唐珂Google Scholar ResearcherID

教授 计算机科学与工程系
唐珂，南方科技大学计算机科学与工程系教授、教育部****、国家“****”青年拔尖人才。主要研究领域为人工智能的共性原理、算法（如演化计算、强化学习、机器学习等），以及人工智能与设计、金融、物流等领域的交叉研究。研究成果曾获教育部一等奖、二等奖、中国电子学会自然科学一等奖。入选英国皇家学会牛顿高级**** (Newton Advanced Fellow)，并获 IEEE Computational Intelligence Society Outstanding Early Career Award。现兼任广东省类脑智能计算重点实验室副主任、深圳斯发基斯可信自主系统研究院副院长。
个人简介工作与教育经历2018 – 至今：南方科技大学，计算机科学与工程系，教授
2011 – 2018：中国科学技术大学，计算机科学与技术学院，教授
2007 – 2011：中国科学技术大学，计算机科学与技术学院，副教授
2003 – 2007：南洋理工大学，博士
1998 – 2002：华中科技大学，学士
荣誉与奖励教育部****
国家****青年拔尖人才
Outstanding Early Career Award, IEEE Computational Intelligence Society
Newton Advanced Fellowship, Royal Society (UK)
教育部新世纪优秀人才
教育部自然科学一等奖（第4完成人）
教育部自然科学二等奖（第 1 完成人）
中国电子学会自然科学一等奖（第 3 完成人）
主要学术服务IEEE Transactions on Evolutionary Computation 副编
Swarm and Evolutionary Computation (Elsevier) 副编
十余次担任 IEEE-CEC、SEAL、IDEAL 等国际会议程序/技术委员会主席
中国演化计算与学习研讨会（ECOLE）创始执委

个人简介

研究领域My work is, in general, about fundamental research on computational approaches for Learning and Optimization, two most important problems in Artificial Intelligence. I'm also frequently attracted by other relevant domains, such as Smart Logistics, Structural and Multi-Disciplinary Optimization and Computational Finance, for which applied research is required to produce application-oriented learning and optimization techniques.
Most of my research could also be viewed as arising from Evolutionary Computation, which is essentially a distributed heuristic search framework widely applicable for modelling, learning and optimization problems, especially for hard problems where limited prior knowledge is available.
Selected topics for fundamental research are listed below.


1. Scalable Evolutionary Search
Research in this direction aims at systematically boosting the capacity of Evolutionary Computation on problems with huge search space, which has been believed as a major challenge for most EAs. Approaches for this purpose include:
? Co-evolutionary Search: Introducing the divide-and-conquer idea to guide EAs adaptively search different regions of the search space
? Parallel Algorithm Portfolios: Leveraging on high performance computing to enhance both the extreme performance and reliability of EAs, without suffering the wall-clock runtime but only computational resources.
? Surrogate-assisted Search: Exploiting data generated during the search course to alleviate the cost of evaluating a future solution.


2. Reinforcement and Evolutionary Learning
Reinforcement Learning is a learning problem that lies exactly in the "backyard" of EAs, because the objective function of most RL tasks so far rely on a noisy and non-differentiable simulator. Thus it’d be quite interesting to see whether EC could offer a promising alternative approach for RL.


3. Learning and Optimization with Uncertainty
Uncertainty is ubiquitous in real-world learning and optimization tasks. It could be due to the dynamically changing physical world, the noise caused by imprecise measurements, or even the unpredictable nature of human behaviors. We are specifically interested in new learning/optimization methods that could handle various forms of uncertainty. This has led to exploration on the following topics:
? Incremental learning with concept drift
? Evolutionary computation for Dynamic optimization
? Learning from crowds (Crowdsourcing Learning)

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教学计算机导论A、人工智能、研究方法、智能数据分析、创新教学实践

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学术成果 查看更多从演化计算、机器学习的基础理论出发，完善了数据驱动的智能算法设计手段，围绕复杂学习与优化问题，形成了可扩放演化搜索方法体系，并针对智慧物流、无人系统、边缘智能等领域的典型问题提出了一系列高效的算法。在IEEE/ACM Trans、CCF-A类学术会议发表论文60余篇。更多信息请点击此处。



新闻动态 更多新闻
我系研究成果入选NeurIPS Spotlight论文2019-11-05

喜报丨我系唐珂教授在IEEE WCCI2018上荣膺IEEE计算智能学会****奖2019-11-05

首届深圳计算智能重点实验室学术交流会圆满结束2019-11-05

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本专业培养具有坚实的计算机科学与技术理论知识，初步掌握前沿的计算机系统的设计原理，拥有相应的研发能力，同时具备英语和计算机应用能力，可从事计算机科学与技术和相关交叉学科领域研发的高素质科技人才。学生毕业后能在该领域内从事计算机系统与应用的设计、研发等方面工作，也适宜继续攻读计算机相关研究生学位，可在科研部门、教育单位、企业、事业、技术和行政管理、服务行业部门从事计算机教学、科学研究和应用的高级专门技术人才。
南科大计算科学与工程系成立于2016年。目前本系已有教授27名，均在海外知名大学获得博士学位或有多年教学与研究的工作经验。其中，IEEE会士3名，IET会士1名。本系力争最终建成拥有50名（不包括纯教学和纯科研）核心教师的国际化高水平的师资队伍。重点发展计算智能、自主智能系统、数据科学、计算机系统和网络、计算理论等五个方向，致力于建设成为计算机学科领域国际知名的研究型计算机科学与工程系。

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