جست‌وجوی استاد و پژوهشگر

Guoquan Huangمشاهده پروفایل

استادیار

• Robotics
• Computer Vision
• Robot Learning
• +۵ مورد دیگر

Guoquan Huang is an Assistant Professor in the Department of Mechanical Engineering at the University of Delaware. He holds a B.Eng. in Automation from the University of Science and Technology, Beijing (2002), and M.Sc. and Ph.D. degrees in Robotics from the University of Minnesota (2009 and 2012). Prior to his current role, he was a Postdoctoral Associate at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on robotics, computer vision, and autonomous systems, emphasizing probabilistic perception, estimation, and control for ground, aerial, and underwater vehicles. He leads the development of the OpenVINS platform for visual-inertial estimation and has contributed to advancements in SLAM (Simultaneous Localization and Mapping), sensor fusion, and multi-robot coordination. Education: B.Eng in Automation (Electrical Engineering), University of Science and Technology, Beijing, 2002 M.Sc. in Robotics, University of Minnesota, Twin Cities, 2009 Ph.D. in Robotics, University of Minnesota, Twin Cities, 2012 His research interests span robotics, computer vision, and autonomous systems , with a focus on: Visual-inertial navigation and SLAM Sensor fusion (LiDAR, IMU, camera) Autonomous vehicle control and safety Multi-robot cooperative localization His recent publications (2023–2025) emphasize robust algorithms for navigation in GPS-denied environments, real-time sensor calibration, and dataset development for aerial visual localization. He has pioneered techniques like decoupled error-state estimation and consistent parallel frameworks for SLAM. Labs/Teams: Leads the development of the OpenVINS research platform, focusing on visual-inertial state estimation. Collaborates on projects involving human-swarm interactions and resilient ground vehicle navigation.

Robert J Brunnerمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Statistical Analysis
• +۳ مورد دیگر

Robert J. Brunner is a Professor at the University of Illinois with primary appointments in the Gies College of Business (Department of Accountancy) and the School of Information Sciences. He holds affiliate roles across multiple departments including Astronomy, Computer Science, and Statistics, as well as research centers like the Beckman Institute and NCSA. His research focuses on applying statistical/machine learning to solve complex problems in astronomy, finance, and large-scale data science. Education: Ph.D. in Astrophysics from Johns Hopkins University (advisor: Alex Szalay). Postdoctoral work at Caltech on the Digital Sky project. Research Interests: Machine learning applications, computational techniques, data management/visualization, and observational cosmology. His work bridges astrophysical data analysis with modern data science methodologies. Recent work includes developing spatio-temporal neural networks for forecasting, evaluating AI-driven financial analysis tools, and planning for the Vera C. Rubin Observatory. He collaborates internationally on large-scale surveys like the Dark Energy Survey and SDSS. Labs/Teams: Leads data science initiatives at the University of Illinois Research Park. Active in interdisciplinary teams at NCSA and Beckman Institute focusing on algorithm optimization and data-intensive research.

• Computer Architecture
• Accelerated Machine Learning
• Computer Science Education
• +۳ مورد دیگر

Thomas Yeh is an Assistant Professor of Teaching in the Department of Computer Science at the University of California, Irvine. His academic background includes a Ph.D. in Computer Science from UCLA and a BS in Electrical Engineering and Computer Science from UC Berkeley. Prior to academia, he gained industry experience across research, architecture, design, verification, marketing, and management roles. His educational credentials: Ph.D. in Computer Science, UCLA BS in Electrical Engineering and Computer Science, UC Berkeley Dr. Yeh's research spans computer architecture, accelerated machine learning, and computer science education. In architecture, he pioneers error-tolerant physics simulation and heterogeneous computing. His ML work focuses on adaptive precision techniques for energy-efficient acceleration. In education, he develops interactive tools for novice programmers and experiential learning frameworks for computer architecture. His cross-disciplinary approach bridges hardware-software co-design with pedagogical innovation. Publication trends reveal consistent focus on computational efficiency across physics simulation, ML acceleration, and educational technology. His work connects real-time systems optimization with emerging AI applications, particularly in interactive environments and physics-based animation. No scientific awards are documented in the provided materials. While advising details and grant funding specifics are absent from available information, his industry-academia transition informs practical research directions. Teaching responsibilities include core courses like Introduction to CS, Data Structures, and Efficient ML Computing. Research infrastructure details remain unspecified, though his publications suggest collaborations in physics simulation and heterogeneous computing environments.

