FLAIR

December 20-21, 2017

Future Leaders of AI Retreat

NYU Shanghai

 
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FLAIR (Future Leaders of AI Retreat) brings together world-class, senior PhD students who are active in the broad area of Artificial Intelligence research from leading labs around the globe. The goal is to set a stage for the future stars to meet and debate against each other, and to meet and communicate with researchers and industrial labs in this region.

The participants are hand-picked by a small committee of reputed top young scholars. Candidates are typically within two years of graduation, and have produced high-quality works in this year. Participants of 2017 FLAIR include students from world-leading research groups such as CMU, Stanford, MIT, Berkeley, Cambridge, NYU, Univ of Washington etc.. The agenda includes theory and core algorithms, natural language processing, computer vision, games and general intelligence, and creative arts with artificial intelligence.

The workshop is initiated by faculties of NYU Shanghai, co-organized by NYU Shanghai Center for Data Science, UniDT Technology, and Amazon AWS labs. Sponsored by Shanghai Municipal Peoples Government, Chinese Academy of Sciences, the Chinese Academy of Engineering.

 

➤ Date

December 20-21, 2017

➤ LOCATION

NYU Shanghai Academic Building

1555 Century Avenue, Pudong District, Shanghai, China

 

Committee

Chair: Zheng Zhang (NYU Shanghai)

Kaiming He (Facebook), Mu Li (Amazon)

Zhengdong Lu (Deeplycurious.ai), Yuandong Tian (Facebook)

David Wipf (Microsoft Research), Gus Xia (NYU Shanghai)

 

Speakers

Theory and core algorithms


Shaolei Du (Carnegie Mellon University)

Simon Du (杜少雷) is a PhD student in the Machine Learning Department at the School of Computer Science, Carnegie Mellon University, advised by Professor Aarti Singh and Professor Barnabás Póczos. He obtained his B.S. in Engineering Math & Statistics and Electrical Engineering & Computer Science from University of California, Berkeley in 2015. His research interests broadly include topics in theoretical machine learning and statistics, such as deep learning, matrix factorization, convex/non-convex optimization, transfer learning, reinforcement learning, non-parametric statistics and robust statistics. Currently he is also developing methods for precision agriculture.


Yuqian Zhang (Columbia University)

Yuqian Zhang (张雨倩) is a Ph.D. candidate in the Electrical Engineering Department at Columbia University, advised by Professor John Wright. She received her B.S. in Electrical Engineering from Xi’an Jiaotong University. Her research spans across optimization, computer vision, signal processing, and machine learning. Specifically, her primary research interest is to develop efficient, reliable and robust algorithms for applications in computer vision, scientific data analysis, etc.


Junbo Zhao (New York University)

Junbo Zhao (赵俊博) is currently a 2nd year PhD student at CILVR lab at NYU, under the supervision of Professor Yann LeCun. His recent main research interests include deep learning and unsupervised learning, on both domains of vision and language. In recent years, Jake has interned at Facebook AI research team, Clarifai engineering team, NVIDIA autonomous driving team. He graduates from Wuhan University majoring in electrical engineering in 2014 and holds a master degree in data science from NYU.


Ravid Shwartz-Ziv (Hebrew University)

Ravid is currently a computational neuroscience PhD candidate at  the Hebrew University of Jerusalem at the Machine Learning Lab under the supervision of Prof. Tali Tishby. Ravid received both his B.A and his M.Sc degrees from the Hebrew University of Jerusalem. Ravid’s research interests lie in the intersection of learning, information and optimization, especially in deep neural networks. Ravid's main focus is exploring learning and dynamics via information for both artificial and biological neural network. 

Application: natural language processing and computer vision


Ronghang Hu (University of California, Berkeley)

Ronghang Hu (胡戎航) is a 3rd-year Ph.D. student in computer science at UC Berkeley, working with Prof. Trevor Darrell. He has been working on a variety of topics in computer vision, and most notably joint vision and language tasks such as visual question answering. In 2017 summer, he was a research intern in Facebook AI Research (FAIR) working with Dr. Ross Girshick. He obtained his B.E. degree from Tsinghua University in 2015. Previously in 2013 and 2014, He was a research intern at Institute of Computing Technology, Chinese Academy of Science (ICTCAS) and was advised by Prof. Shiguang Shan and Prof. Ruiping Wang.


