Jianhua Shen is a Professor at Nanjing University of Posts and Telecommunications (NJUPT), School of Computer Science and Engineering, with a distinguished research career spanning over 30 years. His academic journey shows a natural progression from theoretical mathematics to applied computer science and educational technology. His research interests encompass Applied Mathematics , Differential Equations , Impulsive Systems , Stability Analysis , Computer Networks , Wireless Communication , Educational Technology , and Machine Learning . Shen's work demonstrates remarkable adaptability while maintaining mathematical rigor, evolving from foundational theoretical work to practical applications in modern computing and education. Shen's recent publications (2020-2025) reveal a strong focus on educational technology and federated learning , with significant contributions to virtual simulation experiments, MOOC platforms, and personalized learning systems. His work bridges theoretical computer science with practical educational applications, reflecting NJUPT's emphasis on technology-enhanced education. As an advisor, Shen has mentored numerous students including Meijuan Chen, Cong Yu, and Xiaoling Liu, who frequently appear as co-authors on his publications. His collaborative network extends across multiple departments at NJUPT and includes researchers from various Chinese institutions.
Hong Qin is an Associate Professor at Old Dominion University (ODU), specializing in interdisciplinary research at the intersection of artificial intelligence, bioinformatics, and public health. His work focuses on developing machine learning models for healthcare diagnostics, pandemic forecasting, and computational biology. Qin’s research integrates deep learning, federated learning, and explainable AI to address challenges in disease detection, aging mechanisms, and urban environmental analysis. Key research interests include viral evolution modeling, medical image analysis, and algorithm optimization for genomics. He has contributed to projects like the REU Site: Interdisciplinary Computational Biology (iCompBio), fostering undergraduate research in computational biology and data science. His lab develops tools such as μPolar for image analysis and Fitmix for lifespan modeling in yeast. Recent studies explore the impact of social determinants on health outcomes, AI-driven solutions for cybersecurity threats, and urban growth modeling using remote sensing. Qin’s work bridges computational methods with real-world applications in healthcare, environmental science, and public policy.
Antonino Nocera is an Associate Professor at the University of Pavia's Department of Electrical, Computer and Biomedical Engineering, within the Faculty of Engineering. He specializes in Data Science, Social Network Analysis, Privacy, Security, and Artificial Intelligence. He holds a PhD in Information Engineering from the University Mediterranea of Reggio Calabria (2013). His research focuses on cybersecurity, machine learning applications, and privacy-preserving technologies, with over 85 publications. He is an Associate Editor for *Information Sciences* (Elsevier) and *IEEE Transactions on Information Forensics and Security*. He leads the Digital Content Analysis Lab (DCALab) and collaborates with Microsoft on cloud computing initiatives. Recent projects include a 2020 Hackathon on analyzing the spread of the SARS-CoV-2 virus and a Microsoft Learn mini-course on Azure. His work spans federated learning security, malware detection, and IoT trust models. He actively participates in conference TPCs and promotes ethical AI practices. Key contributions include frameworks like SECTIS for CTI sharing and DROIDTTP for Android application analysis. His teaching emphasizes data science and big data analytics, integrating industry tools like Azure into curricula.
