Large Language Models (LLM) Professors and Supervisors | ApplySharif

جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۷۲۱-۷۳۵ از ۳٬۱۶۷ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Thilo Dieingمشاهده پروفایل

پژوهشگر

University of MannheimGermany

Thilo Dieing is a social data scientist at the Chair of Artificial Intelligence led by Prof. Dr. Heiner Stuckenschmidt at the University of Mannheim's Mannheim Center for Data Science. His research focuses on AI-supported study orientation, including developing chatbots and machine learning models to improve student advising. He contributes to projects such as NEST-BW, aiming to reduce dropout rates by creating personalized degree recommendations via generative AI chatbots, and the TransforMA project, which produced the multilingual recipe chatbot Foodle. His work emphasizes ethical AI use and explainable methods (XAI). Thilo holds a bachelor’s degree in political science and a master’s in data science from Mannheim. His technical expertise spans NLP, large language model optimization, and classification models. Future interests include integrating speech/image recognition and combining LLMs with knowledge graphs for advanced reasoning. His research underscores the critical role of data science in analyzing student data to create tailored educational pathways, ensuring chatbots like Foodle and the NEST-BW advisor deliver safe, effective guidance. Collaboration with student teams (e.g., a six-person TransforMA group) highlights his commitment to mentoring and practical application of AI in education.

پروفایل رسمی دانشگاه

Eon Soo Leeمشاهده پروفایل

دانشیار

New Jersey Institute of Technology (NJIT)United States

Eon Soo Lee is an Associate Professor in the Department of Mechanical and Industrial Engineering at the New Jersey Institute of Technology (NJIT). His primary research focuses on advanced materials engineering, biomedical microfluidics, and assistive technologies for individuals with disabilities. He has led federally funded projects including 'I-Corps: Multiplex Diagnostic Assay Using Interdigitated Nano-Sensing Technology' (NSF, 2023-2025) and 'Innovative Nano Catalysts for Automobile and Fuel Cell Applications' (NSF, 2018). Research Interests: Lee's work spans interdisciplinary areas including: Development of N-doped graphene/MOF composites for energy applications Microfluidic systems for blood plasma separation and antigen detection Design of accessible technologies for visually impaired users, including VR audio descriptions and remote sighted assistance systems Grants and Projects (select): National Science Foundation (2023): $500K for multiplex diagnostic assays National Science Foundation (2018): $300K for nano-catalysts in fuel cells Multiyear collaborations with industry partners on biosensor integration Innovation Highlights: Developed AIGuide: AR hand-guidance system for visual impairments Pioneered omnidirectional audio descriptions for VR music performances Published extensively in Carbon , Biomicrofluidics , and ACM/IEEE accessibility venues

پروفایل رسمی دانشگاه

Thaleia Dimitra Doudaliمشاهده پروفایل

استادیار

IMDEA Software InstituteSpain

Thaleia Dimitra Doudali is an Assistant Professor at the IMDEA Software Institute in Madrid, Spain, leading the Muse research lab. She holds a PhD in Computer Science from Georgia Institute of Technology (2021), advised by Ada Gavrilovska, focusing on hybrid memory management with machine learning. Prior to her PhD, she earned a Diploma in Electrical and Computer Engineering from the National Technical University of Athens (2015). Her research interests span systems for machine learning, machine learning for systems, and computer vision applications in resource management. Her work has been recognized with awards including the 'César Nombela' (2024), 'Juan de la Cierva' (2021), and Rising Star in EECS (2020). She serves on program committees for top conferences (ASPLOS, EuroSys) and organizes workshops. Her lab's projects address cloud resource management, carbon efficiency, and sustainable computing. She advises multiple PhD and intern researchers, contributing to impactful publications in SIGMOD, EuroSys, and other venues. Notable contributions include Cronus (CV-based memory management), CaRE (carbon-efficient cloud-edge orchestration), and foundational work on ML-driven resource forecasting. Her research bridges systems and AI, aiming to optimize data center efficiency and sustainability. She actively promotes diversity in STEM through mentorship roles in ACM-W Greece and Women in HPC initiatives.

