جستجوی استاد راهنمای دانشگاهی

Xin Li is a Professor in the Department of Electrical and Computer Engineering at Duke University and serves as the Associate Vice Chancellor at Duke Kunshan University. He holds a Ph.D. from Carnegie Mellon University (2005) and has held leadership roles in research consortia like the FCRP Focus Research Center and the Center for Silicon System Implementation (CSSI). His research bridges integrated circuits , machine learning , and cyber-physical systems , with applications in autonomous driving, battery lifetime prediction, and smart buildings. Education : Ph.D., Carnegie Mellon University (2005); M.S., Fudan University (2001); B.S., Fudan University (1998) His work emphasizes robust design methodologies for analog/RF circuits, data-driven predictive modeling , and Bayesian inference for high-dimensional variation spaces. Recent publications focus on generative adversarial networks for circuit design, multi-view imputation for incomplete data, and knowledge-driven autonomous systems . He has received numerous accolades, including the NSF CAREER Award (2012) , IEEE Donald O. Pederson Best Paper Awards (2013, 2016) , and IEEE Fellow (2017) . He has served as Editor for journals like IEEE Transactions on Biomedical Engineering and as Chair for conferences including ISVLSI and CAD/Graphics.

• Integrated Circuits
• Signal Processing
• Data Analytics
• +۴ مورد دیگر

Dr. Yu (Chelsea) Jin is an Assistant Professor in the Department of Industrial Engineering at the University at Buffalo, specializing in quality inspection, predictive modeling, and data analytics for advanced manufacturing systems. She holds a PhD in Industrial Engineering from the University of Arkansas, an ME from the University of Michigan, and dual BS degrees in Network Engineering and Finance from Jinan University. Her research focuses on integrating machine learning and physics-based models to optimize manufacturing processes, such as additive manufacturing, PCB assembly, and pharmaceutical distribution systems. She has developed frameworks like ReflowNet for reflow oven optimization and physics-informed neural networks for thermal profile prediction. Her work emphasizes both theoretical advancements and practical applications in smart manufacturing and healthcare logistics. Dr. Jin's recent publications highlight contributions to generative AI for knowledge retrieval, AGV system optimization, and multi-source transfer learning for pandemic modeling. She actively collaborates with industry partners to bridge academic research and real-world manufacturing challenges.

• Quality Inspection and Control
• Predictive Modeling
• Smart Manufacturing Systems
• +۳ مورد دیگر

Damon L. Woodard is a Professor in the Department of Electrical and Computer Engineering at the University of Florida (UF) and serves as Director of the Florida Institute for National Security (FINS) and the Applied Artificial Intelligence (AAI) Group. His research focuses on applied artificial intelligence, hardware security, and biometrics, with particular emphasis on adversarial AI, AI-enabled hardware assurance, and explainable AI (XAI). He holds IEEE and ACM Senior Member status and is a Kavli Frontiers Fellow. Education: Ph.D. in Computer Science and Engineering, University of Notre Dame M.E. in Computer Science and Engineering, Penn State University B.S. in Computer Science and Computer Information Systems, Tulane University Research Interests: Dr. Woodard explores cutting-edge areas such as AI hardware acceleration, multi-modal AI systems, and counter-AI strategies. His work bridges cognitive science and technology through projects like text stylometry and psychological analysis frameworks. Key focus areas include semiconductor reverse engineering, hardware trojan detection via SEM imaging and machine learning, and secure IoT biometric systems. Highlighted Awards: National Academy of Science Kavli Frontiers Fellow IEEE Senior Member ACM Senior Member AAAI Membership Leadership & Contributions: As FINS Director, he oversees national security initiatives integrating AI and hardware assurance. His AAI Group develops resource-efficient AI solutions for constrained environments. Notable projects include the SECURE segmentation metric for IC reverse engineering and the MaGNIFIES GAN framework for electronic system inspection. Labs & Teams: Leads the Applied Artificial Intelligence Group and collaborates across disciplines through FINS, fostering innovation in AI-driven security and semiconductor integrity.

