جست‌وجوی استاد و پژوهشگر

Oswald Lanzمشاهده پروفایل

استاد

• Computer Vision
• Deep Learning
• Machine Learning
• +۶ مورد دیگر

Oswald Lanz is a tenured full professor at the Faculty of Engineering of the Free University of Bozen-Bolzano , leading the Visual Computing Lab . He holds a Ph.D. in Computer Science and a Mathematics degree from the University of Trento. Prior to his current role, he was a researcher and head of research at FBK Trento. He is an endowed professor collaborating with Covision Lab , an AI hub in Bressanone, and coordinates the board of professors for the PhD in Computer Science program since 2025. His research focuses on Computer Vision, Deep Learning, and Video Analytics , with applications in sports technology, medical imaging, and industrial automation. Key achievements include the Amazon AWS Machine Learning Research Award (2020) , ACM Multimedia Best Paper (2015) , and Best Student Paper at ICIAP (2007) . He co-organized the ELLIS-VISMAC Winter School (2025) and chaired ICIAP 2019 . His work spans novel view synthesis, action recognition, and anomaly detection, supported by patents in video tracking and detection. He teaches courses like Deep Learning and Artificial Intelligence in undergraduate and graduate programs. Recent projects such as 5VREAL integrate 5G, edge computing, and AI for sports analysis. His collaborations bridge academia and industry, exemplified by his role in Covision Lab and multidisciplinary initiatives like DSS4LCO for food supply chains. Lanz’s publications emphasize spatiotemporal modeling, neural architecture search, and hybrid machine vision systems.

Shinji Watanabeمشاهده پروفایل

دانشیار

• Automatic Speech Recognition
• Speech Enhancement
• Spoken Language Understanding
• +۳ مورد دیگر

Shinji Watanabe is an Associate Professor at Carnegie Mellon University's Language Technologies Institute and a Courtesy Professor in the Electrical and Computer Engineering department. He holds a Ph.D. (Dr. Eng.) from Waseda University, Japan, and has held research roles at NTT Communication Science Laboratories, Mitsubishi Electric Research Laboratories (MERL), and Johns Hopkins University. His research focuses on automatic speech recognition, speech enhancement, and machine learning for speech processing. Watanabe has published over 300 peer-reviewed papers and received the Best Paper Award at IEEE ASRU 2019. His work emphasizes robust speech processing in challenging environments, multilingual models, and neural audio codecs. He leads the ESPnet toolkit development for end-to-end speech processing systems and contributes to technical committees like IEEE SLTC and APSIPA SLA. Recent research trends include streaming speech systems, universal speech enhancement (URGENT challenges), and fusion of discrete speech units with self-supervised representations. He explores scalable speech foundation models through benchmarks like ML-SUPERB 2.0 and investigates cross-modal audio-visual processing in challenges like MISP 2025. Education : B.S., M.S., Ph.D. (Waseda University) Affiliations : CMU Language Technologies Institute, CMU ECE, Former roles at MERL and Johns Hopkins Key Projects : ESPnet, OpenWhisper-Style Models, URGENT Challenge Frameworks

Maarten de Rijkeمشاهده پروفایل

استاد

• Information Retrieval
• Recommender Systems
• Retrieval Models and Ranking
• +۸ مورد دیگر

Maarten de Rijke is a Professor at the University of Amsterdam and affiliated with the Innovation Center for Artificial Intelligence (ICAI) . He is a leading expert in Information Retrieval , Recommender Systems , and Machine Learning , with over 500 publications and 10,000 citations. His work spans theoretical and applied domains, including conversational recommender systems , domain generalization , and neural ranking models . Research Pillars: Information retrieval, e-commerce search, learning to rank, and empathetic AI systems Awards: Best Paper (2x), Best Student Paper Community Roles: Organized workshops (MANILA25, SIGIR editions) His recent publications focus on robust recommendation systems , cross-domain contract extraction , and brain signal integration for query refinement. He leads the AIRLab (Amsterdam) and collaborates with institutions like Shandong University and the University of Chinese Academy of Sciences. Scientific Contributions : Over 500 publications in ACM Transactions, SIGIR proceedings, and journals Developed novel frameworks for learning-to-rank and user satisfaction modeling Pioneered research on conversational AI and adversarial attacks in retrieval He actively engages in community service, including organizing conferences and advocating for epilepsy research through initiatives like Emma’s collection box (over €24,448 raised). His work bridges theoretical rigor with real-world impact in search and recommendation technologies.