پروفایل رسمی دانشگاه

Saleh Ashkboosمشاهده پروفایل

پژوهشگر

ETH ZurichSwitzerland

Saleh Ashkboos is a Ph.D. student in the Computer Science Department at ETH Zurich, advised by Professors Torsten Hoefler and Dan Alistarh. He is also a Research Assistant at the Scalable Parallel Computing Lab and an affiliated doctoral student of the ETH AI Center. His research focuses on accelerating deep neural network training and developing systems for large-scale graph processing. Prior to ETH Zurich, he earned his Master's degree in Computer Science from Sharif University of Technology, advised by Professor Amir Daneshgar. His work has led to notable contributions, including the best paper award at SC22 for 'ProbGraph.' Recent research emphasizes efficient LLM training and quantization techniques, with publications on topics like 4-bit inference, quantization-aware training frameworks, and scalable meteorological modeling. He has interned at Apple and Microsoft, and his work is accessible via Google Scholar and GitHub. Key projects include GPTQ (post-training quantization for transformers), SliceGPT (LLM compression), and ProbGraph (high-performance graph mining). His technical contributions span distributed systems, neural network optimization, and climate-related machine learning.

پروفایل رسمی دانشگاه

Yang Leiمشاهده پروفایل

پژوهشگر

University of TrierGermany

Yang Lei is an academic affiliated with the University of Melbourne's Department of Computing and Information Systems. Their research focuses on knowledge graphs, FAIR data principles, large language models (LLMs), and ontology engineering. They contribute to initiatives like the Open Research Knowledge Graph (ORKG) and NFDI4DataScience, emphasizing reproducibility, scholarly metadata, and systematic literature reviews. Recent work includes applications of LLMs for abstract summarization, leaderboard extraction, and entity recognition in scholarly documents. Yang also explores challenges in FAIR Digital Objects, machine-actionable workflows, and interoperability in research data management.

پروفایل رسمی دانشگاه

Christian Muiseمشاهده پروفایل

استادیار

Queen's University at KingstonCanada

Christian Muise is an Assistant Professor at Queen's University's School of Computing, part of the Faculty of Arts and Science. He holds a PhD (2014) in Artificial Intelligence from the University of Toronto, where he was advised by Sheila McIlraith and J. Christopher Beck. His research focuses on automated planning under uncertainty, combining planning with learning for applications like goal-oriented dialogue systems and multi-agent coordination. He previously held postdoctoral roles at the University of Melbourne's Agentlab and MIT's CSAIL, and was a Research Staff Member at the MIT-IBM Watson AI Lab. Education: PhD in Artificial Intelligence, University of Toronto (2014) MSc in Computer Science, University of Toronto (2009) BSc in Computer Science, Carleton University (2007) Research Interests: His work bridges automated planning and machine learning, emphasizing robustness in uncertain environments. Key areas include non-deterministic planning, model acquisition with large language models (LLMs), and human-aware planning. He explores applications in healthcare (e.g., treatment response prediction), robotics (autonomous navigation), and dialogue systems for safety-critical domains. Current projects include developing explainable planning systems and mitigating bias in AI decision-making. Awards: Scotiabank Scholar (Scotiabank Centre for Customer Analytics) Advising & Labs: Leads the Mu Lab, supervising PhD and Master's students in topics like model acquisition, dialogue systems, and planning bias. Active in open-source tools (e.g., L2P, MACQ library) to democratize planning research. Collaborates on projects like PRP Rebooted and FixMyPlan to advance FOND planning and LLM integration. Labs/Teams: Mu Lab at Queen’s University, focusing on planning under uncertainty, AI safety, and neuro-symbolic systems.

