JIA LI Phone: (217) 721-0580 Email:[email protected] http://vision.stanford.edu/lijiali/

EXPERIENCE

Head of R&D (Nov.2016 – Now) CloudAI. Our mission is to democratize AI and advance AI. My org focus on both research innovation to solve real world problems and development of the full stack of AI products on Google Cloud to power solutions for diverse industries. Core products include Cloud ML platform, APIs and ML Solutions development at Advanced Solutions Lab. AI areas include computer vision, speech, NLP, translation, recommendation, deep learning, reinforcement learning, ML tools, as well as ML innovations in Healthcare, Education and more.

Head of Research (Feb.2015-Nov.2016) Snap Inc. Built the Research org from scratch. Team's mission is two folds: solve real-world research problems; research to innovate product. Introduced and lead the AI effort at Snap. Team's expertise includes computer vision, machine learning, NLP, speech, graphics, data mining, search, recommendation and more. Responsibilities consist of core technology development, research innovation, prototyping, and product integration.

Group Lead (Aug.2014-Feb.2015) Visual Computing and Learning group at Yahoo! Labs . Initiated and led the deep learning effort at Yahoo Labs. Built the first Deep Learning computing platform at Yahoo Labs. Impact spans computer vision applications, recommendation, NLP and search. Related products include E-Commerce Product Recommendation, Video and Image Ads Recognition, Mobile Image and Video Search. Served as lead scientist collaborating closely with Stanford (MACRO) and CMU (InMind) on large scale vision and machine learning projects.

Research Scientist - Senior Research Scientist (Oct.2011-Feb.2015) Yahoo! Labs . Led Yahoo! Lab's effort of advanced computer vision and deep learning innovation. Developed models for Yahoo! Taiwan Ecommerce to boost the recommendation revenue and search CTR significantly. Innovated deep learning approaches to model multimodality data including social and visual feature for Flickr photo recommendation and personalization. Led the science effort to model social activity and visual recognition for retrieving large-scale aesthetic photos, for example, Yahoo! Weather App, reducing the cost of manually collecting aesthetic photos significantly.

Industrial Advisory Board (2016-Now) Computer Vision Foundation Associate Editor (Jun 2014-Now) The Visual Computer. International Journal of Computer Graphics, Springer Press Consultant (Jul.2011–Oct.2011) Neural ID . Research Intern (May.2008-Aug.2008), Machine Perception Research Group, Google Inc.

EDUCATION

Ph. D. (Jun.2006-Dec.2011) degree from Computer Science, Stanford University . M.Sc. (Jul.2003-Oct.2004) in Computer Control & Automation, School of Electrical and Electronic Engineering, Nanyang Technological University , Singapore. B.Eng. (Sep.1998-Jul.2003) in Dept. of Automation, University of Science and Technology of China (USTC) .

IN PRESS

Snapchat introduces Memories: a searchable, shareable archive of your snaps in The Verge. July 6, 2016 Report described search in memories which is powered by technology developed by Snapchat Research Team. Similar articles in TechCrunch, Mashable, Wired and other reports. Snapchat just took emojis to the next level in Tech Insider . April 13, 2016 Report about 3D stickers powered by mobile computer vision technology built by Snap Research. Similar articles in TechCrunch, The Verge, CNET and other media reports. Snapchat's new 'Story Explorer' feature lets users swipe through photos to experience events from every angle in Business Insider , November 23, 2015. Report about 3D stickers powered by mobile vision technology built by Snap Research. Similar articles in TechCrunch, The Verge, CNET and other media reports. Snapchat is quietly building a research team to do deep learning on images, videos in VentureBeat , April 8, 2015 The Face Detection Algorithm Set to Revolutionize Image Search in MIT Technology Review , February 16, 2015 Yahoo and Flickr mix social computing, geolocation and computer vision to boost image recognition tech in The Next Web. November 27, 2014 Finding an image with an image and other feats of computer vision in Ars Technica. November 24, 2014 Yahoo, Flickr share how social is improving image recognition, search in ZDNet. November 21,2014 The future of content consumption, through the eyes of Yahoo Labs in Gigaom. August 12, 2014 Does Content Matter? in Yahoo! Research Featured Insight. December 9, 2011 Illinois-Princeton team takes first place in robot vision competition in UIUC ECE Headline News . August 22, 2007. Robots surf the web to learn about the world in New Scientist . August 17, 2007. Princeton Wins Robot Vision Competition in Princeton CS News . July 31, 2007.

HONORS Yahoo! Super Star Award, Highest Award at Yahoo!, 2014 Inventor Award, Yahoo!, 2014 LEAP (Labs Excellence Awards Program) Award, Yahoo!, 2014 Hackovation Winner , Yahoo!, 2013 1st Place in the Semantic Robot Vision Challenge Software League , An NSF and AAAI sponsored visual recognition competition. 2007.

