10月26日：金芝：知件、知识工程和深度学习

报告题目：知件、知识工程和深度学习

报告人：金芝  教授

主持人：王晓玲 教授

报告地点：中北校区理科大楼A504

报告时间：10月26日13:00--15:00

报告摘要：

大数据时代为知识工程带来极大的机遇，也提出新的研究问题，比如知识的在线获取与挖掘、知识的时变性和情景性、知识的动态融合与协同推理。本报告将与大家一同回顾知识工程的发展历程，分享大数据时代知识工程的研究心得，重点介绍知件、知识的动态获取、知识的在线服务、以及显式知识和隐式知识的融合等。

个人简介：

金芝，博士，北京大学教授，博士生导师，国家杰出青年基金获得者，973项目首席科学家。现任北京大学高可信软件技术教育部重点实验室常务副主任，兼任国务院学位委员会软件工程学科评议组成员，中国计算机学会CCF会士与常务理事，中国计算机学会软件工程专业委员会主任，《软件学报》执行主编，《计算机学报》副主编，以及其它多个国内外期刊的编委。主要研究兴趣包括：知识工程、软件需求工程、和基于知识的软件工程等。

10月27日：Michael Blumenstein：Machine intelligence in real life: how innovative computing is disrupting our digital future （新增更新）

讲座题目：Machine intelligence in real life: how innovative computing is disrupting our digital future

主讲人：Michael Blumenstein教授 （悉尼科技大学）

主持人：吕岳 教授

开始时间：2016-10-27   10:00am

讲座地址：中北校区理科大楼B1002

主办单位：计算机科学与beat365手机中文官方网站

报告摘要：

The new driver for technological innovation and disruption is the intelligent processing of vast amounts of data, including digital video and images to inform decision-making, and to deliver economic value for industry and the greater community. Recently, a number of significant advances in the domain of computational intelligence research, including the ground-breaking developments in the areas of automated pattern recognition, as well as video and image processing, have been applied in the context of solving the big global challenges of the 21st century.

In this seminar, a number of applications are presented including research into automated document analysis systems (such as video-based text processing, multi-script handwriting recognition and signature verification), in addition to the development of automatic systems for monitoring the activities of visitors at our beaches and coastal zones, drone-based shark detection from video imagery, and intelligent bridge infrastructure management systems to name a few. Further discussion will be dedicated to the future of pattern recognition systems and possible directions for attaining the goal of conscious machines.

报告人简介：

Michael Blumenstein is currently a Professor and Head of the School of Software at the University of Technology Sydney. He formerly served as the Head of the School of Information and Communication Technology at Griffith University, and earlier as the Dean (Research) in the Science, Environment, Engineering and Technology Group.Michael is a nationally and internationally recognised expert in the areas of automated Pattern Recognition and Artificial Intelligence, and his current research interests include Document Analysis, Multi-Script Handwriting Recognition and Signature Verification. He has published over 170 papers in refereed books, conferences and journals. His research also spans various projects applying Artificial Intelligence to the fields of Engineering, Environmental Science, Neurobiology and Coastal Management. Michael has secured internal/nationally competitive research grants to undertake these projects with funds exceeding AUD$4.5 Million. Components of his research into the predictive assessment of beach conditions have been commercialised for use by local government agencies, coastal management authorities and in commercial applications.

Following his work in applying Artificial Intelligence to the area of bridge engineering (where he has published widely and has been awarded federal funding), he was invited to serve on the International Association for Bridge and Structural Engineering’s Working Commission 6 to advise on matters pertaining to Information Technology. Michael was the first Australian to be elected onto this committee. In addition, he was previously the Chair of the Queensland Branch of the Institute for Electrical and Electronic Engineers (IEEE) Computational Intelligence Society. He was also the Gold Coast Chapter Convener and a Board Member of the Australian Computer Society's Queensland Branch Executive Committee. He is a past Chairman of the IT Forum Gold Coast and a former Board Member of IT Queensland. Michael has served on the Australian Research Council's (ARC) College of Experts on the Engineering, Mathematics and Informatics (EMI) panel. In addition, he previously served on the Executive of the Australian Council of Deans of Information and Communication Technology (ACDICT). Michael currently serves on a number of Journal Editorial Boards and has been invited to act as General Chair, Organising Chair, Program Chair and/or Committee member for numerous national/international conferences in his areas of expertise.