Esther Rolfمشاهده پروفایل

استادیار

• Machine Learning
• Geospatial Machine Learning
• Environmental Monitoring
• +۴ مورد دیگر

Esther Rolf is an Assistant Professor in the Department of Computer Science at the University of Colorado Boulder. Her research focuses on blending methodological and applied machine learning techniques to address social and environmental challenges, emphasizing usability, data-efficiency, and fairness. Her work includes developing algorithms for environmental monitoring with satellite imagery and advancing geospatial ML systems. Her research explores the multifaceted role of data representation in machine learning, particularly how spatial distribution and data acquisition impact model fairness and efficacy. Projects include formalizing representivity in training data and tackling evaluation challenges in geospatial ML applications. Recent publications highlight her expertise in satellite-based poverty mapping, multimodal data efficiency, and geospatial foundation models. She integrates specialized architectures and domain-specific benchmarks into her work. Best Paper Award, ICML (2018) Best Paper Award, NeurIPS Workshop on AI for Social Good (2019) NSF Graduate Research Fellowship Google Research Fellowship Esther's lab at CU Boulder recruits PhD students and postdocs interested in statistical/geospatial ML and context-driven research for real-world problems. She teaches graduate courses in machine learning and geospatial ML at CU Boulder.

Jonathan Shihao Jiمشاهده پروفایل

دانشیار

• Deep Learning
• Computer Vision
• Natural Language Processing
• +۵ مورد دیگر

Jonathan Shihao Ji is an Associate Professor in the School of Computing at the University of Connecticut (UConn), leading the Intelligent Systems Lab. He holds a Ph.D. in Electrical and Computer Engineering from Duke University and previously served as an Associate Professor at Georgia State University and Director of the DoD Center of Excellence (CiARE). His research focuses on deep learning applications in computer vision, NLP, robotics, and high-performance computing, with over 50 publications in top venues like CVPR, NeurIPS, and IEEE journals. He has secured grants from NSF, NIH, DoD, and industry partners including VMware and Nvidia. His work emphasizes efficient algorithms for large-scale data processing, parameter-efficient model fine-tuning (e.g., VB-LoRA), and 3D perception benchmarks for UAVs (UAV3D). Notable contributions include sparse network optimization (Dep-L0), energy-based models (M-EBM), and robust defenses against adversarial attacks (Defense-VAE). He is a Senior Member of IEEE and has developed open-source tools like Parallel Word2Vec and WordRank. Recent projects include accelerating Llama2 models on FPGAs (LlamaF) and improving text-to-image synthesis via contrastive learning. His research spans theoretical advancements and practical applications, with industry collaborations in healthcare, robotics, and embedded systems.

LEE Wee Sunمشاهده پروفایل

استاد

• Machine Learning
• Planning Under Uncertainty
• Approximate Inference
• +۳ مورد دیگر

LEE Wee Sun is a Professor in the Department of Computer Science at the National University of Singapore (NUS), where he previously served as Head of Department, Vice Dean of Undergraduate Studies, and Vice Dean of Research. His academic journey began with a B.Eng. in Computer Systems Engineering from the University of Queensland (1992) and a Ph.D. from the Australian National University (1996), followed by research roles at the Australian Defence Force Academy and MIT. Education: Ph.D., Australian National University, Canberra, Australia (1996) B.Eng. in Computer Systems Engineering, University of Queensland, Brisbane, Australia (1992) Research Focus: Professor Lee pioneers work in Machine Learning , Planning Under Uncertainty , and Approximate Inference , with emphasis on integrating AI subfields for holistic reasoning. His current projects include "Learning to Decompose for Reasoning and Planning" (enhancing LLMs via self-supervised problem decomposition) and "Learning to Reason with Visual-Linguistic Inputs" (unifying vision, language, and reasoning in single architectures). Publication Trends: Recent work (2023-2025) centers on bridging LLMs with classical AI techniques, featuring breakthroughs in uncertainty quantification, multi-task optimization, and graph-based reasoning. Key themes include sparsity-aware vehicle routing, epistemic uncertainty for reliable LLMs, and differentiable neural solvers for combinatorial problems. Awards: IJCAI-JAIR Best Paper Prize (2022) RSS Test of Time Award (2021) RoboCup Best Paper Award (IROS 2015) HRATC 1st Place (2015) IPPC POMDP Track 1st Place (2011, 2014) UAI Google Best Student Paper (2014) Semeval-1 1st/2nd Place (2007) J.G. Crawford Prize (ANU 1996) Leadership & Service: As steering committee chair for ACML and area chair for NeurIPS/ICML/AAAI/IJCAI, Professor Lee shapes global AI discourse. His administrative roles at NUS and collaborations with MIT/Singapore-MIT Alliance demonstrate commitment to advancing AI education and research infrastructure. While student advisees aren't listed, his leadership positions imply extensive mentoring. Research Ecosystem: His work drives NUS's AI initiatives including Knowledge@Computing projects on reasoning frontiers. Current efforts focus on making AI systems robust through uncertainty-aware planning and multi-modal integration, with applications in robotics, verification systems, and combinatorial optimization.