Prithviraj Ammanabroluمشاهده پروفایل

استادیار

• Natural Language Processing
• Reinforcement Learning
• Artificial Intelligence
• +۵ مورد دیگر

Prithviraj Ammanabrolu is an Assistant Professor at the University of California, San Diego (UCSD) , affiliated with the Department of Computer Science and Engineering . He leads the PEARLS Lab (Pragmatically Exploring Agents with Reinforced LanguageS) and collaborates with Nvidia as a Research Scientist. Research Focus: Bridging Machine Learning (particularly Reinforcement Learning ) with Natural Language Processing to create trustworthy and responsible language-based AI agents . Key Themes: Contextual language understanding, neurosymbolic world modeling, human preference alignment via feedback, and AI in grounded environments . Before joining UCSD in 2024, he worked on the Mosaic team at the Allen Institute for AI and as a Research Scientist at MosaicML (acquired by Databricks) . His work intersects Cognitive Science and AI scalability . Education: PhD in Computer Science from the Georgia Institute of Technology , advised by Professor Mark Riedl at the Entertainment Intelligence Lab . Projects: RL4LMs, ScienceWorld, Q-BERT, WorldGeneration, and LLMFoundry for Databricks foundation models.

Shuangquan Wangمشاهده پروفایل

استادیار

• Mobile & Wearable Computing
• Activity Recognition
• Smart Health
• +۳ مورد دیگر

Shuangquan (Peter) Wang is an Assistant Professor of Computer Science at Salisbury University. He holds a PhD in Computer Science from the College of William & Mary (2020) and a PhD in Pattern Recognition and Intelligent Systems from Shanghai Jiao Tong University (2008), along with earlier degrees from Wuhan University of Technology and Wuhan Institute of Technology. His research focuses on mobile/wearable computing, activity recognition, smart health, and machine learning. He has over 10 years of experience in academia and industry, including roles at Philips Research East Asia and Nokia Research Center (Beijing). His work emphasizes wearable sensor-based health monitoring, such as fall detection, mastication analysis, and Parkinson’s disease monitoring. He leads the WISH Research Lab and serves as an Associate Editor for Elsevier's Smart Health Journal. Recent contributions include papers on salinity anomaly detection (2024), LLM-based user requirement analysis (2024), and socially acceptable food recognition (2022). His research trends emphasize interdisciplinary applications of machine learning in healthcare and sensor-driven human activity analysis. Professional service roles include coordinating Salisbury University’s Center for Applied Mathematics and Science (2021–2024) and chairing ACM/IEEE CHASE conferences. He has delivered invited talks on artificial intelligence and its societal impacts to diverse audiences, including the Institute of Retired Persons at Salisbury University. His lab, WISH Research Lab, explores innovative solutions in smart health and mobile computing, integrating wearable technologies with machine learning for real-world health applications.

Zhumin Chenمشاهده پروفایل

استاد

• Natural Language Processing
• Artificial Intelligence
• Big Data Processing
• +۷ مورد دیگر

Zhumin Chen is a Professor and Associate Dean at the School of Artificial Intelligence, Shandong University. He serves as a member of multiple committees including the Natural Language Processing Committee of China Computer Federation (CCF) and holds leadership roles in research institutions like the Qingdao Key Laboratory of Big Data Technology Innovation. His research focuses on natural language processing, artificial intelligence, and big data, with a strong emphasis on large language models, healthcare applications, and recommendation systems. He has secured over 30 major grants including National Key Research and Development Programs and has published extensively in top venues like ACL, SIGIR, and NeurIPS. His work has been recognized with awards such as the SIGIR 2024 Best Paper Honorable Mention and the Shandong Science and Technology Progress First Prize. He leads the development of large-scale AI models and platforms, including the Shanda Intelligent Assistant, which supports teaching and research across the university. Dr. Chen collaborates with institutions like Microsoft Research Asia and Purdue University, and has co-founded multiple industry partnerships with companies such as Inspur and Hisense. His academic services include reviewing for top conferences (SIGIR, WWW, ACL) and journals, and he actively mentors students through doctoral and master’s programs.

