جست‌وجوی استاد و پژوهشگر

Zaiqiao Mengمشاهده پروفایل

مدرس

• Natural Language Processing
• Information Retrieval
• Large Language Models
• +۳ مورد دیگر

Zaiqiao Meng is a Lecturer (Assistant Professor) at the University of Glasgow's School of Computing Science, affiliated with the Information Retrieval Group and IDA section. He also holds an Affiliated Lecturer position at the University of Cambridge's Language Technology Lab. His research focuses on the intersection of machine learning, knowledge graphs, and NLP, particularly in biomedical applications. Key areas include AI agents, large language models, and healthcare informatics. Current roles include co-leading the Glasgow AI4BioMed Lab, which develops AI solutions for biomedical knowledge extraction. He has extensive postdoctoral and visiting research experience, including at KAUST's MINE lab. Meng has published widely in top conferences like ACL and EMNLP, with over 40 publications since 2019. His work spans topics such as drug-target interaction prediction, clinical summarization, and knowledge graph construction. Teaching includes courses on Recommender Systems and Data Science at both undergraduate and graduate levels. He advises multiple PhD students on projects involving LLMs, biomedical entity representation, and conversational agents.

Thomas Demeesterمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Natural Language Processing
• Deep Learning
• +۷ مورد دیگر

Thomas Demeester is an Associate Professor at the Internet Technology and Data Science Lab (IDLab), Ghent University - imec, Belgium. Appointed as Assistant Professor in 2019, he leads an AI research group focused on health applications and drug design, co-directing the Text-to-Knowledge research cluster with Prof. Chris Develder. His educational background includes: M.Sc. in Electrical Engineering from Ghent University (2005), completed with thesis work at ETH Zurich Ph.D. in Computational Electromagnetics from Ghent University (2009), funded by Research Foundation - Flanders (FWO) Demeester's research spans artificial intelligence with emphasis on deep learning and neuro-symbolic methods. Current tracks include energy-based models (Hopfield Networks, Deep Equilibrium Models), diffusion models for drug design, and clinical reasoning systems. His work bridges NLP, healthcare informatics, and generative AI with strong industry partnerships. Recent publications (2023-2025) reveal strategic expansion from NLP into health-centric AI: BioLORD biomedical encoders (2023), synthetic medical data frameworks (UAI/NeurIPS 2024), and novel diffusion model guidance (ICLR 2025). This evolution demonstrates convergence of generative modeling, clinical data analysis, and protein design. He actively mentors 24 PhD students across diverse AI domains: Current Research: Conversational agents, emotion analysis, clinical reasoning, antibody design, and diffusion model optimization Recent Graduates: Interpretable language models, biomedical semantics, task-oriented dialogue, and social media knowledge extraction Research is supported by imec funding and collaborations with Flemish biotech companies, building on his post-doctoral experience securing media-sector projects. Within IDLab, he co-leads the Text-to-Knowledge cluster driving NLP innovations for healthcare, legal, and economic applications.

• Computational Linguistics
• Computer Science
• Machine Learning
• +۱ مورد دیگر

Noah A. Smith is an Adjunct Professor of Computer Science and Engineering at the University of Washington. His work focuses on computational linguistics, machine learning, and natural language processing. He holds a Ph.D. in Computer Science from Johns Hopkins University (2006). His research explores ethical AI applications, multimodal systems, and foundational aspects of language models. Key research areas include: Ethical considerations in NLP, such as detecting rights abuses through text analysis Efficient decoding and alignment strategies for large language models Large-scale evaluation frameworks for multitask and multimodal generation Understanding pretraining dynamics and data composition effects Recent work emphasizes transparency in language models (e.g., tracing outputs to training data) and improving alignment through human feedback. He has contributed to open-source projects like OLMo and Dolma, advancing reproducibility in NLP research. No awards explicitly listed in provided texts. No specific advising or grant details available, though extensive publication output indicates active research involvement.

