جست‌وجوی استاد و پژوهشگر

Hannaneh Hajishirziمشاهده پروفایل

دانشیار

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +۱ مورد دیگر

Hannaneh Hajishirzi is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, with adjunct appointments in the Departments of Electrical & Computer Engineering and Linguistics. She leads the H2Lab and serves as Senior Research Director at the Allen Institute for AI (AI2). Her research focuses on advancing large language models through projects like OLMo, retrieval-based language modeling, and post-training optimization. Ph.D. in Computer Science from University of Illinois at Urbana-Champaign (2011) Postdoctoral associate at Disney Research and Carnegie Mellon University (2011-2012) Her work spans NLP, AI, and machine learning, with over 140 publications in top-tier venues (ACL, EMNLP, NeurIPS, ICLR). Key contributions include BiDAF, SciREX, and MedICaT datasets. Recent publications emphasize language modeling , in-context learning , and efficient architectures . Papers explore knowledge representation , multimodal reasoning , and scientific claim verification . 2020 Alfred Sloan Fellowship 2021 NSF CAREER award 2019 Intel Rising Star Faculty Award 2018 Allen Distinguished Investigator Award 2023 Academic Achievement UIUC Alumni Award 2024 Innovator of the Year finalist (GeekWire) She has mentored numerous students and received industry research awards from Amazon, Google, and other tech companies. Her lab's work appears in major media outlets (New York Times, Forbes, MIT Technology Review).

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

Ralph Ewerthمشاهده پروفایل

استاد

• Multimedia Retrieval
• Machine Learning
• Visual Analytics
• +۵ مورد دیگر

Prof. Ralph Ewerth is a Professor leading the Visual Analytics research group at the Technische Informationsbibliothek (TIB) since 2015. His work bridges computer science, information science, and education, focusing on multimedia retrieval, machine learning, and visual analytics. He has pioneered research in areas like content-based video retrieval, image-text relation analysis, and educational technology platforms. Key research interests include: Development of AI-driven tools for educational analytics (e.g., learning platforms for chemistry education) Advancements in multimedia retrieval systems (e.g., TIB AV-Analytics platform) Analysis of sports data through spatiotemporal event detection Design of open research knowledge graphs and journal recommendation systems (B!SON project) Recent publications (2022-2024) emphasize multimodal misinformation detection, search behavior analysis, and fusion of vision-language models. He has edited conference proceedings and contributed to over 150 peer-reviewed works across domains like digital libraries, computer vision, and educational technology. His research infrastructure includes the TIB AV-Analytics platform for scholarly video analysis and the B!SON open access journal recommendation tool. Collaborative projects focus on integrating semantic web technologies with educational and sports data analysis.

Onyeka Emeboمشاهده پروفایل

استادیار

• Data Analytics
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Onyeka Emebo is a Collegiate Assistant Professor at the Department of Computer Science, Virginia Tech. His primary affiliations are with the College of Engineering. He holds a Ph.D., M.S., and B.S. in Computer Science from Covenant University, Nigeria. His research focuses on data analytics, machine learning, natural language processing, and digital education. Notable projects include facial emotion recognition systems, predictive modeling for urban development, and healthcare-related machine learning applications. Emebo also explores requirement engineering methodologies and has contributed to frameworks bridging education policy and disruptive technologies. Recent work emphasizes interdisciplinary applications like climate change modeling in Africa and disease detection systems using Raspberry Pi devices. His publications span 2010–2025, reflecting sustained contributions to software engineering, AI, and computational solutions for societal challenges.

Vinod Lohaniمشاهده پروفایل

استاد

• Computer Supported Research and Learning Systems
• Personalized Learning Systems
• AI Applications in Engineering Education
• +۴ مورد دیگر