Jean-Baptiste Alayracمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• Multimodal Learning
• +۳ مورد دیگر

Jean-Baptiste Alayrac is a Researcher at DeepMind, focusing on structured learning from video and natural language. His academic background includes a PhD from the Sierra and Willow groups at Ecole Normale Supérieure and Telecom ParisTech, where he explored machine learning and computer vision. He has held teaching roles as a Teaching Assistant at Ecole Normale Supérieure and other universities, contributing to courses in statistical machine learning and mathematics. His research interests span multimodal learning, vision-language models, self-supervised learning, and efficient retrieval systems. Notable projects include the Flamingo model for few-shot learning and the Perceiver IO architecture for structured data processing. He has also contributed to foundational works like HowTo100M, leveraging large-scale video-text embeddings. Alayrac's publications emphasize cross-modal interactions, with key contributions in adversarial robustness, layered video representations, and weakly supervised learning. His work often bridges computer vision and natural language processing, with applications in instructional video analysis and cross-lingual translation.

George Hripcsakمشاهده پروفایل

استاد

• Biomedical Informatics
• Electronic Health Records (EHRs)
• Data Mining
• +۴ مورد دیگر

George Hripcsak is the Vivian Beaumont Allen Professor of Biomedical Informatics and Director of Medical Informatics Services at New York-Presbyterian Hospital, Columbia University. He holds affiliations with the Vagelos College of Physicians and Surgeons and the Data Science Institute (DSI). His expertise spans clinical informatics, electronic health records (EHRs), and medical knowledge representation standards. Hripcsak earned degrees in chemistry, medicine, and biostatistics, and is a board-certified internist. Research focuses on leveraging EHR data for clinical research and patient safety through data mining and causal inference techniques. Notable contributions include the Arden Syntax (a national standard for medical knowledge representation) and leadership in the Observational Health Data Sciences and Informatics (OHDSI) network. He chairs the AMIA Standards Committee and has advised federal health informatics policies under HIPAA. His academic awards include Fellowships in the American College of Medical Informatics (1995) and New York Academy of Medicine. Current projects emphasize federated learning, genomic risk prediction, and large-scale real-world evidence analysis through initiatives like LEGEND-T2DM and All of Us Research Program. Educations: MD, Biostatistics, Chemistry Labs/Teams: OHDSI, DSI, Medical Informatics Services Grants & Funding: Not explicitly listed in provided texts

• Child Development
• Self-Regulation
• Diversity Science
• +۶ مورد دیگر

Ying Zhang serves as an Assistant Professor in the Department of Psychology within Clarkson University's Lewis School of Health & Life Sciences, where her research examines developmental trajectories of cognitive, social, and emotional functioning from early childhood through emerging adulthood. Her work emphasizes contextual influences including cultural dynamics, socioeconomic conditions, and family relationships on self-regulation development. Her educational background includes: Ph.D. in Psychology from Syracuse University (2021) M.Ed. in Education from Jilin University (2012) B.Eng. in Engineering from University of Jinan (2008) Grounded in Diversity Science , Dr. Zhang investigates ecological influences on child self-regulation, particularly examining how intimate partner violence exposure, parental mental health, and socioeconomic disparities create pathways for risk and resilience. Her methodological approach integrates advanced statistical modeling with longitudinal designs, latent profile analyses, and machine learning techniques to analyze large secondary datasets, generating insights applicable to real-world interventions for vulnerable populations. Analysis of her 15 most recent publications (2023-2025) reveals three dominant research streams: (1) intricate mechanisms linking intimate partner violence to child self-regulation through parenting stress pathways, (2) socioeconomic and cultural influences on developmental outcomes using machine learning methodologies, and (3) diversity promotion in higher education contexts. Her work increasingly incorporates technology applications, including AI-based violence detection systems. Dr. Zhang actively involves undergraduate researchers in her projects, as evidenced by numerous student co-authorships, and teaches courses including Introduction to Psychology, Cultural Psychology, Diversity Science, and Prenatal/Child Development. Her office is located in Clarkson's Science Center (Box 5825) with open hours Mondays and Fridays 1-3:30pm.