• Artificial Intelligence
• Hardware Security
• Biometrics
• +۵ مورد دیگر

Qi Zhang is an Assistant Professor in the Department of Computer Science and Engineering, AI Institute at the Molinaroli College of Engineering and Computing, University of South Carolina. His research focuses on developing safe, reliable, and trustworthy AI systems through advancements in reinforcement learning and decision-making algorithms for uncertain environments. He holds a Ph.D. from the University of Michigan (2020) and a B.E. from Shanghai Jiao Tong University (2015). Research interests include artificial intelligence, reinforcement learning, multi-agent systems, and decision-making under uncertainty. Key themes involve leveraging domain knowledge for robust AI solutions and ensuring ethical, transparent, and risk-aware system designs. His work spans applications in autonomous systems, healthcare, robotics, and materials science. Current projects emphasize improving algorithmic trustworthiness, safety, and adaptability across diverse contexts. He is affiliated with the AI Institute at USC and maintains an active research lab focused on these areas.

• Artificial Intelligence
• Reinforcement Learning
• Trustworthy AI Systems
• +۴ مورد دیگر

Stephen Ramsey, an Associate Professor at Oregon State University, holds dual appointments in the School of Electrical Engineering and Computer Science (College of Engineering) and the Department of Biomedical Sciences (Carlson College of Veterinary Medicine). With a PhD in Physics from the University of Maryland, his postdoctoral training in computational genomics at the University of Washington, and professional experience at the Institute for Systems Biology and Center for Infectious Disease Research, Ramsey bridges computational methods with biomedical applications. Education : Ph.D., Physics, University of Maryland; M.S., Physics, University of Maryland; Sc.B., Mathematical Physics, Brown University Ramsey specializes in computational systems biology , focusing on bioinformatics , biomedical knowledge graphs , and precision medicine . His research integrates machine learning , gene regulatory network modeling , and multi-omics data analysis to address challenges in rare disease diagnostics , drug monitoring , and inflammatory disease mechanisms . Current work includes AI-driven biomedical translation and electrochemical biosensor development for non-invasive diagnostics . Recent publications highlight knowledge graph applications in translational biomedicine , causal network inference in clinical-environmental data integration , and cross-species cancer transcriptomics . His team develops tools like RTX-KG2 and PloverDB to standardize biomedical data sharing and semantic reasoning . Scientific Awards : 2019 Zoetis Award (Carlson College of Veterinary Medicine) 2016 NSF CAREER Award 2016 PhRMA New Investigator Award 2010 NIH K25 Mentored Quantitative Research Award Ramsey advises in computational biology courses (CS 446/546) and contributes to biomedical AI through projects like mediKanren for rare disease diagnostics . His NSF-funded research explores gene expression noise and regulatory network dynamics , while NIH and PhRMA grants support his translational medicine initiatives. He leads the Ramsey Laboratory , which develops graph-based reasoning tools for biomedical data translation and multi-omics integration . The lab's work spans comparative oncology models, electrochemical biosensors , and knowledge graph infrastructure for clinical decision support .

• Computational Systems Biology
• Bioinformatics
• Precision Medicine
• +۶ مورد دیگر