Monica Lamمشاهده پروفایل

استاد

• Artificial Intelligence
• Privacy-Preserving Technologies
• Compiler Design
• +۴ مورد دیگر

Monica Lam is the Kleiner Perkins, Mayfield, Sequoia Capital Professor in Stanford University's School of Engineering and holds a courtesy professorship in Electrical Engineering. She leads the Stanford Open Virtual Assistant Laboratory and has pioneered work in virtual assistants, privacy protection, and compiler design. Her research includes the Almond virtual assistant, privacy-preserving IoT systems, and the ThingTalk programming language. She co-authored the seminal 'dragon book' on compilers and co-founded Tensilica (now part of Cadence). Education: Bachelor of Science (Honors), Computer Science, University of British Columbia, 1980 Master of Science, Computer Science, Carnegie Mellon University, 1982 Doctor of Philosophy (PhD), Computer Science, Carnegie Mellon University, 1987 Research Interests: Dr. Lam's work focuses on conversational AI with privacy guarantees, compiler optimization for parallel computing, and open-source virtual assistant ecosystems. She is a leader in decentralized systems, having developed frameworks like SociaLite for large-scale graph analysis and Musubi for mobile social networking without centralized platforms. Article Trends: Recent publications emphasize multimodal interactions, multilingual dialogue systems, and LLM-driven applications in areas like question answering, persuasive chatbots, and adaptive assistants. Her work bridges foundational AI research (e.g., semantic parsing) with real-world deployments (e.g., privacy-compliant IoT). Awards & Honors: Member of the National Academy of Engineering ACM Fellow Popular Science's Best of What's New Award (Security, 2019) Advising & Grants: Lam oversees the Open Virtual Assistant Initiative, a collaborative project to build open-source semantic models. Her NSF CNS grant (CNS Core) focuses on federated privacy systems. She has advised over 50 students in AI and systems research, though specific names are not listed in the provided texts. Labs & Teams: Directs the Stanford Open Virtual Assistant Laboratory, collaborates with the Stanford NLP group, and maintains ties to industry through former startup Tensilica's legacy in embedded processors.

Andrew McCallumمشاهده پروفایل

استاد

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

Andrew McCallum is a Distinguished Professor and Director of the Center for Data Science at the University of Massachusetts Amherst. He holds a PhD in Computer Science from the University of Rochester (1995) and a BS from Dartmouth College (1989). His research focuses on machine learning, natural language processing, and information extraction, with applications to scientific literature and knowledge base construction. He has pioneered work on conditional random fields and probabilistic databases, and led the development of systems like Rexa, an advanced research paper search engine. Affiliations: Center for Data Science, Center for Intelligent Information Retrieval, Computational Social Science Institute Key Projects: OpenReview.net, Unified Information Extraction, Automated Knowledge Base Construction His work emphasizes extracting actionable knowledge from unstructured text, with contributions to social network analysis, entity resolution, and semi-supervised learning. McCallum has over 300 publications and has received awards including the NSF ITR Grant, IBM Faculty Partnership Awards, and ACM/AAAI Fellowships. He has advised numerous students and served as ICML General Chair (2012). Recent Research Trends: Probabilistic box embeddings, case-based reasoning for knowledge bases, scalable clustering algorithms, and applications in biomedical informatics.

• Medical Image Understanding
• Radiology Report Generation
• Deep Learning
• +۳ مورد دیگر

Chantal Pellegrini is a Lecturer and PhD student at the Chair of Computer Aided Medical Procedures (Prof. Navab) at Technical University of Munich (TUM). Her research focuses on Deep Learning applications in medical imaging, including explainable AI for radiology report generation and Vision-Language Models for clinical decision support. She actively contributes to the DHM, NARVIS Lab, and RobUSt research groups. Teaching responsibilities include courses such as 'Computer Aided Medical Procedures', 'Medical Augmented Reality', and 'Surgical Robotics'. She supervises student projects in medical AI and healthcare innovation, with recent projects involving multimodal report generation and graph pretraining for medical applications. Education: BSc/MSc Computer Science (TUM), current PhD student since 2022 Labs: DHM (German Heart Center), NARVIS Lab, RobUSt Robotics & Ultrasound Research Keywords: Medical Image Understanding, Radiology Reports, LLMs in Healthcare Her publications span surgical OR dataset development, reinforcement learning for clinical decisions, and explainable X-ray diagnosis systems. She mentors MA/BA students in medical AI and project management for healthcare applications.

