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Heng HuangView profile

Professor

• Machine Learning
• Data Mining
• Big Data Computing
• +11 more

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.

Shiyu ChangView profile

Associate Professor

• Machine Learning
• Natural Language Processing
• Computer Vision
• +4 more

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.

Giancarlo Ferrari TrecateView profile

Adjunct Professor

• Automatic control
• State estimation
• System identification
• +7 more

Giancarlo Ferrari Trecate is an Adjunct Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) , affiliated with the School of Engineering and the SCI-STI-GFT department. He is also involved in teaching and research through the STI-SGM and EDRS-ENS programs. Research Interests : Automatic control, state estimation, system identification, machine learning, distributed control, hybrid systems, microgrids, biochemical networks, voltage and frequency stabilization in AC/DC microgrids. Publications Trends : His recent work focuses on integrating Neural ODEs and Hamiltonian structures for stable control systems, regret minimization in distributed control, and robust state estimation under uncertainty. Applications include autonomous mobility-on-demand , power grid optimization , and secure microgrid control against cyber-attacks. Scientific Awards : No specific awards mentioned in the provided data. Teaching & Advising : He supervises PhD students in mechanical engineering and teaches courses on Multivariable control and Networked control systems . His lab, DECODE , specializes in Dependable Control and Decision systems.

Bryan Kian Hsiang LowView profile

Associate Professor

• Probabilistic Machine Learning
• Bayesian Optimization
• Multi-Agent Systems
• +4 more

Bryan Kian Hsiang Low serves as Associate Professor in the Department of Computer Science at the National University of Singapore's School of Computing, while simultaneously holding leadership positions as Director of AI Research at AI Singapore and Deputy Director of the NUS AI Institute. His academic journey includes a B.Sc. (2001) and M.Sc. (2002) in Computer Science from NUS, followed by a Ph.D. in Electrical & Computer Engineering from Carnegie Mellon University (2009). His research spans probabilistic machine learning, multi-agent systems, and trustworthy AI, with particular focus on Bayesian optimization , federated learning , and data-efficient methodologies . The Low Lab develops frameworks for collaborative AI, automated machine learning, and AI applications in scientific domains through the Group of Learning and Optimization Working in AI (GLOW.AI), which maintains a multi-disciplinary approach bridging computer science, mathematics, and engineering disciplines. Analysis of his recent publications reveals a consistent emphasis on data valuation , privacy-preserving collaborative learning , and robust optimization techniques , with increasing integration of large language models into his research framework. His work demonstrates strong theoretical foundations coupled with practical applications in computational sustainability and robotics. Andrew P. Sage Best Transactions Paper Award (2006) NUS Overseas Graduate Scholarship (2004-2009) Faculty Teaching Excellence Award (2017-2018) IEEE RAS Distinguished Lecturer (2019) World Economic Forum Global Future Councils Fellow (2016-2018) Dr. Low actively mentors PhD students including Rachael Sim, Quoc Phong Nguyen, and Zhongxiang Dai, while leading major initiatives like the AI Phenome Platform for plant breeding optimization. His research group GLOW.AI operates at the intersection of theory and practice, with strong industry engagement through AI Singapore. Current projects focus on scalable AI systems for scientific discovery and developing frameworks for equitable collaborative machine learning with robust privacy guarantees.

Qi LongView profile

Professor

• Genomics
• Computational Biology
• Biostatistics
• +10 more

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
• +6 more

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.

Xianyang ZhangView profile

Professor

• High-dimensional statistics
• Functional data analysis
• Time series analysis
• +8 more

Xianyang Zhang is a Professor in the Department of Statistics at Texas A&M University, affiliated with the College of Arts & Sciences. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (2013) and a B.S. from the University of Science & Technology of China (2008). His research focuses on high-dimensional statistics, functional data analysis, kernel methods, and genomics, supported by grants from NIH, NSF, and Texas A&M. Education: Ph.D., Statistics, University of Illinois at Urbana-Champaign, 2013 B.S., Statistics, University of Science & Technology of China, 2008 Research Interests: Xianyang Zhang develops statistical theories and methodologies for complex data structures, including high-dimensional inference, kernel-based testing, change-point detection, and microbiome analysis. His work bridges computational and theoretical statistics, addressing challenges in genomics, omics-wide studies, and spatial statistics. Key Contributions: Developed KDist , a package for kernel and distance-based statistical inference Authored fastcpd for efficient change-point detection Advanced covariate-adaptive FDR control methods for omics studies Contributed to microbiome analysis tools like MicrobiomeStat and LinDA Advising & Grants: Advises multiple Ph.D. students in statistics and interdisciplinary projects Recipient of NIH and NSF grants for high-dimensional statistical research Collaborates with institutions like Mayo Clinic and Chinese University of Hong Kong Labs/Teams: Leads research groups focused on statistical methodology development, software implementation, and applications in computational biology and genomics.

