geographical Society (2013) He leads significant federally funded initiatives including the CyberCorps Scholarship for Service program and international collaborations like the Qatari Contribution to the Verified Software Initiative, advancing secure computing education and research infrastructure.

Craig Zillesمشاهده پروفایل

استاد

• Computer Science Education
• Learning Analytics
• Computer Architecture
• +۷ مورد دیگر

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.

Reyhaneh Jabbarvandمشاهده پروفایل

استادیار

• Artificial Intelligence
• Programming Languages
• Formal Methods
• +۴ مورد دیگر

Reyhaneh Jabbarvand is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. She leads the Intelligent CAT Lab, focusing on neuro-symbolic approaches to enhance software quality and reliability. Her work integrates AI and mathematical optimization to address real-world software challenges. She holds a PhD from UC Irvine and has been recognized with awards including the Google PhD Fellowship and NSF CAREER Award. Education: PhD in Computer Science, University of California, Irvine (Advisor: Dr. Sam Malek) Research Interests: Her research spans programming languages, software engineering, and AI applications. Key areas include code translation validation, test automation, and energy-efficient testing of mobile apps. She develops tools like AlphaTrans, Croissant, and SEER to tackle industry challenges. Awards & Grants: NSF CAREER Award (2022) Google PhD Fellowship (2018-2020) Rising Star in EECS (2019) IBM Research grants (2023) Advising & Service: Advises PhD students like Yang Chen and Ali Ibrahimzada. Serves on program committees for ICSE, FSE, ISSTA, and journals TOSEM/TSE. Organizes workshops like InteNSE at ICSE/ICSE. Lab & Teams: Intelligent CAT Lab collaborates on projects like neuro-symbolic code repair, LLM-driven testing, and cloud application validation. Partners with institutions like KTH Royal Institute of Technology via C3.ai grants.

Y. Richard Yangمشاهده پروفایل

استاد

• Computer Networks
• Wireless Networking
• Mobile Computing
• +۲ مورد دیگر

Y. Richard Yang is a Professor of Computer Science and Electrical Engineering at Yale University, where he leads research in computer networks, wireless systems, and distributed computing. He is a core member of Yale's Computer Systems Lab and has pioneered Internet standards like ALTO (RFCs 7285/9439/9569). His work on massive MIMO (Argos) laid foundations for 5G cellular networks. Education: Ph.D. in Computer Science, University of Texas at Austin (2001) M.S. in Computer Science, University of Texas at Austin (1998) B.E. in Computer Science, Tsinghua University (1993) Research Interests: Yang's work spans network architecture (ALTO/P4P), wireless innovations (beamforming, superposition coding), and network verification (Flash, Trident). His recent focus includes deep application-network integration and scalable data plane verification for cloud/CERN-scale systems. Publication Trends: Recent articles emphasize SDN programmability (Maple, Trident), Internet-scale optimization (ALTO extensions), and high-speed verification (Flash). His works consistently bridge theory (game-theoretic models) with practical deployment (RFCs, CERN integration). Awards: ACM SIGMobile Test of Time Award (2022) ACM SIGCOMM NAI Best Paper Award (2022) NSF CAREER Award Facebook/Google/Microsoft Faculty Research Awards Students & Funding: Advised recent graduates including Jackie Dong and Lily Zhou. Secured grants from U.S. NSF, Army, U.K. MoD, Facebook, Google, and Microsoft. Labs: Directs projects in the Computer Systems Lab at Yale , collaborating with CERN, Microsoft Research, and Tsinghua/Peking Universities.