Elsayed Issaمشاهده پروفایل

استادیار

• Natural Language Processing
• Second Language Acquisition
• Arabic Linguistics
• +۶ مورد دیگر

Elsayed Issa is an Assistant Professor in the School of Languages and Cultures at Purdue University. Specializing in Computational Linguistics and Arabic, his interdisciplinary research bridges Natural Language Processing (NLP) with Second Language Acquisition (SLA), focusing on conversational AI and speech technology for under-resourced languages. Ph.D. in Linguistics from the University of Arizona (2023) Research integrates NLP, conversational AI, and SLA methodologies Develops tools for computer-assisted pronunciation training (CAPT) Focuses on Arabic dialectology and large language models (LLMs) His work employs Transformer architectures and end-to-end machine learning to enhance language learning systems. Recent projects include ArabiBot development and dialect identification models. He specializes in speech-to-text systems , prosody modeling , and emotional speech analysis for Arabic language learning applications.

• Robotics
• Transfer Learning
• Neuro-fuzzy Systems
• +۲ مورد دیگر

C. S. George Lee is a Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering, located in West Lafayette. His research focuses on Robotics, Transfer Learning, Neuro-fuzzy Systems, Automatic Controls, and Computer Engineering. He holds a BSEE (1973), MSEE (1974) from Washington State University, and a PhD (1978) from Purdue University. His work integrates computational intelligence with AI, robotics, and education technology, emphasizing human-machine co-learning models and bilingual systems. His contributions span domains like quantum computing, generative AI, and knowledge graph applications. He leads the Art Lab at Purdue and has published extensively on topics ranging from humanoid robotics to cross-cultural educational platforms. His research areas include developing intelligent agents for edutainment, robotic assistants for student learning, and advanced machine learning techniques. Notable trends in his publications involve computational intelligence applied to bilingual language models (e.g., Taiwanese/English co-learning), quantum-based AI systems, and human-centric robotics. He has explored applications in healthcare (e.g., blood donor analysis), autonomous navigation, and game AI (e.g., Go). His work often bridges theoretical advancements with real-world implementations, such as Java software tools for motor activity assessment (JKinect) and AI-driven platforms for skill evaluation. Lee's research emphasizes interdisciplinary collaboration, with contributions to IEEE conferences and cross-institutional projects. His lab develops tools for adaptive e-learning, robotic task performance evaluation, and human pose estimation using neural networks. Despite prolific publishing, no specific grants or awards are explicitly mentioned in the provided text. His work continues to explore the intersection of human intelligence and smart machines through platforms like Metaverse integration and BCI (Brain-Computer Interface) applications.

Piotr Indykمشاهده پروفایل

استاد

• High-dimensional computational geometry
• Data stream algorithms
• Sparse recovery
• +۳ مورد دیگر

Piotr Indyk is the Thomas D. and Virginia W. Cabot Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT. He is co-director of the Foundations of Data Science Institute (FODSI) and a member of MIT's Theory of Computation Group, Computer Science and Artificial Intelligence Lab (CSAIL), and multiple research initiatives like Wireless@MIT and Big Data@CSAIL. Education: Magister (MA) in Computer Science, University of Warsaw (1995) Ph.D. in Computer Science, Stanford University (2000), advised by Rajeev Motwani Research Interests: Focuses on high-dimensional computational geometry, data stream algorithms, sparse recovery, compressive sensing, and machine learning. His work includes foundational contributions like locality-sensitive hashing (LSH), the Sparse Fourier Transform, and efficient similarity search algorithms. Key Contributions: Known for developing FALCONN (Fast Approximate Nearest Neighbor Search library), and for pioneering work in sub-linear algorithms, streaming algorithms, and geometric computing. Awards: ACM Paris Kanellakis Award (2012) ACM Fellow (2015) Simons Investigator (2013) Member, National Academy of Sciences (2024) Member, American Academy of Arts and Sciences (2023) Teaching & Mentorship: Advised numerous PhD/MSc students and postdocs, and taught courses on geometric computation, streaming algorithms, and algorithmic aspects of embeddings. Labs & Teams: Leads research in areas like FODSI, geometric algorithms, and data science at MIT's CSAIL.

• Computational Linguistics
• Natural Language Processing (NLP)
• Dependency Parsing
• +۵ مورد دیگر

Joakim Nivre is a Professor at Uppsala University's Department of Linguistics and Philology. He is a leading researcher in computational linguistics, with a focus on dependency parsing, Universal Dependencies (UD) framework development, and multilingual NLP applications. His recent work explores LLMs in climate change discourse analysis, pharmacovigilance explainability, and historical text processing. Key research areas: Dependency parsing theory, Universal Dependencies standardization, LLM evaluation Collaborations: SweSAT-1.0 benchmark development, ClimateEval project, PARSEME integration His 2025-2023 publications demonstrate expertise in explainable AI for healthcare, synthetic data generation for idioms, and multilingual benchmark design. Notably, he co-developed SweSAT-1.0 to evaluate Swedish LLMs and contributed to typology-informed UD revisions. Despite extensive work in NLP, no scientific awards are mentioned in available texts.

