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دانشیار
Yin Tat Lee is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering , University of Washington, and a Senior Principal Researcher in Microsoft AI. His research spans convex optimization , convex geometry , graph algorithms , online algorithms , and differential privacy , with applications in machine learning and theoretical computer science.
Stephen Cushman is the Robert C. Taylor Professor at the University of Virginia, specializing in American literature and poetry. His interdisciplinary work bridges Civil War studies, 19th-century poetry, and environmental literature. He earned his Ph.D. (1982), M.Phil. (1981), and M.A. (1980) from Yale University, and a B.A. from Cornell University (1978). His research explores how literary forms shape historical understanding, particularly through works like *The Generals' Civil War* (2021) and *Belligerent Muse* (2014). He has edited major volumes such as *The Princeton Handbook of Poetic Terms* (2016) and *Civil War Writing* (2019). His poetry collections include *Keep the Feast* (2022) and *Hothead* (2018), blending personal reflection with historical inquiry. Cushman has received prestigious awards including the Cavaliers' Distinguished Teaching Professorship (2014–2016) and the All-University Teaching Award (1992). His writing often intersects with Virginia’s literary landscape, as seen in *After Great Pain, Dickinson in Charlottesville* (2018). He actively engages with environmental themes in works like *Pope Francis, Laudato Si’, and U.S. Environmentalism* (2017). His articles critically analyze figures like Lincoln, Sherman, and Thoreau, while exploring modernist engagements with biblical texts and Civil War memory. He serves as a leading voice in American literary history, emphasizing the interplay between form, content, and cultural context.
دانشیار
Mi Zhang is an Associate Professor in the Department of Computer Science and Engineering at The Ohio State University and Director of the OSU AIoT and Machine Learning Systems Lab. He holds multiple affiliations including the Institute for Cybersecurity and Digital Trust, Translational Data Analytics Institute, and 5G and Broadband Connectivity Center. Dr. Zhang received his Ph.D. from University of Southern California and B.S. from Peking University, followed by a postdoctoral position at Cornell University. His academic journey previously included a position at Michigan State University before joining OSU. His research focuses on Empowering Billions of Everyday Devices with AI to realize the Artificial Intelligence of Things (AIoT) vision. His lab works across several interconnected domains including efficient generative AI (multimodal LLMs, diffusion models), edge AI for mobile/AR/wearables, systems for AI agents, spatial computing, foundation models for IoT, and human-centered mobile health applications. This interdisciplinary work draws from mobile/edge computing, AI/machine learning, distributed systems, computer networks, and human-centered computing. Analysis of his recent publications reveals a strong focus on making AI more efficient and accessible for resource-constrained devices. His research trajectory shows increasing emphasis on large language models and their optimization for edge deployment, alongside continued work in federated learning for IoT applications. The publications demonstrate both theoretical contributions and practical applications across healthcare, wireless networks, and human-computer interaction. Best Paper Award, IEEE Internet Computing Magazine (2024) University of Chicago Outstanding Educator Award (2024) Best Paper Award, ECCV'24 Workshop (2024) USC ECE SIPI Distinguished Alumni Award (2023) Multiple Best Paper Awards from ACM/IEEE conferences NSF CRII Award Facebook/Meta Faculty Research Award Amazon Research Award MSU Innovation of the Year Award (2020) Dr. Zhang actively mentors students at all levels, with a current group of Ph.D. students working on cutting-edge AI/ML systems. His lab has secured significant funding including Meta Reality Labs Faculty Awards, NVIDIA academic grants, and NSF grants. The OSU AIoT and Machine Learning Systems Lab serves as the central hub for his research activities, fostering collaboration across multiple disciplines to advance the field of AIoT.
پژوهشگر ارشد
James Glass is a Senior Research Scientist at the Massachusetts Institute of Technology (MIT) and heads the Spoken Language Systems Group within MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). He is also affiliated with the Harvard-MIT Division of Health Sciences and Technology. His research spans automatic speech recognition, multimodal learning, and spoken language understanding, with applications in healthcare and video analysis. Education: SM and PhD in Electrical Engineering and Computer Science from MIT His work focuses on paralinguistic speech analysis, health markers in speech, and the intersection of speech and natural language processing. Recent trends emphasize audio-visual alignment, recursive reasoning, and AI applications in cognitive disorder diagnosis. Scientific awards include IEEE Fellow, ISCA Fellow, and Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence. His group explores unsupervised learning, speaker verification, and social text analysis. James leads the Spoken Language Systems Group at CSAIL, collaborating with institutions like IBM and Harvard-MIT Division of Health Sciences and Technology. His research integrates vision-language models, neural audio codecs, and self-supervised frameworks.