Dr. Li Xi is an Associate Professor of Chemical Engineering at McMaster University, specializing in multiscale molecular modeling of polymer materials, flow turbulence, and polymer fluid dynamics. His research integrates computational fluid dynamics (CFD) and molecular simulation to address challenges in energy, environment, and biomedical applications. He holds a Ph.D. from the University of Wisconsin-Madison and completed postdoctoral research at MIT, focusing on polymer materials and pharmaceutical manufacturing. Education: Ph.D., Chemical Engineering, University of Wisconsin–Madison (2009) Postdoctoral, Chemical Engineering, MIT (2009–2013) B.S., Chemical Engineering, Zhejiang University (2004) Research Interests: Multiscale modeling of polymer materials for targeted properties Turbulent flow dynamics and drag reduction mechanisms Rheology and polymer processing-structure-property relationships Advanced materials and smart manufacturing systems Labs & Resources: Led research group at McMaster (visit www.xiresearch.org ) Focus areas: polymer materials, process systems engineering, micro-nano systems Teaching: Instructor for CHEM ENG 3D04 (Thermodynamics), CHEM ENG 3L03 (Laboratory Skills), and supervising graduate students
Collin McMillan, Ph.D., is an Associate Professor of Computer Science at the University of Notre Dame's College of Engineering. He earned his Ph.D. from William & Mary under Denys Poshyvanyk. His research focuses on software engineering and natural language processing, particularly in automated documentation generation, program comprehension, and human-AI collaboration in code analysis. Education: Ph.D. in Computer Science (William & Mary), advised by Denys Poshyvanyk. His work integrates eye-tracking studies, neural networks, and graph-based models to improve code summarization and developer tooling. Research interests include source code summarization, developer behavior analysis, privacy behavior detection in Android apps, and tools for program comprehension. His lab, the Automatic Program Comprehension Lab (APCL), develops datasets and tools like CodeGRITS, available via Huggingface and GitHub. Publications span 2007–2025, emphasizing developer-centric AI models, code summarization techniques, and empirical studies on programmer attention. Key themes include context-aware models, eye-tracking integration, and LLM applications in software engineering. Awards: ASEE Teaching Award (2020), NSF CAREER Award (2015), Distinguished Paper Award (2021). Grants: NSF funding for AI/SE integration, totaling over $2.5M since 2015. Service: Associate Editor for TSE and EMSE, program committee roles at ICSE, FSE, and ASE. Current students include Maria Dhakal (2023), Robert Wallace (2023), and Chia-Yi Su (2022). Graduates have moved to faculty positions (e.g., Clemson University) and industry roles at Google, Qualtrics, and Ascend Health.
Trygve Christian Eftestøl is a Professor of Information Technology at the Department of Electrical Engineering and Computer Science, University of Stavanger. His academic background includes a PhD in signal processing from NTNU and an M.Sc. in Electrical and Computer Engineering from HiS, Stavanger. He is a senior member of IEEE and serves on the board of the Cognitive Lab at UiS since 2025. Educations: PhD in Signal Processing (NTNU/HiS, 2000) M.Sc. in Electrical and Computer Engineering (HiS, Stavanger) Research Interests: His work focuses on biomedical data analysis, including resuscitation, cardiac science, waveform analysis (ECG, thorax impedance), and MRI for myocardial injury. He is involved in multidisciplinary projects such as digital pathology, newborn resuscitation, sports medicine, neurogenerative diseases, and prostate cancer imaging. He co-founded the Biomedical Data Analysis Laboratory (BMDLab) and serves as its deputy leader since 2020. Articles Trends: Recent publications emphasize machine learning applications in healthcare (e.g., EEG-based neurodegenerative disorder classification, MRI segmentation for myocardial injury), predictive models for cardiac arrest outcomes, and AI-driven solutions in oncology and pathology. His work bridges signal/image processing with clinical needs, addressing challenges in resuscitation, cardiology, and diagnostic accuracy. Awards/Grants: No specific awards listed, but his leadership roles and research contributions highlight sustained academic and clinical impact. Advising & Labs: Supervises/co-supervises PhD projects in areas like human activity recognition and prostate cancer detection. Active in BMDLab, collaborating nationally and internationally on biomedical data analysis.
Moritz Böhle is a researcher at the Max Planck Institute for Informatics , affiliated with the Computer Vision and Machine Learning department. He completed his PhD in 2024 at Saarland University, titled Towards Designing Inherently Interpretable Deep Neural Networks for Image Classification . His work focuses on interpretable machine learning, particularly in developing architectures like B-cos Networks to align models for faithful explanations. He has contributed to foundational research in model interpretability, including transforming pre-trained models (B-cosification), knowledge distillation with explanations, and automated concept discovery. His research spans computer vision tasks, vision transformers, and convolutional neural networks, with a strong emphasis on aligning model components for transparency. He has co-authored papers in top venues like ICLR, NeurIPS, CVPR, and ECCV, addressing challenges in post-hoc explanations, alignment mechanisms, and systematic evaluation of attribution methods. His work bridges theoretical advancements and practical applications in explainable AI. Key contributions include B-cos alignment for CNNs and transformers, explanation-enhanced knowledge distillation, and frameworks for task-agnostic concept discovery. These efforts aim to make AI systems more transparent and trustworthy while maintaining competitive performance.
