Dr. Meng Fang is a researcher specializing in Artificial Intelligence with a focus on Reinforcement Learning, Large Language Models, and their applications in medical QA, game theory, and causal inference. Their work combines technical innovation with practical problem-solving in safety-critical and domain-specific contexts. Key research areas: Social bias in AI, data augmentation, embodied agents, and model-based reinforcement learning Teaching: Coordinated module COMP532 - Machine Learning and BioInspired Optimisation (2024-25). Recent publications address challenges in offline RL robustness, vision-based safe reinforcement learning, and strategic game generalization.
David Hsu is Provost's Chair Professor in the Department of Computer Science at the National University of Singapore (NUS) School of Computing, where he founded and directs the NUS Artificial Intelligence Laboratory (NUSAIL) and leads the Smart Systems Institute. His academic leadership includes chairing major conferences such as Robotics: Science & Systems (2015) and IEEE ICRA (2016), alongside editorial roles in IEEE Transactions on Robotics and the Journal of Artificial Intelligence Research. He earned a B.Sc. in Computer Science & Mathematics from the University of British Columbia and a Ph.D. in Computer Science from Stanford University. His research spans robotics, AI, and computational biology, with recent focus on robot planning under uncertainty and human-robot collaboration. Current work integrates machine learning with decision-theoretic planning to enable robust human-robot co-existence in unstructured environments. Analysis of his 2023-2025 publications reveals dominant trends in deformable object manipulation (e.g., clothes handling via semantic keypoints), open-world navigation using scene graphs, and LLM-driven multi-agent reasoning for complex tasks. Key innovations include perspective-aware visual grounding for human-centric interaction and functional object arrangement through compositional generative models, reflecting a strong emphasis on real-world applicability. His scientific contributions have earned prestigious recognition: IJCAI-JAIR Best Paper Prize (2022) for foundational AI research Robotics: Science & Systems Test of Time Award (2021) IEEE Fellowship (2018) for contributions to robotic planning RSS Best Systems Paper Award (2017) RoboCup Best Paper Award at IROS (2015) Humanitarian Robotics Award at ICRA (2015) As director of the Adaptive Computing Laboratory, Hsu drives research on fundamental computational frameworks for human-robot interaction. The lab's work on uncertainty-aware decision-making has secured significant research funding through grants from Singapore's National Research Foundation and industry partnerships with robotics firms. While specific student names aren't publicized, his leadership in the NUSAIL indicates extensive mentorship of doctoral candidates in AI and robotics.
Desmond Elliott is an Associate Professor in the Natural Language Processing section at the Department of Computer Science, University of Copenhagen (UCPH). His research focuses on multimodal and multilingual models with specific emphasis on vision-language integration and tokenization-free NLP approaches. He teaches Bachelor and Master's level courses including Advanced Topics in Natural Language Processing (since 2019), Grundlæggende Data Science (since 2023), and previously Data Science (2021-2023). His research interests center on building and understanding multimodal and multilingual models , particularly exploring vision and language interactions through billion-parameter systems. Current work investigates cultural representation disparities in vision-language models, parameter-efficient captioning, and multimodal distributional semantics across diverse domains including food culture and medical imaging. His methodology emphasizes real-world applicability in non-English contexts and ethical considerations in multimodal systems. Elliott's recent publications (2025) demonstrate leadership in multimodal NLP, with significant contributions to vision-language pretraining, multilingual evaluation frameworks, and clinical NLP applications. His work spans theoretical advancements in model architectures and practical implementations addressing challenges in low-resource languages and domain adaptation. Best Long Paper Award at EMNLP 2021 Best Poster Award at COLING 2019 As an active educator, Elliott contributes to courses on Fair and Transparent Machine Learning and previously taught Information Retrieval. His research collaborations span international institutions with particular focus on European and non-English language contexts, reflecting UCPH's recognition as Europe's #1 institution for HCI research over the past decade.
