جست‌وجوی استاد و پژوهشگر

• Natural Language Processing
• AI Ethics
• Multilingual Learning
• +۳ مورد دیگر

Yulia Tsvetkov is an Associate Professor at the University of Washington's Paul G. Allen School of Computer Science & Engineering. Previously, she was an Assistant Professor at Carnegie Mellon University (CMU) and holds a PhD from CMU. Her research focuses on Natural Language Processing (NLP), AI ethics, multilingual learning, and large language models. She leads a research group advancing ethical AI, NLP fairness, and cross-cultural language technologies. Education: PhD in Language Technologies, Carnegie Mellon University Postdoctoral research at Stanford NLP Group M.Sc. in Computer Science (Multi-word Expressions), University of Haifa Research Interests: Hybrid solutions combining machine learning with linguistic theory Addressing ethical challenges in AI Multilingual NLP for underrepresented languages Large language model robustness and alignment Recent Work Trends: Recent publications emphasize ethical AI, bias mitigation, and cross-lingual fairness. Notable contributions include frameworks for knowledge-aware LLMs and methods to detect/hallucination. Awards: Best Paper Award (MatFormer, 2023) Outstanding Paper Award (Don't Hallucinate..., 2024) Wikimedia Foundation Award (Controlled Analyses..., 2021) Teaching: Teaches graduate courses on Ethics in AI and undergraduate NLP at UW. Previously taught Multilingual NLP at CMU. Labs/Teams: Leads the Tsvetkov Lab focused on ethical and equitable NLP systems.

Zirui Neil Zhaoمشاهده پروفایل

استادیار

• Computer Architecture
• Security and Privacy
• Cloud Computing
• +۴ مورد دیگر

Zirui Neil Zhao is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Texas at Austin, specializing in computer architecture, security, and cloud computing. Previously, he was a postdoctoral researcher at NVIDIA Research after completing his PhD at the University of Illinois Urbana-Champaign. His academic journey includes: PhD in Computer Science, University of Illinois Urbana-Champaign (2024), advised by Prof. Josep Torrellas BS in Applied Physics, University of Science and Technology of China (2018), School of the Gifted Young (Yan Jici Talent Program) Dr. Zhao's research centers on side-channel attacks and defenses across cloud-computing stacks, with breakthroughs including end-to-end attacks in Google Cloud (ASPLOS 2024) and low-noise side channels in Intel CPUs (USENIX Security 2022). His defense strategies span information theory, cache systems, OS design, program analysis, and data-oblivious programming, aiming to build attack-resistant public clouds. His work bridges theoretical rigor with real-world cloud environments. His 15 recent publications (2018-2025) reveal a dominant focus on hardware-software security interfaces, evolving from symbolic execution tools to cutting-edge cloud-side-channel research. Key trends include practical attack demonstrations in production clouds, defense frameworks for speculative execution, and cross-layer mitigation strategies from hardware to programming models, consistently targeting real-world deployability. His accolades include: W. J. Poppelbaum Memorial Award for architectural creativity (UIUC, 2023) IEEE Micro Top Picks 2025 and 2024 Honorable Mention Top Picks in Hardware and Embedded Security 2024 Actively recruiting graduate students for hardware/system security research, Dr. Zhao teaches ECE 382N: Security at the Hardware-Software Interface (Fall 2025). His service includes program committees for CCS 2025, HPCA 2024, and Top Picks in Hardware Security 2024. While specific grants aren't detailed, his prolific output in top venues (ASPLOS, ISCA, MICRO, USENIX Security) indicates robust research support. His lab focuses on practical cloud security challenges within UT Austin's ECE ecosystem.

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

Dr. Sriparna Saha is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Patna, India. She holds a Ph.D. from the Indian Statistical Institute Kolkata and has held leadership roles including Head of Department (2021-2023) and Associate Dean for Research and Development (2019-2021). Her research focuses on AI, machine learning, natural language processing, bioinformatics, and multiobjective optimization. She has authored over 400 publications with an h-index of 38 and received awards such as the NASI Young Scientist Platinum Jubilee Award and Google India Women in Engineering Award. Her work spans multimodal systems, medical image analysis, and computational social systems. Education: M.Tech (2005) and Ph.D. (2011) in Computer Science from Indian Statistical Institute Kolkata. Research Interests: Multimodal information processing, NLP, machine learning, bioinformatics, and optimization techniques. Awards: Includes Lt. Rashi Roy Memorial Gold Medal, BIRD Award, and multiple fellowships (Humboldt, CNRS, etc.). Administrative Roles: IEEE Student Branch Councilor, Senate Member, and Visvesvaraya Nodal Officer at IIT Patna. Her recent work includes advancements in multimodal recommendation systems, breast cancer prognosis models, and computational social systems for crisis management. She has also contributed to hate speech detection in multilingual contexts and medical imaging diagnostics.

