Professor Kenny Mitchell is a faculty member at the School of Computing Engineering and the Built Environment at Edinburgh Napier University. With over 60 research outputs listed, he specializes in Interactive Graphics, Virtual Reality, and Augmented Reality technologies. Research Interests Mitchell's work focuses on real-time systems, motion prediction, and human-computer interaction in immersive environments. His research spans generative AI environments , 3D facial reconstruction , and light field rendering . Key themes include AI-driven animation , networked VR experiences , and haptic-visual integration . Article Trends Recent publications emphasize Transformer-based motion prediction (NeFT-Net), speech-to-VR systems (HoloJig), and low-latency avatar synchronization . His work integrates machine learning with computer graphics for applications in telepresence dance and emotionally intelligent avatars . Projects CAROUSEL+ : £929,077 funded by European Commission (2021-2024) for telepresent dance systems DISTRO : £243,804 European Commission grant for 3D graphics training (2015-2018)
Beidi Chen is an Assistant Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), with a visiting research role at FAIR/Meta. She holds a Ph.D. in Computer Science from Rice University (2020) and a B.S. in Electrical Engineering and Computer Science from UC Berkeley (2015). Her research focuses on optimizing machine learning algorithms and models for modern hardware, emphasizing efficiency in large-scale systems. She has been recognized as a Rising Star in EECS by MIT (2019) and UIUC (2021), and her work has received multiple best-paper awards, including runner-up at ICML 2022 and awards at IISA 2018 and USENIX LISA 2014. Education: Ph.D., Computer Science, Rice University (2020) B.S., Electrical Engineering & Computer Science, UC Berkeley (2015) Her research interests span machine learning systems , algorithm-system co-design , and large language models (LLMs) . She explores techniques like structured sparsity, efficient inference, and hardware acceleration to enhance the scalability and performance of ML models. Recent work includes optimizing LLM context lengths, speculative decoding, and memory-efficient training methods. Her publications highlight advancements in LLM acceleration (e.g., 'Megalodon' for unlimited context), memory optimization ('Headinfer'), and scalable training frameworks ('FlexGen'). She also contributes to federated learning and edge deployment solutions for LLMs. Awards: Best Paper Runner-up, ICML 2022 Best Paper Award, IISA 2018 Best Paper Award, USENIX LISA 2014 Rising Star in EECS (MIT 2019, UIUC 2021) Chen collaborates across academia and industry, focusing on bridging algorithmic innovation with practical hardware constraints. Her lab emphasizes scalable, efficient systems for modern AI challenges.
Jingfeng Zhang is a Lecturer at the University of Auckland's School of Computer Science and a Visiting Research Scientist at RIKEN Center for Advanced Intelligence Project. Holding a PhD from the National University of Singapore, he's supervised by Prof. Masashi Sugiyama at RIKEN and Prof. Mohan Kankanhalli at NUS. Education: PhD (2016-2020) at NUS, BEng (2012-2016) at Shandong University's Taishan College Current research focuses on Trustworthy Machine Learning , Adversarial Robustness , and Foundation Model Security His publication history shows a strong focus on adversarial learning techniques across multiple top conferences (ICML, NeurIPS, ICLR). He's developed methods for improving model robustness against various attacks including backdoor attacks, clean-label poisoning, and adversarial noise. The work spans from theoretical foundations to practical implementations in neural network training. He supervises several students including: Zihao Luo (Master's, UoA) Xin Chen (PhD, UoA with Prof. Gill Dobbie) Di Zhao (PhD, UoA with Prof. Yun Sun Koh and Prof. Gill Dobbie) Professional service includes organizing workshops at ACML and TrustML, serving as Area Chair at SOICT, and reviewing for top venues in machine learning and AI.
