جست‌وجوی استاد و پژوهشگر

Heng Huangمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Big Data Computing
• +۱۱ مورد دیگر

Heng Huang is the Brendan Iribe Endowed Professor in the Department of Computer Science and the Department of Electrical and Computer Engineering at the University of Maryland, College Park . He earned his Ph.D. in Computer Science from Dartmouth College and holds prior degrees from Shanghai Jiao Tong University. His research focuses on advancing the foundations and applications of artificial intelligence, particularly in machine learning, data mining, natural language processing, computer vision, and biomedical informatics . His work integrates large-scale optimization, fairness, and robustness in deep learning systems. Heng Huang’s recent publications demonstrate a strong trend in large language models, federated learning, model watermarking, continual learning, and medical image analysis . His work appears consistently in top venues like NeurIPS, ICML, CVPR, ICLR, and MICCAI, reflecting a broad impact across theoretical and applied AI. He actively mentors students and postdocs, seeking highly motivated researchers in machine learning and related domains. His work has significant implications for healthcare, privacy, and trustworthy AI.

Robin Jiaمشاهده پروفایل

استادیار

• Natural Language Processing (NLP)
• Deep Learning
• Artificial Intelligence
• +۷ مورد دیگر

Robin Jia is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC) , where he leads the AI, Language, Learning, Generalization, and Robustness (Allegro) Lab . His research focuses on enhancing the reliability and robustness of large language models (LLMs) through mechanistic understanding, benchmarking under distribution shifts, and neurosymbolic integration. Key affiliations include collaborations with the USC Keck School of Medicine and contributions to legal frameworks like the EU's Digital Services Act. Robin's research spans multiple domains, including: Scientific analysis of LLM capabilities in in-context learning , data memorization , and numerical reasoning Advancements in robust NLP systems , emphasizing uncertainty estimation and calibration Development of methods combining LLMs with symbolic solvers for complex reasoning tasks Interdisciplinary applications in medicine and law , such as privacy-preserving synthetic data generation and medical misconception evaluation . His recent publications (2024-2025) address Fourier-based numerical embeddings (NeurIPS), neurosymbolic planning (NAACL), and multimodal benchmarking (COLM), with a strong emphasis on privacy , fairness , and transparency . Scientific awards include the Google Research Scholar Award (2023) , SoCalNLP Symposium Best Paper Awards , and ACL/EMNLP outstanding papers . He advises PhD students like Johnny Wei and Ameya Godbole, and has secured grants from the NSF , USC-Capital One , and USC-Amazon .

Reza Shokriمشاهده پروفایل

دانشیار

• Data Privacy
• Trustworthy Machine Learning
• Security
• +۷ مورد دیگر

Reza Shokri is a Dean's Chair Associate Professor in the Department of Computer Science at the National University of Singapore (NUS), School of Computing. His research lies at the intersection of data privacy, security, and trustworthy machine learning, with a focus on quantifying privacy risks and developing robust, fair, and interpretable models. PhD in Computer Science, EPFL His research interests center on data privacy and trustworthy machine learning , particularly in the context of deep learning and federated systems. He investigates how machine learning models memorize training data, leading to privacy leakage, and designs frameworks to audit and mitigate such risks. His work bridges theoretical guarantees with practical applications, emphasizing the trade-offs among privacy, fairness, robustness, and utility. His recent publications (2023–2025) reveal a strong trend in analyzing privacy in large language models (LLMs), membership inference attacks, federated learning, and fairness. These works are published in top venues such as NeurIPS, ICML, ICLR, CCS, and FAccT, highlighting his leadership in both AI and security communities. Notable scientific awards include: Asian Young Scientist Fellowship (2023) Intel Outstanding Researcher Award (2023) Best Paper Award, ACM FAccT (2023) IEEE S&P Test-of-Time Award (2021) Caspar Bowden Award for Privacy Enhancing Technologies (2018) NUS Presidential Young Professorship (2019–2023) VMware Early Career Faculty Award (2021) He has advised numerous PhD and Master’s students, many of whom have contributed to high-impact publications. He has also received research grants from major industry partners including Meta, Google, Intel, and VMware. He leads the Data Privacy and Trustworthy Machine Learning Lab at NUS and has served on program committees for top conferences such as IEEE S&P, ACM CCS, and FAccT, including co-chairing roles at HotPETs and Shadow PC of IEEE S&P. He has delivered tutorials at ICML and CCS on privacy auditing in machine learning. His lab focuses on developing tools and frameworks—such as the ML Privacy Meter—for assessing and improving the privacy properties of machine learning models, with applications in regulatory compliance and secure AI deployment.