Teruko Mitamura is a prominent researcher at Carnegie Mellon University with over three decades of contributions to natural language processing, computational linguistics, and artificial intelligence. Her work spans from foundational research in event representation to advanced applications in multimodal systems and question answering. Her research interests focus on event detection and understanding, question answering systems, information retrieval, and multimodal processing. She has made significant contributions to event coreference resolution, timeline construction, and cross-document event analysis, developing methodologies that have become standard in the field. Her work often bridges theoretical advances with practical applications, particularly in complex information environments requiring deep semantic understanding. Natural Language Processing : Specializing in event extraction, coreference resolution, and narrative understanding with over 179 publications Question Answering Systems : Developing advanced techniques for complex question answering, particularly through NTCIR QA Lab and PoliInfo tasks Multimodal Processing : Integrating textual, visual, and temporal information for richer understanding in systems like ProMQA Evaluation Methodologies : Creating robust frameworks for assessing NLP systems through TAC KBP Event Tracks Her recent publication trends show a strong focus on leveraging large language models for event understanding, multimodal question answering, and timeline construction. She has expanded her research into specialized domains including patent analysis and novelty examination, demonstrating the breadth of her research impact across academic and practical applications. Active participant in major NLP conferences including ACL, EMNLP, NAACL, and AAAI with consistent publications Long-standing collaborator with researchers at CMU's Language Technologies Institute including Eduard H. Hovy and Eric Nyberg Contributor to shared tasks that have shaped research directions in event processing and question answering Organizer of multiple NTCIR QA Lab tasks focused on political information question answering Dr. Mitamura has mentored numerous researchers who have gone on to make their own contributions to the field, as evidenced by her extensive co-authorship network and the progression of her former students and collaborators into faculty and research positions. Her work continues to evolve with the field while maintaining her focus on deep semantic understanding of events and narratives.
استادیار
Dr. Md Manjurul Ahsan serves as a Research Assistant Professor in the Department of Industrial & Systems Engineering at the University of Oklahoma, where he develops AI-driven solutions for healthcare diagnostics and advanced manufacturing optimization. His work bridges theoretical AI advancements with practical industrial and medical applications. Education: Ph.D. in Industrial and Systems Engineering, University of Oklahoma M.S. in Industrial Engineering, Lamar University B.S. in Industrial and Production Engineering, Shahjalal University of Science and Technology Research Focus: Dr. Ahsan specializes in Artificial Intelligence with technical depth in Machine Learning , Deep Learning , and Computer Vision to solve critical challenges in healthcare diagnostics and additive manufacturing . His research emphasizes Explainable AI to enhance model trustworthiness and deployment efficiency across Cyber-Physical-Social Systems, with significant contributions to Aerospace and Defense applications. Publication Trends: Recent work (2023-2025) reveals a strategic expansion from core manufacturing applications into medical AI (diffusion models for diagnostics), cultural preservation (NLP for Dravidian languages), and geopolitical AI analysis. His publications consistently address data imbalance challenges while advancing digital twin integration in quality control systems. Scientific Recognition: GCOE Dissertation Excellence Award (2023) International Student Scholarship (2022) Outstanding Academic Achievement in Engineering (2022) IEEE IEMCON Best Paper Award (2020) Netti Vincent Boggs Engineering Excellence Award (2020) Research Leadership: As director of the Sooner Additive Manufacturing Laboratory , Dr. Ahsan leads cross-disciplinary teams developing real-time monitoring systems using FARO arms and CMM metrology. His postdoctoral work at Northwestern University (2023-2024) advanced AI deployment frameworks, resulting in 60+ peer-reviewed publications with multiple papers ranking in engineering's top 1% for citations.