Professor Tao Pan is a leading researcher in RNA biology at the University of Chicago's Department of Biochemistry and Molecular Biology. His laboratory pioneers methodologies for studying tRNA dynamics, epitranscriptomics, and microbiome-host interactions through innovative sequencing technologies. His research spans three interconnected domains: tRNA biology : Developing high-throughput sequencing to measure tRNA abundance, charging, and modifications, revealing roles in translational control and extratranslational functions Microbiome characterization : Creating tRNA-seq as an RNA-based alternative to 16S/shotgun metagenomics to capture dynamic microbiome activity Epitranscriptomics : Investigating mRNA modifications like m6A in gene regulation, discovering the 'm6A switch' mechanism affecting RNA structure and protein binding His publication trends demonstrate consistent innovation in RNA modification mapping techniques, with recent work focusing on nanopore sequencing applications, microbiome-induced epitranscriptomic reprogramming, and therapeutic mRNA engineering. Key recurring themes include tRNA modification dynamics, host-microbiome crosstalk, and RNA modification detection methodologies. Scientific recognition includes: NIH Director's Pioneer Award (2011-2016) NIH EUREKA Award (2009-2013) AAAS Fellow (2015) Multiple early-career cancer research awards His laboratory maintains substantial NIH funding through multiple R01 grants (current awards totaling >$5M annually), focusing on tRNA sequencing applications in cancer (R33CA272357), microbiome-host interactions (R21AI169280), and snoRNA functions (R01HG012780). The Pan Lab has established tRNA-seq as a novel microbiome characterization platform and pioneered discoveries in m6A-mediated RNA structural switching. Research infrastructure includes development of specialized sequencing technologies for simultaneous tRNA modification and fragmentation analysis, with recent expansion into nanopore-based direct RNA modification detection. Current work explores therapeutic applications of tRNA modification knowledge in cancer and infectious disease contexts.

• Functional genomics
• tRNA biology
• Microbiome
• +۴ مورد دیگر

Dr. Kenneth Marino is a Research Scientist at DeepMind , set to join the University of Utah as an Assistant Professor at the Kahlert School of Computing in Fall 2025. He earned his PhD in Machine Learning from Carnegie Mellon University (funded by NDSEG and NSF GRFP fellowships) and completed his undergraduate studies in Computer Engineering with a minor in Computer Science at Georgia Tech . Dr. Marino's research focuses on the intersection of Computer Vision , Natural Language Processing , and Reinforcement Learning , with emphasis on: Multimodal agents operating on the web, in simulation, and in real-world environments Evaluating AI systems and creating high-impact datasets (e.g., A-OKVQA , OK-VQA ) Incorporating semantic knowledge into end-to-end learning frameworks Embodied reasoning through language-guided planners and neural reporters Language model distillation for agent supervision Human-AI collaboration to improve RL generalization His work has been published in top venues including ICML , NeurIPS , ECCV , CVPR , and ICLR , with recent trends emphasizing: Relational reasoning in LLMs Object-centric world modeling Continual learning for embodied agents Reporter neural networks for agent control Legal AI benchmarking (e.g., BriefMe ) Knowledge graph integration for visual classification Scientific recognition includes: NDSEG Fellowship NSF GRFP Fellowship He has served on the MLD PhD Admissions Committee and as an Area Chair for ECCV 2024 , with prior teaching experience at Columbia University (COMS 6998) and Carnegie Mellon University (16-824, 10-401).

• Computer Vision
• Natural Language Processing
• Reinforcement Learning
• +۴ مورد دیگر

Huaping Liu is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University (College of Engineering). He holds a Ph.D. in Electrical Engineering from New Jersey Institute of Technology (1997) and previously worked at Lucent Technologies (1997–2001). His research focuses on wireless communication systems, including MIMO systems, ultrawideband technologies, spread spectrum systems, and channel coding. He has contributed to applications in healthcare monitoring and industrial automation. Education: Ph.D., Electrical Engineering, New Jersey Institute of Technology (1997) M.S., Electrical Engineering, Nanjing University of Posts and Telecommunications (1990) B.S., Electrical Engineering, Nanjing University of Posts and Telecommunications (1987) Research Interests: Wireless communication theories and systems Embodied intelligence and multi-agent robotics Tactile sensing and large-area robotic skin Autonomous navigation and vision-language models Awards and Recognition: 2017 IEEE UEMCON Best Paper Award 2015 OSU College of Engineering Research Collaboration Award 2011 DesignCon Best Paper Award 2010 IEEE VTS Best Paper Award 2007 & 2004 EECS IEEE Professor of the Year Advising and Grants: Dr. Liu emphasizes student-driven learning, fostering critical thinking through inquiry-based teaching. His work involves collaborative projects in robotics, cybersecurity, and sensor systems, supported by grants and industry partnerships. Labs/Teams: Active in developing embodied intelligence frameworks, robotic tactile sensing systems, and cybersecurity solutions for digital twins and vehicular networks. Collaborates on simulation platforms for human-robot collaboration (e.g., HumanTHOR).