Libby Gerardمشاهده پروفایل

دانشیار مدعو

• Science Education
• Educational Technology
• Natural Language Processing
• +۳ مورد دیگر

Libby Gerard is an Associate Adjunct Research Professor at the University of California, Berkeley School of Education and a Research Director for the Technology-Enhanced Learning in Science (TELS) Center. Her work focuses on leveraging innovative technologies to enhance science education through student idea capture, automated assessment, and teacher professional development. Doctorate in Educational Leadership (EdD), Mills College (2008) Bachelor’s in English Literature and Philosophy, Emory University (2000) Her research emphasizes: Automated scoring of student essays using NLP to improve science explanations Real-time instructional customization using embedded assessment data Technology-driven professional development for teachers and principals Social justice integration in science pedagogy Collaborative revision frameworks for inquiry-based learning K-12 education adaptation during the pandemic Recent publications highlight trends in educational technology for science learning, with a focus on NLP applications, interactive inquiry modules, and equitable teaching practices. She has authored studies in journals like Science , Review of Educational Research , and Computers & Education , often exploring how automated systems can enhance teacher-student dynamics. Scientific Awards : Best Paper Award at the AI4EDU Workshop (AAAI Conference, 2020) Libby leads funded projects such as: TIPS (NSF, 2021-2025): NLP for science education ARISE (Hewlett Foundation, 2020-2023): Anti-racism in science education STRIDES (NSF, 2018-2022): Responsive instruction for science teachers PLANS (NSF, 2015-2020): Automated learning support systems She contributes to teacher training through courses like Research Methods for Science Teachers and Apprentice Teaching in Science , emphasizing data-driven pedagogy and inquiry-based instruction.

Cecilia Mascoloمشاهده پروفایل

استاد

• Mobile Systems
• Wearable Computing
• Machine Learning
• +۵ مورد دیگر

Cecilia Mascolo is a Professor of Mobile Systems at the University of Cambridge , specifically in the Department of Computer Science and Technology . She co-directs the Centre for Mobile, Wearable System and Augmented Intelligence and is a Fellow of Jesus College, Cambridge . Her research focuses on mobile systems , machine learning for mobile health , and earable technology . She has been awarded prestigious grants such as the ERC Advanced Research Grant (2019-2025) and the EPSRC Open Research Fellowship (2025-2030). Currently on sabbatical at Harvard University , her work bridges systems and machine learning for health applications. Education: PhD in Computer Science from the University of Bologna, Italy. Previous Affiliation: Faculty at University College London before 2008. Her research spans mobile and wearable systems for health and behavior monitoring, focusing on on-device machine learning , uncertainty-aware models , and audio-based diagnostics . Key areas include federated learning , edge computing , and respiratory disease progression analysis via wearables. She explores earable technology for physiological monitoring, gait analysis, and even toothbrushing tracking using in-ear sensors. Her recent publications highlight advancements in earable-based health monitoring , including heart rate estimation , respiratory rate detection , and ECG analysis using machine learning. She emphasizes longitudinal health data from consumer devices, advocating for scalable diagnostics beyond traditional clinical standards. Scientific Awards: ERC Advanced Research Grant EPSRC Open Research Fellowship Best Paper Award - IEEE Percom 10-Year Impact Award - ACM Ubicomp Computer Laboratory Ring Hall of Fame Best Paper Award Student: Andrea Ferlini - ACM SIGMOBILE Doctoral Dissertation Runner-up She leads the Mobile Systems Research Laboratory , mentoring a team of 15 researchers (postdocs and PhD students), and has graduated over 25 PhD students. Her teaching includes Mobile Health courses at the University of Cambridge, and she serves as Director of Studies for Computer Science at Jesus College.

