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Boqing GongView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Visual Recognition
• +10 more

Boqing Gong is an Assistant Professor in the Department of Computer Science at Boston University. He concurrently serves as a part-time research scientist at Google. His research focuses on advancing visual recognition, video analysis, and the safety/generalization of AI models through novel algorithms in computer vision and machine learning. He holds editorial roles as an Associate Editor for IEEE TPAMI (since 2024) and TMLR. He has organized major conferences including co-chair roles for WACV 2023, CVPR 2022/2025 tutorials, and ICCV 2025 workshops. Education: Ph.D. Computer Science, University of Southern California (2011-2015) Visiting Graduate Student, University of Texas at Austin (Summer 2012) MPhil Information Engineering, Chinese University of Hong Kong (2008-2010) Bachelor's in Electronic Information Engineering, University of Science and Technology of China (2004-2008) Research Interests: Dr. Gong develops algorithms to understand objects, human activities, and scene relationships. His work emphasizes mathematical structures for effective and efficient solutions with strong analytical guarantees. Key areas include domain adaptation, long-tailed recognition, adversarial robustness, few-shot learning, and generative models. He explores intersections between language, vision, and reinforcement learning. Professional Contributions: Senior/Area Chair for CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, AISTATS, AAAI Outstanding Reviewer Awards (CVPR 2017/2021)

Longbing CaoView profile

Professor

• Data Science
• Artificial Intelligence
• Machine Learning
• +6 more

Professor Longbing Cao serves as a Professor at University of Technology Sydney (UTS) in the Faculty of Engineering and Information Technology, specifically within the Data Science Institute. He also holds the position of Distinguished Chair Professor in AI at Macquarie University and is an ARC Future Fellow (Level 3). As a pioneering researcher in AI, data science, and advanced analytics, Professor Cao founded the UTS Advanced Analytics Institute, the first data science initiative in Australia. Professor Cao's research interests span across data science fundamentals, machine/deep learning, behavior informatics, and enterprise data science applications. His work bridges the gap between theoretical research and practical implementation, focusing on non-IID learning, actionable knowledge discovery, and complex behavior modeling. His research has addressed real-world challenges in government services, finance, insurance, banking, telecommunications, transport, and e-commerce sectors. His 15 most recent publications reveal a strong trend toward addressing distributional challenges in machine learning, developing transdisciplinary AI approaches, modeling complex temporal dependencies, and advancing negative sequence analysis. His work consistently integrates theoretical foundations with practical applications, particularly in financial analytics, healthcare, and disaster resilience. Scientific Awards: Eureka Prize for Excellence in Data Science (2019) ACM Distinguished Scientist Professor Cao has made significant contributions to academic leadership and research supervision. He established world-first research degrees at UTS (Master of Analytics and PhD Thesis: Analytics), founded the IEEE Task Force on Data Science and Advanced Analytics, and served as general chair of the KDD conference in 2015 (the first edition in Australia). His funded research portfolio includes multiple ARC grants and industry collaborations focusing on complex data analytics, behavior informatics, and AI applications. He leads the Data Science Lab at UTS, which has become a prominent center for data science research in Australia. The lab focuses on developing fundamental theories and practical applications of data science, with strong industry partnerships that translate research into real-world impact. Professor Cao's leadership in establishing the annual Big Data Summit in 2012 created Australia's first platform for bridging academic research with industry and government applications.

Noam KoenigsteinView profile

Professor

• Recommender Systems
• Machine Learning
• Collaborative Filtering
• +5 more

Prof. Noam Koenigstein is a Professor in the Department of Industrial Engineering at Tel Aviv University's Iby and Aladar Fleischman Faculty of Engineering. His research focuses on developing scalable machine learning solutions for real-world recommendation systems, with extensive industry collaborations including Microsoft (Xbox) and Yahoo! Music. His primary research interests include recommender systems, collaborative filtering, and neural approaches to user modeling. He pioneered neural item embedding techniques like Item2Vec and advanced Bayesian methods for diversity-aware recommendations using Determinantal Point Processes. His work consistently bridges theoretical machine learning with industrial-scale deployment challenges. Analysis of his publication record reveals a strong evolution from traditional matrix factorization toward deep learning and attention-based architectures, always emphasizing practical scalability and addressing cold-start problems. His research demonstrates exceptional continuity in solving core recommendation challenges across domains including e-commerce, music, and video services. Scientific recognition includes: Best paper runner up award at TVX 2014 for group viewing pattern analysis Prof. Koenigstein has led multiple industry-academia partnerships that produced deployable recommendation frameworks, notably for Xbox Movies and Windows Store. His invited RecSys 2017 talk highlighted critical gaps between academic research and industrial requirements in recommender systems, establishing him as a leading voice in practical recommendation science.

• Machine Learning
• Artificial Intelligence
• Graph Neural Networks
• +2 more

Maoying Qiao is a Lecturer at Australian Catholic University's Peter Faber Business School, specializing in machine learning and artificial intelligence. Her research spans graph neural networks, computer vision, and probabilistic modeling, with applications in network analysis and 3D vision. Key publications focus on improving graph convolutional networks through negative sampling, adapting stochastic block models for power-law networks, and developing conditional graphical lasso methods for multi-label image classification. Marine science applications include automated catch detection systems for fisheries.