جست‌وجوی استاد و پژوهشگر

Rocky Slavinمشاهده پروفایل

استادیار

• Computer and information security
• Mobile and IoT privacy
• Natural language processing
• +۲ مورد دیگر

Rocky Slavin is an Assistant Professor in the Department of Computer Science at the University of Texas at San Antonio (UTSA), College of AI, Cyber and Computing. His research focuses on computer and information security, mobile and IoT privacy, security patterns, and application of natural language processing to privacy policy analysis. Education: Ph.D. in Computer Science, University of Texas at San Antonio B.S. in Computer Science, University of Texas at San Antonio Slavin's research spans privacy policy alignment with code, IoT threat detection, and accessibility-focused mobile applications. He employs techniques from large language models, sensor fusion, and regulatory compliance frameworks. Recent work includes holistic AI-enabled health computing, few-shot learning for GDPR compliance, and lightweight collaborative inferencing for IoT security. Slavin's publications demonstrate interdisciplinary expertise in security, privacy engineering, and software development. His work addresses challenges in mobile privacy assurance, ambiguity in natural language policies, and developer-focused tools like PoliDroid-AS and PVDetector.

Anthony Riosمشاهده پروفایل

استادیار

• Biomedical informatics
• Computational social science
• Natural language processing
• +۱ مورد دیگر

Dr. Anthony Rios is an Assistant Professor in the Department of Information Systems and Cyber Security at The University of Texas at San Antonio (UTSA), affiliated with the Alvarez College of Business. He holds a Ph.D. in Computer Science from the University of Kentucky (2018) and a B.S. in Computer Science from Georgetown College (2011). His research focuses on biomedical informatics, computational social science, natural language processing (NLP), and machine learning, with applications in healthcare and social media analysis. Dr. Rios has published extensively in top venues such as EMNLP, NAACL, AMIA, and JAMIA, and has received awards including a distinguished poster finalist award at AMIA 2014 and a best paper finalist at ICHI 2015. His work includes developing robust NLP systems for electronic medical records (EMR) coding, combating bias in AI systems (e.g., dialect biases in privacy policies), and enhancing cybersecurity through SQL-based threat detection frameworks. He leads the UTSA-NLP team, contributing to projects like ArchEHR-QA and Chemotimelines. Current research explores large language model (LLM) validation, ethical AI, and scalable evaluation of data visualization systems. Education: Ph.D. in Computer Science, University of Kentucky, 2018 B.S. in Computer Science, Georgetown College, 2011 Awards: 2014: Distinguished Poster Finalist Award, AMIA 2015: Best Technical Paper Finalist, ICHI Labs/Teams: UTSA-NLP Lab, focused on NLP applications in healthcare and cybersecurity

Yang Shiمشاهده پروفایل

استادیار

• Educational Data Mining
• Learning Analytics
• AI in Education
• +۴ مورد دیگر

Yang Shi is an Assistant Professor of Computer Science at Utah State University, specializing in educational data mining, learning analytics, and AI-driven approaches to enhance computing education. He focuses on developing data-driven methods to improve intelligent tutoring systems and student modeling in programming education. His research spans programming language processing, software analysis, and deep learning applications in education. Shi actively contributes to interdisciplinary conferences and workshops, including EDM, LAK, and SIGCSE, and has organized the CSEDM workshop. His work addresses challenges in scaling educational tools, detecting academic integrity issues in code submissions, and leveraging large language models for programming instruction. He explores novel pedagogical strategies, such as comic-based learning toolkits and the impact of on-demand code examples on novice programmers. Shi's research integrates machine learning and human-AI collaboration to address barriers in computing education, such as anomaly detection in programming datasets and improving code tracing question generation. He investigates how AI feedback systems and generative models can enhance student engagement and learning outcomes. His multi-disciplinary approach bridges computer science and educational theory, emphasizing practical applications to improve STEM education accessibility and effectiveness.

Bevan Koopmanمشاهده پروفایل

دانشیار

• Information Retrieval
• Health Informatics
• Natural Language Processing
• +۲ مورد دیگر

Dr. Bevan Koopman leads the Health Search team at CSIRO's Australian e-Health Research Centre and serves as Associate Professor in Language Technologies at University of Queensland. His research develops AI systems for clinical information retrieval, focusing on evidence-based medicine and health decision support. Current work examines large language models for medical question answering, automated clinical trial matching, and systematic review acceleration. Methodologies combine neural ranking, query refinement, and domain-specific knowledge integration. Publications demonstrate innovations in semantic search for medical records, Boolean query generation for systematic reviews, and conversational agents for agricultural health questions. Recent work evaluates ChatGPT for health information reliability. Teaching emphasizes quantitative skills development in biosciences. Funded projects include MathBench Australia for improving STEM education and automated diagnostic coding systems.