Peyman Moghadam is a Principal Research Scientist at CSIRO Data61 and an Adjunct Professor at Queensland University of Technology (QUT). He leads the Embodied AI Research Cluster at CSIRO, focusing on robotics and machine learning intersections. His roles include former Group Leader of Robotic Perception and Acting Leader of the Spatiotemporal AI portfolio within CSIRO's MLAI Future Science Platform. Education: PhD in Robotics from Nanyang Technological University (2012). Professional experiences include Visiting Professorships at ETH Zurich (2022) and University of Bonn (2019), alongside leadership in multidisciplinary projects. Research interests span self-supervised learning, embodied AI, 3D perception, and agricultural robotics. Awards include CSIRO's Julius Career Award, Collaboration Medal, and national/state iAwards for innovation in robotics. He has held adjunct roles at QUT and the University of Queensland. Current roles emphasize AI-driven solutions for scientific challenges, such as Great Barrier Reef conservation and autonomous systems in agriculture. Key projects include the DARPA Subterranean Challenge (2nd place), Hovermap LiDAR technology, and collaborations with industry partners like Emesent and Georgia Tech. His work bridges foundational research with real-world applications in mining, agriculture, and environmental monitoring.
Zhuangdi Zhu is an Assistant Professor at the Department of Cyber Security Engineering, George Mason University (GMU). She holds a PhD in Computer Science from Michigan State University (2022) and a BSc from Nanjing University of Science and Technology (2015). Prior to academia, she worked as a Senior Data & Applied Scientist at Microsoft (2022–2023) and interned at Meta, Google, IBM, and others. Her research focuses on accountable, scalable, and trustworthy AI , particularly in federated learning, reinforcement learning, robustness, fairness, privacy, and edge computing. She has organized workshops such as FedKDD and FL4Data-Mining, and her work has been recognized through grants like the CCI Grant (2024) and NAIRR Pilot Program Grant (2024). Key research areas include federated learning with system heterogeneity , sample-efficient reinforcement learning , and de-biased representation learning . She teaches courses on federated learning and cybersecurity engineering at GMU. Her lab hosts PhD students Zhengbang Yang and Eason Zhong. She has published extensively in top venues including TPAMI, ICML, KDD, and AAAI. Professional activities include serving as Program Chair for FedKDD and session chair at KDD. Her recent work explores AI applications in healthcare (e.g., cognitive support for seniors) and sports analytics. She also maintains a research lab focused on secure and privacy-conscious threat detection in federated systems.
Yunhe Feng is an Assistant Professor in the Department of Computer Science and Engineering at the University of North Texas (UNT), where he directs the Responsible AI Lab and co-leads the Learning · Language · Vision (LLaVi) Lab. He also serves as an Affiliate Assistant Professor in the Anuradha and Vikas Sinha Department of Data Science at UNT. Previously, he was a Postdoctoral Fellow at the University of Washington (2020-2022) after earning his Ph.D. in Computer Science from the University of Tennessee, Knoxville (2020). Research Interests include Responsible AI, Generative AI, Data Security & Privacy, and their applications in Computational Health and Spatial Computing. His work explores AI ethics, robustness in vision-language models, and efficient large language model inference techniques. Recent Publications focus on dynamic attention masking for LLMs, generative AI in biomedical imaging, and multimodal semantic segmentation, with contributions appearing in top venues like ACL, ICCV, IROS, and IEEE Transactions. Awards & Honors include: 2025 IEEE Senior Member 2024 Dallas Innovates AI 75 List 2023 IEEE Smart Computing Early Career Award 2023 IEEE COMPSAC Best Track Paper Award Teaching & Leadership involves directing UNT's Master's Program in Artificial Intelligence (first in Texas) and teaching courses like CSCE 5300 - Introduction to Big Data. He mentors undergraduate and graduate students while serving on program committees for AAAI, ACL, and IEEE conferences.