• Computer Architecture
• Processor Microarchitecture
• Memory Systems
• +۴ مورد دیگر

Rakesh Kumar is an Associate Professor in the Department of Computer Science at NTNU, affiliated with the Computer Architecture Lab (CAL). He received his PhD from UPC Barcelona in 2014 and previously worked at Uppsala University, the University of Edinburgh, and Intel Barcelona Research Center. His research focuses on improving datacenter efficiency through microarchitecture and memory systems, hardware/software co-design, and dynamic code translation. Education: PhD in Computer Architecture, UPC Barcelona (2014) MEng in Microelectronics, BITS Pilani (2008) BTech in Electronics and Communications, Kurukshetra University (2005) Research Interests: His work emphasizes processor microarchitecture, memory systems, and energy-efficient designs. Key areas include hardware/software co-design (e.g., Nvidia Denver alternatives), dynamic vectorization, and server optimization. Recent projects target server front-end bottlenecks, address translation efficiency, and BTB organization for data centers. Publications: Recent work includes contributions to IEEE/ACM MICRO, HPCA, and ISCA, focusing on topics like uneven block size instruction caches, address translation optimizations, and server architecture improvements. Awards: Intel Spontaneous Level II/Excellence Award (2014) Distinguished Artifact Award at IEEE/ACM MICRO 2023 Teaching & Advising: Teaches courses like TDT4258 Low Level Programming and advises students on projects such as vector unit design and microarchitecture optimization. Active in mentoring PhD candidates and leading the Computer Architecture Lab. Labs/Teams: Affiliated with the Computer Architecture Lab (CAL) and collaborates on projects like DARCO, an infrastructure for HW/SW co-designed virtual machines.

Javed Mostafaمشاهده پروفایل

استاد

• Multimedia Information Retrieval
• Personalization and User Modeling
• Cyberinfrastructure for Research and Learning
• +۷ مورد دیگر

Javed Mostafa is a Professor and Dean of the Faculty of Information at the University of Toronto since September 2023. He previously served as Professor and founding Director of UNC's Carolina Health Informatics Program (2011-2023), Deputy Director for Education & Training at UNC's Biomedical Informatics unit (2010-2023), and as Victor H. Yngve Endowed Professor and Associate Dean at Indiana University (2000s). Education : PhD in Information Science (1994) from The University of Texas at Austin, MA from The Ohio State University, BSc from Northwestern Oklahoma State University Companies : Co-founder of KeonaHealth (2010-present) and Cymantix (2018-present) His research focuses on multimedia information retrieval , personalization/user modeling , and cyberinfrastructure for research . Key contributions include: Development of neurophysiologically-informed information retrieval systems Creation of the Health Data Exchange platform for expert identification Advancements in SNOMED CT-based clinical cohort identification Editorial leadership as Editor-in-Chief of the Journal of the Association for Information Science and Technology Establishment of the Laboratory of Applied Informatics Research (LAIR) at U of T Mostafa's scientific awards include: Victor H. Yngve Endowed Professorship McColl Term Professorship His work spans interdisciplinary collaborations with grants like the Sponsored Research Agreement (2025-2026) for the Society 2025 project. He has authored over 105 peer-reviewed publications and contributed to policy development for health data systems in India, Malawi, and Bangladesh.

Benjamin Van Durmeمشاهده پروفایل

دانشیار

• Natural Language Processing
• Computational Semantics
• Streaming Algorithms
• +۱۰ مورد دیگر

Benjamin Van Durme is an Associate Professor in the Department of Computer Science at Johns Hopkins University's Whiting School of Engineering, with secondary appointments as Senior Research Scientist at the Human Language Technology Center of Excellence (HLTCOE) and affiliation with the Center for Language and Speech Processing (CLSP). He leads Natural Language Understanding research at HLTCOE and serves as research lead at Microsoft Semantic Machines. His educational background includes B.S. and B.A. (2001), M.S. in Language Technologies (2004), M.S. in Computer Science (2006), and Ph.D. (2009), all from the University of Rochester. Van Durme's research spans multiple facets of artificial intelligence with primary focus on natural language processing, computational semantics, and information seeking systems. His work addresses fundamental challenges in machine learning, reasoning agents, multimodal understanding, factuality, and legal reasoning. Current projects include Decomp.io for decompositional semantics, IterX for structured information extraction, Nellie and Treewise for neuro-symbolic reasoning, and MultiVENT for event detection in videos. Analysis of his recent publications reveals a strong emphasis on improving large language models through context compression, safety alignment, and multilingual capabilities. His work bridges theoretical advances in NLP with practical applications in legal reasoning, scientific communication, and information retrieval systems. Van Durme actively collaborates across multiple institutions and leads research efforts that address critical challenges in AI safety, factuality verification, and efficient reasoning systems. His lab produces work that spans from foundational ML methods to cognitive science applications, with particular attention to creating models that can extract structured information, make logical inferences, and handle uncertainty in multilingual contexts.

