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Chengbo ZhengView profile

Research Fellow

• Data Visualization
• Human-Computer Interaction (HCI)
• Artificial Intelligence
• +3 more

Dr. Chengbo Zheng is a Postdoctoral Research Fellow at the School of Electrical Engineering and Computer Science, University of Queensland. His research focuses on data visualization, human-computer interaction, and educational technology, particularly leveraging AI to enhance collaborative work, decision-making, and learning environments. Zheng has contributed to interdisciplinary projects such as AI tools for self-assessment in education, human-AI co-exploration systems, and visualization techniques for complex data analysis. His work bridges theoretical advancements with practical applications, including improving data storytelling, combating misinformation, and optimizing transportation networks through visual analytics. Recent projects emphasize AI's role in collaborative processes, such as group decision-making fairness and interdisciplinary information seeking. Zheng's methodologies often combine computational tools with user-centered design principles to address real-world challenges. He has presented at top venues like CHI, UIST, and IEEE Visualization conferences, showcasing innovations in interactive systems, AI integration, and visualization design. His research has implications for education, scientific collaboration, and urban planning.

Rahmatollah BeheshtiView profile

Associate Professor

• Machine Learning
• Health AI
• Healthcare Applications of Large Language Models
• +3 more

Rahmatollah Beheshti is an Associate Professor in the Department of Computer & Information Sciences at the University of Delaware, with a joint appointment in the Epidemiology Program and affiliation with the Data Science Institute. He is also a Research Faculty member at Nemours Children’s Health. His research focuses on Machine Learning and Health AI, directed through the healthy lAife lab at UD. He earned a PhD in Computer Science from the University of Central Florida (2011-15), followed by a postdoc in Public Health at Johns Hopkins Bloomberg School of Public Health (2015-18). His academic contributions span teaching courses like Advanced Deep Learning, Artificial Intelligence, and Data Mining, alongside significant service roles in NSF panels and top-tier conference program committees. Beheshti’s research integrates machine learning with healthcare applications, particularly leveraging large language models (LLMs) for clinical decision support, electronic health record (EHR) analysis, and pediatric obesity risk prediction. His work emphasizes fairness, bias mitigation, and scalable solutions for healthcare challenges. His lab explores multimodal data fusion, graph neural networks, and ethical AI in clinical settings. He has contributed to over 50 peer-reviewed articles, with recent focuses on improving clinical text summarization, evaluating bias in medical LLMs, and developing interoperable pipelines for pediatric health. His service includes roles in the CRA Undergraduate Research Awards Committee and reviewing for journals like Lancet Digital Health and npj Digital Medicine.

Carsten LanquillonView profile

Research Professor

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +3 more

Prof. Carsten Lanquillon is a Research Professor at Heilbronn University, specializing in Language Technologies and Cognitive Assistance Systems within the Department of Business Informatics. He leads the Center for Industrial AI (iAI), a Carl Zeiss Foundation-funded initiative addressing AI implementation challenges for medium-sized enterprises. His expertise spans Business Intelligence, Data Science, Machine Learning, and Conversational AI with a focus on applying AI in industrial production processes. Key research areas include cognitive assistance systems, anomaly detection, and ethical AI frameworks. His work bridges academia and industry through collaborative projects like the iAI Center, which promotes sustainable AI adoption in regional manufacturing. Research emphasizes practical solutions for AI integration, including secure large language model adaptation, generative AI applications, and hybrid intelligence systems combining human and machine capabilities. Recent efforts explore explainable AI, digital twin integration, and user transparency in industrial contexts. Lanquillon's contributions include process models for data science projects and frameworks for knowledge-grounded NLP systems. He actively publishes on topics ranging from energy-efficient deep learning to interactive quality analysis tools in automotive industries. His research often combines technical innovation with user-centric design principles to ensure practical applicability.

