CIS2016 - Speakers

Wuxi, China

The 12th International Conference on Computational Intelligence and Security

December 16-19, 2016

Speakers


Tutorials in Workshop on Artificial Intelligence in Astronomy:

李菂 Chief Scientist of Radio Division National Astronomical Observatories of China,Project Scientist and Deputy Chief Engineer of Five-hundred-meter Aperture Spherical radio Telescope (FAST). Dr. Li has lead numerous research programs, including spectroscopic and mapping projects of VLA, GBT, Arecibo, FCRAO, SWAS, Spitzer, Herschel, SOFIA and ALMA. He pioneered several observing and data analysis techniques, including HI narrow self-absorption technique and a new inversion solution to the dust temperature distribution. These techniques facilitate important measurements of star forming regions, such as the formation time scale. His work has been featured on《Nature》magazine as a research highlight. He was awarded the National Research Council (US) “residence research fellow” award and was a member of the NASA outstanding team award (2009). He is now leading the science preparation efforts of the Five-hundred-meter Aperture Spherical radio Telescope (FAST). He served on the Steering Committee of Australia Telescope National Facility (ATNF), is now a co-chair of the “Cradle of Life” science working group (SWG) of SKA, a member of the Chinese Academy of Sciences Major-facilities Guidance Group, an adviser to the Breakthrough Listen initiative.

赵永恒  博士, 中国科学院国家天文台研究员、博士生导师,LAMOST运行与发展中心常务副主任,国家天文台兴隆观测基地首席科学家,北京天文学会理事长。曾任国家重大科学工程LAMOST项目总经理,中国天文学会常务理事,国际天文学联合会第5委员会科学组织委员,世界数据中心天文学科中心主任,从事天文学和天体物理研究,主要包括活动天体的理论研究、高能天体的观测分析、多波段观测、数据分析技术、天文信息技术以及LAMOST项目的科学研究和工程管理等工作。

Invited Talks in Workshop on Artificial Intelligence in Astronomy:

范锡龙  物理学博士, 湖北第二师范学院物机学院副教授。主要从事引力波天文学、 星系和尘埃演化等研究。先后在山东师范大学、北京师范大学、德国马普引力所、意大利的里雅斯特大学、英国格拉斯哥大学等学习、访问。在ApJ、A&A、PLB 等国际著名学术期刊发表学术论文,引力波发现PRL作者之一。 2013年度湖北省楚天学子,2012年度英国皇家学会牛顿学者(Newton Fellow). Abstract: The observation of gravitational wave signals from binary black hole mergers, labelled GW150914 and GW151226, heralded the dawn of gravitational wave astronomy. In this talk, I will introduce a Bayesian approach to gravitational wave astronomy. Several data analysis and computing challenges in this research field will also be discussed.

刘青山  博士, 现任南京信息工程大学教授,博士生导师,江苏省大数据分析技术重点实验室主任,IEEE高级会员。2000年4月毕业于中科院自动化所模式识别国家重点实验室获博士学位,随后留实验室工作,2006年4月赴美国Rutger大学访问、工作。2011年9月加盟南京信息工程大学。主要研究方向为图像与视频分析、计算机视觉、和机器学习。已在国内外学术期刊和国际会议发表论文100余篇,其中IEEE Transaction汇刊和CCF A类40余篇。2011年入选江苏省特聘教授(2014年终期考核“优秀”),2012年入选教育部新世纪人才,同年获首届江苏省杰出青年基金资助,2013年入选江苏省双创个人,2014年入选江苏省双创团队领军人才。先后主持承担了国家自然基金项目4项,其中国家自然基金重点项目1项。受邀担任国际学术期刊、《NeuroComputing》、《Signal Processing》编委,长期受邀担任20余种国际知名学术期刊的审稿人,参与组织国际学术会议10余次。

Abstract: 图像理解一直是计算机视觉、模式识别等领域的研究重点。随着数字成像技术的快速发展,图像数据的维度越来越高、图像数据的规模也越来越大,给图像分析带来了巨大的挑战。本报告将从视觉特征学习的角度来汇报我们近年来做的一些工作,主要包括:视觉特征低维表达学习、视觉特征复杂关系表达、复杂深度卷积特征学习等,及其在一些实际问题上的应用研究工作。

李乡儒  Professor in University of South China Normal University (SCNU). He received a Ph.D. from the Institute of Automation, Chinese Academy of Sciences. Before joining SCNU in 2009, he worked at Shandong University of Science and Technology during 2006 and 2009. His research interests are in data mining methods, algorithms and their applications in computer vision, astronomical data analysis.

Abstract: Large-scale and deep sky survey missions are rapidly collecting a large amount of stellar spectra, which necessitate the estimation of atmospheric parameters directly from spectra and make it feasible to statistically investigate latent principles in a large data set. We present two techniques for detecting prominent spectral components and estimating parameters Teff , logg, and [Fe/H] from stellar spectra. With these techniques, we first select some sparse spectral wavelength ranges and extract features from stellar spectra using the LASSO algorithm and wavelet analysis; then, the parameters are estimated from the extracted features using the support vector regression or linear regression methods. The effectiveness of the proposed scheme is shown on a subsample of 50,000 stellar spectra from the SDSS and a theoretical spectral sets.

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