• Database Systems
• Data Science
• Machine Learning
• +۳ مورد دیگر

Dr. H.V. Jagadish is a Professor in the Computer Science and Engineering department, focusing on database systems, data science, and ethical AI. He emphasizes mentorship and independence in research, guiding students to become colleagues through collaborative growth. Research Focus His work spans Data integration and fairness in machine learning Query processing and knowledge graph analysis Reproducibility and ethical data management Advising and Research Dr. Jagadish encourages open communication, frequent feedback, and proactive student-driven research. He supports internships and conference attendance, prioritizing SIGMOD and VLDB for presentations.

Muchao Yeمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• AI Safety
• +۷ مورد دیگر

Muchao Ye is an Assistant Professor in the Department of Computer Science at the University of Iowa. He earned his Ph.D. from Pennsylvania State University's College of Information Sciences and Technology in 2024 and a Bachelor of Engineering in Information Engineering from South China University of Technology. Ph.D., Information Sciences and Technology, Pennsylvania State University (2024) B.Eng., Information Engineering, South China University of Technology His research focuses on the intersection of Artificial Intelligence, Machine Learning, and AI Safety, particularly adversarial robustness in language models and vision-language models. He designs methods to enhance the security and reliability of deep learning systems for safety-critical applications like video surveillance and healthcare. Recent publications highlight adversarial robustness frameworks (e.g., UniT , PAT ), vision-language models for explainable video anomaly detection ( VERA ), and healthcare risk prediction techniques ( MedPath , MedRetriever ). His work appears in top venues such as NeurIPS, KDD, AAAI, ACL, and CVPR. Professional experience includes Applied Scientist internships at Amazon (2022–2023) and teaching roles at the University of Iowa and Pennsylvania State University. He serves as a reviewer for conferences like NeurIPS, ICML, and journals including IEEE TPAMI.

Jia Zhangمشاهده پروفایل

استاد

• Machine Learning
• Natural Language Processing
• Information Retrieval
• +۱۰ مورد دیگر

Dr. Jia Zhang is the Inaugural Robert H. Dedman Jr. Endowed Department Chair and Professor of Computer Science at Southern Methodist University (SMU Lyle School of Engineering). She holds the Cruse C. and Marjorie F. Calahan Centennial Chair in Engineering and has a courtesy appointment in the Department of Operations Research and Engineering Management. Her research focuses on applying machine learning, natural language processing, and information retrieval to data science infrastructure, particularly scientific workflows, provenance mining, software discovery, knowledge graphs, cloud computing, immune AI, and applications in earth science and healthcare. Education: Ph.D. in Computer Science, University of Illinois at Chicago M.S. in Computer Science, Nanjing University B.S. in Computer Science, Nanjing University Dr. Zhang's work emphasizes data science infrastructure and machine learning for scientific workflows and knowledge graphs. Her recent publications highlight deep learning , graph neural networks , and optimization algorithms in cloud computing, cybersecurity, and environmental applications. Key trends include spatiotemporal modeling , hybrid neural architectures , and AI-driven service ecosystems . Scientific Awards: Best Paper Awards IEEE SCC (2011, 2017) Best Student Paper Awards IEEE ICWS (2014, 2018), IEEE ICCC (2018) Distinguished Paper Award ICSOC (2023) First Outstanding Service Award IEEE Technical Committee on Services Computing (2016) She has secured over $5 million in federal grants (as PI) and $11 million as PI/Co-PI from NSF, NASA, NIH, UTSW, Ericsson, SAP, and Google. Her lab (Caruth Hall 308) actively recruits research assistants. She previously served as a faculty member at Carnegie Mellon University, Northern Illinois University, and Nanjing University, and worked in industry as a software architect.