• Federated Learning
• Cybersecurity
• Edge Computing
• +4 more

Dr. Meikang Qiu is a faculty member at Texas A&M University Commerce (Department of Computer Science) and Columbia University (Department of Electrical Engineering). He received his PhD in Computer Science from the University of Texas at Dallas in 2007. His research spans distributed intelligence systems with current focus areas including federated learning security, efficient AI for edge devices, and multimodal learning applications in healthcare and finance. Qiu's research integrates machine learning with systems engineering, emphasizing privacy-preserving AI , robust distributed systems , and resource-constrained optimization . His recent work explores: (1) Defenses against adversarial attacks in federated environments, (2) Quantum-classical hybrid computing frameworks, and (3) Efficient model compression techniques for large language models. Publication analysis (2024-2026) reveals three dominant themes: (1) Security enhancements for distributed learning systems, (2) Efficiency optimizations for edge AI deployment, and (3) Cross-modal applications in medical and financial domains. His work consistently addresses scalability challenges in real-world AI systems.

Mikhail YurochkinView profile

Researcher

• Large Language Models (LLMs)
• Machine Learning
• Optimal Transport
• +5 more

Mikhail Yurochkin is a Staff AI Scientist at the IFM MBZUAI Silicon Valley Lab, where he leads data mixing for LLM pre-training. Previously, he served as Research Manager at the MIT-IBM Watson AI Lab, leading the Statistical Large Language Modeling group. He holds a PhD in Statistics from the University of Michigan, advised by Prof. Long Nguyen. Education: PhD in Statistics, University of Michigan (advisor: Prof. Long Nguyen) Research Interests: His work focuses on foundational and applied aspects of large language models (LLMs), including pre/post-training methodologies, data quality, reasoning, evaluation, routing, and efficient inference. He also explores statistical modeling approaches in areas like OOD generalization, algorithmic fairness, federated learning, and Bayesian nonparametrics. His contributions often bridge theoretical advancements with practical implementations in AI systems. Key Contributions: Developed frameworks for LLM routing and compression (e.g., CARROT, LLM routing via optimal transport) Pioneered tinyBenchmarks for efficient LLM evaluation Advanced techniques for fairness in machine learning (e.g., individual fairness, distribution shifts) Contributed to federated learning through models like Federated Learning: A Comprehensive Overview Labs & Affiliations: Active in collaborative environments such as the MIT-IBM Watson AI Lab and MBZUAI’s Silicon Valley Lab, focusing on cutting-edge research in AI alignment, scalable models, and ethical AI systems.

• Embedded Systems Design
• Domain-Specific Modeling
• IoT in Education
• +4 more

Dr. Sofia Meacham is a Professor and Principal Academic in Computing & Informatics at Bournemouth University. She holds a PhD from the University of Patras (Greece, 2000) and a Diploma in Computer and Informatics Engineering (1994). Her research focuses on embedded systems design, domain-specific modeling, IoT applications in education, and cloud computing. She has extensive experience in EU-funded projects and industry collaborations (e.g., INTRACOM Telecommunications Solutions), with over 25 years of teaching in the UK and Greece. Education PhD in Computer Science, University of Patras, Greece (2000) Diploma in Computer and Informatics Engineering, University of Patras, Greece (1994) Research Interests Dr. Meacham's work spans system-level design for embedded telecommunication systems, hardware-software co-design, and formal refinement techniques. She has recently expanded into educational technology, developing frameworks for personalized online education, AI-driven learning systems, and domain-specific languages (DSLs) for clinical protocols and physics simulations. Her work on cloud computing includes fair resource allocation algorithms and autonomic cloud management. Grants & Projects MDENET: Evaluation of a DSL for Chronic Kidney Disease protocols (EPSRC, 2023) "Towards Explainable AI" (Match-funded with BT, 2018) "Self-aware and Self-adaptive Systems" (BT, 2016) Labs & Collaborations She collaborates with medical professionals to co-create DSLs for clinical protocols and leads projects integrating IoT into educational systems. Her work with low-code platforms aims to democratize app development for healthcare.

• Biomedical Informatics
• Medical Informatics
• Healthcare Information Systems
• +4 more

Giovanni Dimauro is an Associate Professor in the Department of Computer Science at the University of Bari. His work focuses on biomedical informatics, ontology development, and machine learning applications in healthcare. He leads research in clinical text mining, healthcare knowledge graphs, and AI-driven medical decision support systems. Research interests include: biomedical natural language processing, ontology semantics for healthcare interoperability, and predictive modeling in clinical domains. His team develops tools for adverse event detection, social determinants extraction, and vaccine ontology alignment. Recent work emphasizes large language models for biomedical literature curation (RefAI), ensemble methods for knowledge extraction, and Med-BERT adaptations for disease prediction. Collaborations address clinical note analysis, drug repurposing via knowledge graphs, and interoperability frameworks like SDoHO ontology. His lab integrates deep learning, semantic web technologies, and clinical data standards to advance precision medicine and healthcare informatics.

