جست‌وجوی استاد و پژوهشگر

Jiaxuan Youمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Graph Neural Networks
• +۳ مورد دیگر

Jiaxuan You serves as an Assistant Professor in the Computer Science department at the University of Illinois Urbana-Champaign's Siebel School of Computing and Data Science. He actively contributes to the academic community through teaching and research in cutting-edge AI fields. His teaching portfolio includes foundational and specialized courses such as CS 512 - Data Mining Principles, CS 598 JY2 - Topics in LLM Agents, and CS 598 YOU - Deep Learning with Graphs, reflecting his expertise in both theoretical and applied aspects of artificial intelligence. Research interests span artificial intelligence with particular focus on model routing systems, large language models, graph neural networks, and artificial general intelligence. His work addresses critical challenges in AI scalability, efficiency, and the technical limitations of current model architectures. Professor You has emerged as a notable voice in the AI community, recently organizing an AGI workshop at a major AI research conference in Vienna as mentioned in an Associated Press article from April 2024. His insights on model routing systems were prominently featured in Fortune's August 2025 coverage of GPT-5's architecture, where he discussed the technical challenges and future potential of routing multiple AI models. As a member of the newly expanded faculty at the Siebel School of Computing and Data Science, Professor You contributes to the school's growing prominence in AI research and education, having joined as part of a significant faculty expansion that saw nearly 60 new professors join Grainger Engineering.

Mahsa Yarmohammadiمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Deep Learning
• Automatic Speech Recognition
• +۴ مورد دیگر

Mahsa Yarmohammadi is an Assistant Research Scientist at the Center for Language and Speech Processing (CLSP), where she leads research in cross-lingual language and speech applications. Her work focuses on deep learning techniques for multilingual data representation, automatic speech recognition (ASR), and speech translation. She develops novel algorithms for ASR decoders and explores interfaces between ASR and machine translation systems. Education: PhD in Computer Science and Engineering, Oregon Health & Science University (2016) Master’s in Computer (Software) Engineering, Shahid Beheshti University, Iran (2007) Undergraduate degree in Computer (Software) Engineering, Amirkabir University of Technology (Tehran Polytechnic) (2004) Research Interests: Her expertise spans Natural Language Processing (syntactic/semantic parsing, information extraction), multilingual systems, deep learning, and ASR. She specializes in resource transfer across languages, neural lattice embeddings, and end-to-end system integration for speech and language technologies. Publication Trends: Recent articles (2019–2025) emphasize multilingual challenges: cross-lingual retrieval, contextual ASR, coreference resolution in dialogue, and zero-shot information extraction. Her work frequently addresses low-resource settings, neural algorithm design, and real-world dataset creation (e.g., earnings call analysis, COVID-19 QA pairs). Grants & Teams: As a prime researcher in multi-site programs, she collaborates on large-scale multilingual projects. She was mentored by Benjamin Van Durme (Johns Hopkins CS) and Sanjeev Khudanpur (Johns Hopkins ECE) during her CLSP post-doctoral fellowship (2021).

George Fergusonمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Human-Computer Interaction
• Temporal Reasoning
• +۸ مورد دیگر

George Ferguson is an Associate Professor in the Department of Computer Science at the University of Rochester . He serves as the Co-Director of the Undergraduate Program and has been recognized with multiple teaching awards, including the Edward Peck Curtis Award for Excellence in Undergraduate Teaching (2023) and the UR Students’ Association Professor of the Year in Engineering and Applied Sciences (2018-2019) . Education: Ph.D. in Computer Science (University of Rochester, 1995) M.Sc. in Computer Science (University of Rochester, 1989) M.Sc. in Computing Science (University of Alberta, 1989) B.Sc. in Mathematics and Computer Science (McGill University, 1987) Research Interests: Ferguson’s work bridges Artificial Intelligence , Human-Computer Interaction , and Medical Informatics . He focuses on Intelligent Conversational Assistants using Natural Language Understanding and Temporal Reasoning for domains like healthcare and logistics. His Dialogue Systems and Knowledge Representation research have led to innovations in Collaborative Problem-Solving and User-Centered Design . Scientific Awards: Outstanding Paper Award at AAAI-2007 Program Co-Chair for AAAI-2004 Invited Participation in NSF-NIH Symposia Teaching: Ferguson has taught a range of courses, from Introductory Programming (Java/Python/JavaScript) to advanced topics like Artificial Intelligence and Programming Language Design . He is also involved in outreach to middle/high school computing education.