Yiming Yang is a Professor at the Language Technologies Institute and Machine Learning Department within the School of Computer Science at Carnegie Mellon University , where he has held faculty positions since 2003. His research spans foundational and applied aspects of machine learning , artificial intelligence , and scientific computing . Professor, Carnegie Mellon University (2003–Present) Associate Professor, Carnegie Mellon University (1996–2003) Yang's research focuses on LLM-based problem-solving agents , combinatorial optimization , and scalable oversight frameworks . His work explores diffusion models, Langevin dynamics, and Fourier neural operators for NP-hard problems, while advancing reinforcement learning techniques for self-play supervision and principle-driven fine-tuning of large language models. Recent publications highlight his contributions to code synthesis , PDE solving , and multi-agent reinforcement learning . Key methodologies include demonstration-guided control, retrieval-augmented reasoning, and test-time scaling laws. His team has developed frameworks like FEEDER for efficient in-context learning and μTransfer-FNO for zero-shot hyperparameter transfer in PDE solvers. Notable scientific achievements include: Best Student Paper Runner Up (2013) Best Theoretical Paper Award (1994) Best Theoretical Paper Award (1993) Yang has mentored over 20 PhD students and postdocs, including Shengyu Feng , Zhiqing Sun , and Aman Madaan , across domains like graph learning , extreme multi-label classification , and language model alignment .
Sewon Min is an Assistant Professor at UC Berkeley's Electrical Engineering and Computer Sciences (EECS) department and a research scientist at the Allen Institute for AI. Her research focuses on Natural Language Processing (NLP) and Machine Learning, particularly Large Language Models (LLMs), emphasizing data-centric approaches and ethical AI practices. She holds a Ph.D. from the University of Washington (2024) and a B.S. from Seoul National University (2018). Her work includes advancements in retrieval-based models, mixture-of-experts architectures, and data privacy in LLMs. Notable projects include FlexOlmo (flexible data use in LLMs) and OLMoE (open mixture-of-experts models). She has been recognized with the ACM Doctoral Dissertation Award Honorable Mention (2025) and WAGS/ProQuest Innovation in Technology Award. Recent articles highlight her contributions to reasoning models, data tracing (OLMoTrace), and scalable retrieval systems (MassiveDS). She leads the Berkeley NLP Group and collaborates with BAIR, exploring topics like model transparency and ethical data usage.
Lizi Liao is an Assistant Professor at the School of Computing and Information Systems , Singapore Management University (SMU) , specializing in Artificial Intelligence and Conversational AI . Her research bridges Machine Learning , Natural Language Processing , and Multimodal Systems , focusing on proactive dialogue systems, multimodal conversational search, and task-oriented interactions. Education : PhD in Computer Science (2019) from the National University of Singapore (NUS) , advised by Professor Tat-Seng Chua . Research Interests center on principles of human conversational understanding and machine implementation, particularly in proactive conversational agents , multimodal dialogue systems , and target-driven conversation planning . Key applications include emotional support systems , intelligent shopping assistants , and learning companions . Recent Publications (2024-2025) highlight her work on LLM-based proactive dialogue , multimodal emotion recognition , and dynamic graph modeling , often integrating NLP , Multimedia , and Knowledge Graphs . Collaborative projects with her CoAgent Lab team emphasize human-AI interaction and ethical response generation . Scientific Awards : Google South Asia & Southeast Asia Research Award 2023 Lee Kong Chian Fellow Teaching includes Visual Analytics for Business Intelligence (undergraduate) and Text Analytics and Application (graduate). She also serves as Associate Editor for TOIS and TOMM , and organizes tutorials at ACL , SIGIR , and WSDM .
