جست‌وجوی استاد و پژوهشگر

Jackie Chit Kit Cheungمشاهده پروفایل

دانشیار

• Natural Language Processing
• Natural Language Generation
• Automatic Summarization
• +۱۲ مورد دیگر

Jackie Chit Kit Cheung is an Associate Professor in the School of Computer Science at McGill University, where he co-directs the Reasoning and Learning Lab. He holds the Canada CIFAR AI Chair and serves as an Associate Scientific Co-Director at the Mila Quebec AI Institute. He is also a consulting researcher at Microsoft Research Montreal. Academic Background: Ph.D. in Computer Science, University of Toronto (2010–2014) M.Sc. in Computer Science, University of Toronto (2008–2010) B.Sc. (Honours) in Computer Science, minors in Linguistics and German, University of British Columbia (2004–2008) Research Interests: Jackie Cheung's research lies at the intersection of natural language processing, machine learning, and cognitive science. He focuses on natural language generation , automatic summarization , commonsense and pragmatic reasoning , and the evaluation of NLP systems . His work aims to build language models that reflect real-world structure and reasoning, with applications in health, education, and language revitalization. He is particularly interested in how implicit meaning is processed in context, a core concern in linguistic pragmatics. Publication Trends: His recent publications (2024–2025) show a strong emphasis on evaluation methodologies (e.g., COSMIC, ECBD), hallucination and factuality in language models, coreference and reasoning , and long-context modeling . He frequently collaborates across institutions and integrates insights from linguistics and psychology into NLP system design and analysis. Scientific Awards: Best Paper Award, ACL 2018 Outstanding Paper Award, NAACL 2018 Student Research Workshop SAC Award, ACL 2024 (for COSMIC) Best Poster Award, CMDO AI-Health Symposium 2024 Advising and Grants: He advises a large and diverse group of graduate students, including PhD and Master’s candidates, often in co-supervision with other faculty. His group has received support from major AI and health research initiatives, including CIFAR and Mila. He has trained alumni who have gone on to faculty and industry research positions. He has served in leadership roles in top NLP conferences, including as Senior Area Chair, Workshop Chair, and Program Chair of Canadian AI 2018. Labs and Teams: He co-directs the Reasoning and Learning Lab at McGill and is deeply involved with Mila – Quebec AI Institute . He founded the NLP Reading Group at McGill, which brings together researchers from computer science, linguistics, and information studies to discuss theoretical and applied NLP topics.

Byron Wallaceمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Byron Wallace is the Sy and Laurie Sternberg Interdisciplinary Associate Professor at Northeastern University's Khoury College of Computer Sciences, where he also serves as Associate Dean for Research and Director of the BS in Data Science Program. His research focuses on Natural Language Processing and Machine Learning applications in healthcare. Education Details of formal education are not explicitly provided in the available text, though he holds a PhD from Tufts University (mentioned in thesis award context). Research Interests His work centers on developing NLP and ML models for health applications, with particular emphasis on: Biomedical evidence synthesis automation Electronic Health Record processing Model interpretability and trustworthiness Human-in-the-loop systems Learning with limited supervision Research Trends Recent publications demonstrate strong focus on large language model applications in healthcare, including factuality evaluation for medical summarization, evidence extraction from clinical trials, and interpretable risk prediction models. Notable contributions include work on GPT-3 applications in medical evidence synthesis and neural methods for EHR analysis. Scientific Awards ACL Outstanding Paper Award (2022) ICLR Spotlight (top 5% acceptance) (2024) Best Student-led Paper Award at AMIA 2021 NSF CAREER Award (2018-2023) Advising and Grants Currently advises 4 PhD students and has mentored numerous others. Major funding includes: NSF CAREER Award ($500K+) NIH R01 grant for EHR summarization NSF Medium grant for healthcare summarization Support from Army Research Office, Amazon, and Seton Hospital Labs and Teams Leads the Evidence Inference project team working on automated biomedical evidence synthesis. Collaborates with Brigham and Women's Hospital, Mass General Hospital, and Reboot Rx for clinical translation of research.