• Computer Science Education
• Programming Education
• Adaptive Learning Systems
• +۶ مورد دیگر

Barbara Ericson is a Professor in the Computer Science and Engineering department at the University of Michigan, Ann Arbor. Her research focuses on computer science education, programming pedagogy, and broadening participation in computing. She has developed innovative educational tools including interactive ebooks and Parsons problems for teaching programming concepts. Dr. Ericson's research interests span multiple areas of computer science education including adaptive learning systems, CS teacher professional development, and broadening participation in computing, particularly for underrepresented groups. She has pioneered work on Parsons problems as learning tools and has extensively researched how to make computer science education more accessible and effective. Her recent work has focused on AI literacy and the integration of large language models in programming education. Her publication record shows a clear trend toward integrating AI technologies into computer science education while maintaining a strong focus on equity and accessibility. The majority of her recent articles address how to effectively use AI tools like large language models in programming education, develop AI literacy among students, and create more inclusive computing classrooms. Her work bridges educational theory with practical classroom applications. Dr. Ericson has received recognition for her work in broadening participation in computing, particularly through initiatives like Sisters Rise Up and Project Rise Up 4 CS which support underrepresented students in Advanced Placement Computer Science courses. She has been instrumental in developing the Runestone Interactive platform for free, open-source computer science ebooks and has conducted extensive research on how teachers and students use these resources. Her work on adaptive Parsons problems has influenced programming pedagogy across multiple institutions.

Chandan Reddyمشاهده پروفایل

استاد

• Machine Learning
• Natural Language Processing
• Healthcare Analytics
• +۵ مورد دیگر

Chandan Reddy is a Professor in the Department of Computer Science at Virginia Tech. He holds a Ph.D. from Cornell University and an M.S. from Michigan State University. His research focuses on Machine Learning, Natural Language Processing, and their applications in Healthcare, Software, Transportation, and E-commerce. His work has been funded by NSF, NIH, DOE, DOT, and industries, resulting in over 200 peer-reviewed publications. Notable awards include the Best Application Paper at SIGKDD 2010 and the Franz Edelman Award finalist in 2011. Education: Ph.D., Computer Science, Cornell University M.S., Computer Science, Michigan State University Research Interests: Machine Learning and NLP applied to healthcare analytics, big data systems, and complex data challenges. His work emphasizes scalable algorithms, fairness in AI, and generative models. Recent projects include scientific equation discovery via LLMs and bias mitigation in language models. Publications Trends: Recent work explores LLM-driven reasoning, hyperbolic neural networks, and healthcare informatics. Key areas include data privacy (e.g., synthetic medical data), interpretable models (e.g., time-series clustering), and graph-based methods for product search. Awards: Best Application Paper Award at ACM SIGKDD 2010 Best Poster Award at IEEE VAST 2014 Best Student Paper Award at IEEE ICDM 2016 INFORMS Franz Edelman Award Competition Finalist 2011 Advising & Grants: Advises graduate students on machine learning research, with grants supporting interdisciplinary projects in healthcare, transportation, and AI ethics. Leads the GraphZoo toolkit for hyperbolic GNNs and collaborates on large-scale medical data initiatives. Labs/Teams: Directs research in Virginia Tech’s Data Analytics Lab, focusing on big data platforms, healthcare analytics, and explainable AI systems. Active in developing open-source tools like GraphZoo for graph neural networks.

• Conceptual Modelling
• Enterprise Modeling
• Requirements Engineering
• +۳ مورد دیگر

Monique Snoeck is a full professor and coordinator of the Information Systems Engineering Research Group (LIRIS) at KU Leuven's Faculty of Economics and Business (FEB). She serves on the FEB Council and Campus Council while contributing to DigiSoc and Leuven.AI institutes as a member focused on digital society and AI initiatives. Her research pioneers conceptual modelling, enterprise modeling, requirements engineering, and model-driven engineering, notably through the MERODE methodology and MERLIN prototyping tool. Current work integrates AI with learning analytics for education and develops automatic UI generation from conceptual models, alongside capacity-building projects for Cuba's software industry using model-driven approaches. Recent publications reveal converging trends in LLM-enhanced conceptual modeling education, XAI applications for credit risk analysis, and digital process twins for business scenario simulation. Key themes include model validation techniques, IoT application patterning, and multi-model integration for domain-process alignment. She supervises PhD candidates Tiukhova, E. (credit risk/XAI) and Verbruggen, C. (multi-modelling), leading grants including 'Explanations in AI for Learning Analytics' (2025-2029), 'Personalized Feedback Generation' (2024-2028), and 'Cuba Software Industry Capacity Building' (2024-2029) as promotor. As LIRIS leader, she drives model-driven engineering research while collaborating with Leuven.AI on AI integration and DigiSoc on digital public service co-creation initiatives.