Professor Alsayed Algergawy is the substitute for the Chair of Data and Knowledge Engineering at the University of Passau since April 2023. His work bridges semantic web technologies and machine learning to enable heterogeneous data integration across domains. Current focus areas: schema/ontology alignment, entity resolution, knowledge graph construction Active in DFG Collaborative Research Center AquaDiva (data lifecycle management) Domain applications: biodiversity, agriculture, energy His team develops hybrid strategies combining rule-based systems with ML techniques to extract value from both structured and unstructured data sources.
HaoYu Wang is an Assistant Professor of Computer Science at SUNY Albany. His research focuses on parameter-efficient and data-efficient deep learning, particularly in natural language processing and machine learning, aiming to democratize AI access. He holds a Ph.D. from Purdue University's School of Electrical and Computer Engineering, a B.Eng. from the University of Electronic Science and Technology of China, and an MS from SUNY Buffalo. His work includes innovations like RoseLoRA (sparse low-rank adaptation for knowledge editing), LightLT (lightweight quantization for long-tail data), and FedKC (federated knowledge composition for multilingual NLU). He has received awards such as the Future Leaders in Data Science (2024) and Bilsland Dissertation Fellowship. Key research areas include parameter efficiency, cross-lingual NLU, and model fairness. Recent publications span topics like federated learning optimization, robust retrieval-augmented generation, and mitigating token overfitting in LLMs. He advises students on Ph.D. and intern roles, emphasizing CVs and research interests in applications.
Ziyu Yao is an Assistant Professor in the Department of Computer Science at George Mason University , co-leading the George Mason NLP Group . He is affiliated with the C4I & Cyber Center , Center for Advancing Human-Machine Partnership , and Institute for Digital InnovAtion at GMU. PhD in Computer Science and Engineering from Ohio State University (2021) Internships: Microsoft Semantic Machines, Carnegie Mellon University, Microsoft Research, Fujitsu Lab of America, Tsinghua University Research Interests: Focus on Natural Language Processing (NLP) and Artificial Intelligence (AI) , particularly advancing LLM systems through knowledge grounding , reasoning , and planning . Key areas include: Mechanistic Interpretability for LLMs Interactive Semantic Parsing/Code Generation Responsible and Trustworthy NLP Interfaces Interdisciplinary Applications in Mathematics Education and Network Communication Recent Articles (2024-2025) explore trends in LLM cascading for cost efficiency, mechanistic interpretability surveys, vision-language model reasoning, and interdisciplinary educational technology. Collaborations span institutions like Microsoft Research , William & Mary , and University of Cambridge . Scientific Awards: Presidential Fellowship (OSU Graduate School, 2020) Graduate Student Research Award (OSU CSE, 2021) Top Reviewer at NeurIPS 2023 Advising & Grants: Mentors PhD students like Murong Yue , Hao Yan , and Mohamed Aghzal . Leads NSF projects on AI-driven Mathematics Education and LLM Interpretability , alongside grants from Commonwealth Cyber Initiative and Microsoft Accelerate Foundation Models Research . Organized workshops at COLM 2025 and ICML 2025 . Labs & Teams: Co-leads the NLP Lab at GMU and collaborates with the MathVC NSF Project team (w/ Jennifer Suh, William & Mary). Develops platforms like Gentopia for tool-augmented LLMs and IntelliExplain for non-professional programmers.