Jiatao Guمشاهده پروفایل

استادیار

• Machine Learning
• Generative Models
• 3D Generation
• +۳ مورد دیگر

Jiatao Gu is an Assistant Professor in the Department of Computer and Information Science (CIS) at the University of Pennsylvania, with a part-time role as Staff Research Scientist at Apple (MLR). He holds a Ph.D. in Electrical and Electronic Engineering from the University of Hong Kong (2018) and a B.Eng. in Electronic Engineering from Tsinghua University (2014). His research focuses on generative machine learning and AI agent interaction with the physical world, emphasizing multi-modal systems spanning language, images, videos, and 3D. Key themes include efficient modeling , flexible architecture design , and scalable decision-making frameworks . 2025: ICLR paper on DART framework 2024: TMLR work on GFlowNet alignment 2023: NeurIPS research on diffusion stability 2022: ACL papers on speech translation Recent publications explore diffusion models for text-to-image synthesis, 3D reconstruction, and efficient sampling techniques. His work addresses fundamental challenges in attention mechanisms, entropy collapse, and multi-stage distillation while advancing non-autoregressive translation and vision-language reasoning . Prospective students can apply through his recruitment process at UPenn. Prior affiliations include Meta AI (FAIR Labs) and academic collaborations with institutions like New York University's CILVR Lab.

• Natural Language Processing
• Large Language Models
• Digital Libraries
• +۷ مورد دیگر

Min Yen Kan is an Associate Professor and Vice Dean of Undergraduate Studies at the National University of Singapore's School of Computing, Department of Computer Science. With a PhD from Columbia University (2002), he leads the Web Information Retrieval / Natural Language Processing Group (WING.NUS) and serves as ACL Ethics Committee co-chair. His research spans Natural Language Processing , Large Language Models , Digital Libraries , and Information Retrieval , with specific focus on scientific discourse analysis, fact verification, and multimodal systems. Current projects include Scholarly Document Information Extraction (TRL 6), Task-Oriented Dialogue Systems (TRL 4), and Recommendation Systems (TRL 5). Recent publications reveal strong trends in LLM limitations (bias, hallucination, evaluation), conversational recommendation systems , and misinformation detection . His work consistently bridges theoretical NLP with real-world applications in digital libraries and scientific communication. Award highlights include: CIKM 2019 Best Paper Award ACL Distinguished Service Awards Vannevar Bush Best Paper Award (JCDL 2012) ACM Distinguished Speaker designation Kan mentors PhD students with placements at Google and USTC, and serves as associate editor for Information Retrieval and survey editor for Journal of AI Research . His lab WING.NUS develops practical tools like SciWING for scientific document processing and FANG for fake news detection. Media engagements include commentary on AI regulations in Southeast Asia and workforce implications in the AI era.

Dai Zhongxiangمشاهده پروفایل

استادیار

• Large Language Models Inference
• Prompt Optimization
• In-context Learning
• +۵ مورد دیگر

Dai Zhongxiang is an Assistant Professor and Presidential Young Fellow at the School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHKSZ), where he joined in August 2024. Previously, he was a Postdoctoral Associate at MIT's Laboratory for Information and Decision Systems (January-June 2024) and a Postdoctoral Fellow at the National University of Singapore's Department of Computer Science (April 2021-December 2023). He completed his Ph.D. in Artificial Intelligence at NUS under the supervision of Bryan Kian Hsiang Low and Patrick Jaillet. Dr. Dai's research focuses on the intersection of theoretical and practical AI, with particular emphasis on large language models (LLMs) and optimization techniques. His work spans both theoretical foundations of multi-armed bandits and Bayesian optimization, as well as practical applications in LLM inference, including prompt optimization, in-context learning, personalization of LLMs, LLM-based agents, and scaling up test-time computation of LLMs. His research approach often bridges theoretical principles with real-world applications, particularly in AI4Science problems. His recent publications demonstrate a clear trend toward advancing LLM capabilities through optimization techniques, with increasing focus on practical deployment challenges. The research spans both theoretical contributions to optimization theory and applied work on enhancing LLM performance in real-world scenarios. His work on dueling bandits, neural bandits, and zeroth-order optimization has been consistently published in top-tier venues including NeurIPS, ICML, ICLR, and ACL. Presidential Young Fellow, CUHKSZ (2024) Dean's Graduate Research Excellence Award, NUS (2021) Research Achievement Award × 2, NUS (2019 & 2020) Singapore-MIT Alliance Graduate Fellowship (2017) Dr. Dai actively mentors multiple Ph.D. students and research assistants, with several of his students' papers accepted to top conferences. His research has received significant attention, with invitations to serve as Area Chair for NeurIPS 2025 and ICLR 2025, reflecting his growing influence in the machine learning community. His work bridges theoretical machine learning with practical applications in large-scale AI systems.