Hao Peng (University of Washington)

Hao Peng (彭昊) is a second year Ph.D. student in Computer Science and Engineering at the University of Washington, advised by Prof. Noah Smith. He works on natural language processing and machine learning, and is particularly interested in broad-coverage semantics. Previously, Hao received B.S. from Peking University in 2016 (with hornor), and also visited University of Edinburgh and Microsoft Research Asia.


Zhiting Hu (Carnegie Mellon University)

Zhiting Hu (胡志挺) is a PhD student at Machine Learning Department, Carnage Mellon University. His advisor is Prof. Eric Xing. His research is focusing on knowledge-enriched deep learning, Bayesian modeling and inference, large-scale machine learning, and their applications in natural language processing, esp., text generation. His work on harnessing deep neural networks with logic rules was selected as one of the outstanding papers in ACL2016. He is the recipient of 2017 IBM Fellowship.

Application: game and generalizable intelligence


Shixiang Gu (Cambridge University)

Shixiang Gu (顾世翔) is a PhD candidate at University of Cambridge and Max Planck Institute for Intelligent Systems, where he is jointly co-supervised by Richard E. Turner, Zoubin Ghahramani, and Bernhard Schoelkopf. He holds BASc. in Engineering Science from University of Toronto, where he completed this thesis with Professor Geoffrey Hinton. His research interests span deep reinforcement learning, deep learning, robotics, approximate inference and causality, and his research has been featured by MIT Technology Review and Google Research Blog. He also collaborates closely with Sergey Levine from UC Berkeley/Google and Tim Lillicrap from DeepMind.


Jiaji Zhou (Carnegie Mellon University)

Jiaji Zhou (周佳骥) is a PhD student in the Manipulation Lab of the Robotics Institute at Carnegie Mellon University, co-advised by Matt Mason and Drew Bagnell. His work won the ICRA 2016 Best Conference Paper Award. He has interned at GoogleX self-driving car team, Dato and Toyota Research Institute Manipulation Group.  



Xiangyu Kong (Peking University)

Xiangyu Kong (孔祥宇) is a fifth-year Ph.D candidate in Computer Science at Peking University, under the supervision of Prof. Yizhou Wang. He also works very closely with Dr. Bo Xin of Microsoft Research Asia. Prior to that, He graduated from Harbin Institute of Technology with a Bachelor of Computer Science. His current research interest includes computer vision, machine learning (in particular, multi-agent deep reinforcement learning) and their applications in video game playing.

 

Yuke Zhu (Stanford University)

Yuke Zhu (朱玉可) is a fifth-year Ph.D. student in Computer Science at Stanford University, advised by Professor Fei-Fei Li and Professor Silvio Savarese. His research focuses on the principles and applications of computer vision, machine learning, and robotics, in particular, visual knowledge and deep reinforcement learning. Prior to coming to Stanford, he received a BEng. degree from Zhejiang University and a BSc. degree from Simon Fraser University, working with Professor Greg Mori. He also collaborates with research labs including Snap Research, Allen Institute for Artificial Intelligence, and Google DeepMind.

Creativity and music with AI

System


Tianqi Chen (University of Washington)

Tianqi Chen (陈天奇) is a PhD student in University of Washington, working on machine learning and systems. He received his bachelor and master degrees from Shanghai Jiao Tong University. He is recipient of a Google PhD Fellowship in Machine Learning


Minjie Wang (New York University)

 Minjie Wang (王敏捷) is a forth year Ph.D. student at New York University and a member of the NYU systems group. Before joining NYU, Minjie got his master's and bachelor's at Shanghai Jiao Tong University. He also spent two years as a research intern in Microsoft Research Asia, where he found his research interests in machine learning systems and built his first deep learning system: Minerva. Minjie was also one of the founding members of the Deep Machine Learning Community. He is one of the main developers of the MXNet, NNVM, and MinPy projects. He is the recipient of 2016 NVIDIA Graduate Fellowship.



Hiroyuki Osone (University of Tsukuba)

Hiroyuki Osone is a sophomore in Digital Nature Group at University of Tsukuba, advised by Associate Prof. Yoichi Ochai. His work accepted the NIPS 2017 workshop Machine Learning for Creativity Design. He is interested in image generation by GAN and application of that image. 

 

Zhengshan Shi (Stanford University)

Kitty Zhengshan Shi (施正珊) is a current 4th year PhD student at Stanford University in Center for Computer Research in Music and Acoustics (CCRMA). She obtained her bachelor’s degree at Shanghai Conservatory of Music, and a master at New York University. She is interested in intelligent music software design as well as creative music information retrieval. She also enjoys playing piano, accordion, violin, and bagpipes. She is a native Shanghainese. 

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