Jiliang Tangمشاهده پروفایل

استاد

• Graph Machine Learning
• Trustworthy AI
• Deep Learning
• +۳ مورد دیگر

Jiliang Tang is an MSU Foundation Professor in the Department of Computer Science and Engineering at Michigan State University (MSU), part of the College of Engineering. He holds a PhD from Arizona State University (2015) and previously worked as a research scientist at Yahoo Research. His research focuses on graph machine learning, trustworthy AI, and applications in education and biology. He has received numerous awards, including the 2022 AI's 10 to Watch, IAPR J.K. Aggarwal Award, and NSF CAREER Award. Education: PhD in Computer Science, Arizona State University, 2015 (Advisor: Huan Liu) Research Interests: Graph Neural Networks (GNNs) and Deep Learning on Graphs Trustworthy AI: Safety, Robustness, and Fairness AI+X Applications: Education Technology and Biological Data Analysis His work bridges theoretical advancements and practical applications, with contributions to graph representation learning, privacy in generative models, and educational AI systems. Awards & Recognition: Over 8 best paper awards (or runner-ups) Rock Star Award from Association of Chinese Scholars in Computing Extensive media coverage for innovations in AI education and biology Grants & Projects: NSF CAREER Award (2019) for research on signed networks Co-PI on a $1.7M grant for 5G research Leadership in projects like DSE Lab and Data Science initiatives Labs & Teams: Directs the Data Science and Engineering (DSE) Lab at MSU, focusing on advancing AI for real-world challenges. The lab collaborates with industry leaders and publishes widely in top conferences (e.g., KDD, SIGIR, ACL).

Shiyu Changمشاهده پروفایل

دانشیار

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

Shiyu Chang is an Associate Professor in the Department of Computer Science at the University of California, Santa Barbara , focusing on machine learning with applications in natural language processing and computer vision . He previously worked as a research scientist at the MIT-IBM Watson AI Lab alongside Prof. Regina Barzilay and Prof. Tommi Jaakkola, and earned both his B.S. and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign , advised by Prof. Thomas S. Huang. Education : PhD, University of Illinois at Urbana-Champaign BS, University of Illinois at Urbana-Champaign His research centers on enhancing AI systems through human-AI interaction , aiming to improve interpretability , transferability , and adversarial robustness in LLMs. Recent work includes LLM watermarking defense , uncertainty decomposition , and self-denoised smoothing for model robustness. His publications span premier venues like ICML , NeurIPS , CVPR , and ACL , with recurring themes in diffusion models , LLM optimization , and ethical AI (e.g., hallucination detection, unlearning frameworks). He actively mentors students, several of whom are marked as advisees (☆) in his publications.

Mohit Iyyerمشاهده پروفایل

دانشیار

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

Mohit Iyyer is an Associate Professor in Computer Science at the University of Maryland, College Park (UMD), affiliated with the Manning College of Information and Computer Sciences (CICS) and the CLIP lab. Previously, he held roles at UMass Amherst, AI2, and completed his PhD at the University of Maryland. His research focuses on advancing NLP and machine learning, particularly in long-context LLMs, factuality evaluation, and human-LLM collaboration. Education : PhD, Computer Science, University of Maryland, College Park (2017) MS, Computer Science, University of Maryland, College Park (2014) BS, Computer Science, Washington University (2012) Research Interests : His work emphasizes improving instruction-following in LLMs, evaluating long-form text, detecting AI-generated content, and enhancing LLM robustness. He actively explores multilingual LLMs, literary translation, and creative writing support through AI. Key Contributions : Developed influential frameworks like ELMo and QuAC, and introduced benchmarks such as BEARCUBS and BooookScore. His work on watermarking (PostMark) and adversarial attacks (OverThink) addresses security and reliability in AI systems. Awards & Recognition : NSF CAREER Award (2021) Samsung AI Researcher of the Year (2022) Best Paper Awards at NAACL 2018 and CCS 2023 Advising & Grants : Advises a dynamic research group and has received grants for projects like multilingual QA and long-context LLM evaluation. Collaborates with industry partners like AI2 and DeepMind. Labs & Teams : Leads the CLIP lab at UMD, focusing on foundational NLP research and applications in literature, dialogue systems, and computational storytelling.