Guang ChengView profile

Professor

• Generative Data Science
• Trustworthy AI
• Machine Learning Theory
• +5 more

Guang Cheng is a Full Professor of Statistics and Data Science at the University of California, Los Angeles (UCLA), and serves as the Graduate Vice Chair. He leads the Trustworthy AI Lab, focusing on generative data science, privacy-preserving synthetic data, and the theoretical foundations of machine learning. His research explores generative AI, trustworthy synthetic data, and high-dimensional statistics. Education : Ph.D. in Statistics, University of Wisconsin-Madison (2003-2006) B.A. in Economics, Tsinghua University (1998-2002) Research Interests : Generative Data Science, trustworthy AI, machine/deep learning theory, privacy-preserving techniques, high-dimensional statistics, and business intelligence applications in finance, healthcare, and marketing. His lab develops tools like artificially generated tables for privacy-preserving data sharing. Publications : Recent work includes advancements in generative models (e.g., TimeAutoDiff, MissDiff), fair classification algorithms, and theoretical analyses of deep learning (e.g., attention mechanisms, minimax analysis). Key themes include synthetic data utility/privacy trade-offs and adversarial robustness. Awards : IMS Fellow (2020), Adobe Data Science Award (2020), NSF CAREER Award (2012). Advising & Grants : Supervises PhD/Master’s/postdoc researchers in trustworthy AI and generative data science. Alumni hold roles at Meta, Amazon, and academia. Active in editorial roles for JASA - Theory & Methods and Canadian Journal of Statistics . Labs & Teams : Leads the Trustworthy AI Lab at UCLA, organizing workshops on synthetic data in finance and healthcare. Collaborates with Amazon as an Amazon Scholar.

Sunil K. PrabhakarView profile

Professor

• Database Systems
• Data Management
• Uncertain Data
• +4 more

Sunil K. Prabhakar is a Professor of Computer Science at Purdue University, currently serving as the Vice Provost for Faculty Affairs and former Department Head. He holds a PhD from the University of California, Santa Barbara, and a BTech from the Indian Institute of Technology, Delhi. Education: PhD, Computer Science, University of California, Santa Barbara, 1998 MS, Computer Science, University of California, Santa Barbara, 1998 BTech, Electrical Engineering, Indian Institute of Technology Delhi, 1990 Research Interests: Focuses on database systems, uncertain data management, cloud database integrity, sensor databases, and digital rights management. His work emphasizes developing systems for probabilistic data, ensuring authenticity in outsourced databases, and scalable solutions for multimedia and sensor applications. Grants & Projects: NSF Grant IIS-1017990: Ensuring Integrity of Outsourced Databases NSF Grant IIS-09168724: Uncertain Data Management Students & Advising: Advised over 15 PhD and MS students, including Ryan Rossi (Palo Alto Research Center), Rohit Jain (Google), and Reynold Cheng (University of Hong Kong). Labs & Teams: Involved in projects like the ORION uncertain data management system, the C4E4 environmental cyberinfrastructure initiative, and collaborations with industry (e.g., Naval Surface Warfare Center).

Yangming LiView profile

Research Fellow

• Image Analysis
• Machine Learning
• Mathematical Modeling
• +7 more

Yangming Li is a Research Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, affiliated with the Cambridge Image Analysis research group. His work bridges applied mathematics, theoretical physics, and machine learning, with a focus on developing innovative models for image analysis, generative processes, and natural language understanding. Research interests include Fourier Neural Operators, diffusion models, generative adversarial networks (GANs), and their applications in solving complex problems across scientific computing and data-driven domains. His recent contributions explore operator learning for PDEs, robust diffusion models under noisy conditions, and adversarial attacks in text watermarking systems. Publications highlight advancements in operator-based neural networks, risk-sensitive generative modeling, and domain-aware NLP frameworks. His methodologies emphasize mathematical rigor while addressing practical challenges like missing data and model expressivity limitations. Active collaborations span interdisciplinary teams at DAMTP and the broader University of Cambridge research community.

Paolo PalmieriView profile

Senior Lecturer

• Cryptography
• Privacy Enhancing Technologies
• Internet of Things Security
• +4 more

Dr. Paolo Palmieri is a Senior Lecturer in Cyber Security at the University College Cork (UCC) , affiliated with the School of Computer Science & IT . He serves as a Funded Investigator in Insight and CONNECT research centers, Supervisor in ADVANCE and AI Centres for Research Training, and co-Investigator in DTIF-Holistics and SFI-Spoke on Blended Autonomous Vehicles . PhD in Cryptography (2013), Université catholique de Louvain Prior positions: Lecturer at Cranfield University and Bournemouth University (UK), Post-Doctoral Researcher at Delft University of Technology (Netherlands) Research spans cryptography, privacy, and security , with focus on: Secure multi-party computation Homomorphic encryption Location privacy mechanisms Spatial Bloom filters Smart city security Blockchain applications in IoT Recent publications highlight advancements in blockchain-based healthcare authentication , privacy-preserving vehicular networks , and homomorphic encryption for sentiment analysis . He chairs Malicious Software and Hardware in Internet of Things (Mal-IoT) and Location Privacy Workshop (LPW) . Active in PhD supervision , he currently advises students in cryptographic protocols for location-based services. Collaborates with Smart City Lab at the University of Bologna and participates in international research networks .