Dr. Vinod Lohani is a Professor of Engineering Education and an adjunct faculty member in Civil & Environmental Engineering at Virginia Tech. He earned his Ph.D. in Civil Engineering from Virginia Tech and has been a founding faculty member of the Engineering Education (EngE) department. His research focuses on computer-supported learning systems, AI applications in education, hydrology, and international collaboration. He established the LEWAS Lab and directed NSF/REU programs for 12 years, training 115 scholars. Dr. Lohani has published over 100 papers and received multiple awards, including the NSF Director’s Commendable Service Award (2023) and the W.S. “Pete” White Chair for Innovation in Engineering Education (2018-2020). Ph.D., Civil Engineering, Virginia Tech M.S., Asian Institute of Technology, Thailand B.S., Agricultural Engineering, India His work bridges engineering education and hydrology through real-time environmental monitoring systems like LEWAS and OWLS. He has led interdisciplinary NSF grants in advanced manufacturing, AI, and water sustainability, and served as Program Director at NSF (2020-2024) overseeing $100M+ in awards. He also directed Virginia Tech’s ICTAS education initiatives, focusing on diversity and inclusion. Notable scientific awards include the NSF Director’s Commendable Service Award (2023), ASEE’s William Elgin Wickenden Award (2010), and multiple teaching accolades from Virginia Tech. He has advised 10 Ph.D., 12 M.S., and 40+ undergraduate researchers, fostering global collaborations through US-Brazil and IRES programs.

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.

Xuan Wangمشاهده پروفایل

استادیار

• Natural Language Processing
• Data Mining
• AI for Sciences
• +۷ مورد دیگر

Xuan Wang is an Assistant Professor in the Department of Computer Science at Virginia Tech (VT), also affiliated with the Sanghani Center for Artificial Intelligence and Data Analytics. Specializing in Natural Language Processing (NLP), Data Mining, and AI applications in healthcare and sciences, their research focuses on weakly-supervised learning, multi-agent systems, and foundation models. They hold a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (UIUC), with additional M.S. degrees in Statistics and Biochemistry from UIUC, and a B.S. in Biological Science from Tsinghua University, China. Education: Ph.D., Computer Science, UIUC (2022) M.S., Statistics, UIUC (2017) M.S., Biochemistry, UIUC (2015) B.S., Biological Science, Tsinghua University (2013) Research Interests: Focus on NLP with limited supervision, LLM agents for reasoning, multi-modal science foundation models, and healthcare AI. Current projects include small language model agents, EEG-to-text translation, and gene regulatory network inference. Grants & Awards: Recipient of NSF NAIRR Pilot Award (2024-2025), Cisco Research Award (2025), and NAACL Best Demo Award (2021). Active in securing funding for projects in healthcare AI, multi-agent systems, and scientific computing. Lab & Students: Mentors PhD/Master students in NLP, data mining, bioinformatics, and AI. Current advisees include Daniel Hajialigol, Zhenyu Bi, Meng Lu, and others. Alumni Yueyan Gu has contributed to large energy models research. Service & Leadership: Organized workshops at ACL 2025, VL/HCC 2025, and SDM 2025. Served as co-chair for ICDM 2025 Undergraduate Symposium. Active in tutorial development on AI for science and trustworthy LLMs.

Pinar Yanardagمشاهده پروفایل

استادیار

• Deep Learning
• Human-AI Collaboration
• Augmented Creativity
• +۳ مورد دیگر

Pinar Yanardag is an Assistant Professor in the Department of Computer Science at Virginia Polytechnic Institute and State University (Virginia Tech), part of the College of Engineering. Her research focuses on advancing human-AI collaboration, augmented creativity, and deep learning techniques with an emphasis on diffusion models and submodular optimization. She holds a Ph.D. in Computer Science from Purdue University (2016), an M.S. in Computer Engineering from Bogazici University, Turkey (2010), and a B.S. in Computer Engineering from Canakkale Onsekiz Mart University, Turkey (2007). Her work explores interpretable directions in generative models, disentangled representations, and creative content generation. Notable contributions include frameworks like Crea for collaborative AI creativity, LoRAShop for training-free image editing, and FluxSpace for semantic manipulation in diffusion models. She also investigates bias mitigation in AI systems and cross-modal reasoning with vision-language models. Recent trends in her publications emphasize scalable adaptation of diffusion models, contrastive learning for customization, and explainability in AI. Her research bridges theoretical advancements with practical applications in creative industries and interactive systems. Dr. Yanardag's research is supported by grants focused on AI creativity, explainable AI, and generative modeling. She collaborates with industry partners on developing user-friendly tools for style exploration and content co-creation. Her lab actively engages in open-source projects to democratize access to advanced AI techniques.