PROFESSIONAL ACTIVITIES

Conference Organizer Program Chair, ACM Multimedia, 2017 Area Chair, IEEE ICCV, 2017 Corporate Relations Chair, IEEE Computer Vision and Pattern Recognition (CVPR), 2016 Volunteers Chair, IEEE Computer Vision and Pattern Recognition (CVPR), 2010 Conference and Journal Reviewer CVPR (Since 2006), ICCV (Since 2007), ECCV (Since 2008), NIPS (Since 2009), Proceeding of the IEEE (Since 2009), Pattern Recognition and (Since 2008)

SELECTED INVITED TALKS AND SEMINARS

From Insights to Solutions, An Invitation to the AI Journey, Distinguished Speaker at IEEE AI Symposium, Nov. 2017 Why Democratizing AI Matters: Computing, Data, Algorithms and Talent, Keynote, O’Reilly AI Conference , Sep. 2017 How Businesses are using AI, Keynote, the Future of Go Summit , May. 2017 How Businesses are using AI, Keynote, Global Machine Intelligence Summit , May. 2017 Large Scale Visual Recognition, Keynote, BigVision, CVPR , Jun., 2016 Guest Lecture at Deep Learning for Computer Vision Class, CMU , Oct., 2014 Large Scale Visual Recognition in Real World Images. CMU VASC Seminar , Oct., 2014 Object Recognition and Deep Learning for E-Commerce Product Recommendation . Yahoo! Labs , Aug., 2014 Using the Wisdom of the Crowds for Aesthetic Photo Discovery . Hackovation , Sept., 2013 Large Scale Image Social Network Analysis, Stanford Vision Lab , Feb., 2013 Semantic Image Understanding: From the Web, in Large Scale, with Real-World Challenging Data , Robotics Institute, VASC Seminar, Carnegie Mellon University , Mar., 2011 Semantic Image Understanding: From the Web, in Large Scale, with Real-World Challenging Data , Computational Vision Group Seminar, California Institute of Technology , Jan., 2011 Object Bank: An Object-Level Image Representation for High-Level Visual Tasks , Guest Lecture at IEEE RAS SCV Chapter , Sep., 2010 Total Scene Understanding: Classification, Annotation and Segmentation in an Automatic Framework , PAIL seminar series, Stanford University , Aug., 2009 Semantic image understanding and its application, Computational Vision Group Seminar, California Institute of Technology , Aug., 2008 Semantic image understanding and its application, Computer Vision Group Seminar, University of California, Berkeley , Aug., 2008 Semantic image understanding and its application, PIXL & PICASso Lunch Seminar Series, Princeton University , Apr., 2008

SELECTED PUBLICATIONS

Book Chapters Where the Photos Were Taken: Location Prediction by Learning from Flickr Photos Large-Scale Visual Geo-Localization, Li-Jia Li , Rahul K Jha, Bart Thomee, David A Shamma, Liang-Liang Cao, Yang Wang, Springer Press , 2016 What, Where and Who? Telling the Story of an Image by Activity Classification, Scene Recognition and Object Categorization. Li Fei-Fei and Li-Jia Li . Computer Vision: Recognition, Registration, and Reconstruction - Eds. R. Cipolla, S.Battiato, G.M. Farinella - Studies in Computational Intelligence – Springer-Verlag press, 2010

Selected Papers Thoracic Disease Identification and Localization with Limited Supervision, Zhe Li, Chong Wang, Mei Han, Wei Wei, Emily Xue, Li-Jia Li , Li Fei-Fei, in submission to IEEE Conference on Computer Vision and Pattern Recognition (CVPR ), 2018

Iterative Visual Reasoning Beyond Convolutions, Xinlei Chen, Li-Jia Li , Abhinav Gupta, Li Fei- Fei, in submission to CVPR, 2018

Regularizing Very Deep Neural Networks Trained on Corrupted Labels, Lu Jiang, Thomas Leung, Zhenyuan Zhou, Li-Jia Li , Li Fei-Fei, in submission to CVPR 2017

Attention-based Graph Neural Network for Semi-supervised Learning, Kiran Koshy, Chong Wang, Sewoong Oh, Li-Jia Li , in submission to International Conference on Learning Representations (ICLR), 2018

A Goal-oriented Neural Conversation Model based on Information Isolation and Action States, Wei Wei, Andrew Dai, Quoc Le, Li-Jia Li , in submission to ICLR 2018