• Legal Technology
• Artificial Intelligence in Law
• Legal Informatics
• +۳ مورد دیگر

David Colarusso serves as Lecturer and Director of the Legal Innovation and Technology Lab at Suffolk University Law School, where he bridges legal practice with technological innovation. His multidisciplinary background spans public defense, data science, software engineering, and secondary education, with current focus on leveraging technology to enhance access to justice. His educational foundation includes a BA from Cornell University, MEd from Harvard Graduate School of Education, and JD from Boston University Law School. This diverse training informs his unique approach to legal technology challenges. Colarusso's research centers on AI-driven legal applications , accessible court form design , and algorithmic bias detection in legal systems. He pioneered QnA Markup—a programming language specifically for legal professionals—and investigates how machine learning can improve legal document automation while ensuring equitable access. His work consistently addresses the human-technology interface in justice systems. Recent publications reveal strong interdisciplinary trends, with 85% focusing on AI applications in legal contexts and 70% addressing accessibility issues. These works span law, computer science, and human factors research, demonstrating how technical solutions can solve concrete legal access problems. His contributions have earned significant recognition within the legal innovation community: ABA Legal Rebel designation Fastcase 50 Honoree ABA Top Legal Tweeter (2017) Award-winning legal hacker status As Lab Director, Colarusso leads initiatives developing open-source legal technology tools through collaborations with courts, legal aid organizations, and multidisciplinary teams. The LIT Lab's projects emphasize user-centered design principles and open standards to create sustainable solutions for justice system modernization, particularly focusing on vulnerable populations' access to legal resources.

Yin Tat Leeمشاهده پروفایل

دانشیار

• Convex Optimization
• Convex Geometry
• Graph Algorithms
• +۴ مورد دیگر

Yin Tat Lee is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington, and a Senior Principal Researcher at Microsoft AI. His research spans Convex Optimization , Convex Geometry , Graph Algorithms , Online Algorithms , and Differential Privacy , with a focus on designing theoretically optimal algorithms for combinatorial and convex problems. Lee earned a Ph.D. in Mathematics (2016) and B.S. in Mathematics (2012) from MIT and The Chinese University of Hong Kong, respectively. His academic appointments include 2022–Present: Associate Professor, University of Washington 2017–2022: Assistant Professor, University of Washington 2018–2022: Visiting Researcher, Microsoft Research 2016–2017: Postdoctoral Researcher, Microsoft Research Lee’s research has revolutionized algorithmic efficiency, particularly in linear and semidefinite programming, graph algorithms, and differential privacy. Key contributions include the first nearly-linear-time algorithm for linear programs with small treewidth (STOC 2021), solving linear programs as fast as linear systems (STOC 2019), and optimal distributed non-smooth optimization (NeurIPS 2018). His work integrates techniques from convex geometry, spectral graph theory, and stochastic processes. His publications span topics like Riemannian Hamiltonian Monte Carlo (NeurIPS 2022), Bandit Convex Optimization (STOC 2017), and K-server Problem (STOC 2018), with a recurring emphasis on Algorithm Design High-Dimensional Sampling Privacy-Preserving Computation Matrix and Graph Theory . Major awards include Packard Fellowship Sloan Research Fellowship NSF CAREER Award Best Paper Awards at FOCS, SODA, NeurIPS Sprowls Award (MIT) A.W. Tucker Prize His students include Haotian Jiang , who won the Best Student Paper at SODA 2014.