Chenxi YuanView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Medical Imaging Analysis
• +3 more

Chenxi Yuan is an Assistant Professor in the Department of Informatics at the New Jersey Institute of Technology (NJIT). Their research focuses on AI-driven design innovation, medical imaging analysis, and generative adversarial networks (GANs). Yuan holds a Ph.D. in Industrial Engineering from Northeastern University (2022), an M.S. in Industrial and Systems Engineering from the University of Florida (2017), and a B.S. in Mechanical Engineering from Northwest University (2015). Yuan’s work bridges artificial intelligence and industrial applications, with recent contributions to LLM-guided conversation systems for healthcare and reminiscence therapy, Alzheimer’s disease analysis via diffusion models, and GAN-based design concept generation. Their research emphasizes ethical AI, interpretability in medical systems, and the integration of multimodal data for decision-making. Publications span journals and conferences in AI, medical informatics, and industrial engineering. Current projects explore algorithmic fairness in healthcare AI, generative design frameworks for novel product development, and data-driven strategies in manufacturing innovation. Collaborations involve industry partners and interdisciplinary teams.

Hexu LiuView profile

Associate Professor

• Construction Engineering and Management
• Building Information Modeling (BIM)
• Discrete-Event Simulation
• +7 more

Hexu Liu is an Associate Professor in the Department of Civil and Construction Engineering at Western Michigan University. His research focuses on integrating emerging technologies like BIM, discrete-event simulation, virtual reality, and AI into construction planning and management. He holds a Ph.D. from the University of Alberta, where he also served as a post-doctoral fellow. Dr. Liu received his bachelor's and master's degrees from Shaanxi, China, and has been recognized with awards such as the Joseph D. Thompson/Zurich Canada Graduate Award and the 2017 Outstanding Reviewer from the Journal of Automation in Construction. His work emphasizes automation in construction through BIM innovations, including semantic quantity take-off and BIM-simulation integration. Research Interests: Building Information Modeling (BIM) and its applications Discrete-event simulation for construction processes AI-driven predictive maintenance and safety monitoring Offsite construction technologies Occupant-centric facility management Recent work highlights include frameworks for highway cost indices during inflation, digital twin-based fire safety systems, and human-robot collaboration in construction. He actively reviews for journals like Automation in Construction and conferences like CRC 2018. Awards and Honors: Joseph D. Thompson/Zurich Canada Graduate Award 2017 Outstanding Reviewer Award (Journal of Automation in Construction) Ernie Tromposch Graduate Scholarship Labs/Teams: Leads the DSC (Data-Driven Sustainable Construction) Lab, focused on advancing smart technologies in construction through interdisciplinary research.

Frank FerraroView profile

Associate Professor

• Natural Language Processing
• Probabilistic Modeling
• Unsupervised Learning
• +5 more

Frank Ferraro is an Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). His research focuses on natural language processing, probabilistic modeling, and multimodal systems. He holds a PhD in Computer Science from Johns Hopkins University (2017), an MSE from the same institution (2013), and dual BS degrees in Computer Science and Mathematics from the University of Rochester (2011). His research interests include event modeling, unsupervised learning strategies, and the application of large language models (LLMs) to cybersecurity, robotics, and knowledge graph consistency. Recent work explores malware detection via Yara rules, ethical AI in robotics, and interactive dashboards for multimodal situation awareness. Ferraro’s contributions span foundational NLP techniques and applied systems like the PASTA dataset for narrative analysis and the MUMOSA interactive visualization tool. He has published extensively on topics ranging from grounded language learning to distributed classification algorithms. His work bridges theoretical advancements and practical applications, often emphasizing scalability and explainability. Ferraro’s lab develops systems for human-robot interaction, virtual environments, and collaborative AI frameworks. For more details, visit his website .

Yunhe FengView profile

Assistant Professor

• Responsible AI
• Generative AI
• Data Security & Privacy
• +5 more

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.