• Natural Language Processing
• Computer Science Education
• Large Language Models
• +۲ مورد دیگر

Dr. Suma Pallathadka Bhat is an Assistant Professor at the University of Illinois with joint appointments in the Siebel School of Computing and Data Science, Electrical and Computer Engineering, Coordinated Science Lab, Educational Psychology, and the National Center for Supercomputing Applications. Her research bridges computer science and education, focusing on natural language processing, large language models, and educational technology innovations. Her work explores how AI transforms learning systems, particularly in programming education and instructional video design. Recent publications demonstrate strong emphasis on cognitive modeling, multimodal learning, and language understanding systems.

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.

• Reinforcement Learning
• Multi-Agent Systems
• Foundation Models
• +۵ مورد دیگر

Xinrun Wang is an Assistant Professor of Computer Science and Lee Kong Chian Fellow at Singapore Management University's School of Computing and Information Systems. He joined SMU in July 2024 after completing his PhD at Nanyang Technological University in 2020. His research focuses on fundamental decision making through single- and multi-agent reinforcement learning, decision making applications including RL for FinTech and MARL for urban security, and foundation agents for computer control and auto research. His work spans AI for Science, financial technology, and theoretical computer science with a particular emphasis on NP-hard problems. Wang's recent publications demonstrate strong trends in foundation agents, reinforcement learning for complex decision making, and applications of AI to financial technology and scientific discovery. His research group has produced significant work on autonomous alloy discovery, financial workflow automation with LLMs, and solving NP problems through reasoning benchmarks. Outstanding Student Paper Award (GameSec 2019) Lee Kong Chian Fellow Wang currently serves as Research Advisor to ZHOU Shunchao and is actively recruiting PhD and Master's students for research in decision making, foundation agents, and AI for Science. His research is supported through his Lee Kong Chian Fellowship and likely other grants given his extensive publication record. He leads a research group focused on developing frameworks for autonomous decision making and foundation agents. His laboratory work centers on developing general-purpose AI agents capable of computer control, financial trading, and scientific discovery, with projects like Cradle, AgentStudio, and TradeMaster forming the core of his research infrastructure.

Anurag Agarwalمشاهده پروفایل

دانشیار

• Number Theory
• Cryptography
• Algebra
• +۷ مورد دیگر

Anurag Agarwal is an Associate Professor in the School of Mathematical Sciences at Rochester Institute of Technology (RIT), College of Science. He holds a BS and MS from the Indian Institute of Technology (India) and a Ph.D. from the State University of New York at Buffalo. Dr. Agarwal's research spans multiple areas of mathematics with a focus on Number Theory , Cryptography , and Algebra . His specific interests include Diophantine equations and approximations, Continued Fractions, Finite Field Theory, Algebraic Number Theory, Abstract Algebra, and Combinatorial Mathematics. His work bridges theoretical mathematics with practical applications, particularly in the field of cryptography and mathematical information retrieval. Dr. Agarwal's publications demonstrate a strong focus on graph theory, geometric properties of mathematical objects, and mathematics education. His research combines theoretical rigor with practical applications, particularly in the areas of combinatorial mathematics and number theory. The interdisciplinary nature of his work connects pure mathematics with computer science applications, especially in cryptography. Dr. Agarwal has been actively involved in developing innovative educational tools, including MathDeck, an online search interface for mathematical formulas that allows users to create, edit and lookup sophisticated math formulas. Created by an interdisciplinary team of faculty and students at RIT, MathDeck aims to make math notation interactive and easily shareable. He teaches a variety of courses including Discrete Mathematics, Linear Algebra, Number Theory, Abstract Algebra, and Cryptography at both undergraduate and graduate levels. His teaching portfolio reflects his expertise across theoretical and applied mathematics, with courses ranging from foundational topics like Project Based Calculus to advanced subjects like Topics in Number Theory & Cryptography.