Alistair Windsorمشاهده پروفایل

دانشیار

• Dynamical Systems
• Ergodic Theory
• STEM Education
• +۴ مورد دیگر

Alistair Windsor is an Associate Professor of Mathematical Sciences at the University of Memphis, where he also serves as Director of the Institute for Intelligent Systems. His research bridges dynamical systems, ergodic theory, and natural language processing (NLP), with a focus on applications in STEM education and AI ethics. Notably, he collaborates with Kristopher Kyle on Verb Argument Constructions (VACs) analysis using the TAASSC tool, examining linguistic patterns in human vs. AI-generated texts. His work extends to machine learning and computer vision, including contributions to the CvT (Convolutional Vision Transformers) project on GitHub, which merges CNNs with Vision Transformers for improved image recognition. Research interests include computational linguistics, AI-driven education, and interdisciplinary projects like analyzing medical/financial advice quality in ChatGPT versus human experts. He actively publishes in venues addressing education, political linguistics, and healthcare literacy, emphasizing practical applications like improving teaching assistant feedback in CS courses. His technical expertise spans software testing methodologies and open-source tools for reproducibility in computational workflows. Windsor’s interdisciplinary approach integrates mathematical rigor with real-world societal challenges, from urban policy analysis to pandemic governance studies. He maintains an active GitHub presence, contributing to projects like CvT and tools for text coherence measurement in social media.

Xiaohong Wangمشاهده پروفایل

استاد

• Software Engineering
• Computer Science Education
• Scientific Computation
• +۵ مورد دیگر

Xiaohong (Sophie) Wang is a Professor and Chair of the Computer Science Department at Salisbury University. She holds a B.Sc. from Jilin University (China), and M.Sc. and Ph.D. in Computer Science from the University of Victoria (Canada). Her research focuses on software engineering, computer science education, scientific computation, GIS integration, programming languages, and artificial intelligence. She leads the NSF-funded Rural AI Solutions and Engagement (RAISE) project ($380K) and has managed over 100 capstone software engineering projects for real-world clients. Dr. Wang has received the 2020 Salisbury University Alumni Association Faculty Appreciation Award for her innovative teaching methods and leadership. She chairs the Computer Science Department, overseeing curriculum development, lab infrastructure, and strategic decisions. Her grants include the NSF Convergence Accelerator Track K project ($799K), Maryland Department of Emergency Management grants, and NSF-funded projects like the CCC* Regional initiative. Her research spans climate change impacts, coastal inundation modeling, and educational technology. Notable projects include storm surge prediction for Maryland, salt contamination forecasting via SaltCast.io, and K-8 math tutorial development. She advises undergraduate research in areas like 3D printing, parallel programming, and Android app development. Dr. Wang teaches advanced courses such as Software Engineering I/II, Compiler Construction, and Operating Systems. She emphasizes applied learning and has developed concept-based pedagogy frameworks. Her work bridges academia and real-world challenges, fostering student innovation and environmental resilience initiatives.

Ana Bellمشاهده پروفایل

مدرس ارشد

• Computer Science Education
• Python Programming
• Computational Biology
• +۳ مورد دیگر

Ana Bell is a Senior Lecturer in the Department of Electrical Engineering and Computer Science (EECS) at MIT. She specializes in computer science education, particularly in introductory programming courses using Python. Her teaching portfolio includes courses like 6.100A/6.0001, 6.100B/6.0002, and 6.UWTD, which are available on MIT OpenCourseWare and edX. Bell holds a B.A.Sc. from the University of British Columbia (2007), an M.A. (2009), and a Ph.D. (2013) from Princeton University, where her research focused on computational biology in the Troyanskaya Lab. Her research interests bridge educational technology and computational biology, with publications on MOOC student performance and gene interaction networks. She authored Get Programming: Learn to Code with Python (Manning Publications) and a companion video series, Get Programming with Python in Motion . She also created Doodle Debug , a coloring book for programmers. Bell has been featured on podcasts such as Python Podcast.__init__ and Women in Tech with Ariana , discussing programming education and her academic journey. Her work emphasizes making programming accessible to beginners and leveraging technology for educational outreach.

Adam Chlipalaمشاهده پروفایل

استاد

• Programming Languages
• Formal Verification
• Compiler Verification
• +۲ مورد دیگر