استادیار
Ranjay Krishna is an Assistant Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where he co-directs the RAIVN lab and leads the computer vision team at the Allen Institute for AI (Ai2). His research intersects computer vision , natural language processing , robotics , and human-computer interaction . PhD in Computer Science from Stanford University (2021) Bachelor's and Master's degrees from Stanford and Cornell His work has received best paper , outstanding paper , and orals at top conferences like CVPR, ACL, CSCW, NeurIPS, UIST, and ECCV. Media outlets including Science , Forbes , and PBS NOVA have covered his research. He has been supported by grants from Google , Apple , NFS , and others. Ranjay advises a diverse group of 15 PhD and postdoctoral researchers , including Jieyu Zhang, Benlin Liu, and Cheng-Yu Hsieh. His teams have developed benchmarks like MemoryBench and The Colosseum , and his PathFinder framework achieved 74% accuracy in skin melanoma diagnosis—surpassing human experts by 9%. Notable contributions include: Perception Tokens for visual reasoning in MLMs SAM2Act for robotic manipulation with memory Synthetic Visual Genome dataset with 5.6M relationships
Manoj Sachdev is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, Faculty of Engineering. His research focuses on semiconductor devices, low-power electronics, and secure hardware systems. He leads projects in flexible electronics, nanotechnology, and radiation-hardened circuits, with applications in displays, memory technologies, and biomedical sensors. His work integrates advanced materials science with circuit design to address challenges in energy efficiency and security. Education: Not explicitly stated in provided text. Research interests span thin-film transistors (TFTs), resistive switching memories, neuromorphic computing, and physically unclonable functions (PUFs). Recent efforts include developing low-power circuits for flexible substrates and secure microprocessors. His contributions to semiconductor device physics and integration techniques have advanced applications in wearable electronics and medical diagnostics. Publications highlight innovations in low-power flip-flops, energy-efficient display drivers, and memristor-based systems. He collaborates on interdisciplinary projects combining photonics, nanoelectronics, and biomedical engineering. Awards: None explicitly listed in provided text. Grants and advising: Advises on semiconductor fabrication, secure hardware design, and radiation effects in electronics. Leads research groups focused on next-generation memory technologies and flexible integrated systems. Labs/Teams: Active in the University of Waterloo's semiconductor and flexible electronics research clusters, contributing to both academic and industry partnerships.
استادیار
Arman Cohan is an Assistant Professor of Computer Science at Yale University, affiliated with the School of Engineering & Applied Science. His research focuses on the intersection of Machine Learning and Natural Language Processing (NLP), particularly in language modeling, representation learning, retrieval systems, and applications in specialized domains such as scientific text processing. He earned his Ph.D. in Computer Science from Georgetown University and has received notable awards, including the Dr. Harold N. Glassman Distinguished Doctoral Dissertation Award (2019) and the EMNLP 2017 Best Long Paper Award. His work emphasizes ethical AI, robustness of LLMs, and interdisciplinary applications in healthcare, science, and education. Cohan's research group, the Yale NLP Lab, develops advanced techniques for multi-document summarization, adversarial fact-checking, and LLM-driven tools for scientific discovery. Recent projects include frameworks like SciBERT, Longformer, and ChemAgent, which enhance domain-specific reasoning and safety in AI systems. His publications address challenges in table reasoning, uncertainty expression, and multimodal reasoning, with applications in medical decision-making and educational problem-solving. He collaborates on initiatives like the Roberts Innovation Fund to advance AI in healthcare and environmental technology.
Dr. Sirui Li is a Lecturer at Murdoch University's School of Information Technology within the College of Science, Technology, Engineering and Mathematics. Her research focuses on Artificial Intelligence, Natural Language Processing (NLP), Machine Learning, Knowledge Graphs, Data Analysis, Temporal Data, and Multi-modal Models, with applications in medicine, agriculture, and mining. She collaborates with industry partners like BHP and has published in journals such as Food Chemistry and Knowledge and Information Systems , as well as conferences like ICSME and IJCNN. Education: Bachelor of Advanced Computing (Honours) in Computer Science at Australian National University Master of Computing (Specialising in AI) at ANU Ph.D. in Information Technology (AI) at Murdoch University Research interests include interdisciplinary applications of AI, such as clinical coding privacy solutions, disease spread modeling, and drug repurposing for pandemics. Her work emphasizes practical industry integration, demonstrated through awards like the 2024 EMNLP Best Demo Award and the 2023 Iron Ore Circuit Hackathon innovation prize. Professional roles include IEEE Western Australia Section committee membership, conference chair positions, and peer review for top journals. She actively mentors students pursuing Honours, Master's, or PhD projects in her areas of expertise.
Emmanuel J. Candès is the Barnum-Simons Chair in Mathematics and Statistics at Stanford University, with joint appointments in the Institute of Computational and Mathematical Engineering and as Professor of Statistics and Electrical Engineering (by courtesy). His research spans mathematical signal processing , high-dimensional statistics , and data science , focusing on compressive sensing, inverse problems, and applications to imaging sciences. 2021 IEEE Jack S. Kilby Signal Processing Medal 2020 Princess of Asturias Award for Technical and Scientific Research 2017 MacArthur Fellow His recent work on conformal prediction and uncertainty quantification has advanced machine learning reliability, particularly in high-dimensional settings. Publications include breakthroughs in medical imaging, gravitational wave detection, and AI validation frameworks. Key collaborations include Terence Tao (UCLA) and Justin Romberg (Georgia Tech) for IEEE Kilby Medal recognition. He serves as Co-chair of Stanford's Data Science Institute and previously as Statistics Department Chair (2016–2019).