Learning from Noisy Labels with Distillation, Yuncheng Li, Jianchao Yang, Yale Song, Liangliang Cao, Jiebo Luo, Li-Jia Li , IEEE Intern. Conf. in Computer Vision (ICCV), 2017

Deep Reinforcement Learning-based Image Captioning with Embedding Reward, Zhou Ren, Xiaoyu Wang, Ning Zhang, Xvtao Lv, Li-Jia Li , CVPR , 2017

Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations, Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li Jia-Li , David Ayman Shamma, Michael Bernstein, Li Fei- Fei , International Journal of Computer Vision (IJCV) , 2017

Bi-directional Joint Inference for User Links and Attributes on Large Social Graphs, Carl Yang, Ling Zhong, Li-Jia Li , Jie Luo , Intern. Conf. on World Wide Web Companion (WWW) , 2017

Attention based CLDNNs for short-duration acoustic scene classification, Jinxi Guo, Ning Xu, Li- Jia Li , Abeer Alwan, Proc. Interspeech 2017

Dense Captioning with Joint Inference and Visual Context, Linjie Yang, Kevin Tang, Jianchao Yang, Li-Jia Li , CVPR 2017

AutoScaler: Scale-Attention Networks for Visual Correspondence, Shenlong Wang, Linjie Luo, Ning Zhang, Li-Jia Li , British Machine Vision Conference (BMVC), 2017

YFCC100M: The New Data in Multimedia Research, Bart Thomee, David A. Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, Li-Jia Li , Communications of the ACM , 59(2), 2016

Boosted Convolutional Neural Networks, Mohammad Moghimi, Mohammad Saberian, Jian Yang, Li-Jia Li , Nuno Vasconselos, Serge Belongie, BMVC , 2016

CelebrityNet: A Social Network Constructed from Large-Scale Online Celebrity Images, Li-Jia Li , David A. Shamma, Xiangnan Kong, Sina Jafarpour, Roelof van Zwol, and Xuanhui Wang, ACM Transaction of Multimedia , 2015

Best of Both Worlds: Human-Machine Collaboration for Object Annotation, Olga Russakovsky, Li-Jia Li , Li Fei-Fei, CVPR , 2015

Image Retrieval using Scene Graphs, Justin Johnson, Ranjay Krishna, Michael Stark, Li-Jia Li , David Ayman Shamma, Michael Bernstein, Li Fei-Fei, CVPR, 2015

Co-localization in Real-World Images, Kevin Tang, Armand Joulin, Li-Jia Li , Li Fei-Fei, CVPR , 2014.

Large-Scale Multi-Label Learning with Incomplete Label Assignments, Xiangnan Kong, Zhaoming Wu, Li-Jia Li , Ruofei Zhang, Philip S. Yu, Hang Wu and Wei Fan, SIAM International Conference on Data Mining (SDM) , 2014.

Object Bank: A High-Level Image Representation for Scene Classification and Semantic Feature Sparsification. Li-Jia Li *, Hao Su*, Eric. P. Xing and Li Fei-Fei. Proceedings of the Neural Information Processing Systems (NIPS), 2010. (* indicates equal contribution)

Large Margin Learning of Upstream Scene Understanding Models. Jun Zhu, Li-Jia Li , Li Fei-Fei and Eric P. Xing. NIPS, 2010

Building and Using a Semantivisual Image Hierarchy, Li-Jia Li *, Chong Wang*, Yongwhan Lim, David Blei and Li Fei-Fei. CVPR , 2010 . (* indicates equal contribution)

Towards Total Scene Understanding: Classification, Annotation and Segmentation in an Automatic Framework. Li-Jia Li , Richard Socher and Li Fei-Fei. CVPR , 2009.

ImageNet: A Large-Scale Hierarchical Image Database. Jia Deng, Wei Dong, Richard Socher, Li- Jia Li , Kai Li and Li Fei-Fei, CVPR, 2009.

What, where and who? Classifying event by scene and object recognition. Li-Jia Li and Li Fei- Fei. ICCV, 2007.

OPTIMOL: automatic Object Picture collecTion via Incremental MOdel Learning. Li-Jia Li , Gang Wang and Li Fei-Fei. CVPR , 2007.

OPTIMOL: a framework for Online Picture collecTion via Incremental MOdel Learning . Li-Jia Li , Juan Carlos Niebles and Li Fei-Fei. Association for the Advancement of Artificial Intelligence (AAAI) Robot Competition and Exhibition , 2007.

Output frequency response functions of nonlinear Volterra systems. Z Q Lang, S A Billings, R Yue, and J Li , Journal of Automatica , Vol. 43, pp805-816, 2007

Nonlinear weighted discriminant analysis. Hai Jiang, Er Meng Joo, Jia Li , The 12th European Signal Processing Conference, 2004