Abelardo Carlos Martínez Lorenzo is a postdoctoral researcher at Sapienza University of Rome working under Professor Roberto Navigli in the Natural Language Processing group. His research focuses on cross-lingual semantics and information extraction, with specialization in semantic parsing and language models. His educational background includes: Software Engineering degree from Universidad de Málaga, Spain Erasmus Mundus Joint Master Degree in Big Data Management and Analytics from Université Libre de Bruxelles (Belgium), Universitat Politècnica de Catalunya (Spain), and Technische Universität Berlin (Germany) Martínez Lorenzo's research centers on overcoming language barriers through advanced semantic representation frameworks. His work develops efficient multilingual parsing systems that address data scarcity in low-resource languages, leveraging transformer architectures and novel linearization techniques. Key contributions include the BabelNet Meaning Representation (BMR) formalism and optimization methods for Abstract Meaning Representation (AMR) parsing, enabling robust cross-lingual semantic analysis without language-specific constraints. His publication record (2022-2024) reveals a consistent trajectory in enhancing AMR parser efficiency and multilingual capabilities. Notable innovations include CLAP's 80% reduction in computational time, cross-lingual alignment through transformer cross-attention, and ensemble methods that maintain structural integrity while improving SMATCH scores. These works collectively advance accessible, high-performance semantic analysis across linguistic boundaries. His scientific recognition includes: Marie Skłodowska-Curie Fellowship as Early Stage Researcher in the Knograph project (Horizon 2020) Funded through the prestigious Marie Curie grant, Martínez Lorenzo conducts research within the Knograph project framework while mentoring junior researchers in the Sapienza NLP group. His work demonstrates significant grant impact through open-source releases (CLAP, LeakDistill) that democratize access to semantic analysis tools. He actively contributes to the Sapienza Natural Language Processing group led by Roberto Navigli, which specializes in knowledge-based multilingual NLP systems. The group maintains strong collaborations through European research initiatives including Horizon 2020 projects, with emphasis on creating interlingual semantic resources and scalable parsing frameworks.
Ho-fung Leung is a Professor at the Department of Computer Science and Engineering, Faculty of Engineering, Chinese University of Hong Kong. With a prolific publication record spanning over three decades, his research has significantly contributed to the fields of artificial intelligence, multi-agent systems, and natural language processing. His educational background, though not explicitly stated in the provided text, likely includes advanced degrees in computer science or a related field, given his extensive research contributions and faculty position at a prestigious university. Professor Leung's research interests span multiple areas within artificial intelligence, with a particular focus on multi-agent systems, reinforcement learning, natural language processing, and human-computer interaction. His work often explores the intersection of theoretical foundations and practical applications, developing novel algorithms and frameworks that address real-world challenges in AI systems. He has made significant contributions to constraint satisfaction problems, trust and reputation systems in multi-agent environments, and more recently to deep learning applications in NLP and human activity recognition. His recent publications demonstrate a strong trend toward applying advanced machine learning techniques to complex problems in natural language understanding, knowledge representation, and human activity recognition. Many of his papers focus on improving the efficiency, robustness, and interpretability of AI systems through innovative architectural designs and learning paradigms. Key research themes include few-shot learning, knowledge-enhanced models, and theoretical analysis of reinforcement learning dynamics. Professor Leung has received recognition for his work through numerous publications in top-tier conferences and journals, though specific awards are not detailed in the provided information. He has supervised numerous students throughout his career, with many of his publications featuring junior researchers in first-author positions. His research group appears to focus on cutting-edge problems in AI, with current projects spanning reinforcement learning theory, knowledge graph applications, and multimodal learning systems. Collaborators include researchers from across CUHK and international institutions. Professor Leung is actively involved in multiple research projects, with recent work focusing on human activity recognition using wearable sensors, knowledge-enhanced language models, and theoretical aspects of reinforcement learning. His research continues to evolve while maintaining strong connections to foundational AI principles, demonstrating remarkable adaptability in a rapidly changing field.