Mark Hasegawa-Johnson is a Professor of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign, where he has been faculty since 1999. He holds affiliations with the College of Engineering and leads the Statistical Speech Technology Group. His academic roles include serving as Editor-in-Chief of the IEEE Transactions on Audio, Speech and Language, and membership in the ISCA Diversity Committee. Education: PhD in Electrical Engineering and Computer Science from MIT (1996), postdoctoral research at UCLA (1996-1999). Research interests span automatic speech recognition, machine learning applied to phonetics and prosody, and accessibility technologies for under-resourced languages and speech disorders. Key projects include the Speech Accessibility Project, which improves speech recognition for individuals with dysarthria, and international competition successes in audio event detection and multilingual broadcast retrieval. Scientific achievements include Fellowships from the IEEE (2020), Acoustical Society of America (2011), and ISCA. Awards also include NIH’s National Research Service Award (1998-1999) and the Frederic Vinton Hunt Post-Doctoral Fellowship (1996-1997). Teaching focuses on courses like Artificial Intelligence, Multimedia Signal Processing, and Speech Processing. Research supervision emphasizes undergraduate projects in signal processing and speech recognition, with notable student contributions to prosody-dependent speech recognition and audio source separation. Labs/Teams: Leads the Speech Accessibility Project and collaborates with interdisciplinary teams on projects like Mandarin language education tools and audio-visual speech models. Current work explores unsupervised learning, cross-lingual speech recognition, and AI-driven accessibility solutions.
Cong Shen is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Virginia, where he leads a research group focused on machine learning, wireless communications, and networking. He is affiliated with the UVA Link Lab and serves as Deputy Director of Collaboration at SpectrumX, an NSF Spectrum Innovation Center. He has previously held faculty positions at the University of Science and Technology of China (USTC) and maintains strong industry ties with companies such as Qualcomm, SpiderCloud Wireless, Silvus Technologies, and Xsense.ai. Education: B.E. and M.E., Electronic Engineering, Tsinghua University, China Ph.D., Electrical Engineering, University of California, Los Angeles (UCLA), USA His research lies at the intersection of machine learning and communication systems, with a focus on in-context learning, transformers, federated learning, reinforcement learning, distributed optimization, multi-armed bandits, and AI for wireless . His work aims to bridge theoretical foundations with engineering applications in next-generation wireless networks and intelligent systems. His recent publications (2023–2025) reveal a strong trend toward integrating foundational models with communication constraints, particularly in federated and decentralized settings. Key themes include in-context learning with provable guarantees, efficient prompt optimization using bandit methods, privacy-preserving federated learning, and reinforcement learning for wireless resource management. His work frequently appears in top-tier venues such as NeurIPS, ICML, ICLR, AISTATS, and IEEE ICC. Scientific Awards: NSF CAREER Award (2022) Best Paper Award, IEEE ICC (2021) Excellent Paper Award, ICUFN (2017) Best Paper of 2024, Science Robotics Finalist for Best Student Paper Award, Asilomar (2024) Dr. Shen advises a dynamic group of graduate and undergraduate students, including PhD candidates Chengshuai Shi, Zhoubin Kou, Di Wu, and others. He leads multiple NSF-funded projects, including initiatives under the SWIFT, ECCS Core, MLWiNS, and CAREER programs, focusing on spectrum access, resource rationing in wireless FL, and domain-knowledge-enriched RL for network optimization. His lab actively contributes to open science through GitHub repositories and code releases. He also serves as an associate or editor for several IEEE Transactions journals and participates in program committees of major AI and communications conferences.