پروفایل رسمی دانشگاه

Els Lefeverمشاهده پروفایل

دانشیار

Ghent UniversityBelgium

Els Lefever is an Associate Professor at Ghent University, where she works with the LT3 (Language and Translation Technology) research team. Her position focuses on computational linguistics and natural language processing research, with strong ties to both theoretical and applied aspects of language technology. Dr. Lefever earned her PhD in Computer Science from Ghent University in 2012 with her dissertation titled "ParaSense: Parallel Corpora for Word Sense Disambiguation." Her academic journey began as a computational linguist at the R&D department of Lernout & Hauspie Speech Products before transitioning to academia. Els Lefever's research spans multiple areas within computational linguistics with particular expertise in multilingual natural language processing. Her work focuses on computational semantics, cross-lingual word sense disambiguation, and multilingual terminology extraction. Recent research directions include automatic detection of irony in online text, argumentation mining in social media, sentiment analysis of financial news, language modeling for low-resourced languages, and computational approaches to Byzantine Greek epigrams. Her research demonstrates a consistent pattern of bridging theoretical computational linguistics with practical applications across diverse language domains and historical periods. Professor Lefever actively supervises PhD research on several cutting-edge topics including terminology extraction from comparable corpora, event extraction and sentiment mining of financial news, language modeling for low-resourced languages, argumentation mining in social media, and the automatic detection of links between Byzantine Greek epigrams. Her supervision portfolio demonstrates her commitment to advancing multiple frontiers of computational linguistics simultaneously. As an educator, Professor Lefever teaches courses in Terminology and Translation Technology, Language Technology, Localisation, Digital Text Analysis, and Digital Humanities. Her teaching reflects her research interests, providing students with both theoretical foundations and practical skills in language technology applications. The LT3 research group, where Professor Lefever is a key member, maintains strong connections with both academic and industry partners. The group has participated in numerous international conferences and shared tasks including SemEval competitions across multiple years, demonstrating consistent contributions to benchmark datasets and evaluation methodologies in natural language processing.

پروفایل رسمی دانشگاه

Yousra Aaferمشاهده پروفایل

استادیار

University of WaterlooCanada

Yousra Aafer is an Assistant Professor in the Department of Computer Science at the University of Waterloo. Her research focuses on mobile and smart device security, system security, and software security, particularly in the context of Android and cyber-physical systems. She holds a Ph.D. and M.Eng. from Syracuse University. Education: Ph.D., Syracuse University, United States (2016) M.Eng., Syracuse University, United States (2012) Her research interests include analyzing vulnerabilities in Android systems, binary analysis, IoT security, and fuzzing techniques. She explores methods to enhance security through formalized protocols, probabilistic protection recommendations, and leveraging large language models for vulnerability detection. Her work addresses critical areas such as cross-language buffer overflow detection, residual API audits in custom ROMs, and cyber-physical inconsistency in robotic vehicles. Her recent publications span topics like Android security, binary disassembly (e.g., D-ARM), and IoT security protocols (e.g., ProFactory). She has also contributed to frameworks like Poirot for probabilistic protection recommendations and StochFuzz for efficient binary fuzzing. While no awards are explicitly mentioned, her extensive publication record indicates active recognition in the security research community. She advises on multiple projects but no student names are listed here. Her work often involves collaboration on tools and frameworks for practical security applications.

پروفایل رسمی دانشگاه

Sandeep Reddyمشاهده پروفایل

استاد

Queensland University of TechnologyAustralia

Professor Sandeep Reddy is a leading academic in healthcare management and digital health at Queensland University of Technology (QUT), where he heads disciplines in Healthcare Management, Health Information Management, and Health, Safety, and Environment. He co-leads the 'Health, Human Biology and MedTech' theme at QUT's Centre for Data Science. With a PhD in Healthcare Management and a medical degree, Reddy holds multiple professional fellowships, including the Australasian Institute of Digital Health and the UK Higher Education Academy. Roles: Professor, Academic Leader, Researcher, Editor Affiliations: QUT, WHO Digital Health Roster, European Academy of Translational Medicine Professionals His research focuses on AI integration in healthcare delivery, governance frameworks, and translational methodologies. Key areas include AI in chronic disease management, medical imaging, and facial recognition technology. Reddy has authored over 80 publications, including a 2024 textbook on translational AI applications. Reddy's work spans global health policy, having advised the WHO and participated in international panels. He teaches courses on health informatics, implementation science, and digital health, with over 20 years of academic and industry experience. Scientific awards and fellowships reflect his expertise: Fellow of AIDH, ACHSM, and Senior Fellow of the UK Higher Education Academy. His editorial roles include PLOS ONE, Computational and Structural Biotechnology Journal, and Frontiers in AI. Current research supervision focuses on AI in medical education, healthcare access, and explainability in AI systems. He advocates for equitable digital health solutions, addressing the rural-urban digital divide through empirical studies.