• Wireless Communication Systems
• Signal Processing
• MIMO Systems
• +۷ مورد دیگر

Raffi Khatchadourian is an Associate Professor in the Department of Computer Science at Hunter College, part of the City University of New York (CUNY). He specializes in Software Engineering, Programming Languages, and Machine Learning Systems. His research focuses on automated software evolution, secure software engineering, and refactoring techniques, with notable contributions to tools like Hybridize Functions and μAkka. He holds a PhD in Computer Science from Ohio State University, along with MS and BS degrees in Computer Science from Ohio State and Monmouth University, respectively. His educational background includes advanced training in Computer Science, with a focus on theoretical and practical aspects of software systems. His research interests span automated refactoring, program analysis, and the integration of machine learning with traditional software engineering practices. Key projects include frameworks for assessing safety in deep learning systems (ReLESS) and mutation testing for actor concurrency in Akka (μAkka). Khatchadourian’s work emphasizes tool development and open-source contributions, such as the Common-Eclipse-Refactoring-Framework and Hybridize-Functions-Refactoring. He has secured grants from the National Science Foundation (NSF) for combating technical debt in machine learning systems and improving imperative-to-graph execution migration. His teaching spans graduate courses on programming languages and software engineering, reflecting his commitment to advancing computational education. His lab, Ponder Lab, focuses on empirical studies and tool creation for software evolution and concurrency. Notable grants include SHF-funded projects addressing practical analyses and safe transformations in deep learning programs. His research has addressed challenges in migrating deep learning code to graph execution, optimizing Java 8 streams, and analyzing legacy software refactoring.

• Software Engineering
• Programming Languages
• Machine Learning Systems
• +۳ مورد دیگر

Anja C. Roden, MD is a Professor at Mayo Clinic College of Medicine , Department of Laboratory Medicine and Pathology, Anatomic Pathology. Board certified in anatomic and clinical pathology, she specializes in thoracic pathology with expertise in mediastinal tumors, lung tumors, malignant mesothelioma, interstitial lung diseases, and pulmonary transplant pathology. Clinical Roles : Medical Director, Immunostains Laboratory Research : 190+ peer-reviewed manuscripts, 40+ book chapters, Co-editor of "Mediastinal Lesions" and "Pulmonary Pathology" atlases Leadership : Past President, International Thymic Malignancy Interest Group; Current Chair, Thymic Tumor Subgroup, IASLC; Vice-Chair, European Society of Pathology Education : 2009: Surgical Pathology Fellowship, Mayo Clinic 2008: Anatomic & Clinical Pathology Residency, Mayo Clinic 1992: Medical Degree, Technical University of Dresden Research Focus spans mediastinal neoplasms, malignant mesotheliomas, biomarker development (diagnostic/predictive/theranostic), interstitial lung diseases, and pulmonary transplant pathology. Recent work emphasizes AI integration in ILD diagnosis, direct patient-pathologist interactions in transplant medicine, and molecular characterization of thoracic tumors. Scientific Awards : 2017: President's Pulmonary Pathology Society Innovation Award 2020: Best Author Award, Pathology Outlines Professional Activities : Active in clinical guideline development (WHO thoracic tumors 2021), serves on multiple international committees including the European Lung Cancer Congress Program Committee, Pulmonary Vascular Disease Steering Committee, and International Mesothelioma Panel. Regularly contributes to peer-review processes across major journals.