Anja Belz is a Professor at Dublin City University's School of Computing, specializing in Natural Language Processing research. She leads the Natural Language Processing Research Group and has established herself as a leading expert in human evaluation methodologies, reproducibility in NLP, and data-to-text generation systems. Her work bridges theoretical research and practical applications with significant contributions to medical text generation and evaluation standards. Her research interests center on creating robust evaluation frameworks for NLP systems, with particular focus on human evaluation methodologies, reproducibility assessment, and quality criteria standardization. She has pioneered work on the Human Evaluation Data Sheet (HEDS) and the QCET (Quality Criteria for Evaluation Taxonomy), addressing critical gaps in evaluation comparability across NLP research. Her work on reproducibility spans multiple shared tasks (ReproNLP, ReproGen) that have become benchmarks in the field, examining how different experimental conditions affect evaluation outcomes. Analysis of her recent publications reveals a clear research trajectory focused on making NLP evaluation more rigorous, transparent, and comparable. Her work increasingly incorporates large language models while maintaining critical scrutiny of their evaluation methodologies. She has made significant contributions to understanding when LLM-based evaluation correlates with human judgments, and has developed frameworks for assessing the reproducibility of NLP evaluation results in quantified terms. Professor Belz has organized numerous workshops and shared tasks focused on human evaluation and reproducibility in NLP, including multiple ReproNLP shared tasks that have attracted international participation. Her research has been consistently published in top-tier NLP conferences including ACL, EMNLP, and INLG, with a strong emphasis on methodological rigor and practical applicability to real-world NLP evaluation challenges. She has also contributed significantly to NLP research in under-resourced languages, particularly Irish, Welsh, Breton, and Maltese. Her work on medical text generation has practical implications for healthcare applications, particularly in automating clinical documentation and systematic reviews. She has developed methods for biomedical synthesis generation that could significantly reduce the time and cost of keeping medical practitioners updated with research. Her research on consultation checklists aims to standardize the human evaluation of medical note generation systems, addressing critical challenges in clinical safety and quality assessment.
Adway Girish is a third-year Ph.D. candidate and Doctoral Assistant at the School of Computer and Communication Sciences (IC), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He is affiliated with the Information Processing Group (IPG) and the Laboratory for the Theory of Information (LTHI), working under the supervision of Prof. Emre Telatar. He has also collaborated with leading researchers including Michael Gastpar, Hyeji Kim, and Shlomo Shamai. His educational background includes a B.Tech. in Electrical Engineering with honors and a minor in Mathematics from the Indian Institute of Technology Bombay (IITB), completed in 2022. Adway's research centers on information theory and its applications in communication, security, and machine learning. He is particularly interested in foundational aspects of information measures, entropy optimization, and the theoretical underpinnings of modern deep learning architectures such as transformers and large language models. His recent work explores rate-distortion frameworks for prompt compression, learning dynamics in transformers, and entropy-constrained communication channels. His research bridges theoretical rigor with practical implications in AI and communication systems. The trend in his recent publications shows a shift from classical signal processing and micro-Doppler analysis during his undergraduate years to advanced topics in information theory and machine learning during his Ph.D. His contributions are published in top-tier venues such as ISIT, NeurIPS, ICLR, and ICML workshops, indicating a strong trajectory in theoretical computer science and applied mathematics. Scientific Awards and Recognitions: ICLR 2025 Spotlight Paper (awarded to top 5% of accepted papers) Oral presentation at ICML 2024 Workshop on Theoretical Foundations of Foundation Models (selected as one of top 4 out of 58 submissions) Adway advises no students currently, as he is himself a doctoral candidate. However, he plays an active role in collaborative research projects involving multiple co-authors across institutions. He has not received specific mention of external grants in the provided text, but his position as a Doctoral Assistant at EPFL suggests institutional funding. His collaborations with renowned researchers suggest involvement in larger research initiatives and potential access to grant-supported projects. He is a core member of the Information Processing Group (IPG) at EPFL, a research lab focused on theoretical and applied aspects of information science, including coding, communication, learning, and data analysis. The group fosters interdisciplinary research and hosts regular seminars, candidacy reviews, and internal presentations, all of which Adway actively participates in.