Stephen Spectorمشاهده پروفایل

استاد

• Medieval English Literature
• Religious Studies
• Biblical Literature
• +۳ مورد دیگر

Stephen Spector is a Professor of English at Stony Brook University since 1972, with a PhD from Yale University (1973). His work bridges medieval literature, religious studies, and language history. He has held visiting positions at the Hebrew University (Jerusalem, 1996–97) and fellowships at the National Humanities Center and Wesleyan University. His research focuses on medieval English literature, particularly mystery plays and biblical texts, alongside modern topics like Christian Zionism and the Ethiopian Jewish rescue operation (Operation Solomon). He has authored influential books such as Evangelicals and Israel (2009), which won the London Book Festival Prize, and Operation Solomon (2005), honored with the JDC-Herbert Katzki Award. Education: B.A. in English and Psychology from Penn State University (1968), M.Phil. and Ph.D. from Yale University (1973). He has taught courses ranging from the history of the English language to medieval literature and the Bible as literature. Research highlights include pioneering studies on anti-Semitism in medieval mystery plays, textual editing of the N-Town Play, and modern analyses of religious-political intersections. His recent work explores biblical prophecy’s influence on US foreign policy and contemporary grammar usage guides. Awards include the Van Courtlandt Elliott Prize (1980) for medieval scholarship, National Endowment for the Humanities grants, and SUNY Research Foundation fellowships. He has also contributed op-eds on topics like Sarah Palin’s religious influences and Israel-Evangelical relations. Grants and fellowships span decades, including directing NEH summer seminars on Jewish presence in early English literature (1993, 1996). His work often integrates historical methodologies with contemporary cultural analysis. Labs/Teams: Not explicitly listed, but his research collaborations are evident in co-edited volumes like The Olde Daunce (1991) and contributions to interdisciplinary projects like the Early English Text Society.

Bryan Pardoمشاهده پروفایل

استاد

• Machine Learning
• Generative Modeling
• Signal Processing
• +۷ مورد دیگر

Bryan Pardo is a Professor of Computer Science at Northwestern University and head of the Interactive Audio Lab. He co-directs the Northwestern Center for Human Computer Interaction + Design and chairs the Computer Science Diversity Committee. He teaches courses in Deep Learning, Machine Learning, Generative Modeling, and Digital Music Instrument Design. PhD in Computer Science and Engineering, University of Michigan MMus in Jazz and Improvisation, University of Michigan MS in Computer Science, Ohio State University BMus in Jazz Composition, Ohio State University His research focuses on machine understanding and manipulation of sound, particularly in music and speech domains. Key areas include Machine Learning (e.g., automated gradient clipping), Signal Processing (e.g., Multi-scale Common-fate Transform), and Human Computer Interaction. Applications involve inclusive audio interfaces, audio search engines, source separation, natural language-controlled audio effects, privacy-preserving adversarial attacks on voice recognition, and music co-creation tools. Recent publications highlight advancements in neural watermarking (MaskMark), masked acoustic modeling (VampNet), and real-time adversarial privacy systems for speech. His lab's work has been applied in Adobe's AI-powered audio editor and Lexie B2 hearing aids. Scientific Awards: $1.8 million NSF Future of Work award $440K NSF grant for accessible music programming $200K Toyota grant $100K Sony grant TorchCrepe pitch tracker: 20 million+ downloads Bryan Pardo advises PhD student Max Morrison and collaborates with researchers like Patrick O'Reilly, Zeyu Jin, and Prem Seetharaman. His lab develops technologies for blind and visually impaired audio creators, including HaptEQ and Eyes-free tools.

Franziska Boenischمشاهده پروفایل

عضو هیئت علمی

• Privacy-Preserving Machine Learning
• Trustworthy Machine Learning
• Differential Privacy
• +۵ مورد دیگر

Franziska Boenisch is a tenure-track faculty member at the CISPA Helmholtz Center for Information Security , where she co-leads the SprintML lab for Secure, Private, Robust, Interpretable, and Trustworthy Machine Learning. Her research lies at the intersection of privacy-preserving machine learning and trustworthy ML , with a focus on differential privacy , model inversion attacks , and privacy risks in federated learning . She completed her PhD at Freie Universität Berlin and was a postdoctoral fellow at the Vector Institute for Artificial Intelligence under Prof. Nicolas Papernot. Her work has been recognized with awards such as the Academics Rising Start Award (3rd Prize) and the GI Junior-Fellow honor. Her research spans a wide range of topics including memorization in diffusion models , watermarking generative models , membership inference attacks , and privacy-preserving federated learning . She has published extensively in top-tier venues like ICML , NeurIPS , ICLR , and CVPR . She is actively involved in the academic community, serving as an Area Chair for NeurIPS , Track Chair for ACM AsiaCCS , and co-organizing workshops at ICML . She is currently hiring PhDs, postdocs, and research interns for her group.