Dr. Bo Liu is an Associate Professor in the School of Computer Science at the University of Technology Sydney (UTS), where he serves as a core member and director of the AI Security and Privacy (AISP) Research Lab at the Australian Artificial Intelligence Institute (AAII). With expertise spanning cybersecurity, privacy protection, AI and machine learning, and wireless communications, Dr. Liu has established himself as a leading researcher in the field of AI security and privacy. Dr. Liu earned his PhD from the Department of Electronic Engineering at Shanghai Jiao Tong University in 2010. His academic journey at UTS has progressed from Senior Lecturer (November 2019-December 2022) to his current position as Associate Professor (January 2023-present). Dr. Liu's research focuses on the critical intersection of artificial intelligence and security, particularly addressing emerging threats in the age of advanced AI systems. His work spans multiple dimensions of security and privacy, including deepfake detection, privacy-preserving data synthesis, AI model security, and fair machine learning. He has pioneered approaches to detect AI-generated content, protect visual privacy through de-identification techniques, and address the complex relationship between algorithmic fairness and privacy preservation. His publication record demonstrates significant contributions across multiple cutting-edge research areas, with particular emphasis on detecting and mitigating threats from generative AI systems. His recent work reveals a strong focus on deepfake detection across multiple modalities (images, video, and audio), privacy-preserving techniques for sensitive data, and the security implications of emerging AI architectures like Retrieval-Augmented Generation systems. Dr. Liu has secured substantial research funding, including as Lead Chief Investigator on multiple ARC Discovery and Linkage Projects, totaling over $3.5 million AUD. His industry collaborations include partnerships with the NSW Department of Planning and the Reserve Bank of Australia, demonstrating the practical applicability of his research. As an academic leader, Dr. Liu serves as Associate Editor for IEEE Transactions on Broadcasting and actively contributes to the academic community through conference organization, peer review for top-tier venues, and assessment for ARC grant schemes. He also teaches courses including Penetration Testing, Ethical Hacking and Offensive Security, and supervises Masters and PhD students in cybersecurity and privacy research.
Jun Li is a Full Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame's College of Science. He specializes in developing statistical and computational methods for big data, with a focus on interdisciplinary applications in bioinformatics, machine learning, and data mining. His career includes tenure as an Assistant Professor (2012–2017) and promotion to Associate Professor (2017) before becoming Full Professor (2020). Dr. Li holds a Ph.D. in Statistics from Stanford University (2012), supervised by Robert Tibshirani, and earlier degrees from Tsinghua University: a B.E. in Automation (2004) and an M.S. in Pattern Recognition and Intelligent Systems (2007). Research Interests : Dr. Li’s work centers on advancing computational frameworks for handling large-scale datasets, integrating statistical rigor with algorithmic innovation. Recent themes include AI-driven code improvement, ethical LLM applications in HCI, and GUI automation. His methodologies emphasize human-AI collaboration and transparency in algorithmic systems. Publications : His 2025 work explores LLM vulnerabilities in GUI agents, AI-assisted education tools like GLITTER, and ethical challenges in HCI research. Earlier studies (2024–2023) address topics such as natural language database queries, privacy-preserving app promotion analysis, and multimodal task learning. Lab/Teams : Affiliated with Notre Dame’s computational statistics research groups, focusing on interdisciplinary projects bridging statistics, computer science, and applied mathematics. His work often involves collaborations with industry and academic partners to translate theoretical advancements into practical applications.
Aniket 'Niki' Kittur is a Professor in the Human-Computer Interaction Institute at Carnegie Mellon University's School of Computer Science. His research focuses on AI-augmented cognition, exploring how human and machine intelligence can collaborate to enhance creativity, decision-making, and innovation. He leads projects like the Semantic Reader and Skeema browser extension, aiming to reduce cognitive overload through intelligent systems. Education: BA in Psychology & Computer Science from Princeton University; PhD in Cognitive Psychology from UCLA. His work bridges HCI, crowdsourcing, and cognitive science, with 100+ publications and 17 best paper awards. He advises industry partners including Google, Microsoft, and Toyota while maintaining a lab focused on real-world impact. Research interests center on accelerating knowledge acquisition via systems that scaffold sensemaking (e.g., Selenite for web exploration) and fostering analogical innovation through crowdsourced/AI hybrid approaches. Notable contributions include CrowdForge (human-machine workflows) and Kinetica (touch-based data visualization). Awards include NSF CAREER Award, Allen Newell Award, and CHI Academy membership. His lab's Skeema tool has achieved 79% 30-day retention in beta, reflecting impactful user-centered design principles. Current projects emphasize LLM integration for composite cognition, aiming to create systems where 'LLMs + Humans > Either Alone.' Funding来自NSF, NIH, ONR, and industry partners like Bosch and Wikimedia. Teaching includes PhD bootcamps and user-centered research courses. Over 100 students have contributed to his projects, many advancing to tech leadership roles.