Cihang Xie is an Assistant Professor of Computer Science and Engineering at the University of California, Santa Cruz, where he conducts cutting-edge research at the intersection of computer vision and machine learning. He co-leads the Vision · Learning · Assured Autonomy (VLAA) Lab with Professor Yuyin Zhou, focusing on building human-level computer vision systems with robust performance under distribution shifts and developing deep representation learning with minimal supervision. Dr. Xie received his Ph.D. from Johns Hopkins University under the supervision of Bloomberg Distinguished Professor Alan Yuille. During his doctoral studies, he gained valuable industry experience as a research intern at Facebook AI Research (FAIR) working with Kaiming He and Laurens van der Maaten, and at Google Brain with Quoc Le. His research spans multiple critical areas including adversarial machine learning, vision-language models, 3D vision, and robust AI systems. Dr. Xie's work often bridges theoretical foundations with practical applications, particularly in developing methods that maintain performance under challenging conditions such as distribution shifts and adversarial attacks. His recent focus has expanded to include large language model safety evaluation, high-quality image editing datasets, and efficient transformer architectures. Dr. Xie's publication record demonstrates consistent output across top-tier computer vision and machine learning venues including CVPR, ICCV, ECCV, NeurIPS, and ICML. His work shows a clear progression from foundational research in adversarial robustness to more recent explorations in vision-language alignment, 3D representation learning, and efficient model architectures. Awards and Recognition 2020 Facebook Fellowship Dr. Xie has advised numerous Ph.D. students at UC Santa Cruz and maintains collaborations with students from Johns Hopkins University and other institutions. His service to the academic community includes serving as Area Chair for major conferences including CVPR (2024/2023), ICLR (2024/2023/2022), ICML (2024/2023), ICCV (2023/2021), and NeurIPS (2024/2023/2022), as well as Senior Program Committee for AAAI 2022 and IJCAI 2021. The VLAA Lab, which Dr. Xie co-leads, has multiple openings for summer interns and visiting students, indicating active growth and research momentum. Recent lab achievements include releasing Recap-DataComp-1B, HQ-Edit dataset, and D-iGPT with impressive ImageNet performance.
Lin Li is a Researcher in the Department of Computer Science at the University of Oxford, where he conducts postdoctoral research in the Oxford Applied and Theoretical Machine Learning (OATML) Group under Professor Yarin Gal's supervision. His work bridges theoretical advances in AI safety with real-world applications across healthcare, robotics, and economics, positioning him at the forefront of robust and trustworthy artificial intelligence development. His academic journey includes: PhD in Machine Learning from King’s College London (supervised by Prof. Michael Spratling), focusing on model robustness MSc in Computing from Imperial College London (advised by Prof. Wayne Luk) Bachelor of Music in Finance from Xiamen University (advised by Prof. Zheng Qiao) Li's research centers on critical AI safety challenges including hallucination mitigation, jailbreaking defenses, and safety alignment in large language models. He pioneers LLM-based agent systems for complex decision-making and societal simulation while developing practical applications in healthcare diagnostics, robotic dexterity, and economic modeling. His work uniquely combines adversarial machine learning techniques with domain-specific implementations to address real-world deployment challenges. Analysis of his publication record reveals a strategic evolution from foundational adversarial robustness research toward safety-critical applications. Recent work demonstrates increasing emphasis on healthcare domains—particularly women's health and ophthalmology—while maintaining strong contributions to core AI robustness through novel benchmarks like OODRobustBench and techniques such as semantic entropy regularization. His publications consistently bridge theoretical innovation with practical implementation across computer vision, natural language processing, and multimodal systems. Recognition includes: USERN Prize nomination (2025) for early-career research excellence Li actively shapes the field through program committee roles for MICCAI 2025, CVPR 2025, and AAAI 2025, while reviewing for top venues including ICML, NeurIPS, and IEEE Transactions journals. He actively seeks motivated students for collaborative research in AI safety and robustness through the OATML Group, which provides interdisciplinary infrastructure for theoretical and applied machine learning research with strong industry partnerships including Tencent's Robotics X Lab.
Hui Guo is an Adjunct Assistant Professor in the Department of Civil Engineering. Their research intersects computer vision, machine learning, and image processing, with a focus on advanced restoration and super-resolution techniques. Recent publications highlight contributions to image super-resolution challenges (NTIRE 2024/2025), vision transformers (QID), diffusion models for video restoration, and continual learning frameworks (PCL). Work spans OCR-free visual document analysis, adversarial degradation modeling (AND), and parametric image restoration. Contact: guoh15
Liangyan Gui is a Research Assistant Professor at the University of Illinois at Urbana-Champaign, affiliated with the Department of Computer Science within the College of Engineering. His research focuses on cutting-edge topics in Artificial Intelligence, particularly in computer vision, 3D modeling, human motion prediction, and robotics. He teaches courses such as CS 446 (Machine Learning) and CS 598 GUI (Efficient & Predictive Vision). Gui's research interests span 3D scene understanding, human-object interaction, and generative models. He explores AI-driven solutions for tasks like motion editing, physics-based simulation, and multimodal reasoning. His work bridges computer vision and robotics, with applications in 3D reconstruction, animation, and AI for dynamic environments. Recent publications highlight advancements in 3D photo editing, human-object interaction generation, and self-supervised learning. His research often integrates textual inputs with visual data to enhance AI systems' capabilities in reasoning and generation. Gui has been involved in initiatives such as the AICE Center grants (as part of a team), though specific grant details are not explicitly attributed to him in the provided text. His contributions extend to foundational AI models and their practical implementations in real-world scenarios.