Christiane Fellbaumمشاهده پروفایل

استاد

• Theoretical Linguistics
• Computational Linguistics
• Corpus Linguistics
• +۳ مورد دیگر

Christiane Fellbaum serves as Lecturer with Rank of Professor in Princeton University's Program in Linguistics and Department of Computer Science, where she has been a senior research scholar since returning in 1987 after postdoctoral work at the University of Paris. Her foundational contributions to computational linguistics include co-developing WordNet and co-founding the Global WordNet Association. Her educational background features: Ph.D. in Linguistics, Princeton University (1980) Postdoctoral Fellowship, University of Paris Fellbaum's research integrates theoretical linguistics with computational applications, specializing in lexical semantics, corpus analysis, and semantic network construction. Her work bridges computational linguistics and lexicography through projects like WordNet and Medical WordNet, with recent emphasis on multilingual resources, bias analysis in embeddings, and African language technology development. She examines semantic phenomena including idioms, verb alternations, and emotion scales through both corpus linguistics and formal ontological frameworks. Analysis of her publication trajectory reveals sustained innovation in lexical resource development since the 2000s, evolving from foundational WordNet studies to contemporary work on large language model adaptation and social bias mitigation. Current research demonstrates increasing interdisciplinary collaboration across NLP, cognitive science, and social justice applications. Her scientific recognition includes: Wolfgang Paul Prize from the German Humboldt Foundation (2001) Antonio Zampolli Prize (2006) Fellbaum has secured continuous research funding from the U.S. National Science Foundation, European Union Seventh Framework, Frank Moss Foundation, and Tim Gill Foundation. She actively mentors junior researchers through Princeton's Independent Work seminars and hosts the North American Computational Linguistics Olympiad (NACLO), while leading major international collaborations including the KYOTO and SIERA European projects. As director of the WordNet project and permanent fellow at the Berlin-Brandenburg Academy of Sciences, she maintains leadership in global lexical resource initiatives through the Princeton Language and Intelligence initiative and Natural and Artificial Minds research group.

• Machine Learning
• Deep Learning
• Graph Analysis
• +۶ مورد دیگر

Michalis Vazirgiannis is a Professor at LIX, École Polytechnique (France) leading the Data Science and Mining (DaSciM) group. With academic backgrounds in Physics (Athens University), AI (Heriot-Watt University), and Informatics (Athens University), he has conducted research at Fraunhofer, Max Planck MPI, and INRIA/FUTURS while teaching at institutions across Greece, France, China, and Spain. His research spans Machine/Deep Learning for Graphs (GNNs, graph kernels, embeddings) Text Mining & NLP (Graph-of-Words, biomedical text analysis) Combinatorial Optimization for pandemic forecasting and energy systems Event/Anomaly Detection in time series and sensory data Industrial collaborations with Airbus, Google, Tencent, and BNP . He has supervised 29 completed PhD theses, published over 250 papers, and received prestigious awards including Marie Curie and Tencent Rhino-Bird Fellowships. His team leads the ANR-HELAS Chair (2020-2025) focusing on heterogeneous data deep learning.

Vivek Srikumarمشاهده پروفایل

دانشیار

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

Vivek Srikumar is an Associate Professor in the Kahlert School of Computing at the University of Utah, co-leading the Utah NLP group and affiliated with the Utah Center for Data Science. His research focuses on Machine Learning and Natural Language Processing, particularly in structured prediction, bias mitigation, and healthcare NLP applications. He teaches Machine Learning (CS 6350/DS 4350) and has been supported by NSF, NIH, and corporate grants from Intel, Google, and others. Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (2013) Postdoctoral Researcher at Stanford University's NLP Group (2013-2014) Visiting Researcher at Allen Institute for Artificial Intelligence (2022 sabbatical) Research Interests: Srikumar explores text understanding, structured learning, and robust AI systems. His work addresses challenges in table-based reasoning, adversarial robustness, and ethical AI. He develops methods to ensure models use appropriate evidence and mitigate biases in representations. Grants & Collaborations: Supported by NSF, NIH, BSF, and industry partnerships with Intel, Google, Verisk, Bloomberg, and Nvidia. Notable projects include table QA systems (TempTabQA), bias mitigation (OSCaR/VERB), and crisis counseling NLP tools (ClientBot). Advising: Supervised over 30 students, including 15+ Ph.D./M.S. alumni now in academia and industry (e.g., Google, Amazon, Microsoft). Current advisees focus on multimodal reasoning, healthcare NLP, and AI ethics. Labs/Teams: Utah NLP Group and Utah Center for Data Science. Active in reproducibility efforts (LogFlux) and open-source tools (CogCompNLP/Pylon frameworks).