In 2009 Michael was named as one of Australia’s Top 10 Emerging Leaders in Innovation in the Australian’s Top 100 Emerging Leaders Series supported by Microsoft. Michael is a Fellow of the Australian Computer Society and a Senior Member of the IEEE.

10月27日：Ce Zhang：Accessible Data Sciences with Efficient Data Systems（时间有更新）

报告题目：Accessible Data Sciences with Efficient Data Systems

报告人：Ce Zhang 助理教授（ https://www.inf.ethz.ch/personal/ce.zhang/）

主持人：姚俊杰 副研究员

报告时间：10月27日 16:00—17:30（时间已经更新，以此为准）

报告地点：中北校区数学馆201室

报告摘要：

One important problem for the current state of data science is that many of the techniques needed to unleash the next big thing are available but still far from accessible. Specifically, the current machine-learning ecosystems are difficult to use by non-computer science users and they are still far from achieving the full potential that can be provided by modern hardware. With more than five ongoing data sciences applications here at ETH Zurich, ranging from genomics, social sciences, and astronomy, our dream is to design the next generation of data science ecosystems that are fast, scalable, and easier to use. In this talk, I will first describe the abundant opportunities for data sciences at ETH Zurich, and then describe two enabling techniques that are being developed by my group. The general direction of these techniques is the co-design of machine learning (or artificial intelligence) with modern hardware and systems. I will talk about our recent work that introduced a data structure for dense linear regression. It can potentially reduce the memory bandwidth by 20x while training. Then I will introduce a novel database architecture, which makes the production system of a leading security company 100x faster. It contains an SMT solver to answer queries that it was not originally designed for.

报告人简介：

Ce is an Assistant Professor in Computer Science at ETH Zurich. He believes that by making data—along with the processing of data—easily accessible to non-computer science users, we have the potential to make the world a better place. His current research focuses on building data systems to support machine learning and help facilitate other sciences. Before joining ETH, Ce was advised by Christopher Ré. He finished his PhD by round-tripping between the University of Wisconsin–Madison and Stanford University, and spent another year as a postdoctoral researcher at Stanford. His PhD work produced DeepDive, a trained data system for automatic knowledge-base construction. He participated in the research efforts that won the SIGMOD Best Paper Award (2014) and SIGMOD Research Highlight Award (2015), and was featured in special issues including CACM Research Highlight (2016), "Best of VLDB" (2015), and Nature magazine (2015).

10月27日：Hemant Kumar Singh：Evolutionary Search and Decision Making for Computationally Expensive Multi-objective Design Optimization Problems （取消）

（因故取消）

报告题目：Evolutionary Search and Decision Making for Computationally Expensive Multi-objective Design Optimization Problems

报告人：Hemant Kumar Singh博士，澳大利亚新南威尔士大学

主持人：周爱民

报告时间 ：2016年10月27日14:00-15:00点 

报告地点：中北校区理科大楼B816室

报告摘要：Simultaneous optimization of multiple conflicting criteria is a problem commonly encountered in several domains, such as engineering, operations research, finance and more. The solution to such problems consists of not one but a set of best trade-off designs, known as the Pareto Front (PF) in the objective space. Metaheuristics such as Evolutionary algorithms (EAs) are commonly used to solve these problems owing to a number of advantages, which include parallelizability, global nature of search and ability to deal with generic and even black-box functions. However, if each design evaluation is done using a computationally expensive experiment (such as Finite Element Analysis, Computational Fluid Dynamics, etc.), the direct use of EA become untenable. This talk discusses some of the recent efforts in overcoming this challenge using spatially distributed surrogates and decomposition based methods. Thereafter, the aspect of decision-making is discussed. Since PF may hundreds of thousands of designs, it is not trivial to select one (or a few) designs from the set for final implementation. Two recently developed mechanisms to support decision making will be discussed in this regard: the Pareto interpolation method and the recursive expected marginal utility method. These mechanisms can be used interactively during the search, or offline, to identify certain solutions or regions of interest.