Kangwook Leeمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Information Theory
• +۵ مورد دیگر

Professor Kangwook Lee is a faculty member in the Department of Electrical & Computer Engineering at the University of Wisconsin-Madison. His research focuses on machine learning, deep learning, information theory, and coding. He holds a PhD from UC Berkeley and has received numerous awards including the NSF CAREER Award and Amazon Research Awards. His work spans theoretical advancements and practical applications in AI, federated learning, and data privacy. Education: PhD 2016, University of California-Berkeley MS 2012, University of California-Berkeley BS 2010, KAIST (Korea Advanced Institute of Science and Technology) Research Interests: Machine Learning: Focuses on improving model efficiency, generalization, and robustness in large language models (LLMs) and neural networks. Information Theory: Explores coding techniques and optimization strategies for distributed systems and federated learning. Federated Learning: Develops privacy-preserving methods and algorithms for decentralized data environments. Recent Research Trends: His publications emphasize LLM capabilities, parameter-efficient fine-tuning, and novel approaches to federated learning. Recent efforts include benchmarking LLM agents in video games and advancing transformer architectures for length generalization. Awards: 2024 NSF CAREER Award 2024 Amazon Research Award 2023 MLSys Best Paper Award Multiple fellowships from Brain Korea 21, GCORE, and KSEA Advising & Teaching: Teaches graduate courses in artificial neural networks, ethics of data engineering, and research methodologies. Supervises students in machine learning and federated learning projects.

Brian W. Pattersonمشاهده پروفایل

دانشیار

• Clinical Informatics
• Geriatric Emergency Medicine
• Predictive Analytics
• +۵ مورد دیگر

Dr. Brian W. Patterson is an Associate Professor in the Department of Emergency Medicine at the University of Wisconsin-Madison School of Medicine and Public Health, with additional appointments in Biostatistics & Medical Informatics and Industrial & Systems Engineering. He serves as Physician Administrative Director for Clinical AI and Medical Informatics Director for Predictive Analytics at UW Health, leading AI strategy and clinical decision support implementation. Dr. Patterson's educational background includes: Bachelor of Science in Bioengineering from Pennsylvania State University Master of Public Health and Medical Degree from Northwestern University Feinberg School of Medicine Emergency Medicine Residency at Northwestern Memorial Hospital (Chief Resident) His research focuses on clinical informatics and geriatric emergency medicine, using routinely collected clinical data to improve care for older adults through automated risk scoring and AI-driven interventions. Current work targets fall prevention after emergency department visits via predictive analytics and automated referral systems. Collaborating across the Wisconsin School of Business, College of Engineering, and Biostatistics department, his 2025 publications reveal strong emphasis on AI implementation science, NLP applications, and equity-focused clinical decision support. Key themes include ambient AI evaluation, real-time prediction systems, and LLM validation for medical tasks. Notable awards include: Fellow of the American Medical Informatics Association (2025) UW Health Physician Excellence Award (2025) AGEM Author Spotlight (2019) Dual Faculty Scholarship Awards (2015, 2019) Dr. Patterson leads active research funded by AHRQ and NIA, currently as PI on an R18 grant preventing falls in older adults and Co-I on dementia care initiatives. Past funding includes AHRQ K08/R18 career development awards and Wisconsin Partnership Program projects. Current Grants: AHRQ R18 (PI), NIA U19 (Co-I), NIH P30 (Co-I) Past Grants: AHRQ K08 (PI), AHRQ R18 (Collaborator), Wisconsin Partnership (Co-PI) He directs the Emergency Care Systems Lab, applying human factors engineering to develop and evaluate clinical AI tools within UW Health's learning health system framework, with strong focus on clinician-IT collaboration and workflow integration.

• Electrical Engineering
• Computer Engineering
• Signal Processing
• +۷ مورد دیگر

Ming Zhu is an Assistant Professor in Residence in the Department of Electrical and Computer Engineering at the University of Nevada, Las Vegas (UNLV). His work bridges electrical engineering and computer science to solve real-world problems in transportation, industrial systems, and healthcare. His research interests include Signal Processing , Machine Learning , Unmanned Aerial Vehicles (UAVs) , and Edge Computing . He applies these to Rail Infrastructure Monitoring , Industrial Fault Detection , and Health Monitoring Systems , using technologies like LiDAR, acoustic sensors, and generative models. Analysis of recent publications reveals a focus on non-invasive rail track measurement (using UAVs and LiDAR), sound-based malfunction detection in machinery, and efficient edge computing for video streaming and healthcare applications. His work demonstrates a strong trend towards practical, real-time systems for critical infrastructure.

Daniel Zingaroمشاهده پروفایل

استاد

• Computer Science Education
• Educational Technology
• Programming Pedagogy
• +۳ مورد دیگر

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.

