جست‌وجوی استاد و پژوهشگر

• Natural Language Processing
• Computational Linguistics
• Formal Concept Analysis
• +۴ مورد دیگر

Dmitry Alekseevich Ilvovsky is an Associate Professor at the Department of Data Analysis and Artificial Intelligence within the Faculty of Computer Science at the National Research University Higher School of Economics (HSE University) in Moscow. He also serves as a Research Fellow at the International Laboratory of Intelligent Systems and Structural Analysis. Having joined HSE in 2011, he has accumulated over 10 years of scientific and teaching experience in the field of computational linguistics and artificial intelligence. Dr. Ilvovsky holds a Candidate of Technical Sciences degree (2017) and a Specialist degree in Applied Mathematics and Computer Science from the Moscow Aviation Institute (2010). His professional interests focus on natural language processing, formal concept analysis, and discourse-based approaches to text analysis. He has made significant contributions to developing methods for detecting disinformation, propaganda, and unreliable information in text data. His recent research demonstrates a clear trend toward integrating discourse structure with deep learning approaches for various NLP tasks. His work spans fact-checking systems, dialogue management, propaganda detection, and text complexity assessment. He has pioneered approaches using discourse trees and structural linguistic information to enhance the performance of language models, particularly in identifying manipulative content and verifying claims against trusted sources like Wikipedia. Gratitude from HSE University (January 2024) Letter of gratitude from the Vice-Rector of HSE (August 2021) Letter of Gratitude from the Faculty of Computer Science at HSE (August 2017) Rector's personal allowance (2016-2017) Academic Work Allowance (2020-2021) Bonus for publication in journal from List A (2023-2026) Bonus for publication in international peer-reviewed journal (2017-2023) Dr. Ilvovsky actively supervises PhD research, notably guiding A. Chernyavskiy's work on models for automatic detection and verification of unreliable information. His teaching portfolio includes courses such as Automatic Text Processing for Bachelor's students and Mentor's Seminar for Master's students. He has also contributed to the development of the International Laboratory of Intelligent Systems and Structural Analysis, where he has worked since 2012, organizing international conferences including the Concept Lattices and Their Applications conference in 2016—the first time it was held in Russia.

• Fetal Monitoring
• Machine Learning in Obstetrics
• Biomedical Engineering
• +۵ مورد دیگر

Antoniya Georgieva serves as Associate Professor and Group Lead for Oxford Labour Monitoring at the University of Oxford, holding joint appointments at the Big Data Institute and Nuffield Department of Women's and Reproductive Health within the Medical Sciences Division. She is also a Research Fellow at Wolfson College where she leads the Cross-disciplinary Machine Learning Cluster. Her educational background includes a BSc(Hons) in Applied Mathematics from Technical University of Sofia (Bulgaria) and a PhD in Computer Science from Portsmouth University. She joined Oxford in 2007 for post-doctoral work and established her independent research group in 2016 after receiving a NIHR Career Development Fellowship. Georgieva specializes in applying machine learning to intrapartum fetal monitoring, leading development of the OxSys data-driven CTG interpretation system using the world's largest birth cohort (100,000 deliveries). Her research bridges biomedical engineering, clinical obstetrics, and artificial intelligence to replace empirical CTG interpretation with quantified risk assessment. Her recent publications demonstrate strong focus on deep learning applications for fetal heart rate analysis, adverse outcome prediction, and clinician-AI interaction frameworks. The work shows progressive refinement from foundational algorithm development (2023) to clinical implementation studies (2024-2025). Performer in Wellcome LEAP In-Utero programme (2022-2024) NIHR Product Development Award (£1.12M, 2021-2025) NIHR Career Development Fellowship (£559K, 2017-2021) EPSRC Healthcare Technologies Grant (£600K, 2021-2024) Norwegian HOME-study collaboration (£1.3M, 2021-2025) Georgieva leads a multidisciplinary team developing OxSys - a real-time tablet application deployed at John Radcliffe Hospital that analyzes CTG data with clinician collaboration. Her Wolfson College cluster facilitates cross-departmental machine learning knowledge exchange while her Wellcome LEAP project pioneers novel fetal monitoring technologies to prevent stillbirth.