Adam Chlipala is the Arthur J. Conner Professor of Computer Science at MIT, with appointments in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Department of Electrical Engineering and Computer Science. His research integrates programming languages and formal methods to develop verified computer systems. Education: PhD in Computer Science, University of California, Berkeley MS in Computer Science, University of California, Berkeley BS in Computer Science, Carnegie Mellon University Research Focus: Dr. Chlipala's work spans: Verified compilers and programming tools Cryptographic protocol implementation Hardware description languages Proof automation in theorem provers High-performance parallel computing His current projects focus on rethinking abstractions for parallel systems with end-to-end formal verification. Teaching: He has created and teaches courses on verified software engineering (6.S057), programming fundamentals (6.1010), and formal reasoning about programs (6.5120). His educational materials include textbooks on Certified Programming with Dependent Types and Formal Reasoning About Programs. Awards and Honors: His research has been recognized with the German IT Security Award, Humies Gold Award for evolutionary computation, and multiple teaching prizes including the Jamieson Prize. He is an ACM Distinguished Member and NSF CAREER awardee. Research Teams: Dr. Chlipala leads the Programming Languages & Verification Group at MIT CSAIL. His startup Nectry develops no-code enterprise software based on the Ur/Web language and UPO proof system.

Martin Robillardمشاهده پروفایل

استاد

• Software Engineering
• Information Privacy
• Human-Computer Interaction
• +۴ مورد دیگر

Martin Robillard is a Professor at McGill University's School of Computer Science, where he leads the McGill Software Technology Lab. He holds a Ph.D. from the University of British Columbia. His research focuses on software engineering, emphasizing human aspects of development, including documentation design, test suite quality, and information privacy. He has authored the textbook Introduction to Software Design with Java and developed the JetUML tool, recognized with the 2024 ACM SIGSOFT Influential Educator Award. He has co-chaired major conferences (FSE 2012, ICSE 2017) and is a Distinguished Member of the ACM. Research Interests: His work bridges software engineering, AI, and HCI, addressing challenges in API usability, automated documentation generation, and privacy-preserving software design. Recent projects include dynamic documentation tools like Casdoc and studies on developer documentation formats. Professional Activities: He serves on editorial boards for IEEE TSE and Empirical Software Engineering, and chairs workshops like DySDoc. His contributions include over 100 publications, six SIGSOFT Distinguished Paper Awards, and leadership in software engineering education. Teaching: Teaches courses on software design (COMP 303), information privacy (COMP 555), and software architecture. His courses emphasize practical skills and ethical considerations in software development. Labs & Collaborations: The McGill Software Technology Lab investigates knowledge discovery in software systems, with projects on API documentation, test quality, and privacy in health contexts. Collaborators include industry partners like ActiveEon and General Motors.

Raymond Pettitمشاهده پروفایل

استادیار

• Computer Science Education
• Human-Computer Interaction
• Programming Pedagogy
• +۳ مورد دیگر

Raymond Pettit is an Assistant Professor in the Department of Computer Science at the University of Virginia, where he focuses on computing education research and teaching. With 12 years in industry and 16 years in academia, he transitioned from full-time industry work to academia in 2004, completing a Ph.D. as a graduate assistant. His career includes roles at Abilene Christian University before joining UVA in 2018. He specializes in bridging academia and industry needs through research in programming education, AI integration in teaching, and human factors in computing. Education: Earned a Ph.D. in Computer Science (institution unspecified) while working as a graduate assistant. Prior industry experience included software development for private companies and government research organizations. Research Interests: Computer Science Education with a focus on AI in education, programming pedagogy, and error message design. Key areas include: Generative AI impact on teaching methodologies Metacognitive strategies for novice programmers Gender dynamics in online learning platforms Human factors in compiler error messages Publications: Recent work explores AI integration in courses, collaboration policies, and emotional impacts of AI tools. Themes include bridging academia-industry gaps and improving error message usability. Awards: No awards explicitly listed in the provided text. Advising & Grants: While no specific grants or advisees are mentioned, his teaching roles and research activities suggest involvement in educational initiatives. He has transitioned from adjunct to full-time roles, indicating sustained academic commitment. Labs/Teams: No specific lab affiliations or collaborative teams mentioned in the text.