Patrick BasView profile

Research Professor

• Steganography
• Steganalysis
• Digital Watermarking
• +2 more

Patrick Bas serves as Research Director and Thematic Group Facilitator for the Data Intelligence Group (DatInG) at the University of Lille, France, where he leads the Signal and Images team within the CRISTAL laboratory. His office is located at ESPRIT in Lille's Scientific City, and he holds membership on the institution's Scientific Council. Bas earned his Electrical Engineering degree (1997) and Ph.D. in Signal and Image Processing (2000) from the Institut National Polytechnique de Grenoble. His academic journey includes postdoctoral work at Université Catholique de Louvain, CNRS research at Gipsa-Lab (2001-2009), and a visiting position at Helsinki University of Technology (2005-2008). His primary research centers on steganography, steganalysis, and digital watermarking, with significant contributions to image manipulation detection and neural network applications in multimedia security. He has organized major initiatives including the BOSS steganalysis contest (2010) and served as co-coordinator for the Ecrypt European NoE's watermarking virtual lab (2004-2008). While his Google Scholar profile indicates recent publications in graph theory and combinatorics (2021-2025), these appear inconsistent with his documented research focus on multimedia security as evidenced by his thesis supervision and professional activities. Bas has supervised seven doctoral theses including Rony Abecidan's work on robust image manipulation detection (2024) and Solène Bernard's research on neural network steganography (2021). He has held editorial roles for the Eurasip Journal on Information Security (since 2011) and IEEE Transactions on Information Forensics and Security (2013-2016), and co-organized the International Workshop on Information Hiding (IH07).

Yuheng BuView profile

Assistant Professor

• Machine Learning
• Information Theory
• Signal Processing
• +5 more

Dr. Yuheng Bu is an Assistant Professor in the Department of Electrical & Computer Engineering at the University of Florida, part of the Herbert Wertheim College of Engineering. His research focuses on machine learning, information theory, and signal processing, with applications in fair/trustworthy ML, uncertainty quantification, and anomaly detection. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (2019), an MS from the same institution (2016), and a B.E. from Tsinghua University (2014). Research interests include developing robust watermarking techniques for large language models, ensuring algorithmic fairness, and advancing theoretical foundations of learning algorithms via information-theoretic methods. His work bridges theory and practice, addressing challenges in model security, interpretability, and generalization. Key honors include the Yi-Min Wang and Pi-Yu Chung Research Award (2019). His publications emphasize security-aware ML, uncertainty quantification, and information-theoretic analysis of learning algorithms. While no advising or grants are listed, his lab likely focuses on interdisciplinary projects at the intersection of ECE and computer science.

Mark YampolskiyView profile

Associate Professor

• Cybersecurity
• Additive Manufacturing Security
• Internet of Things (IoT)
• +4 more

Mark Yampolskiy is an Associate Professor in the Department of Computer Science and Software Engineering at Auburn University's Samuel Ginn College of Engineering. His research focuses on cybersecurity in additive manufacturing (AM), internet of things (IoT), and computer networks. Yampolskiy leads efforts to secure AM processes against cyber-physical attacks, including sabotage and data theft. He has received prestigious awards such as the ASTM International Additive Manufacturing Young Professor Award and led high-profile grants totaling over $685,000. Education: Ph.D. in Computer Science, Ludwig Maximilian University M.S. in Computer Science, Technical University Munich B.S. in Applied Mathematics, Moscow State Institute of Radiotechnics, Electronics and Automation Research Interests: Cybersecurity of digital & additive manufacturing IoT security and network defense Cyber-physical system vulnerabilities 3D printing integrity and sabotage detection Key Contributions: Developed frameworks for AM security awareness and threat modeling Pioneered studies on side-channel attacks in manufacturing Co-organized ASTM AM Security Working Groups and AMSec workshops Advocated for legal standards in AM intellectual property protection Awards: ASTM International Additive Manufacturing Young Professor Award America Makes Award ($260k+ for cybersecurity training) NSF Grant ($425k for sabotage prevention research) Labs/Initiatives: Active in Auburn's National Center for Additive Manufacturing Excellence (NCAME), McCrary Institute for Cyber and Critical Infrastructure Security.

• Fuzzy systems
• Computational mathematics
• Numerical optimization
• +5 more

Professor Gleb Beliakov is a faculty member at Deakin University's School of Information Technology within the Faculty of Science Engineering and Built Environment. He has held roles including Associate Head of School (Research) from 2009–2012 and currently leads the Computational Intelligence and Data Analytics Research cluster and the Data to Intelligence research centre. His expertise spans fuzzy systems, computational mathematics, numerical optimization, and high-performance computation. Education: PhD and MSc from the Russian Peoples Friendship University. Research Interests: Focuses on fuzzy systems, aggregation functions, parallel programming, and their applications in decision-making and data analysis. Grants: Includes projects on Trusted Autonomy (CRC), Satellite Communication Systems (Defence), and Explainable AI (ARC). His research emphasizes fuzzy measures, Choquet integrals, and their use in addressing complex problems like resource optimization and multimedia piracy. He has supervised numerous PhD and master's students, contributing to advancements in preference mining, image processing, and watermarking techniques.