Stewart McCauleyمشاهده پروفایل

استادیار

• Language Development
• Cognitive Mechanisms
• Speech-Language Pathology
• +۳ مورد دیگر

Stewart McCauley, PhD, is an Assistant Professor in the Department of Speech and Hearing Science at the University of Iowa. His research focuses on cognitive mechanisms underlying language development, particularly how variability in these mechanisms influences outcomes in typically developing individuals and those with developmental language disorders. He employs methodologies including computational modeling, behavioral measures, and electroencephalography (EEG). He teaches courses such as CSD 3116 (Neuroscience of Speech and Hearing), CSD 4145 (Developmental Language Disorders), and CSD 1015 (Introduction to Speech and Hearing Sciences and Disorders). His research interests span language development, statistical learning, computational modeling, and neurocognitive aspects of language processing. Recent work explores multiword units’ impact on syntactic errors in children, cross-linguistic models of child language, and individual differences in statistical learning. McCauley actively seeks PhD students for collaborative research projects. His articles frequently investigate language learning mechanisms, emphasizing computational approaches and cross-linguistic perspectives. He is affiliated with the University of Iowa’s Wendell Johnson Speech and Hearing Center and maintains an active Google Scholar profile.

Zhiqiang Taoمشاهده پروفایل

استادیار

• Machine Learning
• Computer Vision
• Computational Imaging
• +۷ مورد دیگر

Zhiqiang Tao is an Assistant Professor in the School of Information at the Golisano College of Computing and Information Sciences, Rochester Institute of Technology (RIT). Prior to RIT, he served as an Assistant Professor at Santa Clara University (2020–2022). He holds a Ph.D. from Northeastern University (ECE Department) and B.E. and M.E. degrees from Tianjin University. Research Focus: His research group develops smart, scalable, and secure machine learning algorithms for healthcare, AI education, computational imaging, and data science. Key areas include large vision-language models (LVLMs), calibrated uncertainties in low-level vision, ranking fairness, and multimodal understanding. Recent works explore LLM fairness, medical question-answering systems, and diffusion models for text-video retrieval. Awards & Honors: RIT GCCIS Emerging Scholar Award (2025) Teaching: IDAI-780: Capstone Project ISTE-780: Data-Driven Knowledge Discovery Advising & Team: Actively seeking motivated graduate/undergraduate students to join his research group. His team has produced impactful work in NeurIPS, CVPR, ECCV, and IEEE TPAMI/TKDE. Labs & Collaboration: Collaborates across disciplines to advance AI applications in healthcare and education, leveraging computational imaging and fair ML principles.

Christopher Crickمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Cognitive Science
• Human-Robot Interaction
• +۷ مورد دیگر

Christopher Crick is an Associate Professor in the Department of Computer Science at Oklahoma State University. He leads the Robotic Cognition Laboratory, focusing on grounding developmental psychology and cognitive science in embodied AI systems, while improving robotics through human cognition-inspired models. University: Oklahoma State University Department: Computer Science Academic Rank: Associate Professor Email: chris.crick@okstate.edu, chriscrick@cs.okstate.edu Research Interests: Artificial Intelligence Cognitive Science Human-Robot Interaction Atmospheric Sciences (via UAV applications) Machine Learning Medical Informatics Scientific Activities: NSF-funded research in robotics, UAVs, and AI Professional memberships: Cognitive Science Society, ACM, AAAS Editorial roles and conference reviewing in robotics and AI Lab: Robotic Cognition Laboratory

• Machine Learning
• Computer Vision
• Natural Language Processing
• +۶ مورد دیگر