Chutima Boonthum-Deneckeمشاهده پروفایل

استاد

• Artificial Intelligence
• Natural Language Processing
• Computational Linguistics
• +۶ مورد دیگر

Dr. Chutima Boonthum-Denecke is a Professor in the Department of Computer Science at Hampton University's School of Science. She joined Hampton University in 2006 as an Assistant Professor and now serves as Director of the Information Assurance and Cyber Security Center (IAC@HU). She leads the NSF CyberCorps Scholarship for Service program and has contributed to NSF initiatives like ARTSI and STARS Alliances. Her educational background includes a Ph.D. in Computer Science from Old Dominion University (2007), an MS in Applied Computer Science from Illinois State University (2000), and a BS in Computer Science from Srinakharinwirot University (1997). Dr. Boonthum-Denecke's research integrates artificial intelligence, natural language processing, and cybersecurity. Key interests include: Developing intelligent tutoring systems and educational games Secure coding practices for software engineering NLP applications in information retrieval and assessment tools Cyber-physical security for IoT and robotics Her recent publications (2016-2021) focus on machine learning applications in cybersecurity, including sentiment analysis for threat detection, blockchain-enhanced IoT security, and vulnerability assessments of emerging technologies. Collaborative work with students frequently addresses privacy ethics in AI assistants, RFID implants, and cloud systems. She mentors students through the IAC@HU lab, resulting in award-winning conference presentations on cybersecurity topics. As Principal Investigator of NSF CyberCorps, she oversees scholarship programs that bridge academic research with national security needs.

Claude DELPHAمشاهده پروفایل

استاد

• Signal Processing
• Fault Diagnosis and Prognosis
• Electrical Engineering Systems
• +۴ مورد دیگر

Claude DELPHA is a Full Professor at Université Paris Saclay, affiliated with CentraleSupélec’s Laboratoire des Signaux et Systèmes (L2S). He holds an IEEE Senior Member status and has been with L2S since 2001. His expertise spans signal processing, fault diagnosis, electrical engineering systems, and machine learning. He leads the Modelling and Estimation team (GME) at L2S and oversees engineering admissions at Polytech Paris Saclay. Education: PhD in Instrumentation & Measurements and Signal Processing from Université de Metz, with a focus on intelligent sensor systems. Graduate degree in Electrical and Signal Processing Engineering. Research Interests: Multidimensional/statistical signal processing, fault diagnosis/prognosis (modeling, detection, estimation), electrical systems (drives, converters, PV), data hiding (watermarking), and pattern recognition (machine/deep learning). Active in energy systems, industry 4.0, and health/biology applications. Professional Roles: Director of GME research team, Polytech admissions lead, member of Polytech’s executive and academic boards, and IUT department council member. Engaged in labs like SYCOMORE and ILOCOS. Publications: Over 200 works since 2015, focusing on fault diagnosis in electrical systems, photovoltaic modules, bearings, and tidal turbines. Key methods include Kullback-Leibler divergence, Jensen-Shannon divergence, Mahalanobis distance, and PCA-based approaches. Awards: Not explicitly listed in provided texts.

Qi Longمشاهده پروفایل

استاد

• Genomics
• Computational Biology
• Biostatistics
• +۱۰ مورد دیگر

Qi Long is a Professor at the University of Pennsylvania, holding joint appointments in the Department of Biostatistics, Epidemiology and Informatics (Perelman School of Medicine), Department of Computer and Information Science (School of Engineering and Applied Science), and Department of Statistics and Data Science (The Wharton School). He serves as Founding Director of the Center for Cancer Data Science, Associate Director of the Penn Institute for Biomedical Informatics, and Associate Director for Quantitative Data Science at the Abramson Cancer Center. His research bridges statistical and machine learning (ML/AI) method development with biomedical applications, focusing on precision medicine and population health. Education : Ph.D. (2005) and M.S. (2003) in Biostatistics from University of Michigan; B.S. (1998) in Computer Science from University of Science and Technology of China. Research interests include: Robust statistical and ML/AI methods for big health data (-omics, EHRs, imaging, mHealth) Multimodal data integration and subgroup heterogeneity analysis Missing data, causal inference, Bayesian methods, and clinical trials Data privacy, algorithmic fairness, and responsible AI in healthcare Foundation models and agentic AI for biomedicine His publications focus on privacy-preserving AI, fairness-aware ML, and integrative models for multi-omics and EHRs. Recent work explores LLMs and watermark detection in hybrid human-AI settings. Scientific Awards : Elected fellow: AAAS, ASA, IMS, ISI, AMIA He leads large NIH- and ARPA-H-funded initiatives, directing statistical coordinating centers for national clinical trials. His lab trains numerous PhD/Master’s students and postdocs, many of whom hold prestigious academic or industry positions.