Jie Liuمشاهده پروفایل

استاد

• Big Data Intelligent Analysis
• Machine Learning Systems
• Intelligent Software Engineering
• +۳ مورد دیگر

Jie Liu is a Researcher at the Institute of Software, Chinese Academy of Sciences and a Professor and Doctoral Supervisor at University of Chinese Academy of Sciences. He is also a Member of the Youth Innovation Promotion Association of the Chinese Academy of Sciences and an Executive Committee Member of the System Software Committee of the CCF Computer Society. His research is conducted within the Software Engineering Technology R&D Center. Dr. Liu received his Ph.D. from the University of Science and Technology of China in 2011 and his B.A. from the same institution in 2004. He has progressed through the ranks at the Institute of Software, CAS, starting as an Assistant Research Fellow (2011-2014), then Associate Research Fellow (2014-2024), and currently as a Researcher (since 2024). His research spans Big Data Intelligent Analysis Models and Systems at the intersection of AI, Software Engineering, and System Software. Specifically, his work covers three main areas: Big Data and Machine Learning Systems (statistics and AI algorithm model libraries, data quantitative analysis tools, LLM reasoning optimization, Earth Big Data); Intelligent Software Engineering (code model constraint decoding, data science agents, system log analysis agents); and Knowledge-Enhanced Intelligent Model Construction (knowledge extraction, knowledge graphs, domain AI model design). His research has resulted in innovative approaches to handling complex data analysis challenges across multiple domains. Dr. Liu's research has produced significant outcomes including EarthDataMiner, which supports SDG indicator calculations and won the 2024 Beijing Municipal Science and Technology Progress First Prize. His work on RISC-V software migration technology has been integrated into the Ruiqian tool (https://rvpt.top/), demonstrating practical applications of his research in emerging computing architectures. Beijing Science and Technology Progress Award, First Prize, 2024 2023 Surveying and Mapping Science and Technology Award, Special Prize, 2023 DASFAA Best Paper Runner-up, Second Prize, 2013 Dr. Liu has successfully guided numerous graduate students who have secured positions at major technology companies including Alibaba, ByteDance, Southern Power Grid, and Agricultural Bank of China. He has secured funding through multiple National Natural Science Foundation projects, National Key R&D Program projects, and over ten other research initiatives. His research collaborations span industry leaders like Huawei, JD.com, and TravelSky, as well as academic institutions within the Chinese Academy of Sciences. He teaches graduate courses such as 'Machine Learning Systems' and 'Cloud Computing and Big Data Technology' at University of Chinese Academy of Sciences, and has established a research group focused on developing innovative solutions at the intersection of AI and software engineering with real-world applications in earth sciences, healthcare, and intelligent systems.

Kijung Shinمشاهده پروفایل

دانشیار

• Data Mining
• Graph Algorithms
• Network Science
• +۴ مورد دیگر

Kijung Shin is an Associate Professor at KAIST (Korea Advanced Institute of Science and Technology), holding dual appointments in the Kim Jaechul Graduate School of AI and the School of Electrical Engineering (Computer Division). He leads the Data Mining Lab and teaches multiple courses including Graph Mining and Social Network Analysis, Data Mining and Search, and other foundational courses in electrical engineering and AI. Education Ph.D. in Computer Science, Carnegie Mellon University (February 2019) M.S. in Computer Science, Carnegie Mellon University (December 2017) B.S. in Computer Science and Engineering, Seoul National University (August 2015) B.A. in Economics (Double Major), Seoul National University (August 2015) Research Interests Professor Shin's research primarily focuses on data mining, graph algorithms, and network science, with particular expertise in hypergraph analysis, tensor decomposition, and graph neural networks. His work bridges theoretical foundations with practical applications, developing algorithms that can efficiently analyze complex real-world networks. His recent research has expanded into multimodal learning, integration of large language models with graph neural networks, and applications in recommendation systems, satellite imagery analysis, and biological data analysis. His approach combines rigorous mathematical analysis with practical implementation, resulting in numerous open-source software tools that have been widely adopted in both academia and industry. His research has significant implications for social network analysis, fraud detection, recommendation systems, and scientific discovery in various domains. Research Trends Professor Shin's recent publications show a clear trajectory toward more complex network structures, particularly hypergraphs that capture higher-order interactions beyond simple pairwise relationships. His work increasingly integrates traditional graph algorithms with deep learning approaches, especially focusing on how graph neural networks can be improved and made more interpretable. There's also a growing emphasis on practical applications in areas like satellite imagery analysis, medical data, and recommendation systems that address real-world challenges. Scientific Awards Received the PAKDD Best Survey Paper Award for 'Multi-Behavior Recommender Systems: A Survey' (2025) Selected as one of the best short paper candidates of ACM RecSys 2024 (top 7) for 'Revisiting LightGCN' (2024) Selected for oral presentation (2.6% of accepted papers) at AAAI 2024 for 'VITA: 'Carefully Chosen and Weighted Less' Is Better in Medication Recommendation' (2024) Received the IEEE ICDM Best Student Paper Runner-up Award for 'TensorCodec: Compact Lossy Compression of Tensors without Strong Data Assumptions' (2023) Received the SIGKDD Best Research Paper Award and CogX Award for Best Student Paper in AI for 'FRAUDAR: Bounding Graph Fraud in the Face of Camouflage' (2016) Received the Best Senior Thesis Award from Seoul National University (2015) Received the Samsung Humantech Paper Award (1st in Computer Science) (2015) Teaching and Mentoring Professor Shin has taught multiple graduate and undergraduate courses at KAIST since 2019, including Graph Mining and Social Network Analysis, Data Mining and Search, and foundational courses in electrical engineering. He has also co-organized tutorials at major conferences including AAAI, KDD, ICDM, and CIKM on advanced topics in hypergraph neural networks and real-world hypergraph analysis. As the leader of the Data Mining Lab, he mentors numerous graduate students and postdoctoral researchers, fostering a collaborative research environment that has produced significant contributions to the field of data mining and network analysis. Research Leadership Professor Shin leads the Data Mining Lab at KAIST, which focuses on developing novel algorithms for analyzing complex networks and high-dimensional data. The lab has produced numerous influential software tools including D-Cube, M-Zoom, CoreScope, and DenseAlert, which are widely used in both academic research and industry applications. His research group maintains active collaborations with institutions worldwide and has received funding from various sources to support their innovative work in data mining and network analysis.