Deng Xiaotieمشاهده پروفایل

استاد

• Algorithmic Game Theory
• Internet Economics
• Parallel Computing
• +۱ مورد دیگر

Xiaotie Deng is a distinguished academic and Chair Professor at Peking University (since 2018), with prior roles at Shanghai Jiao Tong University (2013–2017), the University of Liverpool (2010–2013), and City University of Hong Kong (1997–2013). His research focuses on algorithmic game theory, internet economics, and parallel computing. He holds prestigious fellowships including ACM Fellow (2008) and IEEE Fellow (2019). Deng has led significant grants, including a RMB 5M project on algorithmic game theory at Peking University (2018–2020) and NNSFC-funded research on market competitiveness and fairness (2018–2020). Education: PhD in Computer Science from Stanford University (1989), MSc from Chinese Academy of Sciences (1984), BSc from Tsinghua University (1982). Research interests span computational game theory, equilibrium analysis, and blockchain applications. Notable contributions include foundational work on Nash equilibrium complexity and mechanism design for resource allocation. He has advised numerous PhD students and serves on editorial boards of top journals like SIAM Journal on Computing and IEEE Transactions on Cloud Computing. Key awards: ACM and IEEE Fellowships, JSPS Invitation Fellowships (2005, 1996), and NSERC International Fellowship (1991). Active in conference organizing, including PC chairs for WINE 2020 and SAGT 2018. Consultancy includes work with Cryptape, Ant Financial, and Microsoft Research Asia, reflecting his industry engagement in algorithmic solutions and blockchain technologies.

Vagelis Papalexakisمشاهده پروفایل

دانشیار

• Data Science
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Vagelis Papalexakis is an Associate Professor and Ross Family Chair in the Computer Science & Engineering Department at the University of California, Riverside. His research focuses on data science, machine learning, and tensor methods, with applications in multi-aspect/multi-modal data analysis. He holds a Ph.D. from Carnegie Mellon University and a Diploma/M.Sc. from the Technical University of Crete. Affiliations: Ross Family Chair, Bourns College of Engineering, UCR Education: Ph.D. in Computer Science, Carnegie Mellon University M.Sc./Diploma in Electronic & Computer Engineering, Technical University of Crete His work emphasizes interpretable insights from complex datasets, including tensor-based defenses against adversarial attacks, graph representation learning, and scalable algorithms for high-dimensional data. Notable awards include the NSF CAREER Award (2021), IEEE DSAA Next Generation Award (2021), and ICDM Tao Li Award (2022). Grants include NSF funding for railway safety (CISE MSI: RPEP CPS), USDOT transportation research, and NVIDIA GPU grants. He leads projects in AI ethics, misinformation detection, and gravitational wave analysis. His lab collaborates with industry (e.g., Cisco, Instacart) and national labs (e.g., Lawrence Livermore).

• Machine Learning
• Computer Vision
• Data Science
• +۳ مورد دیگر

Bohan Chen is a Postdoctoral Scholar Research Associate in the Department of Computing and Mathematical Sciences. Their work focuses on advancing graph-based machine learning techniques and their applications in environmental science, remote sensing, and AI-driven analysis. Research interests include graph neural networks, active learning strategies, hyperspectral image analysis, and knowledge graph integration. Notable contributions include the development of the GLL layer for neural networks, the CUSP permafrost dataset, and hybrid models for multispectral image processing. Key research trends span theoretical advancements in graph-based learning and practical applications such as environmental monitoring, SAR data analysis, and pandemic modeling. Their work bridges computational methodologies with real-world environmental and societal challenges. Advising and grants: No student advisees listed. Contributions include foundational research without explicit grant mentions in provided text. Labs/teams: No specific lab or team affiliations mentioned in the text.