• Machine Learning
• Computer Vision
• Signal Processing
• +۶ مورد دیگر

Dongwook Kim is affiliated with the Korea Advanced Institute of Science & Technology (KAIST) as a faculty member in the Department of Business and Technology Management under the College of Business. His research spans multiple domains including machine learning, robotics, signal processing, and biomedical engineering. Key contributions in Computer Vision (CNN-based semantic segmentation, 3D point cloud analysis) Significant work in Hardware Design (energy-efficient processors, neuromorphic computing) Interdisciplinary expertise in Medical Imaging (bone age assessment, retinal biomarkers) and Cybersecurity (attack detection, network analytics) Publications since 2015 demonstrate sustained innovation in AI applications , Signal Processing , and Smart City Governance . His work often integrates theoretical advances with practical implementations in real-world systems. No scientific awards or student mentorship details are explicitly documented in the provided records.

Yongyi Maoمشاهده پروفایل

استاد

• Communications
• Machine Learning

Yongyi Mao is a Professor at the School of Electrical Engineering and Computer Science, University of Ottawa. He holds a Ph.D. in Electrical Engineering from the University of Toronto and has a multidisciplinary background in medical biophysics and engineering. His research focuses on communications and machine learning, with notable contributions to federated learning, information theory, and adversarial robustness. Professor Mao has held academic roles since 2003, advancing from Assistant to Full Professor by 2012. Education: B.Eng., Southeast University, 1992 M.D., Nanjing Medical University, 1995 M.S., University of Toronto (Medical Biophysics), 1998 Ph.D., University of Toronto (Electrical Engineering), 2003 Research interests span machine learning frameworks, federated learning, adversarial attacks, and domain adaptation. His work often bridges theoretical foundations (e.g., generalization bounds) with practical applications in text classification and watermarking. Publications reflect a strong emphasis on machine learning theory and NLP applications, with recent trends toward improving model robustness and generalization. No scientific awards are explicitly listed, though his prolific output suggests significant recognition in the field. Advising and grants: While specific student names or grant details are not provided, his position as a Full Professor indicates active research supervision and likely grant involvement. His lab focuses on advancing AI and communication technologies through interdisciplinary approaches.

Hongwei Wangمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Interconnected Systems
• Large Language Models
• +۶ مورد دیگر

Dr. Hongwei Wang is a Senior Researcher at Tencent AI Lab Seattle , specializing in applied machine learning for Natural Language Processing and Interconnected Systems . His work bridges Knowledge Graphs , Recommender Systems , and Graph Neural Networks , with a focus on large language models and retrieval-augmented generation. Ph.D. (2018), B.E. (2014) in Computer Science from Shanghai Jiao Tong University Postdoctoral Researcher : Stanford University (2019-2021), University of Illinois Urbana-Champaign (2021-2022) Dr. Wang’s research explores integrating Knowledge Graphs with Graph Neural Networks to enhance recommendation systems, language models, and information retrieval. His work spans Retrieval-Augmented Generation , Representation Learning , and GAN-based Graph Modeling , with recent papers on State-Space Exploration for LLM agents and Semantic Watermarking . His 15 most recent publications (2022-2024) focus on Retrieval Granularity , Interactive Memory , and Agent Systems , with keywords spanning Computer Science , Machine Learning , and Knowledge Graphs . Trends highlight advancements in Token-Level Semantic Matching , Schema-Guided Event Prediction , and Multi-Document Summarization . Scientific Awards: 2020 CCF Outstanding Doctoral Dissertation Award 2018 Google Ph.D. Fellowship Dr. Wang contributes to open-source projects like DKN and RippleNet , with 11 repositories on GitHub. He actively engages in Knowledge Graph Conferences (KDD, WWW, AAAI) and studies Chinese Classical Poetry and Film Arts .