جست‌وجوی استاد و پژوهشگر

Byung-Cheol Minمشاهده پروفایل

دانشیار

• Human-Robot Interaction
• Multi-Robot Systems
• Robotics
• +۷ مورد دیگر

Dr. Byung-Cheol Min is an Associate Professor and University Faculty Scholar in the Department of Computer and Information Technology at Purdue University. He serves as the inaugural director of the Applied AI Research Center (AARC) and leads the SMART Lab (www.smart-laboratory.org). His research focuses on human-robot interaction, multi-robot systems, and robotics, with an emphasis on distributed algorithms, learning, and socio-technical collaboration. Dr. Min holds a Ph.D. from Purdue University (2014), an M.S. from Kyung Hee University (2010), and a B.S. from Kyung Hee University (2008). He has received prestigious awards including the NSF CAREER Award (2019), Purdue University Faculty Scholar (2021), and multiple recognition for research excellence and mentorship. His work spans robotics applications in precision agriculture, construction automation, and assistive technologies. Dr. Min has secured over $10 million in grants and has published over 100 peer-reviewed articles, contributing to advancements in AI, control systems, and human-robot collaboration. Key roles include leadership in the Realizing the Digital Enterprise (RDE) initiative (2022–2024) and interdisciplinary affiliations with the Robotics Accelerator, ICON, and ISF institutes. His labs develop innovative systems such as the ROS2-based SMARTmBOT platform and multimodal datasets for cognitive workload assessment.

Tianyi Liمشاهده پروفایل

استادیار

• Human-AI Collaboration
• Crowdsourcing
• Sensemaking
• +۴ مورد دیگر

Tianyi Li is an Assistant Professor in the Department of Computer and Information Technology at Purdue University, part of the Purdue Polytechnic Institute. Her research focuses on human-AI collaboration, crowdsourcing, and sensemaking, with applications in domain-specific decision-making. She holds a Ph.D. in Computer Science from Virginia Tech (2020) and a B.Eng. from the University of Hong Kong (2015), with additional studies at UC San Diego and Shanghai Jiao Tong University. Dr. Li's work integrates design, experimental, and computational methods to enhance collective intelligence through crowdsourcing and improve human-AI interaction. She has received an NSF IUSE Award (2023) and contributed to projects like the Event Detection Explorer and Purposeful AI initiatives. Her teaching includes courses on database programming, web development, and discrete structures. Research Grants: NSF IUSE Award for fostering culturally relevant programming learning experiences (2023) Professional Experience: Former Assistant Professor at Loyola University Chicago, and roles at Microsoft Research AI, Cloudera, and Informatica Her lab explores tools for computer-supported cooperative work, including visual analytics for hyperparameter tuning and crowd-based sensemaking systems. Current research emphasizes inclusive learning content development and human-centric AI interaction patterns.

Julia Rayzمشاهده پروفایل

استاد

• Artificial Intelligence
• Natural Language Processing
• Knowledge Representation and Reasoning
• +۵ مورد دیگر

Julia Rayz is a Professor and Associate Department Head in the Department of Computer and Information Technology at Purdue University's Purdue Polytechnic Institute. She specializes in Natural Language Processing (NLP), Artificial Intelligence, and Knowledge Representation. Her work focuses on enabling informal human-computer communication, with a particular interest in computational humor detection and uncertainty management. Education: PhD, Computer Science and Engineering, University of Cincinnati, 2008 MS, Computer Science, University of Cincinnati, 2004 BA, Mathematics, University of Cincinnati, 1999 BS, Computer Science, University of Cincinnati, 1999 Research Interests: Natural language understanding Knowledge representation and reasoning Cognitive science Phishing detection Online predation analysis Recent grants include the TaskBot Challenge Competition (2023), SaTC-EDU AI-based Social Engineering Training (2021), and collaborative research on Robots and Sensors for Human Well-being. She leads the Applied Knowledge Representation and Natural Language Understanding Lab, advancing projects in smart home energy systems, chatbot reliability, and ethical AI.

Dan Goldwasserمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Dan Goldwasser is an Associate Professor of Computer Science at Purdue University's Department of Computer Science (College of Science). He joined the faculty in 2014 and focuses on Artificial Intelligence, Machine Learning, and Natural Language Processing. His work emphasizes analyzing social media discourse, political communication, and integrating large language models (LLMs) into interactive systems. Education: PhD in Computer Science from the University of Illinois (2012). Research interests include multimodal understanding, ethical reasoning in AI, and social context modeling. Recent work explores LLMs' roles in uncovering latent arguments, cultural context grounding, and morality frame analysis in social media debates (e.g., vaccination campaigns, climate policy). Notable projects include VIBE (visual language model analysis), EmoGist (visual emotion understanding), and frameworks for political discourse analysis. His work frequently addresses fairness, bias detection, and contextual reasoning in AI systems.