Hao Peng is an Assistant Professor at the Siebel School of Computing and Data Science, part of the Grainger College of Engineering at the University of Illinois Urbana-Champaign. He holds a B.S. from Peking University (2016) and a Ph.D. from the University of Washington’s Paul G. Allen School of Computer Science & Engineering (2022). His research focuses on Natural Language Processing (NLP) , Machine Learning , Large Language Models (LLMs) , and AI for Science , with particular emphasis on improving LLM efficiency, factuality, and interdisciplinary applications. Recent courses include 'CS 598 PEN - Efficiency in NLP' and 'CS 598 PEN - LLM Post-pretraining.' Hao’s work spans advancing LLM generalization capabilities, mitigating hallucinations, and addressing hardware constraints. In 2024, he co-authored an award-winning paper on 'LM-Infinite,' enabling zero-shot extreme length generalization in LLMs. He collaborates internationally, including with the Hebrew University of Jerusalem under a joint research grant since 2019. He also contributes to Argonne National Laboratory’s AI initiatives through invited lectures. Education : B.S., School of Electronics Engineering and Computer Science, Peking University, 2016 Ph.D., Paul G. Allen School of Computer Science & Engineering, University of Washington, 2022 Key Collaborations : Interdisciplinary research with Hebrew University of Jerusalem (2025) Joint seed grant program with HUJI since 2019 His advising includes graduate student Chi Han, who contributed to the NAACL award-winning work. Grants and seed funding focus on accelerating economic development through tech innovation. No specific lab affiliations are explicitly mentioned, but his research aligns with Argonne’s AI Distinguished Lecture series and open-source platforms like OpenDevin/OpenHands.
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.
Esperanza Román-Mendoza is Professor of Spanish Linguistics at George Mason University with joint appointments in Modern and Classical Languages and Digital Learning Initiatives. She holds a Ph.D. in Spanish Linguistics from The Catholic University of America and has developed over 30 technology-enhanced courses spanning basic Spanish to graduate seminars. Her research pioneers critical approaches to AI-mediated language education, heritage language pedagogy, and digital equity frameworks. Recent publications analyze generative AI's impact on Spanish instruction and advocate for justice-oriented technopedagogy. She has directed study abroad programs across Spain and Latin America and serves on editorial boards for CALICO, EUROCALL Review, and International Journal of Educational Technology. Her book Aprender a aprender en la era digital establishes critical frameworks for technology-enhanced language education.
Bo Wang is an Assistant Professor of Computer and Information Science at The University of Mississippi. He holds a Ph.D. in Computer Science from the University of Utah (2015). His research focuses on computer vision, medical imaging, and machine learning, with particular emphasis on 3D modeling, human pose estimation, and image processing. He currently works in Weir Hall and can be reached at bwang3@olemiss.edu. Research interests include developing algorithms for occlusion-robust human pose estimation, hierarchical 3D modeling using octree structures, and medical imaging applications such as tumor localization and ultrasound tracking. His work integrates techniques like deep learning, domain adaptation, and contrastive learning to address challenges in computer vision and biomedical engineering. Recent publications highlight advancements in zero-shot human-object interaction detection, low-light pose estimation, and personalized 3D head modeling. His research trends emphasize interdisciplinary approaches combining computer vision with medical applications, particularly in radiotherapy guidance and anatomical modeling. Bo Wang has not listed any awards or grants in the provided materials. His academic contributions span over 30 publications since 2005, reflecting a sustained focus on advancing computational methods for medical imaging and 3D reconstruction.