Dongfang Liuمشاهده پروفایل

استادیار

• Artificial General Intelligence (AGI)
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Dr. Dongfang Liu is an Assistant Professor in the Department of Computer Engineering at the Rochester Institute of Technology (RIT), affiliated with the Kate Gleason College of Engineering. He holds a Ph.D. from Purdue University (2021) and specializes in artificial general intelligence (AGI), vision-language systems, and interdisciplinary AI applications such as autonomous robotics, healthcare, and energy systems. His research is supported by the NSF and industry partnerships. Education: Ph.D. in Computer Science/Engineering, Purdue University (2021) Research Interests: Dr. Liu focuses on developing AGI systems to address societal challenges. His work integrates computer vision, robotics, and machine learning, with recent projects including vision-anchored UAV automation using RGB-D cameras. He emphasizes ethical AI, robustness against adversarial attacks, and real-world deployment in domains like autonomous driving and medical diagnostics. Service & Leadership: Area Chair, CVPR (2023–present) Senior Program Committee Member, AAAI/IJCAI (2023–present) Associate Editor, IEEE TCSVT, Neurocomputing, MTAP, ACM JATS (2023–present) Key Projects: "Vision-Anchored UAV Automation": Full automation of bird-sized UAVs in cluttered indoor environments using vision-only sensors, funded by NSF Grant #2242243. Developing AGI tools for fusion energy simulation, medical diagnostics, and sustainable energy. Teaching: CMPE-679 Deep Learning CMPE-789 Special Topics IMGS-890/891 Research & Thesis Supervision Grants/Awards: NSF Grant #2242243 (2023–2025): Supports UAV automation research.

• Machine Learning
• Data Science
• Artificial Intelligence
• +۷ مورد دیگر

Jing Rui He is a Professor in the School of Information Sciences at the University of Illinois at Urbana-Champaign, and Director of the MSIM Program. He holds affiliate positions in the Siebel School of Computing and Data Science and the Center for Digital Agriculture at the National Center for Supercomputing Applications (NCSA). His research focuses on machine learning, graph theory, and AI ethics, with emphasis on anomaly detection, federated learning, and trustworthy AI systems. He has received multiple accolades including AAAI Senior Member (2023), ACM Distinguished Member (2023), and the NSF CAREER Award (2019). Research interests include graph-based learning, temporal networks, and ethical AI applications. He leads initiatives in climate data modeling and digital agriculture through NCSA collaborations. Notable contributions include developing robust federated learning frameworks and graph anomaly detection algorithms. His work bridges theoretical advancements with practical applications in cybersecurity, recommendation systems, and climate science. Recent publications highlight innovations in large language model safety, multi-modal climate benchmarks, and fair anomaly detection. He actively participates in interdisciplinary projects, such as the TrustLOG workshop on trustworthy graph learning. His research is supported by grants from NSF and industry partnerships, emphasizing translational research with societal impact. Awards: AAAI Senior Member (2023), ACM Distinguished Member (2023), IEEE Senior Member (2020) Grants & Advising: NSF CAREER Award (2019), advises on interdisciplinary projects at NCSA and Siebel School Labs/Teams: Center for Digital Agriculture, TrustLOG workshop leadership, GLB (Graph Learning Benchmarks) initiatives

Tianlong Zuمشاهده پروفایل

استادیار

• Physics Education Research
• STEM Education
• Cognitive Science
• +۳ مورد دیگر

Tianlong Zu is an Assistant Professor of Instruction and Director of Introductory Physics Labs at Northwestern University's Department of Physics and Astronomy. He holds a PhD from Purdue University (2017). His research focuses on enhancing college-level physics education through innovative pedagogical strategies, particularly combining retrieval practice and scientific argumentation to improve problem-solving skills. He also develops mixed reality learning modules to deepen conceptual understanding. Education: PhD in Physics, Purdue University, 2017 Research Interests: Zu investigates cognitive load theory in educational contexts, leveraging eye-tracking technology and machine learning to analyze student learning processes. His work bridges cognitive science and physics education, emphasizing multimedia learning and instructional design. Key themes include optimizing problem-solving strategies, validating cognitive load measurement tools, and evaluating the impact of visual and video-based interventions. Publications: His recent work spans 2014–2025, focusing on educational technology applications, cognitive load measurement, and physics pedagogy. Notable trends include the use of eye-tracking data for predictive analytics and the integration of video solutions in lab instruction. Themes of multimedia design and active learning strategies recur across studies. Teaching & Leadership: As Director of Introductory Physics Labs, Zu oversees curricular design and implementation. His role emphasizes translating research insights into classroom practices. While no specific grants or awards are listed, his contributions to physics education methodology are evident through publication output. Labs/Teams: Leads the Introductory Physics Labs team at Northwestern, fostering student engagement through evidence-based practices.