Xhemal Zenuniمشاهده پروفایل

استاد

• Computer Science
• Machine Learning
• Internet of Things (IoT)
• +۷ مورد دیگر

Xhemal Zenuni is a Full Professor and current Dean at the Faculty of Contemporary Sciences and Technologies, South East European University (Tetovo, North Macedonia). His academic career spans multiple roles from IT Administration Assistant (2003-2004) to progressively senior positions including Young Assistant (2004-2006), Assistant (2006-2008), Assistant Professor (2013-2018), Associate Professor (2018-2023), and now Full Professor since 2023. Education: PhD in Computer Sciences (2006-2012, Technical University of Sofia), specialization in Computer Systems, Complexes and Networks Master of Sciences in Informatics (2004-2005, New Bulgarian University), specialization in Internet Software Technologies Bachelor of Sciences in Computer Sciences (2001-2003, SEEU) With expertise in Machine Learning , IoT , Cloud Computing , and Database Systems , his research focuses on applying AI/ML techniques across multiple domains. Recent work includes predictive modeling in education, hate speech detection for Albanian social media, smart agriculture systems, and microservices architecture migration. His publications demonstrate consistent engagement with modern computational challenges, particularly around low-resource language processing , smart city infrastructure , and educational technology . While serving as Dean and Vice-Dean, he has maintained active research output through collaborations with colleagues like Mentor Hamiti, Jaumin Ajdari, and Florije Ismaili.

Francesco Bellottiمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Embedded Systems
• Automated Driving
• +۳ مورد دیگر

Francesco Bellotti is an Associate Professor at the University of Genoa's Department of Naval, Electrical, Electronic and Telecommunications Engineering (DITEN). He teaches advanced courses including Computational Intelligence, Cyber Physical Systems, and Machine Learning for Automated Driving across master's programs in Electronic Engineering and Engineering Technology for Strategy and Security. His institutional role includes membership in the steering committee of the Italian Excellence Center for Logistics, Transport, and Infrastructure (CIELI). Bellotti's research focuses on artificial intelligence and embedded systems , with applications in automotive technology, edge computing, and TinyML. Key areas include real-time driver distraction detection, deep reinforcement learning for autonomous parking, neural architecture optimization for microcontrollers, and cross-domain activity recognition. His work bridges theoretical machine learning with resource-constrained hardware implementations. Recent publications (2024-2025) demonstrate a strong emphasis on optimizing deep learning models for edge deployment, particularly in automotive and IoT contexts. Dominant themes include real-time neural network inference on microcontrollers, adversarial training for robust autonomous agents, video preprocessing for driving scenarios, and benchmarking lightweight vision models. The research consistently targets efficiency-critical applications, reflecting Bellotti's focus on scalable embedded AI solutions.

Mark Steedmanمشاهده پروفایل

استاد

• Computational musical analysis
• Computational linguistics
• Combinatory logic
• +۶ مورد دیگر

Mark Steedman is a Professor of Cognitive Science at the School of Informatics , University of Edinburgh, and an Adjunct Professor in the Department of Computer and Information Science at the University of Pennsylvania. His research bridges Artificial Intelligence , Cognitive Science , and Computational Linguistics , with a focus on Combinatory Categorial Grammar (CCG) , Prosody and Intonation , and Temporal Semantics . He has led the Institute for Language, Cognition, and Computation and contributed to interdisciplinary research at the Human Communications Research Center and Centre for Speech Technology Research . Research Interests : Steedman's work explores the intersection of formal grammar, computational models, and cognitive processes. He investigates how CCG parsing can enhance semantic inference, how prosodic features improve speech processing, and the role of temporal semantics in language understanding. His projects often integrate language models with entailment graphs for question answering and dialogue systems. Scientific Awards : Fellow of the American Association of Artificial Intelligence (1993) Fellow of the Royal Society of Edinburgh (2002) Fellow of the British Academy (2002) Member of Academia Europaea (2006) Best Paper Awards at ACL 2023 and AACL/IJCNLP 2023 Influential Paper Award (IFAAMAS 2017) Recent Trends in Publications : His recent work emphasizes language models for semantic inference , entailment graphs in multilingual settings, and incremental parsing for brain-language interfaces. Papers address challenges in hallucination , cross-lingual transfer , and prosody-text alignment .