报告人简介：

Dr Hemant Singh is a Lecturer in the School of Engineering and Information Technology at the University of New South Wales, Australia. He obtained his Bachelors in Technology in Mechanical Engineering from the Indian Institute of Technology (IIT) in 2007, and Doctor of Philosophy from the University of New South Wales in 2011. He worked in General Electric Aviation for two years before the current academic appointment in UNSW. The main focus of Dr Singh’s research is development of computationally efficient evolutionary computation methods for design optimization with a focus towards engineering problems. Over the years, he has worked on a number of strategies towards addressing the challenges in the domain, including constraint handling, robust optimization, Pareto corner based dimensionality reduction and surrogate assisted evolutionary algorithms. Dr Singh has published nearly 50 peer reviewed papers in the various journals, books and conferences relating to evolutionary computation and design optimization. He is the recipient of the Australian Society for Defence Engineering Prize 2011, the World Congress on Structural and Multidisciplinary Optimization ECR fellowship 2015 and the Australia Bicentennial Fellowship 2016. He was the Publicity and Proceedings Chair of the Australasian Conference on Artificial Life and Computational Intelligence (ACALCI) 2016, and Program Chair of the upcoming Asia Pacific Symposium on Intelligent Evolutionary Systems (IES) 2016. He is a professional member of IEEE, ACM and ISSMO, and the Activities Chair of the Canberra chapter of IEEE Computational Intelligence Society. He is a member of IEEE task forces on Nature-inspired constraint handling and Multi-objective Evolutionary Algorithms. He is a reviewer of a number of major journals in the field including IEEE Transactions on Evolutionary Computation, IEEE transactions on Cybernetics and ASME Journal of Mechanical Design.

10月28日：胡星标：可积系统与正交多项式的交叉研究及其应用

报告题目：可积系统与正交多项式的交叉研究及其应用

报告人：胡星标 中科院研究员

主持人：陈勇 教授

报告时间：2016年10月28日 10:00-11:30

报告地点：中北校区理科大楼B1002  

报告摘要：可积系统和正交多项式有着密切的关联。本报告将介绍我们在这一方向所取得的若干进展。

报告人简介：胡星标，男，中科院计算数学与科学工程计算研究所研究员，博士生导师。《应用数学学报》杂志中文版执行编委；《Advances in Mathematical Physics》杂志编委；《African Journal of Mathematical Physics》杂志编委；《Pacific Journal of Applied Mathematics》杂志编委。研究方向：孤立子与可积系统、动力系统以及反问题中的理论与算法。主要科研成果：主要研究孤立子和反问题以及它们的数值解，包括非线性系统的可积性，借助符号计算的孤立子方程的双线性方法，地震构造成像和参数反演等。在国内外本领域较高学术刊物上发表论文数十篇。著作：《带自相容源的孤立子方程》，清华大学出版社。

10月28日：刘青平：Darboux 变换 --- 从经典到超对称

报告题目：Darboux 变换 --- 从经典到超对称

报告人：刘青平 教授

主持人：陈勇 教授

报告时间：2016年10月28日 15:00-16:30

报告地点：理科大楼B1102          

报告摘要：Darboux变换是可积系统理论的重要组成部分。在回顾Darboux变换的发展历程的基础上，本报告将围绕典型的超对称可积系统如超对称KdV方程、超对称MKdV方程、超对称NLS方程以及N=2 超对称KdV方程介绍超对称可积系统的Darboux变换的构造和应用。

报告人简介：刘青平， 男，教授，博导。国务院政府特殊津贴获得者，北京市教学名师。现任中国矿业大学（北京）理学院院长。刘青平教授主要从事数学教学和数学物理及非线性科学的分支之一――孤立子理论及其应用研究等工作。1992年10月至1994年10月，在中国科学院理论物理研究所做博士后。1999年，2001年两次访问国际理论物理中心。1997年入选江苏省“333“人才工程。1998年获第八届孙越崎青年科技奖。2000年入选煤炭行业专业技术拔尖人才。2002年入选教育部跨世纪人才计划。