پروفایل رسمی دانشگاه

Stephan Schlöglمشاهده پروفایل

استاد

MCI Management Center InnsbruckAustria

Stephan Schlögl is a Full Professor at MCI - The Entrepreneurial School in Austria, leading research and teaching in Human-Computer Interaction (HCI), Artificial Intelligence (AI), and Information Systems. He holds editorial roles at journals like the Springer Discover Artificial Intelligence Journal and MDPI Multimodal Technologies and Interaction Journal. His work spans over two decades, with key roles including Postdoctoral Research Fellow at Télécom ParisTech (2012–2013) and PhD Researcher at Trinity College Dublin (2008–2012). Schlögl’s research focuses on HCI, AI-driven conversational systems, and assistive technologies for aging populations. Education includes a PhD in Computer Science from Trinity College Dublin, an MSc in Human-Computer Interaction from University College London, and a Mag.(FH) in Applied Informatics & Management from MCI. His teaching spans Software Engineering, Business Intelligence, and Research Methods. Research interests emphasize natural language interfaces, AI ethics, and technology’s societal impact. Notable projects include the EU-funded EMPATHIC initiative (2017–2021), developing an empathic virtual coach for elderly care, and the CRYSTAL project (2024–present) on conversational systems for emotional support. Schlögl has supervised numerous theses on AI applications, chatbots, and UX design. He co-organized major conferences like CUI (Conversational User Interfaces) and received awards for best papers in AI-HCI and CHIRA. His work bridges academia and industry, addressing challenges in AI adoption, digital well-being, and ethical technology design.

پروفایل رسمی دانشگاه

Barbara Carminatiمشاهده پروفایل

استاد

University of InsubriaItaly

Barbara Carminati is a Professor at the Department of Theoretical and Applied Science, University of Insubria, Italy. Her work focuses on security, privacy, and trust management in decentralized systems, particularly online social networks, IoT, and emerging technologies like blockchain and digital twins. She has contributed extensively to access control, risk assessment, and collaborative frameworks. Her research spans trust modeling , privacy-preserving mechanisms , and malware detection , with a strong emphasis on decentralized social networks and UAV security . She actively explores the application of blockchain for secure workflows and information sharing. Recent publications highlight her engagement with large language models for security optimization, IoT botnet detection , and metadata leakage analysis . Her work bridges theoretical foundations with practical implementations in cybersecurity, social network management, and edge computing. Contact: barbara.carminati@uninsubria.it

پروفایل رسمی دانشگاه

Xue Linمشاهده پروفایل

دانشیار

Northeastern UniversityUnited States

Xue Lin is an Associate Professor in the Department of Electrical and Computer Engineering at Northeastern University, with a courtesy appointment in Khoury College of Computer Science. She joined Northeastern in 2017 and holds a PhD from the University of Southern California (2016) and a bachelor’s from Tsinghua University. Her research focuses on robust and secure machine learning, deep learning on edge devices, and cyber-physical systems. She leads the High Energy-Efficiency & Performance System Lab, which develops efficient algorithms and systems for applications like autonomous vehicles and medical AI. Dr. Lin’s work is supported by NSF, DARPA, and the U.S. Department of Transportation, among others. Notable achievements include a $1M DARPA grant for adversarial diagnosis systems, a 1st Place ISLPED 2020 Design Contest win, and multiple best paper awards. She has advised students such as Kaidi Xu (PhD’21), Mengshu, and Siyue, who have contributed to impactful projects like adversarial T-shirt attacks and FPGA-based DNN accelerators. Her research also addresses security in autonomous systems and inclusive design challenges for older and visually impaired passengers. Key grants include NSF CPS Small Awards, SaTC Medium Awards, and collaborations with institutions like the University of Maine and Michigan State University. Awards include the 2024 Faculty Fellow Award and recognition in Stanford’s top 2% cited scientists. Her lab’s projects span secure autonomous systems, energy-efficient inference frameworks (e.g., GRIM), and robust neural network verification techniques.