Michael Rießlerمشاهده پروفایل

استاد

• Endangered Languages
• Linguistic Typology
• Sociolinguistics
• +۶ مورد دیگر

Michael Rießler is Professor of General Linguistics in the Foreign Languages and Translation Studies department at the School of Humanities, University of Eastern Finland. Holding both a PhD and Docent qualification, he is actively involved in research on endangered languages, linguistic diversity, and language policy in Northern Europe, particularly in the Barents region. Dr. Rießler's research focuses on multilingualism and language diversity in the Barents region , with specialization in documenting endangered languages using digital methods, sociology of language, minority language policy, language contacts, and linguistic typology. His work spans across Uralic languages, particularly Saami languages and Komi, with emphasis on language documentation, preservation, and technological applications for endangered languages. His recent publications demonstrate a strong trend toward integrating digital humanities and computational linguistics with traditional language documentation. Rießler's work consistently addresses the challenges of documenting and preserving endangered languages through innovative technological solutions, including speech recognition systems, OCR for historical materials, and data management strategies that balance accessibility with ethical considerations. Currently leading the project 'Hundred Sweden Finnish and Tornedalian linguistic autobiographies' (2021-2026) and active in the General Linguistics research group since 2020, Professor Rießler continues to make significant contributions to the field of endangered language documentation and Uralic linguistics.

Mona Diabمشاهده پروفایل

استاد

• Natural Language Processing
• Responsible AI
• Controllable Natural Language Generation
• +۷ مورد دیگر

Dr. Mona Diab is the Director of the Language Technologies Institute (LTI) at Carnegie Mellon University and a Full Professor in the School of Computer Science. She is a globally recognized leader in computational linguistics and natural language processing (NLP), with significant contributions to Arabic NLP, responsible AI, and cross-lingual systems. Trustworthy NLP and Responsible AI Controllable Natural Language Generation Culturally aware generative AI modeling Cross-lingual/multilingual processing (low-resource contexts) Computational social science and health analytics Her recent research focuses on cultural alignment in LLMs, embedding compression via wavelet transforms, and automating AI documentation through CARDGEN . Publications demonstrate her expertise in bias mitigation, conversational AI, and scientific content democratization. ACL Fellow King Salman Global Arabic Academy award recipient Elected member of CRA board (2025) Dr. Diab leads the R3LIT Lab at CMU and has mentored numerous students across her academic career at CMU, George Washington University, and industry roles at Amazon AWS and Meta.

Matthew R. Gormleyمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Dialogue Systems
• +۷ مورد دیگر

Matthew R. Gormley is an Associate Professor at Carnegie Mellon University , affiliated with the School of Computer Science and the Machine Learning Department . He also serves as an affiliate of the Language Technologies Institute and directs the ML Minor/Concentration program. Research interests include Natural language processing for dialogue systems Summarization (multi-document and long-document) NLP applied to medical text analysis Low-resource language and domain adaptation Syntactic and semantic parsing Autoregressive and approximation-aware machine learning Computationally efficient models His work spans both theoretical and applied domains, focusing on unsupervised learning and multilinguality. Recent publications highlight advancements in long-context modeling, data contamination taxonomies, and medical summarization. His collaborations with prominent researchers like Graham Neubig, Jason Eisner, and Thomas Schaaf demonstrate his interdisciplinary approach. Scientific contributions include Developing novel training methods for conditional random fields Advancing neural finite-state transducers Building concrete NLP pipelines for Chinese Creating annotated datasets like Annotated Gigaword Teaching roles at CMU include co-instructing 10-301/10-601 Introduction to Machine Learning and 10-423/10-623 Generative AI . He has mentored numerous PhD and Master's students in areas such as multilingual NLP, medical text analysis, and structured data processing.