Elena Leah Glassmanمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Artificial Intelligence
• Programming Languages
• +۳ مورد دیگر

Elena Leah Glassman is an Assistant Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences. She leads the Variation Lab, focusing on AI-resilient interfaces that enhance human collaboration with AI systems. Her work bridges Human-Computer Interaction (HCI) and Artificial Intelligence (AI), emphasizing cognitive science principles to improve usability and safety in open-ended tasks like text/code writing and sensemaking. Education: B.S., M.Eng., and Ph.D. in Electrical Engineering and Computer Science from MIT, followed by a postdoctoral fellowship at UC Berkeley. Recognized as a 2023 Sloan Research Fellow and National Academy of Sciences Kavli Fellow. Research interests include AI-assisted creativity tools, ethical AI interfaces, program synthesis visualization, and accessible text systems. Notable projects include CorpusStudio, ChainForge, and DynaVis. Recent awards include CHI 2024 Best Paper and multiple Honorable Mentions at top HCI venues. Teaches CS 178/179 on interactive system design, emphasizing needfinding, creativity, and usability. Advises a lab of PhD students and postdocs working on HCI-AI intersections. Active in conference communities (CHI, UIST) and open-source tool development. Labs/Teams: The Variation Lab focuses on augmenting human intelligence through interface design. Collaborates with industry and academic partners on projects like accessible text tools and AI evaluation frameworks.

Mo Liمشاهده پروفایل

استاد

• Condensed Matter
• Photonics
• Quantum Information
• +۵ مورد دیگر

Mo Li is a Professor jointly appointed in the Department of Electrical & Computer Engineering and the Department of Physics at the University of Washington (UW), part of the College of Arts & Sciences. He joined UW in 2018 after serving as faculty at the University of Minnesota, Twin Cities (2010–2018) and postdoctoral research at Yale University (2007–2010). His expertise spans condensed matter physics, photonics, and quantum information, with a focus on integrated photonic systems, optoelectronics, and quantum photonics. Education: Ph.D., Applied Physics, California Institute of Technology, 2007 M.S., Physics, University of California, San Diego, 2003 B.S., Physics, University of Science and Technology of China, 2001 Research Interests: Dr. Li leads the Laboratory of Photonic Devices, investigating hybrid nanoscale devices, optoelectronic materials, and quantum phenomena. His work integrates experimental and theoretical approaches to explore photon-electron-phonon interactions, with applications in photonic circuits, quantum communication, and biomedical sensing. Key areas include 2D materials, spintronics, and MEMS/NEMS systems. Publications & Patents: Over 80 peer-reviewed papers and 6 U.S. patents. Recent research emphasizes graph-based algorithms (e.g., Color framework), enterprise data integration (BEAVER benchmark), and causal relational learning. Lab & Team: The Laboratory of Photonic Systems at UW develops cutting-edge photonic technologies. Openings exist for postdocs (photonic interconnection projects) and graduate students in ECE/Physics programs.

• Human Factors
• Cognitive Ergonomics
• Human-Automation Interaction
• +۲ مورد دیگر

Brandon Pitts is the Elliott Sigal Rising Star Associate Professor at Purdue University's Edwardson School of Industrial Engineering and a Faculty Associate at the Center on Aging and the Life Course. He holds a PhD in Industrial and Operations Engineering from the University of Michigan (2016), an MSE from the same institution (2013), and a BS in Industrial Engineering from Louisiana State University (2010). His research focuses on cognitive ergonomics, human-automation interaction, multimodal information processing, adaptive interface design, and aging-technology integration. Recent investigations include driver-automation trust dynamics, aging populations' technology adaptation, and aviation human factors. Dr. Pitts' publications demonstrate a consistent focus on human performance in automated systems, with emerging themes in AI-assisted decision-making and cross-generational technology usability. Research spans automotive interfaces, aviation safety systems, and healthcare technology accessibility. Scientific Awards: Elliott Sigal Rising Star Professorship He maintains collaborations through the Center on Aging and leads research on adaptive interfaces for older adults in transportation contexts. Laboratory activities focus on human-automation testing and physiological monitoring during system interactions.

David Demeterمشاهده پروفایل

استادیار

• Artificial Intelligence
• Natural Language Processing
• Medical Diagnostics
• +۳ مورد دیگر

David Demeter is an Assistant Professor of Instruction in the Department of Computer Science at Northwestern University. His research focuses on advancing artificial intelligence, natural language processing, and medical diagnostics through innovative applications of machine learning. He has contributed to projects spanning legal technology, healthcare solutions, and foundational studies of language models. His work includes developing LawLLM for legal system applications, creating diagnostic tools for Congenital Central Hypoventilation Syndrome (CCHS), and exploring prompt engineering strategies for large language models. His interdisciplinary approach bridges domains like sentiment analysis, spectrum sensing algorithms, and compiler-based neural training systems. No scientific awards or notable grants are explicitly mentioned in the provided texts. While his publications reflect active research collaboration, no advising relationships or lab affiliations are detailed in the current data.