پروفایل رسمی دانشگاه

Fabio Crestaniمشاهده پروفایل

استاد

University of Italian SwitzerlandSwitzerland

Fabio Crestani is a Full Professor of Informatics at the Università della Svizzera italiana (USI) since 2007, serving as Pro-rector for Internationalisation since March 2024. He previously held roles at the University of Strathclyde (UK) and conducted sabbaticals at institutions like UC Berkeley and Xerox PARC. His expertise spans Information Retrieval, Text Mining, and Digital Libraries, with over 250 publications and editorial leadership roles, including Editor-in-Chief of Information Processing and Management (2008–2015). Education: PhD and MSc in Computing Science, University of Glasgow (UK) Degree in Statistics, University of Padova (Italy) Research Interests: Advanced information access systems Conversational search and user interaction models Machine learning for text analysis Early risk prediction (e.g., mental health via social media) Grants & Collaborations: Funded by Swiss National Science Foundation, Hasler Stiftung, and EU projects. Collaborations with institutions in UK, Italy, Spain, USA, and Malaysia. Labs & Teams: Lead the Information Retrieval Group at USI, which focuses on distributed IR, personalization, and mobile information access. The group includes 10+ researchers and has produced influential work in top-tier venues like SIGIR and ACL.

پروفایل رسمی دانشگاه

Bowen Xuمشاهده پروفایل

استادیار

Singapore Management UniversitySingapore

Bowen Xu is an Assistant Professor in the Department of Computer Science at North Carolina State University (NC State), College of Engineering. His research focuses on software engineering, machine learning, and program analysis, particularly in securing AI models and improving code quality. He holds a PhD from Singapore Management University (SMU), where he also conducted postdoctoral research. Education: PhD in Computer Science, Singapore Management University (SMU) Postdoctoral Researcher, SMU School of Computing and Information Systems Research Interests: AI for Code, Backdoor Attacks on Code Models, Vulnerability Detection Code Representation Learning, Model Compression, Safety of AI Systems Chatbot Development for Developers, Automatic Code Review Key Contributions: Developed PTM4Tag+, a Stack Overflow tag recommendation system using pre-trained models Explored stealthy backdoor attacks in code and reinforcement learning systems Pioneered work on automatic vulnerability repair using LLMs and broader input analysis Awards: 2022: Honorable Mention Award (ACSAC) 2018: Highly Commended Full Paper Award (ESEM) Service Roles: Editorial Board Member, Empirical Software Engineering Journal Program Committee Co-chair for ICSE/FSE Research Tracks Organized workshops like FORGE, MaLTeSQuE, and SEA4DQ Labs & Teams: Leads the Softmax Lab at NC State, advising 12+ students across PhD, Master's, and undergraduate levels. Alumni include industry professionals at Microsoft, Barclays, and Marvell Semiconductor.

پروفایل رسمی دانشگاه

Alexander Miserlis Hoyleمشاهده پروفایل

پژوهشگر

University of Maryland, College ParkUnited States

Alexander Hoyle is a Researcher at the ETH Zürich AI Center, concurrently contributing to natural language processing/machine learning and social science groups. He holds a PhD in Computer Science from the University of Maryland (advised by Philip Resnik) and a Master's in Computational Statistics from University College London (advised by Sebastian Riedel and Jeff Mitchell). His research focuses on computational social science, emphasizing methods for latent construct identification (e.g., topic models, ideal point models) and evaluation frameworks grounded in validity. Key areas include bias/fairness in AI, political science applications, and mental health constructs like suicidality. He pioneered frameworks like PairScale (attitude measurement via pairwise comparisons) and TopicGPT (prompt-based topic modeling). Education: PhD in Computer Science, University of Maryland (2020-2023) MS in Computational Statistics & Machine Learning, University College London (2018) Bachelor's degree (pre-PhD details omitted) Research Interests: Combining NLP with social science needs, particularly in evaluation rigor and interpretability. Active in interdisciplinary work between NLP and computational social science (e.g., measuring attitude evolution on Reddit, improving topic model validity). Advocates for human-in-the-loop approaches to address LLM limitations in tasks like document clustering and sentiment analysis. Grants & Projects: Contributed to a landmark $2.2B DOJ settlement on NYC public housing via econometric modeling at The Brattle Group. Active in graduate labor advocacy (Maryland state legislature testimony) and mentorship (Científico Latino's mentorship program). Labs & Teams: Leads initiatives at the ETH Zürich AI Center, collaborating with groups like Microsoft Research (FATE) and AI2's AllenNLP. Involved in multi-university projects (e.g., University of Maryland's Computational Linguistics lab).

پروفایل رسمی دانشگاه