جستجوی استاد راهنمای دانشگاهی

Chenyan Xiong is an Associate Professor at the Language Technologies Institute (LTI) at Carnegie Mellon University (CMU). Previously, she spent five years (2018-2023) at Microsoft Research, focusing on conversational search, dense retrieval, healthcare AI, and large-scale pretraining. She earned her Ph.D. at LTI, CMU in 2018 under Jamie Callan, after completing her undergraduate degree at Wuhan University (2009) and a master's at the Institute of Software, Chinese Academy of Science (2012), with internships at Microsoft Research Asia. Her research groups actively contribute to information retrieval, machine learning, and natural language processing communities, with a current focus on foundation and large language models (LLMs). She explores data-centric approaches to improve model efficiency, embedding learning for multimodal representation, and new application scenarios enabled by Generative AI, particularly in healthcare and next-gen information retrieval. Her work includes developing methods for loss-less compression and functional operations in embedding spaces. Current Research Interests: Foundation and Large Language Models Data-Centric AI Embedding Learning Generative AI Applications Healthcare LLMs Multi-Modality Systems Recruitment Plans: Ph.D. students at CMU (2024 Fall and 2025 Fall) for pretraining and application of foundation models Specialized Ph.D. student for healthcare LLMs (2024 Fall)

• Information Retrieval
• Machine Learning
• Natural Language Processing
• +۷ مورد دیگر

Ce Zheng is a Research Fellow in the Robotics Institute at Carnegie Mellon University, affiliated with the Computational Behavior (CUBE) Lab. His work focuses on advanced methodologies for human pose estimation and 3D mesh recovery using deep learning and transformer architectures. Research Interests: Robotics and human-computer interaction Artificial intelligence and machine learning Computer vision for pose and mesh recovery Transformer networks and neural architectures Domain adaptation and sim-to-real frameworks Biomechanics and neural networks Article Trends: Recent publications emphasize domain adaptation, multimodal integration (e.g., audio-visual reasoning), and efficient algorithms for 3D reconstruction. Key themes include diffusion models, hardware-algorithm co-design, and leveraging transformer architectures for spatiotemporal analysis in robotics applications. Labs & Teams: Affiliated with the Robotics Institute and the Computational Behavior (CUBE) Lab , contributing to interdisciplinary research in computational models for human behavior analysis.

• Robotics
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Suresh Kumaar Jayaraman is a Postdoctoral Fellow at the Robotics Institute , Carnegie Mellon University . His research focuses on Human-Robot Interaction , Explainable AI , and Trust Modeling in autonomous systems, particularly in automated driving applications. Robotics Institute, Carnegie Mellon University Research Fellow His work emphasizes behavior-aware control systems , pedestrian-AV interaction modeling , and machine teaching frameworks. Key themes include trust calibration , context-adaptive decision-making , and replicability standards in HRI studies. Recent publications highlight real-time trust estimation for automated vehicles, multimodal pedestrian behavior modeling , and user-centric explainable AI systems. He employs inverse reinforcement learning and hybrid automaton models to enhance human-AV interaction safety. He is affiliated with the TBD Lab at Carnegie Mellon University, focusing on robotics and autonomous systems development.

• Robotics
• Human-Robot Interaction
• Machine Learning
• +۴ مورد دیگر

Robert Kass is the Maurice Falk University Professor of Statistics and Computational Neuroscience at Carnegie Mellon University, with affiliations in the Department of Statistics & Data Science, Machine Learning Department, and Neuroscience Institute. He has served as Department Head of Statistics for 9 years and held leadership roles in academic societies. Ph.D. in Statistics (University of Chicago, 1980) B.A. in Mathematics (Antioch College) Postdoctoral training at Princeton University His research bridges statistics and neuroscience, focusing on: Statistical methods for neural data analysis Bayesian inference and graphical models Point process modeling of spike trains High-dimensional and time-series analysis Computational neuroscience education Recent publications span computational neuroscience, emphasizing phase coupling analysis, latent variable modeling, and cross-population dynamics, with methodological innovations in neural data interpretation. As founding Editor-in-Chief of Bayesian Analysis and Executive Editor of Statistical Science , he shaped academic discourse in statistics. Key scientific recognitions: Outstanding Statistical Application Award (ASA) Distinguished Achievement Award (COPSS) National Academy of Sciences member (2023) He co-authored the influential textbook Analysis of Neural Data and developed international workshops like Statistical Analysis of Neuronal Data (2002-2017), advancing neuroscience methodology globally.

• Computational Neuroscience
• Bayesian Inference
• Graphical Models
• +۷ مورد دیگر

Richard Scheines is a Professor and Dean at Carnegie Mellon University, with affiliations in the Machine Learning Department and the Human-Computer Interaction Institute. His research focuses on causal discovery, particularly through the TETRAD project, which develops algorithms for inferring causal structures from statistical data. He also explores educational technologies, including intelligent tutoring systems and virtual labs for causal reasoning. Scheines’ work bridges computer science, philosophy, and education, emphasizing practical applications of causal inference in biomedical research and policy analysis. His research interests include causal modeling, machine learning, and the development of interactive educational software. Scheines has collaborated extensively on projects like the CSR online courseware and the Center for Causal Discovery. His contributions span theoretical advancements in causal inference, empirical studies on learning strategies, and interdisciplinary applications in healthcare and public policy. Publications highlight his work on measurement error, multidimensional data analysis, and the integration of AI into education. He has also engaged in policy-related research, such as evaluating environmental risk assessment frameworks. Scheines’ academic leadership and innovative approaches to causal discovery and educational technology have positioned him as a key figure in computational and applied philosophy.

• Causal Discovery
• Machine Learning
• Educational Software
• +۳ مورد دیگر