Naoko Kurokawaمشاهده پروفایل

مدرس ارشد

• Japanese Linguistics
• Foreign Language Pedagogy
• Technology in Language Learning
• +۳ مورد دیگر

Naoko Kurokawa is a Senior Lecturer in the Department of Asian and Middle Eastern Studies at Duke University. She holds an M.A. from the University of Wisconsin, Madison (2001), focusing on Japanese Linguistics and Foreign Language Pedagogy. Her research emphasizes innovative teaching methodologies, particularly leveraging technology and cross-cultural communication to enhance language learning. Her work explores topics such as social media in language instruction, digital tools for cultural literacy, and corpus-driven approaches to grammar acquisition. She has pioneered the use of multimedia resources like digital comics, iPod-based learning, and video analysis to engage students. Her publications span from 2001 to 2018, reflecting her sustained contribution to Japanese language education. Teaching emphasizes foundational Japanese language courses like JPN 101 and 102. She has explored pedagogical innovations such as peer feedback systems, fixed-form verses in instruction, and internet-mediated communication between international students. Her research also intersects with cultural studies, examining how media like commercials and medical terminology reflect societal values.

Kun Shan Carolyn Leeمشاهده پروفایل

استاد

• Foreign Language Curriculum Development
• Study Abroad Education
• Curriculum Articulation
• +۴ مورد دیگر

Professor Kun Shan Carolyn Lee holds the rank of Professor of the Practice in Asian & Middle Eastern Studies at Duke University, within the Trinity College of Arts & Sciences. She has been in this role since 2014, after progressing through positions such as Assistant and Associate Professor of the Practice since 1995. Her research focuses on foreign language curriculum development, study abroad education, and Chinese language pedagogy, with notable contributions to CLAC (Cultures and Languages Across the Curriculum) integration and multimedia-based language resources. Education includes multiple Master of Arts degrees (1992–1993) and a Bachelor of Arts (1989), all from the University of Iowa. She has led grants like the Department of Defense-funded Project GO (2015–2018) to enhance ROTC Chinese language education. Teaching spans courses such as First Year Chinese for Bilingual Learners and Debate in Chinese: Survey of Great Chinese Thinkers . Notable publications include textbooks like Acting Chinese (2020) and research on curriculum articulation, task-based instruction, and digital tools for character learning. Her work emphasizes cross-cultural communication and technology in education, with a focus on advancing Chinese language programs through innovative teaching methods and materials development.

• Computer Vision
• Machine Learning
• Robotics
• +۷ مورد دیگر

Yuxiong Wang is an Assistant Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC). His research focuses on computer vision, machine learning, and robotics, particularly in open-world perception, meta-learning, generative modeling, and agent learning. He previously held a postdoctoral position at Carnegie Mellon University and obtained his Ph.D. from the Robotics Institute under Prof. Martial Hebert. He has received notable awards including the Best Paper Honorable Mention at ECCV 2020 and Best Paper Finalist awards at CVPR 2019 and 2022. His work bridges generative and discriminative AI, with contributions to 3D vision, human-object interaction, and vision-language models. Recent research highlights include RandAR (decoder-based visual generation), InterMimic (physics-based human-object control), and Argus (vision-centric reasoning). He teaches courses such as CS 445 (Computational Photography) and CS 598 LTL (Learning to Learn). His group actively collaborates with NVIDIA and Electronic Arts, and his work has been funded by grants from NASA and the NCSA. Current projects explore meta-learning, open-world learning, and applications in robotics, healthcare, and materials science.

Bradly C Stadieمشاهده پروفایل

استادیار

• Reinforcement Learning
• Robotics
• Large Language Models
• +۵ مورد دیگر

Bradly C. Stadie is an Assistant Professor in the Department of Statistics and Data Science at Northwestern University, affiliated with the Weinberg College of Arts & Sciences. His research focuses on reinforcement learning (RL), with applications in robotics, biological systems, and large language models (LLMs). He holds a PhD from UC Berkeley and a BA in Mathematics from the University of Chicago, with prior roles as a Research Assistant Professor at TTIC and a Research Scientist at OpenAI. Research interests include RL-driven behavior teaching, diffusion models for shared autonomy, and the intersection of LLMs with physical task planning. Notable projects include using ChatGPT to control robots and developing systems where humans and diffusion models jointly control robots. His work bridges theoretical RL advancements with practical applications in robotics and healthcare safety. Educations: PhD, UC Berkeley (2018); BA, Mathematics, University of Chicago (2014) Previous affiliations: TTIC (2018-202X), OpenAI (2016-2017) Publications span RL theory, robotics, causal inference, and AI safety Current research explores LLM integration with physical systems, safety frameworks in medical AI, and novel RL exploration strategies. Active in open-source development, including repositories like third-person imitation learning and krazyworld for exploration benchmarks.