• Software Engineering
• Deep Learning
• Human-Computer Interaction
• +۷ مورد دیگر

Prof. Dr. Chunyang Chen is a full professor in the Department of Computer Science at the Technical University of Munich (TUM) in Heilbronn, Germany, and an adjunct professor at Monash University, Australia. He is a core member of the Munich Data Science Institute, board member of Heilbronn Data Science Center, and fellow at Fortiss. His research spans Software Engineering, Deep Learning, and Human-Computer Interaction (HCI), focusing on AI/ML, NLP, and program analysis applications. PhD from Nanyang Technological University (NTU), Singapore His research explores three primary domains: AI-Driven Mobile App Development : Focused on requirement elicitation, UI design, code generation, and GUI testing using LLMs, NLP, and lightweight program analysis. Software Repository Mining : Involves library/API migration, collaborative editing, query reformulation, and thesaurus construction. Deep Learning Security : Examines robustness and security in mobile apps, including on-device model attacks, backdoor attacks, phishing attacks, and defense mechanisms. His recent publications highlight trends in LLM applications for software testing, bug replay, accessibility, and dataset construction. Awards include Best Paper Honorable Mention at CHI 2024, DECRA, ACM SIGSOFT Early Career Award, multiple Distinguished Paper Awards, and the Chinese Government Award for Outstanding Self-financed Students Abroad. PhD students: Shengcheng Yu, Yanqi Su, Mark Huasong Meng, Wenchao Gu, Zhen Tao, Kai Huang, Sidong Feng, Yujin Huang, Jian Gu Master/Bachelor students: Dustin Minh Duc Vu, Jason Siu, Ruiqi Dong, Minh Nguyen, An-Chieh Chang, Chao Wu, Yujin Huang, Ishan Joshi, Xinyan Li Chunyang Chen has supervised numerous students across institutions including TUM, Monash University, Chinese Academy of Sciences, ANU, and others. His teaching portfolio includes courses on Generative AI, software engineering, and programming foundations. He actively contributes to academic service as a guest editor, program committee member, and reviewer for top-tier conferences and journals.

Jay Pujaraمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Knowledge Graph Construction
• +۵ مورد دیگر

Dr. Jay Pujara is a Research Associate Professor of Computer Science at the University of Southern California (USC) and Director of the Center on Knowledge Graphs. He is also a Principal Scientist at the Information Sciences Institute (ISI) and leads research teams in data science and AI. Ph.D., University of Maryland, College Park (2016) M.S. and B.S. in Computer Science, Carnegie Mellon University Research Interests include artificial intelligence, probabilistic models, knowledge graph construction, statistical relational learning, NLP, and streaming inference. His work focuses on scalable algorithms for big data and uncertainty modeling in dynamic environments. Recent Publications highlight advancements in knowledge graphs, LLM reasoning, and table understanding. Notable topics include non-verbal abstract reasoning , faithful conversational datasets , and KGQA re-ranking . Scientific Awards : SWSA Ten-Year Award (2023), Outstanding Paper (IUI 2019), Top Reviewer (NeurIPS 2018), Best Paper (SRL Workshop 2016) Advising & Grants : Mentored 12+ graduate students, including Ph.D. advisees on topics like causal modeling and neuro-symbolic tasks. Secured NSF funding for table understanding in paleoclimate studies.

Kathleen M. Carleyمشاهده پروفایل

استاد

• Network Science
• Social Cybersecurity
• Computational Social Science
• +۴ مورد دیگر

Kathleen M. Carley is a full professor at Carnegie Mellon University's School of Computer Science with courtesy appointments in Engineering and Public Policy, Heinz School, and Electrical and Computer Engineering. As director of the Center for Computational Analysis of Social and Organizational Systems (CASOS) and the Center for Informed Democracy and Social-Cybersecurity (IDeaS) , she leads interdisciplinary research at the intersection of network science, cognitive modeling, and cybersecurity. Ph.D. in Sociology from Harvard University SB degrees in Economics and Political Science from MIT Her research focuses on Dynamic Network Analysis (DNA) and Social-Cybersecurity (SC) , developing tools like ORA (network analysis), AutoMap (semantic mining), Construct (influence simulation), and BotHunter (bot detection). She has over 400 publications and 15+ active research projects addressing disinformation, cognitive security, and organizational resilience. Recent work examines LLM-powered bots , multi-platform misinformation dynamics , and public health analytics . As an IEEE Fellow, she contributes to standards in computational social science while teaching courses on network analysis and complex socio-technical systems.

