基本信息
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个人简介
My passion centers around enabling organizations to get the most value from AI, especially through mathematical optimization and structured methods for decision-making. Right now, I’m focused on democratizing the use of mathematical optimization by making it easier and faster to deploy, and taking game theory science into the practical decision-making realm. In this way, I hope to promote better real-life choices in both single and multi-party settings based on the true constraints of the situation or environment—making it practical for use in enterprise decision-making.
Since 2001, I have been researching advanced mathematical, analytical, and optimization techniques. Some of this involved applying advanced mathematical and analytical techniques to customer problems in a variety of domains including workforce optimization and scheduling, advanced water management, and logistics. This has been based on a strong foundation in a variety of areas including artificial intelligence, optimization, complex event processing, computer science, software engineering, operations research, and simulation.
Current research focus
Mathematical optimization can provide significant benefits to making better decisions, but creating, maintaining and updating the required relevant models and solutions requires an enormous amount of time and effort, and a scarce skill set. Therefore, one of my current primary research focuses is leading work that uses AI to simplify the creation and lifecycle management process of decision optimization solutions; the idea is to radically reduce the time and skills required to create and maintain such solutions .
While significant benefits can be achieved by classical decision optimization, decision optimization typically involves making decisions in an environment that is oblivious to the decision-maker. Many real-world scenarios require making decisions in settings in which there are multiple self-interested parties, whose decisions can impact the benefits obtained by the other participants. Game theory is the mathematical science for analyzing such situations. However, it has significant gaps when it comes to providing prescriptive, computationally efficient solutions. To address this, I am leading a team focused on advancing the core scientific basis of game theory and algorithmic game theory (for example, by coupling it with multi-agent reinforcement learning techniques) to enable better decision-making in real world situations—providing the best options given the actual constraints and real limitations of the environment.
Vision
I believes in the power of science and making decisions based on data and sound scientific principles. The driving principle for my work is to enable more widespread use of science in making and implementing better decisions, in both single and multi-party settings. I believe that decision optimization and game theory can become much more fundamental in our lives by providing insights for the scientific outcomes of decisions being implemented.
Knowledge areas
Mathematical optimization, AI, reinforcement learning , game theory and algorithmic game theory, complex event processing
Main research areas:
Decision optimization, and particularly using AI to radically simplify the creation, maintenance and updating of decision optimization models
Enabling practical decision making in multi-party settings by building upon and enhancing game theory, algorithmic game theory and multi agent reinforcement learning
Since 2001, I have been researching advanced mathematical, analytical, and optimization techniques. Some of this involved applying advanced mathematical and analytical techniques to customer problems in a variety of domains including workforce optimization and scheduling, advanced water management, and logistics. This has been based on a strong foundation in a variety of areas including artificial intelligence, optimization, complex event processing, computer science, software engineering, operations research, and simulation.
Current research focus
Mathematical optimization can provide significant benefits to making better decisions, but creating, maintaining and updating the required relevant models and solutions requires an enormous amount of time and effort, and a scarce skill set. Therefore, one of my current primary research focuses is leading work that uses AI to simplify the creation and lifecycle management process of decision optimization solutions; the idea is to radically reduce the time and skills required to create and maintain such solutions .
While significant benefits can be achieved by classical decision optimization, decision optimization typically involves making decisions in an environment that is oblivious to the decision-maker. Many real-world scenarios require making decisions in settings in which there are multiple self-interested parties, whose decisions can impact the benefits obtained by the other participants. Game theory is the mathematical science for analyzing such situations. However, it has significant gaps when it comes to providing prescriptive, computationally efficient solutions. To address this, I am leading a team focused on advancing the core scientific basis of game theory and algorithmic game theory (for example, by coupling it with multi-agent reinforcement learning techniques) to enable better decision-making in real world situations—providing the best options given the actual constraints and real limitations of the environment.
Vision
I believes in the power of science and making decisions based on data and sound scientific principles. The driving principle for my work is to enable more widespread use of science in making and implementing better decisions, in both single and multi-party settings. I believe that decision optimization and game theory can become much more fundamental in our lives by providing insights for the scientific outcomes of decisions being implemented.
Knowledge areas
Mathematical optimization, AI, reinforcement learning , game theory and algorithmic game theory, complex event processing
Main research areas:
Decision optimization, and particularly using AI to radically simplify the creation, maintenance and updating of decision optimization models
Enabling practical decision making in multi-party settings by building upon and enhancing game theory, algorithmic game theory and multi agent reinforcement learning
研究兴趣
论文共 67 篇作者统计合作学者相似作者
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IJCAI 2023pp.2862-2869, (2023)
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AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systemspp.242-250, (2023)
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arxiv(2022)
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Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2022)
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Segev Wasserkrug, Orit Davidovich,Parikshit Ram,Pavankumar Murali,Dzung Phan,Nianjun Zhou,Lam M. Nguyen
semanticscholar(2021)
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