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个人简介
Dr Vaishak Belle is a Chancellor’s Fellow and Faculty at the School of Informatics, University of Edinburgh, an Alan Turing Institute Faculty Fellow, a Royal Society University Research Fellow, and a member of the RSE (Royal Society of Edinburgh) Young Academy of Scotland. At the University of Edinburgh, he directs a research lab on artificial intelligence, specialising in the unification of logic and machine learning, with a recent emphasis on explainability and ethics. He has given research seminars at numerous academic institutions, tutorials at AI conferences, and talks at venues such as Ars Electronica and the Samsung AI Forum. He has co-authored over 50 scientific articles on AI, at venues such as IJCAI, UAI, AAAI, MLJ, AIJ, JAIR, AAMAS, and along with his co-authors, he has won the Microsoft best paper award at UAI, the Machine learning journal best student paper award at ECML-PKDD, and the Machine learning journal best student paper award at ILP. In 2014, he received a silver medal by the Kurt Goedel Society. Recently, he has consulted with major banks on explainable AI and its impact in financial institutions.
Research Interests
machine learning, knowledge representation, artificial intelligence, scalable probabilistic inference and learning, probabilistic programming, statistical relational learning, automated planning, reasoning about knowledge and uncertainty, cognitive robotics
Research Interests
machine learning, knowledge representation, artificial intelligence, scalable probabilistic inference and learning, probabilistic programming, statistical relational learning, automated planning, reasoning about knowledge and uncertainty, cognitive robotics
研究兴趣
论文共 159 篇作者统计合作学者相似作者
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Leonardopp.298-306, (2024)
Machine Learningno. 3 (2024): 1421-1443
Synthesis Lectures on Artificial Intelligence and Machine LearningToward Robots That Reason: Logic, Probability & Causal Lawspp.149-162, (2023)
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Synthesis Lectures on Artificial Intelligence and Machine LearningToward Robots That Reason: Logic, Probability & Causal Lawspp.67-78, (2023)
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Synthesis Lectures on Artificial Intelligence and Machine LearningToward Robots That Reason: Logic, Probability & Causal Lawspp.103-116, (2023)
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THEORY AND PRACTICE OF LOGIC PROGRAMMINGno. 4 (2023): 865-883
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