Maksym Andriushchenkoمشاهده پروفایل

پژوهشگر

• AI Safety
• Adversarial Robustness
• LLM Alignment
• +۳ مورد دیگر

Maksym Andriushchenko is an incoming faculty member at the ELLIS Institute Tübingen and Max Planck Institute for Intelligent Systems , where he will lead the AI Safety and Alignment group as a principal investigator. Currently, he is a Doctoral Assistant at the Theory of Machine Learning Laboratory (TML) within the Department of Computer Science (IINFCOM) at Swiss Federal Institute of Technology in Lausanne (EPFL) . His work focuses on AI safety, adversarial robustness, and alignment of large language models (LLMs) with societal values.

José Eduardo Villalobos Grailletمشاهده پروفایل

استادیار

• Spanish Film Studies
• Spanish Medieval Literature
• Latin American and Caribbean Studies
• +۵ مورد دیگر

José Eduardo Villalobos Graillet is an Assistant Professor of Spanish at Idaho State University , specializing in Modern Peninsular Literature and Culture with a focus on visual culture (film and television) . He previously held a position as Spanish Instructor at Wilfrid Laurier University in the Department of Languages and Literatures. Research Areas : Spanish Film Studies, Spanish Medieval Literature, Latin American and Caribbean Studies, Teaching Spanish as a Foreign Language, Visual Culture, and Critical Pedagogy. His scholarly work examines film and television adaptations of 'La Celestina' , analyzing censorship dynamics across Francoist Spain, the Democratic Transition, and contemporary Spain. He also contributes to Spanish pedagogy , developing technological tools like Lang-8 for written expression and culturally immersive teaching units on topics such as winter festivals and Caribbean music. His publications span both critical academic analysis and methodological innovations for language instruction. Key Themes in Publications : Adaptation fidelity, political censorship, intercultural pedagogy, and the role of digital platforms in language learning.

Sebastian Riedelمشاهده پروفایل

استاد

• Natural Language Processing (NLP)
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Sebastian Riedel is a Professor at University College London (UCL) and a Researcher at DeepMind, leading the UCL NLP Lab. His work focuses on teaching machines to read, reason, and write, integrating Natural Language Processing (NLP) with Machine Learning. He holds an Allen Distinguished Investigator award and has held roles at FAIR, UMass Amherst, Tokyo University, and the University of Edinburgh. Education: PhD in Computer Science from the University of Edinburgh (advisor: Ewan Klein), postdoctoral research at UMass Amherst (advisor: Andrew McCallum), and research at Tokyo University (advisor: Tsujii Junichi). Research Interests: NLP, machine learning, information extraction, and multimodal models like Gemini. He develops tools such as UCLEED (BioNLP event extractor), frontlets (Scala map wrappers), and thebibbrag (BibTeX to HTML converter). Awards: Allen Distinguished Investigator. Software contributions include GitHub repositories for NLP, machine learning, and data tools. Contact: s.riedel@ucl.ac.uk | Office: 1st Floor, 90 High Holborn, London WC1V 6LJ | Office Hours: Mondays 11 AM–12 PM.

Vicente Talanquerمشاهده پروفایل

استاد

• Chemistry Education Research
• Student Reasoning
• Assessment of Learning
• +۳ مورد دیگر

Vicente Talanquer is a University Distinguished Professor in the Department of Chemistry and Biochemistry at the University of Arizona, College of Science. His academic journey includes a B.S., M.A., and Ph.D. in Chemistry from the Universidad Nacional Autónoma de México (UNAM), followed by postdoctoral research at the University of Chicago's James Frank Institute. He has held various academic roles, including Professor and Associate Professor at UNAM before joining the University of Arizona in 2000. His research focuses on chemistry education, particularly student reasoning, assessment, and the development of chemical thinking. Key areas include understanding how students conceptualize chemical reactions, design learning progressions, and improve formative assessment strategies. He has led initiatives such as the Instructional-Teams Model to enhance teaching practices in STEM courses. Dr. Talanquer has received numerous awards, including the ACS Award for Achievement in Research (2021) and Arizona Professor of the Year (2015). He actively contributes to national and international educational committees, editorial boards, and professional societies such as the American Chemical Society and NARST. His work emphasizes curriculum innovation, teacher preparation, and fostering equity in science education. His research is supported by grants from the National Science Foundation and the UA Provost Investment Fund, focusing on active learning, large-enrollment course redesign, and systems thinking approaches. Talanquer's publications span over 200 peer-reviewed articles, textbooks, and educational resources in chemistry education, reflecting his commitment to advancing both research and practice in the field.