• Thoracic Pathology
• Surgical Pathology
• Mediastinal Tumors
• +۵ مورد دیگر

Nathaniel Daw holds the Huo Professorship in Computational and Theoretical Neuroscience at the Princeton Neuroscience Institute , Princeton University. His research integrates computational, neural, and behavioral approaches to study decision-making through trial-and-error learning and reward/punishment processing. Research Interests: Computational Neuroscience Decision-Making Under Uncertainty Model-Based and Model-Free Learning Neural Mechanisms of Self-Control Publications (2025-2019) explore intersections of machine learning and neuroscience, focusing on reward-guided behavior, memory systems, and psychiatric implications like anorexia nervosa and obsessive-compulsive disorder. Key themes include neural replay, cognitive effort allocation, and predictive modeling of human and animal learning. Scientific Awards: Princeton-Rutgers $16M Research Grant for mental illness studies Grants and Collaborations: Highlighted by a major interdisciplinary grant with Rutgers University to advance understanding of mental illness through computational frameworks. Labs and Teams: Leads the Daw Lab at Princeton Neuroscience Institute, focusing on neurocomputational models of decision-making and self-control.

• Computational Neuroscience
• Decision-Making
• Reinforcement Learning
• +۳ مورد دیگر

Md Ferdous Alam is a Postdoctoral Associate at the Massachusetts Institute of Technology School of Engineering , affiliated with the Department of Mechanical Engineering. Previously, he earned his Ph.D. from Ohio State University and worked at Autodesk AI Lab as a research intern and collaborator. Academic Affiliations: Current: MIT (Postdoctoral Associate) Ph.D.: Ohio State University Research Collaborator: Autodesk AI Lab Research Interests focus on generative AI, reinforcement learning, and optimal control theory for manufacturing automation. His work integrates AI with hardware for real-time autonomous systems, particularly in 3D CAD/CAM and robotic manufacturing. Key projects include: Developing GenCAD for image-conditioned CAD generation Building a universal manufacturing operating system with AI at its core Advancing data-efficient reinforcement learning algorithms Creating multimodal benchmarks like DesignQA Scientific Awards include the Google Research Scholar Award 2024 in Applied Science and Best Paper Award at MSEC 2020. His publications span journals like Journal of Mechanical Design and conferences including IDETC-CIE. He is active in open-source development, maintaining repositories for autonomous manufacturing and machine learning frameworks.

• Generative AI
• Reinforcement Learning
• Optimal Control Theory
• +۳ مورد دیگر

Naimul Hoque is an Assistant Professor in the Department of Computer Science at the University of Iowa, within the College of Liberal Arts and Sciences. His research is centered on the intersection of Human-Computer Interaction (HCI), Information Visualization, and Artificial Intelligence, with a focus on fairness, transparency, provenance, and accessibility in AI-assisted applications. His educational background includes a Ph.D. in Information Studies from the University of Maryland, College Park; a Master’s in Computer Science from Stony Brook University; and a BSc in Computer Science from the University of Dhaka, Bangladesh. Hoque's research explores how visualization can enhance human understanding and trust in AI systems. Key themes include explainable AI , intelligent writing assistants , accessible data representation , and social bias detection . His work emphasizes inclusive and ethical design, particularly in media and creative applications. His recent publications span top venues like ACM CHI, IEEE VIS, ACM CSCW, and ACM DIS, showing a consistent focus on interactive, human-centered AI tools. Trends include the development of design spaces for writing assistants, provenance tracking in AI-generated text, and multimodal interfaces for diverse user needs. Although no formal scientific awards are listed in the provided text, his work has been accepted in premier conferences, and he is actively contributing to the research community through workshops and collaborative projects. Hoque advises students and leads interdisciplinary collaborations involving HCI, NLP, education, and social computing. He is involved in organizing the workshop on accessible visualization at IEEE VIS and maintains active projects such as DashGuide, HaLLMark, and Belief Miner. He began his faculty position at the University of Iowa in January 2025, indicating active research and teaching engagement. He is associated with research labs and teams focused on human-centered AI and visualization, including collaborations with researchers across HCI, NLP, and education. His project A Design Space for Intelligent Writing Assistants is a living artifact maintained on GitHub, inviting community contributions.