Yu Caoمشاهده پروفایل

استاد

• Medical Imaging
• Multimodal Deep Learning
• Computer Vision
• +۵ مورد دیگر

Yu Cao, Ph.D., is a tenured full professor at the Miner School of Computer & Information Science, University of Massachusetts Lowell, where he also serves as Director of the UMass Center for Digital Health. His academic journey includes faculty positions at The University of Tennessee (2010-2013) and California State University (2007-2010), followed by a Visiting Fellowship at Mayo Clinic. Dr. Cao holds a Ph.D. in Computer Science from Iowa State University (2007), where he also earned his M.S. (2005), along with an M.Eng. from Huazhong University of Science and Technology (2000) and a B.Eng. from Harbin Engineering University (1997), all in Computer Science. His educational background includes: Visiting Fellow, Biomedical Engineering, Mayo Clinic (2007) Ph.D., Computer Science, Iowa State University (2007) M.S., Computer Science, Iowa State University (2005) M.Eng., Computer Science, Huazhong University of Science and Technology, China (2000) B.Eng., Computer Science, Harbin Engineering University, China (1997) Dr. Cao's research spans multiple domains of knowledge discovery from complex data, with particular focus on Medical Imaging, Multimodal Deep Learning, Computer Vision, Artificial Intelligence, and Digital Health. His work emphasizes intelligent, multi-modal, and data-intensive medical image analysis and retrieval; motion tracking, analyzing, and visualization; and intelligent data analysis for electronic medical records and pervasive healthcare monitoring. His research program has produced over 150 peer-reviewed publications with more than 8,000 citations and an h-index of 40+, appearing in top venues including IEEE CVPR, IJCAI, ICLR, ACM MM, and IEEE ICME, as well as prestigious journals like IEEE TNNLS, TBME, TPAMI, TSC, and JBHI. Analysis of Dr. Cao's recent publications reveals a strong focus on applying deep learning techniques to medical imaging problems, particularly in endoscopy and diagnostic imaging. His work spans multiple subfields including polyp detection in colonoscopy videos, tuberculosis detection in chest X-rays, diabetic retinopathy analysis, and food recognition systems for dietary assessment. The publications demonstrate a consistent pattern of applying cutting-edge AI techniques to solve practical healthcare challenges, with increasing emphasis on multimodal approaches and real-world deployment considerations. Dr. Cao has received numerous accolades for his work, including Best Paper Awards from ACM/IEEE CHASE (2023), IEEE IJCNN (2020), and IEEE NAS (2015). His paper was the most downloaded from Smart Health Journal by Elsevier (2017-2018), and he was recognized for having the highest number of peer-reviewed publications among faculty members in the College of Sciences (2017-2018). He was named a Senior Member of IEEE in 2013, an honor granted to only 8% of IEEE members worldwide. His research has been supported by dozens of NSF/NIH/Industry sponsored grants totaling approximately $10 million. Notable projects include NIH/NSF Award #1R01EB021900 ($1.29 million) as Principal Investigator, NSF Award #1547428 ($500,000) as Co-PI, and NSF Award #1541434 ($1 million) as Co-PI. Dr. Cao has successfully mentored numerous graduate and undergraduate students, with current advisees working on medical image retrieval, data analysis for body sensor networks, and motion tracking and visualization. He has served on organizing committees for over 30 international conferences and workshops, demonstrating strong leadership in the academic community. As Director of the UMass Center for Digital Health, Dr. Cao leads a multidisciplinary team focused on developing innovative solutions for healthcare challenges using digital technologies. His lab maintains active collaborations with medical institutions including Mayo Clinic, Harvard Medical School, and Erlanger Hospital, facilitating the translation of research findings into clinical practice. The center's work spans multiple research areas including medical video/image analysis, motion tracking and visualization, context-aware data analysis for body area sensor networks, and risk analysis for acute coronary syndromes.