Manoj Sachdevمشاهده پروفایل

استاد

• Semiconductor Devices
• Low-Power Electronics
• VLSI Design
• +۷ مورد دیگر

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.

Paulo Bliksteinمشاهده پروفایل

دانشیار

• Technology in teaching and learning
• Learning design
• Computer modeling in Social and Behavioral Sciences
• +۶ مورد دیگر

Paulo Blikstein serves as Associate Professor of Communications, Media and Learning Technology Design at Columbia University. Previously, he was Assistant Professor of Education and (by courtesy) Computer Science at Stanford University and co-founded the Lemann Center for Brazilian Education (2008-2018). Education: Ph.D. in Learning Sciences, Northwestern University (2009) M.A. in Media Arts & Sciences, MIT Media Lab (2002) M.Eng. in Electronic Engineering, University of São Paulo (2000) B.S. in Metallurgical Engineering, University of São Paulo (1998) His research pioneers constructionist learning environments through digital fabrication, educational robotics, and tangible interfaces—focusing on equitable access for underserved communities. Inspired by Paulo Freire and Seymour Papert, he develops open-source tools like the GoGo Board robotics platform and leads the global FabLab@School initiative establishing fabrication labs in schools across four continents. Current work emphasizes multimodal learning analytics to study student interactions in maker-centered classrooms. Publications reveal strong focus on democratizing invention through maker education, with recurring themes in constructionist theory application, multimodal assessment, and context-specific technology adaptation. Brazilian education reform and low-cost computational solutions form significant threads, particularly in 2016-2017 publications. Scientific Awards: Two Google Faculty Awards National Science Foundation Early Career Award (highest U.S. government honor for early-career scientists) Blikstein directs the Transformative Learning Technologies Lab (TLTL) and co-founded Stanford's Center for Educational Entrepreneurship and Innovation in Brazil. His FabLearn conference established the first academic forum on Maker Movement applications in education. While specific grant details aren't listed, the NSF CAREER Award signifies major federal research funding. He spearheads the FabLab@School project deploying advanced fabrication labs in K-12 institutions worldwide and founded the FabLearn conference series. His work integrates teams of engineers, educators, and designers to create scalable solutions for resource-constrained learning environments.

Shih-Fu Changمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Multimedia Information Retrieval
• +۳ مورد دیگر

Shih-Fu Chang is the Dean of Columbia Engineering and holds the Morris A. and Alma Schapiro Professorship at Columbia University. His research focuses on computer vision, machine learning, and multimedia information retrieval. He is recognized as a foundational figure in the field of content-based visual search and has pioneered innovations in image/video search engines, crime prevention systems, and brain-machine interfaces. His leadership roles include Chair of Columbia's Electrical Engineering Department (2007-2010), Editor-in-Chief of the IEEE Signal Processing Magazine (2006-2008), and Senior Executive Vice Dean at Columbia Engineering, where he drives strategic planning and international collaboration. Dr. Chang has received prestigious awards including the ACM Multimedia Technical Achievement Award, IEEE Signal Processing Technical Achievement Award, and IEEE Kiyo Tomiyasu Award. He is a Fellow of AAAS, ACM, and IEEE, and an Academician of Academia Sinica. His recent work emphasizes multimodal reasoning, few-shot learning, and vision-language systems, with applications in healthcare diagnostics and multimedia benchmarking. His research spans cross-modal understanding, event extraction, and adaptive AI systems. Key contributions include systems like Ferret-v2 for multimodal grounding and RESIN for schema-guided event tracking. He has advised multiple startups and actively contributes to curriculum development in AI and engineering education.

Cosette Crisanمشاهده پروفایل

دانشیار

• Mathematics Education
• Curriculum Development
• Teacher Education
• +۳ مورد دیگر