• Human-Computer Interaction
• Information Visualization
• Artificial Intelligence
• +۹ مورد دیگر

Dr. Apan Dastider is a tenure-track Assistant Professor in the Division of Engineering Technology at Wayne State University’s James and Patricia Anderson College of Engineering. He leads research in Robotics and AI, focusing on adaptive robotic systems, human-robot interaction, and generative AI for intelligent control. Previously, he earned his Ph.D. and M.S. in Electrical Engineering from the University of Central Florida, where he worked as a Graduate Research Assistant in the Autonomous Robotic Computing Lab. Education: Ph.D. in Electrical Engineering, University of Central Florida, 2025 M.S. in Electrical Engineering, University of Central Florida, 2023 B.S. in Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, 2017 Research Interests: Dr. Dastider’s research lies at the intersection of robotics, AI, and control theory. His work emphasizes: Knowledge distillation across heterogeneous domains Human-robot interaction and motion synthesis Generative AI for adaptive robotic control Non-parametric reinforcement learning Low-dimensional manifold learning for motion planning Publications Overview: His recent publications demonstrate a strong focus on real-time robotic planning, diffusion models for motion generation, and safe human-robot collaboration. Notable works include APEX (dual-arm manipulation), RETRO (reactive trajectory optimization), and unified frameworks for interception and obstacle avoidance using diffusion variational autoencoders. Awards & Recognition: UCF ECE Outstanding Graduate Researcher Award Reviewer for IEEE ICRA and IROS conferences Teaching & Mentorship: Dr. Dastider is committed to developing the next generation of AI and robotics professionals. He teaches courses like MIT5700 - Industrial Robots Modeling and Simulation at Wayne State University and actively mentors students in cutting-edge research projects.

• Human-Robot Interaction
• Generative AI for Robotic Control
• Knowledge Distillation
• +۴ مورد دیگر

Mohit Bansal is the John R. & Louise S. Parker Distinguished Professor and Director of Graduate Admissions in the Computer Science Department at the University of North Carolina Chapel Hill. He leads the MURGe-Lab (UNC-AI Group) and serves as Lead (Core AI) for the ENGAGE NSF-AI Institute. Previously, he was a Research Assistant Professor at TTI-Chicago. Dr. Bansal earned his Ph.D. from UC Berkeley in 2013 under Dan Klein and his B.Tech. from IIT Kanpur in 2008. His research spans Natural Language Processing and Multimodal Machine Learning , with specific expertise in multimodal generative models, grounded and embodied semantics (language with vision/speech for robotics), faithful language generation, reasoning and planning agents, and interpretable deep learning. He employs techniques from structured prediction, reinforcement learning, and model editing to address challenges in compositional generalization and robustness. His recent work focuses on multimodal understanding, vision-language navigation, model merging, and evaluating/factuality in generative models. Trends show increasing emphasis on trustworthy AI, with projects addressing hallucination reduction, cultural bias diagnosis, and safe generation. His publications span top venues including ACL, CVPR, NeurIPS, and ICML, with significant contributions to multimodal foundation models and parameter-efficient learning. AAAI Fellow (2025) Presidential Early Career Award for Scientists and Engineers (PECASE) (2025) IIT Kanpur Young Alumnus Award (2023) DARPA Director's Fellowship (2019) NSF CAREER Award (2019) Microsoft Investigator Fellowship (2019) Outstanding Paper Awards at ACL, CVPR, EACL, COLING, and CoNLL Dr. Bansal has advised numerous PhD students who now hold positions at top institutions including UT Austin, NTU Singapore, JHU, Meta, and Adobe. His lab secures substantial funding from NSF, DARPA, NIH, and ONR, including the $20M NSF-AI Institute on Engaged Learning where he serves as Core AI Lead. Current projects include DARPA's Environment-driven Conceptual Learning (ECOLE) and ONR's Science of Artificial Intelligence program. The MURGe-Lab (Multimodal Understanding, Reasoning, and Generation) develops foundational models for multimodal tasks, with recent work on VideoTree for long video reasoning, SELMA for skill-specific text-to-image experts, and LASeR for adaptive reward model selection. The lab collaborates extensively with industry partners including Google, Meta, and Microsoft.

• Natural Language Processing
• Multimodal Machine Learning
• Grounded and Embodied Semantics
• +۴ مورد دیگر