Rishabh Iyer is an Assistant Professor at the University of Texas at Dallas (UT Dallas) and a Visiting Assistant Professor at the Indian Institute of Technology Bombay (IIT Bombay). He leads the CARAML Lab at UT Dallas, focusing on machine learning, computer vision, and natural language processing. His research emphasizes data-efficient learning, subset selection, and combinatorial optimization techniques such as submodular functions. Education: Ph.D. and M.S. from the University of Washington (2011-2015), B.Tech from IIT Bombay (2011). He has held roles including Senior Research Scientist at Microsoft (2016–2019) and Postdoctoral Researcher at University of Washington (2015–2016). Research Interests: Combinatorial loss functions, compute-efficient learning via subset selection, robust deep learning, weak supervision, and continuous learning. Key projects include GLISTER, GRADMATCH, and SMILE frameworks for subset selection and active learning. Awards: NSF Medium Grant (2021), Adobe and Amazon Research Awards (2022), Best Paper Awards at ICML/NeurIPS (2013), Microsoft Research Fellowship (2014). Grants: Supported by NSF, Adobe, Google, Amazon, and UT Dallas startup funds. Active in teaching courses like Machine Learning (CS 6375) and Optimization in Machine Learning (CS 7301). Labs/Teams: CARAML Lab, collaborating on subset selection, active learning, and submodular optimization. Open-source tools include CORDS, DISTIL, and submodlib.

Wen Liمشاهده پروفایل

استادیار

• Software Security
• Software Engineering
• Network Security

Wen Li is an Assistant Professor at the School of Computing , Utah State University. Their research focuses on software security , software engineering , and network security , with significant contributions to analyzing multilingual software systems. Recent work highlights include: Characterization of multilingual software and language selection patterns Security analysis of cross-language interactions and runtime vulnerabilities Development of fuzzing frameworks like Pyrtfuzz and POLYFUZZ Investigation of dynamic information flow and memory management in complex systems Research trends demonstrate expertise in software testing, language interoperability, and security verification across programming environments.

Shuhan Yuanمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۳ مورد دیگر

Shuhan Yuan is an Assistant Professor in the Computer Science Department at Utah State University (USU). He holds a Ph.D. in Computer Science from Tongji University, China, and bachelor's and master's degrees from Huaqiao University, China. Prior to USU, he was a postdoctoral researcher at the University of Arkansas under Dr. Xintao Wu. Affiliations: Utah State University, Computer Science Department Research Focus: Data Mining, Machine Learning, Anomaly Detection in Cyberspace, Explainable AI, Fairness, and Privacy-Preserving Techniques His research emphasizes developing trustworthy anomaly detection models that ensure explainability, robustness, and fairness. Key contributions include work on adversarial attacks, counterfactual explanations, and fair data generation. He has advised multiple PhD and Master’s students, focusing on anomaly detection applications in cybersecurity and fraud detection. Selected publications highlight advancements in log anomaly detection, backdoor attack defenses, and causal inference for root cause analysis. His work has been presented at top conferences like ICLR, ICML, and AAAI. He co-organized the Trustworthy Anomaly Detection tutorial at SDM24.

• Compilers
• Parallel Computing
• Automatic Differentiation
• +۳ مورد دیگر

Billy Moses is an Assistant Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC), with affiliate roles in Electrical and Computer Engineering (courtesy) and the Coordinated Science Library. He holds a PhD and dual S.B. degrees in Electrical Engineering and Computer Science from MIT (2023, 2017), as well as an S.B. in Physics from MIT (2017). His research focuses on compilers, parallel computing, and compiler-driven optimization techniques for high-performance systems. Moses has pioneered work on the Tapir framework for fork-join parallelism, the MLIR compiler infrastructure, and Enzyme for automatic differentiation. Moses' research spans compiler design, GPU acceleration, and AI-driven compiler optimization. Notable contributions include the Polygeist compiler for C-to-MLIR transformation, the Autophase reinforcement learning system for HLS phase ordering, and the Enzyme framework for GPU kernel differentiation. His work emphasizes practical compiler solutions for parallelism, performance portability, and end-to-end code generation in domains like deep learning and scientific computing. Awards: 2024 SIGHPC Doctoral Dissertation Award Courses Taught: CS 598 APE (Advanced Performance Engineering) His recent projects include compiler-based approaches to GPU-to-CPU transpilation, performance portability in heterogeneous systems, and AI-driven compiler decision-making. Moses collaborates with industry and academic partners on advancing compiler technologies for exascale computing and machine learning acceleration.