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.

Francis Yanمشاهده پروفایل

استادیار

• Networked Systems
• Computer Networks
• Machine Learning for Systems
• +۳ مورد دیگر

Francis Yan is an Assistant Professor of Computer Science at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Siebel School of Computing and Data Science and the Electrical & Computer Engineering department. He leads the Illinois Networked Systems and AI (NSAI) Lab, focusing on integrating practical machine learning into networked systems. Previously, he was a Senior Researcher at Microsoft Research Redmond, working on systems and networking research. His work emphasizes deployable ML solutions, validated through large-scale platforms like Puffer and Pantheon. Education: Ph.D. in Computer Science (Stanford University, 2020); B.S. in Computer Science (Tsinghua University, 2015) and B.A. in Economics (Tsinghua University, 2015). Research Interests: Networked systems, machine learning for systems and networking, intelligent video systems, and cloud resource management. His lab develops frameworks like DeDe, Mowgli, and Autothrottle to enhance system performance and robustness. Awards: USENIX NSDI Outstanding Paper Award (2024), Applied Networking Research Prize (2021), and USENIX ATC Best Paper Award (2018). He serves on program committees for NSDI, SIGCOMM, and ACM workshops. Grants & Service: Co-chair of SIGCOMM 2024 Artifact Evaluation. Active in open-source projects and research platforms supporting reproducible networking experiments. Labs/Teams: Directs the NSAI Lab, collaborating on projects such as Puffer (400k-user video service), Teal (WAN optimization), and Genet (curriculum learning for networking).

Minjia Zhangمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +۹ مورد دیگر

Minjia Zhang is a tenure-track Assistant Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Department of Electrical and Computer Engineering (ECE) and the Center for Artificial Intelligence Innovation (CAII) at the National Center for Supercomputing Applications (NCSA). He leads the Supercomputing System and AI Lab (SSAIL) and previously served as a Principal Researcher at Microsoft Research Redmond from 2016 to 2023, contributing to projects like DeepSpeed and DeepCPU. Education: Ph.D. in Computer Science and Engineering from Ohio State University (2016), advised by Prof. Michael D. Bond. Prior to UIUC, he held visiting positions at Microsoft Research during his doctoral studies. His research focuses on efficient machine learning systems, including training/inference optimization on parallel/distributed/heterogeneous hardware, model compression, and large-scale AI applications such as agentic AI and vision-language models. Research Interests: Large-scale DL/AI applications (e.g., agentic AI, multimodal systems), effective efficiency algorithms (model compression, data efficiency), and high-performance ML systems. He emphasizes cross-disciplinary collaboration, integrating system design with AI advancements to tackle scalability challenges in scientific discovery and industrial deployment. He has been recognized with the NSF CAREER Award (2024), Honorable Mention of the ICLR 2024 Outstanding Paper Award, and OOPSLA 2015 Distinguished Paper and Artifact Awards. His work has been applied to Microsoft products like Bing and Azure, enhancing latency and cost efficiency. Advising and Grants: Actively mentoring students in PhD/Master's/Undergraduate programs, emphasizing systems and AI. Recipient of NSF grants and the Amazon PhD Fellowship (Xinyu Lian). His lab's VecFlow and MiniKV projects highlight contributions to vector databases and efficient LLM inference. Labs/Teams: SSAIL Lab, collaboration with Microsoft Research (DeepSpeed), BigScience consortium (BLOOM 176B model), and NCSA's CAII. His work bridges systems research and AI, targeting high-impact applications in science and industry.

Fabio Massacciمشاهده پروفایل

استاد

• Cybersecurity
• Software Security
• Security Economics
• +۵ مورد دیگر

Prof. Fabio Massacci is a Full Professor at the Faculty of Science (Computer Systems Department) and Network Institute of Vrije Universiteit Amsterdam. He concurrently holds a part-time professorship in Computer Science at Università degli studi di Trento. His research focuses on cybersecurity, software supply chain risks, security economics, and experimental methods for vulnerability analysis. He earned a PhD in Computer Science from La Sapienza University of Rome (1998). Research interests include software ecosystem security, malware analysis, security policy design, and risk assessment frameworks. Notable contributions include the 2015 IEEE RE Ten Years Most Influential Paper Award for work on socio-technical security systems. Recent studies explore AI in vulnerability detection, cloud configuration security, and GDPR compliance in educational institutions. Teaching responsibilities include courses on Security and Safety Engineering, Security Experiments, and Data Structures for AI. Key projects include the HEWSTI initiative (Hybrid Explainable Workflows for Threat Intelligence) and studies on cybersecurity training in European education programs. Collaborations span global institutions, with datasets published on Zenodo and IEEE DataPort.