Duen Horng Chauمشاهده پروفایل

استاد

• AI Security
• Explainable AI
• Visual Analytics
• +۶ مورد دیگر

Roles & Affiliations: Duen Horng (Polo) Chau is a Professor in the School of Computational Science and Engineering at Georgia Tech. He co-directs the MS Analytics program and leads industry relations for The Institute for Data Engineering and Science (IDEaS) and corporate relations for The Center for Machine Learning. He teaches Data & Visual Analytics (CSE6242/CX4242) to over 1,000 students annually. His affiliations include the GVU Center, Institute for People and Technology (IPaT), and ML@GT. Education: PhD in Machine Learning (Carnegie Mellon University, 2012), MS in Machine Learning (CMU), MA in Human-Computer Interaction (CMU), B.Eng. in Information Engineering (The Chinese University of Hong Kong). Research: Focuses on human-centered AI, interpretable machine learning, adversarial robustness, graph visualization/mining, and social good applications (e.g., healthcare, anti-human trafficking). His lab develops tools like ActiVis (for neural network exploration), Diffusion Explainer (for text-to-image models), and TrafficVis (to combat trafficking). Research is funded by NSF, NIH, DARPA, NASA, and industry partners (Google, Intel, Meta). Awards: 17+ best paper awards, Google/Intel/Meta Faculty Awards, Outstanding Undergraduate Research Mentor (2023), Outstanding Mid-Career Faculty (2022), and the Carnegie Mellon Dissertation Award (2012). Grants & Labs: Leads projects on AI safety, robust speech recognition, and graph vulnerability. Collaborates with Children’s Healthcare of Atlanta on surgical planning via AR. His work influences industry platforms (e.g., Meta’s ML tools used by 25% engineers).

• Artificial Intelligence
• Multimodal Interaction
• Natural Language Processing
• +۴ مورد دیگر

Arash Eshghi is an Assistant Professor in the School of Mathematical & Computer Sciences at Heriot-Watt University, specializing in the Department of Computer Science. His research focuses on multimodal interaction, embodied AI, and dialogue systems, with applications in human-AI collaboration, vision-language models, and incremental processing. He leads projects like the EMMA and AlanaVLM frameworks, which explore embodied agents in 3D environments and egocentric video understanding. His work emphasizes ethical considerations in conversational AI, including dementia-friendly voice assistants and faithfulness in large language models. He collaborates internationally, with contributions to benchmarks like the BURCHAK corpus and the Block World repair framework. Eshghi’s research bridges computational linguistics, cognitive science, and robotics, addressing challenges in ambiguity resolution, spatial reasoning, and incremental dialogue processing. Key collaborations involve institutions like the University of Edinburgh and MIT, focusing on multimodal learning, dynamic syntax, and interactional semantics. His lab develops tools for grounded language learning and real-time interaction, with a focus on systems that adapt to human feedback and contextual dynamics.

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

Lucila Ohno-Machado, MD, PhD, MBA, is the Waldemar von Zedtwitz Professor of Medicine and Biomedical Informatics and Data Science at Yale University. She serves as Deputy Dean for Biomedical Informatics and Chair of the Department of Biomedical Informatics and Data Science at the Yale School of Medicine. Her leadership roles include overseeing informatics infrastructure for Yale’s academic health system and fostering interdisciplinary collaboration across departments such as Medicine and the Halicioğlu Data Science Institute (previously at UCSD). Ohno-Machado holds an MD from the University of São Paulo (Brazil), an MBA from Fundação Getúlio Vargas (Brazil), and a PhD in Medical Information Sciences and Computer Science from Stanford University. She has held faculty positions at Harvard Medical School, MIT’s Health Sciences and Technology Division, and the UCSD Health Department of Biomedical Informatics, where she pioneered federated learning and privacy-preserving AI methodologies. Her research focuses on predictive analytics, federated learning, quantum computing in healthcare, and blockchain applications to enhance data security. She emphasizes addressing algorithmic bias and promoting health equity through data-driven solutions. Recent work includes developing frameworks for medical device safety evaluation and guiding principles to mitigate disparities in algorithmic healthcare applications. Key achievements include the Inaugural Helen M. Ranney Award (2024), election to the National Academy of Medicine (2024), and the William W. Stead Award (2019). She has led NIH-funded informatics centers and contributed to the first large-scale clinical data-sharing initiative across five UC medical systems. Her grants span AHRQ, PCORI, NSF, and blockchain-related initiatives through the IT/NIST Challenge Award. Ohno-Machado advises on translational research strategies and mentors teams in YBIC (Yale Biomedical Informatics & Computing). Her lab collaborates globally, leveraging federated models and AI to advance personalized medicine while prioritizing patient privacy. She also chairs the OHER Awards for Yale Research Excellence, promoting interdisciplinary health equity research.