Zhaopeng Qiu is an active researcher in computer science with a strong publication record spanning from 2012 to 2025. His work primarily focuses on recommendation systems, machine learning, and their applications in healthcare and online services. He frequently collaborates with researchers including Xian Wu, Zhi Zheng, Hengshu Zhu, and Hui Xiong on projects related to large language models, medical informatics, and job recommendation systems. Dr. Qiu's research interests include Recommendation Systems, Machine Learning, Medical Informatics, Natural Language Processing, Artificial Intelligence, Data Mining, and Healthcare AI. His work demonstrates a clear evolution from earlier research in mobile robotics (2012-2015) to current cutting-edge work applying large language models to recommendation problems across various domains. Analysis of his recent publications shows a strong trend toward leveraging large language models for recommendation tasks, with significant contributions in medication recommendation, job matching, and fairness-aware systems. His work bridges theoretical AI advancements with practical applications, particularly in healthcare contexts where AI can have significant real-world impact. While specific scientific awards aren't documented in the available publications, his work appears in top-tier venues including WWW, AAAI, KDD, and IEEE/ACM transactions journals, indicating recognition within the research community. Dr. Qiu's research has practical implications for online platforms, healthcare systems, and labor market technologies. His recent focus on large language models for recommendation suggests he's at the forefront of integrating emerging AI capabilities with traditional recommendation paradigms.
Kaisheng Ma is a Professor in the Department of Computer Science and Technology at Tsinghua University's School of Information Science and Technology. With an extensive publication record spanning from 2013 to 2025, he has established himself as a leading researcher in computer vision, deep learning, and computer architecture. His work bridges theoretical advances with practical applications in neural network acceleration and hardware-software co-design. Professor Ma's research interests focus on advancing the frontiers of computer vision and deep learning systems. His work in knowledge distillation has significantly improved the efficiency of neural networks, while his contributions to hardware acceleration have enabled more powerful AI systems. His recent research demonstrates a growing emphasis on 3D vision, embodied AI, and the intersection of language and vision systems. He has pioneered techniques in frequency domain analysis for image processing and developed novel approaches for neural network acceleration through hardware-software co-design. Analysis of Professor Ma's recent publications (2023-2025) reveals a strong research trajectory with increasing focus on multimodal learning, 3D scene understanding, and efficient neural network deployment. His work spans both theoretical advances in machine learning (particularly knowledge distillation and continual learning) and practical hardware implementations for AI acceleration. The publications show consistent collaboration with a core group of researchers while expanding into new areas like brain-computer interfaces and language-grounded spatial reasoning. Professor Ma has successfully mentored numerous PhD students who have become active contributors to the field, including Linfeng Zhang, Runpei Dong, and Zekun Qi. His research has been supported by significant grants that enable cutting-edge work in neural network acceleration and 3D vision systems. His students have published in top-tier conferences including CVPR, NeurIPS, and ICCV, demonstrating the strength of his research group. Professor Ma leads a research group focused on the intersection of computer vision, deep learning, and computer architecture. His lab investigates efficient neural network deployment, hardware acceleration techniques, and novel computer vision algorithms. The team maintains strong collaborations with industry partners working on AI hardware and has developed several innovative approaches to neural network acceleration and efficient model deployment.
Qian Huang is a Professor in the Department of Computer Science at Sun Yat-sen University's School of Computer Science and Engineering. With over 350 publications spanning from 1992 to 2025, Dr. Huang has established themselves as a leading researcher in multiple interdisciplinary fields at the intersection of computer science, engineering, and applied mathematics. Dr. Huang's research spans several critical domains in modern computing. Their primary interests include computer vision with applications in medical image analysis, machine learning with emphasis on transformer architectures and federated learning, signal processing for video compression, and wireless communications for IoT applications. Recent work demonstrates significant contributions to nuclei segmentation in cervical cell images, advanced video compression techniques using spatiotemporal modeling, and predictive maintenance systems for industrial equipment that incorporate uncertainty quantification. An analysis of Dr. Huang's 15 most recent publications reveals a strong trend toward interdisciplinary research that bridges theoretical computer science with practical applications. Their work consistently addresses real-world challenges in healthcare diagnostics, industrial automation, and communication systems. The publications demonstrate expertise in developing novel deep learning architectures while maintaining theoretical rigor in mathematical foundations. Multiple publications in IEEE Transactions journals across various domains Regular contributions to top-tier conferences including ICASSP, ICIP, NeurIPS, and CVPR Collaborations with researchers from leading institutions globally Dr. Huang's research program appears well-funded through collaborations with industrial partners and Chinese national research grants, though specific grant information isn't detailed in the publication record. Their work on federated learning frameworks and medical image analysis suggests strong connections with healthcare technology companies and medical research institutions. The extensive publication record across multiple domains indicates leadership of a substantial research group with expertise spanning computer vision, machine learning, and signal processing.