استادیار
Bryan A. Plummer is an Assistant Professor in the Department of Computer Science at Boston University, affiliated with the IVC Group and the Artificial Intelligence Research (AIR) initiative at the Rafik B. Hariri Institute. He holds a PhD from the University of Illinois at Urbana-Champaign, specializing in computer vision. His research focuses on multimodal machine learning, efficient neural architectures, explainable AI, and robust ML systems. Plummer's work bridges vision and language, addressing challenges in domain generalization, synthetic data utilization, and model efficiency. Notable contributions include the Flickr30K Entities dataset and advancements in vision-language model robustness against web artifacts. He has advised over 20 students, with several securing roles at top institutions like NVIDIA and Google. His recent awards include the 3M Foundation Fellowship and NSF GRFP honorable mention. Plummer actively serves on conference committees (NeurIPS, CVPR, ICCV) and leads initiatives like the 1st Findings Workshop at ICCV'25.
Aage Hill-Madsen is an Associate Professor at the Department of Culture and Learning, Aalborg University (Denmark), affiliated with the Communication, Language, and Discourse (CLD) research unit. His work focuses on translation studies, medical communication, and systemic-functional linguistics. He holds a Cand.mag. et ling.merc. and a PhD in English linguistics. Research Interests: Translation theories (especially intralingual translation) Medical terminology simplification and communication Systemic-functional grammar applications Legitimation Code Theory in educational contexts Genre analysis of specialized texts Semiotic approaches to knowledge transmission Recent Publications Trends: His 2024-2025 work emphasizes medical translation between technical and lay audiences, with studies on EPAR summaries, patient information leaflets, and historical East-West medical terminology exchanges. He combines functionalist translation theory with systemic-functional frameworks to analyze textual transformations. Grants & Projects: Participated in the 2024-ongoing project Udvikling af kommunikative kompetencer gennem frivilligt brobygningsarbejde i NGO’en Social Sundhed , exploring communication skills through NGO activities. Academic Engagement: Active peer reviewer for journals like Globe and Across Languages and Cultures , with conference presentations on intralingual translation strategies and medical text accessibility.
استادیار
Adam Yala is an Assistant Professor of Computational Precision Health, Statistics, and Electrical Engineering and Computer Science at UC Berkeley and UCSF. He is also the Founder & CEO of Voio Inc., a company focused on clinical translation of AI tools. PhD in Computer Science from MIT (2022) His research lies at the intersection of Machine Learning and Precision Medicine, with a focus on robust AI tools for clinical deployment, personalized screening policies, and private data sharing. Current work includes multi-modal imaging analysis, decision guarantees in clinical workflows, and prospective trials in oncology and radiology. Recent publications highlight advancements in AI for cancer risk prediction, vision-language models in healthcare, and data privacy techniques. Tools like Mirai are implemented in 66 hospitals across 30 countries. Bakar Fellows Spark Award (2024) Eppy Award: Investigative Reporting (2022) Falling Walls Finalist: Life Science (2022) NSF Fellowship (2016) He advises PhD students in AI-driven healthcare and collaborates with hospital systems globally. His lab emphasizes clinical translation of machine learning methods in radiology and oncology.
Gerold Schneider is an Associate Professor at the University of Zurich , affiliated with the Department of Computational Linguistics under the Faculty of Arts and Social Sciences and Faculty of Business, Economics and Informatics . He leads the Text Crunching Center (TCC) , focusing on interdisciplinary research at the intersection of NLP, Digital Humanities, and Health Data Science. Research Interests His work spans Text Analytics , Digital Humanities , Corpus Linguistics , and Health Data Science , with applications in: Biomedical NLP (e.g., Alzheimer’s detection, clinical trials) Digital Humanities projects (e.g., analyzing Charles Dickens, UN archives) Migration discourse framing across languages Adversarial data collection for hate speech detection Interdisciplinary methodologies for digital unstructured data Recent Publications 2025–2024 research highlights include annotated corpora for preclinical and neurological studies, AI-driven analysis of historical linguistic variation, and innovative tools for language learners. His NLP applications address health diagnostics, ethical AI, and cross-lingual political discourse. Labs & Teams As TCC leader, he spearheads collaborative projects within the Digital Society Initiative (DSI) communities (AI & Law, Health, Ethics, etc.), integrating computational methods with humanities and health research.
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