• Multilingual Language Education
• Genre Theory
• Systemic Functional Linguistics
• +۵ مورد دیگر

Francis Troyan is an Associate Professor in the Department of Teaching and Learning at The Ohio State University, affiliated with the College of Arts and Sciences and the Center for Languages, Literatures and Cultures. He specializes in multilingual language education, genre theory, and culturally sustaining pedagogy. Troyan holds a PhD in Language Education from the University of Pittsburgh (2013), an MA in French Language Education (2001), and a BS in Elementary Education (1998). His research focuses on genre-based instruction, systemic functional linguistics, and immersion education, particularly exploring how teachers leverage linguistic repertoires to create inclusive classrooms. He has conducted Fulbright-funded research in France and Colombia, examining multilingualism in education systems. Troyan leads the Buckeye Language Education Resource Center and directs the online Graduate Certificate Program in Core Practices for World Language Education, emphasizing practice-based teacher training. Key contributions include co-editing Genre in World Language Education (2020) and co-authoring the upcoming Handbook of Research in World Language Instruction (2025). His work addresses teacher agency, assessment design, and antiracist pedagogy in language education, supported by grants and partnerships with institutions like the Universidad de Córdoba. Rewards: 2021-2022 Fulbright FLEX Award Professional Roles: Co-Director of the Buckeye Language Education Resource Center, Faculty Advisor for World Language Education programs

Daisy Zhe Wangمشاهده پروفایل

استاد

• Database Systems
• Data Science
• Machine Learning
• +۴ مورد دیگر

Dr. Daisy Zhe Wang is a Professor in the Department of Computer & Information Science & Engineering at the University of Florida, affiliated with the Herbert Wertheim College of Engineering. She directs the Data Science Research (DSR) Lab, focusing on advanced data analysis systems using machine learning and probabilistic methods. Her work spans databases, data science, and informatics, with specializations in probabilistic knowledge graphs, multimodal fusion, and healthcare analytics. Education: PhD in Computer Science from the University of California, Berkeley (2011). Awards include the Arnold and Lisa Goldberg Rising Star Professorship in Computer Science (2019-2021) and the UF Term Professorship (2018). Her lab's current projects include developing systems for tree species classification using remote sensing and improving surgical risk prediction algorithms like MySurgeryRisk. Research emphasizes bridging data science with domain-specific applications, including electronic health records (EHR) analysis, environmental remote sensing, and knowledge graph-driven decision systems. She has pioneered frameworks like RAMQA for multimodal QA and M3 for multi-hop retrieval, advancing both theoretical and applied aspects of data science. Grants and collaborations include large-scale data competitions and partnerships with healthcare institutions. The DSR Lab maintains active research in AI ethics, explainable AI, and scalable data management systems, with ongoing work on neuro-symbolic architectures and multimodal learning.

Parisa Rashidiمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Biomedical Informatics
• +۷ مورد دیگر

Parisa Rashidi is a Professor and UF Research Foundation Professor in the Department of Biomedical Engineering at the University of Florida, co-directing the IC3 (Intelligent Clinical Care) initiative. She holds affiliations with the Herbert Wertheim College of Engineering and leads the intelligent Health Lab (i-Heal). Her research focuses on developing AI-driven patient monitoring systems, particularly in ICU and outpatient settings, leveraging wearable sensors and machine learning for real-time care optimization. Education: BSc in Computer Engineering, University of Tehran (2005) MSc and PhD in Computer Science, Washington State University (2008, 2011) Research Interests: Machine learning for healthcare analytics Scalable algorithms for ICU monitoring and delirium prediction Wearable sensor integration in clinical workflows Federated learning for privacy-preserving healthcare collaboration Explainable AI in surgical and critical care decision-making Honors & Awards: 2024 Presidential Early Career Award for Scientists and Engineers (PECASE) 2023-2026 UF Research Foundation Professorship 2018 NSF CAREER Award Advising & Grants: Leads interdisciplinary research funded by NIH, NSF, and industry partnerships Focuses on translational projects bridging computer science and clinical practice Labs & Teams: The i-Heal Lab develops cutting-edge tools for clinical decision support, including real-time acuity prediction systems and AI frameworks for surgical risk assessment.