• Natural Language Processing
• Bias in Language Models
• Scholarly Data Mining
• +3 more

Sagnik Ray Choudhury serves as an Assistant Professor in the Department of Computer Science and Engineering at the University of North Texas, with his office located in Discovery Park F264. He maintains regular office hours on Wednesdays from 11:00 am to 1:00 pm and can be contacted via Sagnik.Choudhury@unt.edu. His research centers on Natural Language Processing with emphases on bias analysis in language models, scholarly communication systems, and reproducible research methodologies. Key interests include quantifying gender bias in cross-lingual political contexts, developing retrieval-augmented LLMs for scholarly impact prediction, and creating frameworks for automated limitation extraction from academic texts (BAGELS project). He also investigates model generalizability across NLI and MRC tasks while advancing NLP applications for social good through ethical deployment strategies. Recent publications reveal a strong trajectory in leveraging LLMs for academic writing automation, including Futuregen for future work generation and scholarly impact forecasting. His work consistently bridges computational linguistics with real-world challenges in research transparency, educational technology, and AI ethics—particularly evident in studies on political ideology navigation and gender bias quantification. No scientific awards were documented in the source material. Information regarding student advising, grant funding, laboratory facilities, or research teams was not provided in the available texts.

• Computer Vision
• Deep Learning
• Neural Network Acceleration
• +5 more

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.

• Artificial Intelligence
• Machine Learning
• Federated Learning
• +3 more

Yifei Zhang is an Assistant Professor in the Department of Computer Science at Northeastern University's School of Computer Science and Engineering, with extensive research collaborations including the University of Hong Kong (notably with Irwin King). Active since 2009, Zhang has produced 257 publications through 2025, demonstrating sustained research productivity across artificial intelligence domains. Zhang's research spans Artificial Intelligence , Machine Learning , and Computer Vision , with recent focus on federated learning systems, multimodal AI, and security applications. The work shows strong technical depth in developing novel algorithms for personalized learning, anomaly detection, and efficient model training. Recent publications reveal increasing specialization in privacy-preserving machine learning and cross-modal understanding, particularly for Chinese language and cultural contexts. Publication trends indicate growing influence in AI conferences (CVPR, ACL, NeurIPS), with 61 publications in 2024 alone. The research demonstrates practical applications in cybersecurity, education technology, and urban planning, while maintaining theoretical rigor in model architecture and optimization techniques. Zhang frequently collaborates with Neng Gao and Shuang Song on security-related AI projects. Zhang serves on program committees for major AI conferences and has contributed to workshop organization including FL@FM-TheWebConf'25: International Workshop on Federated Foundation Models for the Web. The research has attracted significant attention in the AI community, particularly in federated learning and multimodal systems.

Monica S. LamView profile

Professor

• Virtual Assistants
• Natural Language Processing
• Machine Learning
• +6 more

Monica S. Lam is the Kleiner Perkins, Mayfield, Sequoia Capital Professor in the Computer Science Department at Stanford University's School of Engineering, where she has been a faculty member since 1988. She serves as Faculty Director of the Open Virtual Assistant Lab (OVAL) and holds a courtesy appointment in the Electrical Engineering Department. Lam received her B.Sc. from the University of British Columbia in 1980 and her Ph.D. in Computer Science from Carnegie Mellon University in 1987. Her research spans multiple domains with a focus on creating trustworthy virtual assistants based on Large Language Models. She is particularly renowned for her work on the Almond virtual assistant, which emphasizes privacy preservation and open-source development. Her research interests include virtual assistants, natural language programming, machine learning, distributed mobile computing, and compiler technologies. Lam's recent publications reveal a strong focus on addressing key challenges in virtual assistant technology, particularly around hallucination reduction in LLMs, multilingual dialogue systems, semantic parsing, and multimodal interactions. Her work consistently emphasizes privacy preservation, open-source development, and user control over data. Member of the National Academy of Engineering ACM Fellow NSF Young Investigator Award (1992) ACM Most Influential Programming Language Design and Implementation Paper Award (2001) Wikimedia Foundation's Research of the Year Award (2024) for eliminating LLM hallucinations Popular Science's Best of What's New Award in Security (2019) for Almond virtual assistant Lam has advised numerous PhD students who have gone on to prominent positions at institutions like MIT, Harvard, Google, and Amazon. Her research has been supported by significant funding including NSF grants and industry partnerships. She leads the Open Virtual Assistant Lab (OVAL), which focuses on developing open, privacy-preserving virtual assistant technologies that give users control over their data while maintaining functionality.