• Computer Graphics
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Dr. Frederick Li is an Associate Professor in the Department of Computer Science at Durham University, UK. He holds editorial roles as Associate Editor of Frontiers in Education (Digital Education) and Editorial Board Member of Virtual Reality & Intelligent Hardware. His research focuses on Computer Graphics, Machine Learning, Geometric Modelling, Collaborative Virtual Environments, Visual Aesthetics, and Educational Technologies. He earned his B.A. (Hons) and M.Phil. from The Hong Kong Polytechnic University and his Ph.D. in Computer Graphics from City University of Hong Kong. Prior roles include Assistant Professor at HK PolyU and project manager of a Hong Kong Government ITF-funded project. **Education**: B.A. (Computing Studies) and M.Phil. from HK PolyU; Ph.D. in Computer Graphics (CityU Hong Kong). **Research Interests**: His work spans mesh saliency detection, human-object interaction recognition, cloud modeling, face beautification, and educational technology. Recent achievements include awards for papers (e.g., Best Paper at ITiCSE 2014) and recognition such as EPSRC Peer Review College membership. He leads Durham's Undergraduate Board of Examiners and has been an external examiner at Northumbria University. **Awards**: Best Paper (ACM ITiCSE 2014), Outstanding Paper (ICALT 2013), EPSRC Peer Review College (2024), Outstanding BMVC 2024 Reviewer. **Grants & Labs**: His research is supported by grants from EPSRC and others. He collaborates with the Centre for Vision and Visual Cognition, VIViD, and AIHS group at Durham.

Wenxuan Zhangمشاهده پروفایل

استادیار

• Natural Language Processing (NLP)
• Large Language Models (LLMs)
• Multilingual Language Models
• +۵ مورد دیگر

Dr. Wenxuan Zhang is a tenure-track Assistant Professor at the Information Systems Technology and Design (ISTD) Pillar of Singapore University of Technology and Design (SUTD), supported by the prestigious SUTD Assistant Professorship (SAP) award. He holds a PhD from The Chinese University of Hong Kong and previously worked as a research scientist at Alibaba Group Singapore. His research focuses on advancing large language models (LLMs) to be both inclusive (supporting multilingual capabilities) and trustworthy (ensuring safety and robustness). Key projects include SeaLLMs (specialized for Southeast Asian languages), Babel (serving 90% of global speakers), and M3Exam (LLM evaluation framework). Education: PhD in Computer Science, The Chinese University of Hong Kong Previous roles: Research Scientist at Alibaba Singapore (2022) Research Interests: Multilingual LLMs, AI safety, model evaluation, and cross-lingual adaptation. He leads projects addressing LLM trustworthiness through safety mechanisms and fair evaluation practices. Awards & Recognition: SUTD Assistant Professorship (2025) Alibaba Star (2022) ITU Best Innovate for Impact Award (2024) Service & Leadership: Area Chair for NeurIPS 2025, ACL 2025, and multiple other top conferences. Actively contributes to program committees for conferences like ICLR and EMNLP. Current Projects: Multilingual LLMs, model compression, safety frameworks, and evaluation methodologies. Openings for PhD/Postdoc researchers in these areas.

Yuanbo Xiangliمشاهده پروفایل

پژوهشگر

• 3D Computer Vision
• Deep Generative Modeling
• Photorealistic Scene Reconstruction
• +۳ مورد دیگر

Yuanbo Xiangli is a postdoctoral researcher at Cornell University , advised by Prof. Noah Snavely. Previously, he obtained his Ph.D. from the Multimedia Lab in the Department of Information Engineering at the Chinese University of Hong Kong (CUHK) , supervised by Prof. Dahua Lin. His research focuses on 3D computer vision and deep generative modeling for urban scene reconstruction. 3D scene reconstruction from sparse images Neural rendering and Gaussian splatting Deep generative modeling for urban environments Multi-source geospatial data processing City-scale modeling and synthetic datasets His recent work includes advanced NeRF extensions (BungeeNeRF, GridNeRF), Gaussian splatting enhancements (GSDF, Scaffold-GS), and urban scene datasets (MatrixCity, OmniCity). A pioneer in combining classical vision techniques with modern deep learning approaches. ICLR 2020 Spotlight Award Collaborates with leading researchers in photorealistic rendering, including Noah Snavely and Dahua Lin. Develops systems enabling efficient 3D reconstruction from diverse data sources like satellite imagery and street-level panoramas.