• Machine Learning
• Artificial Intelligence
• Federated Learning
• +۳ مورد دیگر

Dr. Jing Jiang is an Associate Professor in the School of Computer Science and a core member of the Australian Artificial Intelligence Institute (AAII) at the University of Technology Sydney (UTS). As an ARC DECRA Fellow, she has secured over AU$2 million in research funding through multiple ARC grants, CSIRO/Data61 projects, and industry collaborations. Her work bridges theoretical advances in machine learning with practical applications across various domains. Dr. Jiang's research focuses on machine learning, particularly federated learning, reinforcement learning, and foundation models. She explores how to make these technologies work effectively in heterogeneous environments, addressing challenges like data privacy, non-IID data distributions, and efficient communication. Her work spans both theoretical foundations and practical implementations for real-world applications. Her publications demonstrate a strong trend toward personalized federated learning approaches, with significant contributions to recommender systems, time series analysis, and weather forecasting. She has developed novel techniques like variational autoencoder approaches for federated collaborative filtering and adaptive prompt learning for foundation models on devices. Dr. Jiang has received several notable recognitions: ARC DECRA Fellow Awardee of the Australian International Postgraduate Research Scholarship (IPRS) Dr. Jiang has successfully led multiple major research projects, including two ARC Discovery Projects, one ARC Linkage Project, and a CSIRO/Data61 CRP project where she served as lead Chief Investigator. She has supervised numerous PhD and Master's students and actively collaborates with industry partners on applied research. As a core member of the Australian Artificial Intelligence Institute (AAII) at UTS, Dr. Jiang contributes to a vibrant research ecosystem focused on cutting-edge AI research. She collaborates closely with Professor Guodong Long and other researchers on various machine learning projects, and serves in leadership roles including program co-chair for major conferences like ADMA2023.

Ted Pedersenمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Processing
• Word Sense Disambiguation
• +۳ مورد دیگر

Ted Pedersen is a Professor in the Department of Computer Science at the University of Minnesota Duluth, where he is part of the Swenson College of Science and Engineering. He serves as the Study Abroad Advisor for the CS Department and was a member of the UMN AI Task Force from January 2025 to August 2025, where he addressed questions regarding AI use at the University of Minnesota. Dr. Pedersen's research focuses on computational linguistics and natural language processing, with particular expertise in word sense disambiguation, semantic similarity measurement, and biomedical text analysis. His work bridges theoretical computational linguistics with practical applications in healthcare informatics and social media analysis. He has developed several widely-used open-source tools including SenseClusters, WordNet::Similarity, and WordNet::SenseRelate that have become standard resources in the NLP community. His publication record shows a clear evolution from foundational work in semantic similarity and word sense disambiguation toward contemporary applications in social media analysis, bias detection, and multilingual processing. Recent work demonstrates increasing focus on ethical AI considerations, particularly in sentiment analysis, offensive language detection, and news credibility assessment. His research maintains strong connections between theoretical linguistic frameworks and practical computational implementations. Multiple publications in AMIA Annual Symposium Proceedings Extensive work on semantic similarity measures in biomedical contexts Development of open-source NLP tools adopted by the research community Active participation in SemEval shared tasks since 2017 Dr. Pedersen actively mentors students through research opportunities in natural language processing, with current opportunities available for both undergraduate and graduate students. His teaching portfolio includes courses on Computer Ethics, Algorithms, Race and Computing, and advanced Natural Language Processing topics. He has taught consistently since at least 1999, demonstrating a long-standing commitment to computer science education. His laboratory work focuses on developing computational approaches to linguistic problems, with particular emphasis on unsupervised and semi-supervised methods for semantic analysis. Current projects appear to focus on multilingual processing, social media analysis, and ethical considerations in AI systems.

Nate Fosterمشاهده پروفایل

استاد

• Programming Languages
• Networking
• Formal Verification
• +۳ مورد دیگر

Nate Foster is a Professor of Computer Science at Cornell University and a Visiting Researcher at Jane Street . During 2023-24, he also holds a Visiting Professor position at EPFL in the Data Center Systems Laboratory. His research focuses on Programming Languages and Networking , with significant contributions to formal verification of network data planes and domain-specific language design. Awarded NSF CAREER Award , Sloan Research Fellowship , ACM SIGCOMM Rising Star Award , and ACM SIGPLAN Robin Milner Award Active in program committees for conferences like POPL, PLDI, SPLASH, and ICFP Research Trends : His recent work explores intersections of programming language theory with networking, including symbolic verification tools like KATch , infinite-state network analysis with StacKAT , and active learning frameworks for network automata. He applies formal methods to practical challenges in software-defined networking and hypervisor verification. Scientific Awards : NSF CAREER Award Sloan Research Fellowship ACM SIGCOMM Rising Star Award ACM SIGPLAN Robin Milner Award Academic Leadership : Serves as Session Preview Co-Chair for POPL 2024 and organizes workshops like RPLS 2025. He has chaired tutorials on P4 programming and mentored researchers through PLMW programs.