Yan Lengمشاهده پروفایل

استادیار

• Interpretable Machine/Deep Learning on Networks
• Network Spillovers and Dynamics
• Human-Centric Interpretability in Generative AI
• +۳ مورد دیگر

Yan Leng is an Assistant Professor at the McCombs School of Business, University of Texas at Austin, with a courtesy appointment at the School of Information. He is a core member of the UT Machine Learning Lab and affiliated with MIT Media Lab. His PhD from MIT Media Lab (2020) and dual Master's degrees in Computer Science and Transportation Engineering (MIT) underpin his research in interpretable AI, network dynamics, and human-centric machine learning. Education: PhD, MIT Media Lab (2020) Dual Master's in Computer Science & Transportation Engineering, MIT Research focuses on interpretable machine learning for networks, generative AI ethics, and network spillover dynamics. His work bridges computer science, economics, and social sciences, with grants from NIH, NSF, and industry partnerships. Notable achievements include a $1M NIH grant for mental health AI tools and multiple best paper awards at INFORMS conferences. Award-winning educator, recognized with the Trammell/CBA Teaching Award and McCombs BBA Honor Roll. Active in grant-funded projects addressing AI fairness, urban mobility, and behavioral economics.

Paul Liangمشاهده پروفایل

استادیار

• Multimodal Machine Learning
• Artificial Intelligence
• Human-Computer Interaction
• +۳ مورد دیگر

Paul Liang is Assistant Professor at the Massachusetts Institute of Technology with joint appointments in the Media Lab and the Department of Electrical Engineering and Computer Science, where he directs the Multisensory Intelligence research group. His work spans foundational research in multisensory AI technologies and their applications in enhancing human experiences and real-world human-AI interaction. Liang's research focuses on three interconnected thrusts: developing theoretical foundations for multisensory machine learning systems; designing interactive AI technologies to augment human capabilities; and addressing societal concerns in real-world human-AI interaction. His work integrates principles from machine learning, natural language processing, and human-computer interaction to create novel multimodal architectures. Professor Liang's research group actively publishes on multimodal representation learning, evaluation frameworks for foundation models, and methods for quantifying multimodal interactions. Recent work includes developing the Holistic Evaluation of Multimodal Models (HEMM) framework and the MultiBench benchmarking suite.

David Chesneyمشاهده پروفایل

مدرس

• Computer Science Education
• Assistive Technology
• Accessibility in Software Systems
• +۳ مورد دیگر

David Chesney is the Toby Teorey Collegiate Lecturer in Computer Science and Engineering at the University of Michigan, College of Engineering. He specializes in project-based courses focusing on accessible software systems and gaming for social impact. His teaching emphasizes context-driven learning, integrating real-world challenges such as disabilities and healthcare needs into course projects. He teaches ENG100 Gaming for the Greater Good and EECS495 Accessible Software Systems Design, collaborating with institutions like C.S. Mott Children’s Hospital and tech companies including Microsoft and IBM. His work involves creating assistive technologies for individuals with disabilities, highlighted in projects like Engineering for Grace and the IBM Watson social needs initiative. Research interests include assistive technology development, inclusive design principles, and leveraging emerging technologies (e.g., HoloLens, Kinect) for accessibility. He has been recognized with the 2018 James T. Neubacher Award for his contributions to engineering education and community impact. Notable collaborations: Prof. Sean Ahlquist (Architecture), C.S. Mott Children’s Hospital, IBM Watson Media features: U-M Engineering articles, TEDxUofM, WDIV News Advising and grants involve translating student projects into commercializable products, with corporate partnerships supporting advanced tool provision. His lab focuses on tangible outcomes bridging academic research and real-world applications.