Dr Cosette Crisan is an Associate Professor (Teaching) in Mathematics Education at University College London’s IOE, ranked World Number 1 in Education. She specializes in curriculum design, subject-specific mentoring, and integrating digital technologies into mathematics education. Prior to joining UCL IOE in 2010, she taught mathematics at secondary and university levels for 16 years. Her research focuses on enhancing mathematics teaching practices through collaborative mentorship frameworks and technology integration. She co-leads the Curriculum and Subject Specialism Research Group and the ROPE group, driving pedagogical innovation. Notable achievements include the 2021 UCL Faculty Education Team Award for pandemic-era teaching support and leadership roles such as Academic Head of Learning and Teaching (2020–2023). She holds a PhD in Mathematics Education from London South Bank University and is a Principal Fellow of the Higher Education Academy. Current initiatives include developing a Mathematics and Secondary Mathematics Education BSc (QTS) Teacher Degree Apprenticeship program. Her research emphasizes revitalizing geometry education and preparing mentors to effectively guide novice teachers. Publications span topics like computer-aided assessment (STACK), pandemic-era teaching adaptations, and cross-cultural mentorship studies. Dr Crisan’s work bridges theory and practice, advocating for mathematics education as a design science. She actively contributes to the London Mathematical Society’s Education Committee, promoting public engagement with mathematics. Awards include the UCL Faculty Education Team Award (2021) and recognition for her role in transitioning teaching to online platforms during the pandemic. Her teaching portfolio includes leadership of the MA Mathematics Education program and supervision of doctoral candidates. Recent interests include exploring intersections between mathematics and cognitive neuroscience, inspired by her daughter’s career path.

Ying Xuمشاهده پروفایل

استادیار

• Early Education
• Evidence-Based Intervention
• Families and Community
• +۵ مورد دیگر

Ying Xu is an Assistant Professor at the Harvard Graduate School of Education (HGSE), specializing in the design of AI technologies to support children's language, literacy, STEM learning, and well-being. Her research emphasizes creating AI systems that act as interactive learning companions and language partners for children while fostering human-AI collaboration with educators and families. She holds a Ph.D. in Language, Literacy, and Technology from the University of California, Irvine, and previously served as an Assistant Professor at the University of Michigan from 2022 to 2024. Her work focuses on understanding how AI can complement human interactions, particularly through conversational agents integrated into media like books and educational TV programs. Collaborations include partnerships with PBS KIDS, GBH Education, and Sesame Workshop. Funded by organizations such as the National Science Foundation and Schmidt Futures, her research bridges developmental psychology, education, and human-computer interaction. Xu's studies highlight AI's potential for personalized learning while addressing ethical considerations like AI literacy and social impact. Key achievements include numerous best paper awards and recognition as an Early Career Interdisciplinary Scholar by SRCD. Her interdisciplinary approach involves designing technologies that reflect community values and linguistic/cultural diversity, ensuring equitable access to AI-driven educational tools. Xu advocates for balanced AI integration in children's lives, emphasizing the need for transparency about AI's limitations and the importance of maintaining human connections. She explores how AI can empower stakeholders (e.g., educators, parents) to co-create technologies tailored to their needs, ensuring AI serves as a complement—not replacement—for human interaction. Current projects investigate conversational AI's design principles, children's perceptions of AI, and strategies for fostering critical evaluation of AI-generated content. She also examines how AI affects social interactions and developmental processes, advocating for ethical guidelines to maximize benefits while mitigating risks.

• Human-Computer Interaction (HCI)
• Quantitative HCI
• Mixed Reality
• +۴ مورد دیگر

Jay Henderson is an Assistant Professor in the Department of Computer Science at Memorial University of Newfoundland, leading the Human-Computer Interaction (HCI) Lab. His research focuses on quantitative HCI, measuring human performance with emerging technologies such as mixed reality and generative AI. He holds a PhD from the University of Waterloo and has held roles including Postdoctoral Fellow at Carleton University and Senior Research Scientist at Huawei Technologies Canada. Education: PhD in Computer Science, University of Waterloo (2021) BSc Hons in Computer Science (minors in Mathematics and Psychology), Mount Allison University (2016) Research Interests: Henderson’s work bridges computer science with psychology and mathematics, emphasizing objective measurement of user interactions. Key areas include mixed reality productivity, generative AI interfaces, gesture-based input, and multimodal feedback systems. His quantitative approach ensures practical insights for technology design. Key Grants & Awards: NSERC Discovery Grant ($155,000 over 5 years) NSERC Discovery Launch Supplement ($12,500) David R. Cheriton Graduate Scholarship ($20,000 over 2 years) Teaching & Supervision: Teaches courses like COMP 4303 (AI for Games) and supervises multiple graduate and undergraduate researchers. Notable supervisees include Daniel Jo (MSc) and Arunav Saha (BSc Honours). Service Contributions: Served on ACM committees including Graphics Interface and CHI Late Breaking Work. Acted as Virtual Chair for ACM ASSETS 2024 and contributed to ACM’s name change policy as a transgender advocate.