Chengbo Zhengمشاهده پروفایل

پژوهشگر ارشد

• Data Visualization
• Human-Computer Interaction (HCI)
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Chengbo Zheng is a Postdoctoral Research Fellow at the School of Electrical Engineering and Computer Science, University of Queensland. His research focuses on data visualization, human-computer interaction, and educational technology, particularly leveraging AI to enhance collaborative work, decision-making, and learning environments. Zheng has contributed to interdisciplinary projects such as AI tools for self-assessment in education, human-AI co-exploration systems, and visualization techniques for complex data analysis. His work bridges theoretical advancements with practical applications, including improving data storytelling, combating misinformation, and optimizing transportation networks through visual analytics. Recent projects emphasize AI's role in collaborative processes, such as group decision-making fairness and interdisciplinary information seeking. Zheng's methodologies often combine computational tools with user-centered design principles to address real-world challenges. He has presented at top venues like CHI, UIST, and IEEE Visualization conferences, showcasing innovations in interactive systems, AI integration, and visualization design. His research has implications for education, scientific collaboration, and urban planning.

• Computer Science
• Programming Languages
• Software Engineering
• +۳ مورد دیگر

Xinyu Wang is a Research Fellow at the University of Michigan's Department of Physics, affiliated with the School of Literature, Science, and the Arts (LSA). Their primary office is located in the Randall Laboratory, and they are also associated with the Homer A. Neal Lab. Wang's work focuses on advancing program synthesis techniques, web automation systems, and data-driven methodologies to enhance software development and user interaction. Their research addresses challenges in SQL query verification, cloud computing simplification, and integrating human feedback into automated processes. While specific educational background details are not provided in the text, Wang's research demonstrates expertise in computer science, programming languages, and software engineering. Their projects include developing tools like VeriEQL for SQL equivalence verification, Miwa for user-controlled web automation, and SlabCity for query optimization. These efforts highlight a trend toward creating systems that blend formal methods with practical usability. Key Research Themes: Program synthesis, compiler design, database systems, human-computer interaction, neurosymbolic AI, and automated workflows Wang’s contributions include innovations in data structure refinement, cloudless computing architectures, and tensor transformation tools. Their work often emphasizes reliability, user confidence, and scalable solutions. No formal advisees or grants are explicitly listed in the provided texts. However, their involvement in advising resources like 'Advising Career Resources' suggests a role in supporting academic and professional development within the department. Wang collaborates with labs and teams focused on physics and computational sciences, contributing to interdisciplinary projects. Their contact information includes the email wongxy@umich.edu and office phone numbers 734-764-4437 and 734-763-9694.

William Arthurمشاهده پروفایل

مدرس

• Computer Security
• Computer Architecture
• Compilers
• +۵ مورد دیگر

William Arthur is a Lecturer in the Department of Electrical Engineering and Computer Science at the University of Michigan. He currently teaches EECS 183: Elementary Programming Concepts , with teaching experience dating back to Fall 2014. His office hours are held remotely during the Winter semester, and he has also served as visiting faculty at Addis Ababa Institute of Technology in Ethiopia for the course ECEG 66514. His research interests span computer security , computer architecture , compilers , system security , network security , hardware/software co-design , software engineering and test , and engineering education . Publications focus on control-flow integrity, security bug detection, and network attack modeling, primarily using C++ as a teaching language in EECS 183. Key publications address Hardware-based control-data isolation (2016) Control-flow management in security contexts (2015) Scalable security profiling tools (2013) DDOS attack analysis in phase space (2011) These works emphasize secure computing frameworks and vulnerability mitigation strategies. Contact: warthur@umich.edu

Andrew DeOrioمشاهده پروفایل

استاد

• Computer Science Education
• Software Engineering
• Medical Device Security
• +۳ مورد دیگر

Andrew DeOrio is a Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan. He specializes in computer science education, software engineering, and medical device security. His teaching includes large courses such as EECS 280 (Programming and Data Structures) and EECS 485 (Web Systems), with a focus on innovative pedagogical strategies. He has developed tools like Madoop (a lightweight MapReduce framework) and contributed to open-source projects like TapirX for medical device discovery. His research spans hardware debugging, medical device cybersecurity, and educational technologies. Notable contributions include work on post-silicon validation of multi-core processors and anomaly detection in medical devices. He has won the Provost's Teaching Innovation Prize and the ECE Division Best Diversity Paper Award. DeOrio advises numerous students, many of whom pursue careers in tech or academia. His labs and collaborations include OpenHealthSoftware and the University of Michigan’s Center for Research on Learning and Teaching.