Carlos Toxtli is an Assistant Professor at Clemson University where he leads the Human-AI Empowerment Lab. His research applies Human-Centered AI to create fair workplace tools, focusing on NLP, computer vision, and crowdsourcing ethics. He holds a PhD in Computer Science from Northeastern University and previously worked at Google, Microsoft Research, and Snap Inc. Education: Ph.D. Computer Science, Northeastern University M.S. Innovation & Technological Entrepreneurship, Monterrey Institute of Technology MBA, IEDE Business School B.S. Computer Science, University of the Valley of Mexico B.S. Computer Engineering, National Autonomous University of Mexico Research Focus: Dr. Toxtli develops AI systems promoting workplace fairness through ethical frameworks. His work spans: 1) Human-AI collaboration for task management, 2) Bias mitigation in gig economies, 3) Multimodal interfaces for accessibility, and 4) Culturally adaptive systems. His lab explores how AI can augment human capabilities while ensuring transparency. Publication Trends: Recent works demonstrate strong focus on LLM reliability (7 papers), human-AI teaming (4 studies), and ethical AI frameworks (3 publications). Over 60% of 2024-2025 output addresses real-world deployment challenges. Awards: UNESCO IRCAI Global Top 100 project Google Apps Developer Challenge 1st Place Facebook Developers World Hack 1st Prize Intel Innovation Latin App 1st Prize NSF STIR Labs Research Grant Leadership: Founded the Human-AI Empowerment Lab securing $1.2M in NSF/industry grants. Supervises 8 PhD students in human-centered computing projects. Previously co-founded 5 tech startups including ComproPago (acquired by Coca-Cola FEMSA).
Jiaoyan Chen is a Lecturer (Assistant Professor) in the Department of Computer Science at The University of Manchester, set to become a Senior Lecturer (Associate Professor) from July 2025. Previously, she served as a Senior Researcher at the University of Oxford and held postdoctoral roles at Heidelberg University. Her research focuses on neural-symbolic knowledge representation, ontology engineering, and integrating large language models with knowledge graphs. Education: PhD in Knowledge Reasoning and Predictive Analytics (Zhejiang University, 2011-2016) and BEng in Computer Science (Zhejiang University, 2007-2011). She also spent time as a visiting scholar at Zurich University (2014-2015). Research Interests include: Knowledge Graphs, Ontologies, Large Language Models, Retrieval Augmented Generation, and Machine Learning applications in knowledge-aware systems. She leads major grants such as the EPSRC New Investigator Award (EP/Y017706/1) and collaborates internationally through initiatives like the Manchester-Melbourne-Toronto Fund. Teaching: Leads units like 'Data Engineering Technologies' and 'Advanced Topics in Knowledge Representation'. She actively advises PhD students and co-develops tools like OWL2Vec* and DeepOnto. Service roles include Associate Editor of Transactions on Graph Data and Knowledge (TGDK), membership in the EPSRC Peer Review College, and leadership in ontology alignment initiatives like OAEI Bio-ML Track.
Professor Yizhou Sun is affiliated with the University of California Los Angeles (UCLA) and the Henry Samueli School of Engineering and Applied Science . Her academic work focuses on Machine Learning , Artificial Intelligence , and Graph Neural Networks within the Computer Science department. Her research spans High-Level Synthesis , Causal Inference , and Computational Biology , with recent publications addressing neural network compression, language model safety, and dynamical system modeling. The trends in her recent 2025 and 2024 publications emphasize Deep Learning , Graph Theory , and Language Model Optimization , reflecting interdisciplinary applications in Biomedical Data , Hardware Design , and Physical Simulation .
