Education PhD, KTH Royal Institute of Technology;

Dec. 2011 - Sep. 2016 RPL Department – Robotics, Perception,

and Learning Department School of Computer Science and Communication Research directions: Machine Learning, Computer Vision Supervisor: Prof. Hedvig Kjellström Co-supervisor: Lecturer. Carl Henrik Ek

Master of Science, KTH Royal Institute of Technology; Aug. 2009 - Sep. 2011 System, Control and Robotics Program; School of Electrical Engineering

Bachelor of Science, Beijing Jiao Tong University (BJTU); Sep. 2005 - Jul. 2009 Major: Mechanism Manufacturing and Mechanical Automatization; Minor: Logistics; School of Economics and Management

Experience Researcher, Microsoft Research, Cambridge; Mar. 2018 - present Machine Intelligence and Perception Group Research Topic: Machine Learning and applications

Postdoctoral Research Associate, Disney Research Pittsburgh; Jan. 2017 - Dec. 2017 Machine Learning Group Advisor: Dr. Stephan Mandt Research Topic: Probabilistic Modelling and Approximate Inference

Researcher, KTH Royal Institute of Technology; Sep. 2016 - Feb. 2018 RPL Department – Robotics, Perception, and Learning Department Advisor: Hedvig Kjellström Research Topic: Machine Learning in Health Care

Researcher, Research Project under Mentor4Research; May. 2015 - Dec. 2016 Research Project on Online Diagnose.

Collaborative Project together with MD. PhD. Bo Christer Bertilson from Karolinska Institutet (KI).

Research Intern, Microsoft Research Cambridge; Jun. 2014 - Sep. 2014 Infer.Net team, Machine Learning and Perception Group Hosts: John Bronskill, Yordan Zaykov, Tomas Minka Research Topic: Generative Group Recommendation System

Visiting Research Fellow, University of Sheffield; Oct. 2013 - Nov. 2013 Translational Neuroscience Department

Hosts: Dr. James Hensman, Prof. Neil Lawrence, Prof. Magnus RattrayResearch Topic: Inference of Transcript Expression Using RNA-seq Data

Cheng Zhang

Address: Rådmansgatan 8a LGH 1101 11425, Stockholm Sweden

Email: chengz@kth.se URL: http://cheng-zhang.org/

Research Engineer, KTH Royal Institute of Technology; Jul. 2011 - Nov. 2011RPL Department – Robotics, Perception, and Learning DepartmentAdvisor: Prof. Hedvig KjellströmResearch Topic: 3D Object Detection with Kinect

Research Engineer, KTH Royal Institute of Technology; Jun. 2010 - Sep. 2010 RPL Department – Robotics, Perception, and Learning Department

Advisor: Marianna Madry, Prof. Hedvig Kjellström, Prof. Danica KragicResearch Topic: Object Classification and Cue Integration

Intern, Cummins (China) Investment Company Limited; Jul. 2008 - May. 2009 Precision Command System (PCS) Team

PCS is an enterprise information system

Publication Advances in Variational Inference, C. Zhang, J. Butepage, H. Kjellstrom and S. Mandt, IEEE Transactions on Pattern Analysis and Machine Intelligence (in review) 2017

Causal Feature Selection for Individual Characteristics Prediction T. Ding, C. Zhang and M. Bos, 

https://arxiv.org/abs/1712.07708

Diversified Mini-Batch Sampling using Repulsive Point Processes C. Zhang, C. Oztireli and S. Mandt NIPS Workshop on Advances in Approximate Bayesian Inference, 2017

Causality Refined Diagnostic Prediction M. Klasson, K. Zhang, B. Bertilson, C. Zhang and H. Kjellström NIPS Workshop on Machine Learning for Health (ML4H), 2017 Perturbative Black Box Variational Inference C. Zhang*, R. Bamler*, M. Opper and S. Mandt* (* indicates equal contribution) Conference on Neural Information Processing Systems (NIPS) 2017

Determinantal Point Processes for Mini-batch Diversification C. Zhang, H. Kjellström and S. Mandt Uncertainty in Artificial Intelligence (UAI 2017). (accepted as plenary talk)

Bridging Medical Data Inference to Achilles Tendon Rupture Rehabilitation

A. Qu, C. Zhang, P. Ackermann and H. Kjellström NIPS Workshop on Machine Learning for Health, 2016

Balanced Population Stochastic Variational Inference

C. Zhang, S. Mandt and H. Kjellström NIPS Workshop on Advances in Approximate Bayesian Inference, 2016 Structured Representation Using Latent Variable Models C. Zhang PhD Thesis, 2016

Diagnostic Prediction Using Discomfort Drawing with IBTM C. Zhang, H. Kjellström, C.H. Ek and B.C. Bertilson Machine Learning in Health Care (MLHC), 2016

Inter-Battery Topic Representation Learning C. Zhang, H. Kjellström and C.H. Ek European Conference on Computer Vision (ECCV), 2016

Diagnostic Prediction Using Discomfort Drawing with Multimodal Topic Model C. Zhang, H. Kjellström, and B.C. Bertilson UAI Workshop on Machine Leaning for Health, 2016 GroupBox: A Generative Model for Group Recommendation C. Zhang, M. Gartrell, T. P. Minka, Y. Zaykov and J. Guiver Microsoft Research Technical Report; MSR-TR-2015-61

How to Supervise Topic Models C. Zhang and H. Kjellström

ECCV 2014 Workshop on Graphical Models in Computer Vision (oral)

Supervised Hierarchical Dirichlet Processes with Variational Inference C. Zhang, C.H. Ek, X. Gratal, F. T. Pokorny and H. Kjellström IEEE ICCV 2013 Workshop on Inference for Probabilistic Graphical Models (oral)

Contextual Modeling with Labeled Multi-LDA C. Zhang, D. Song and H. Kjellström

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2013

Factorized Topic Models C. Zhang, C.H. Ek and H. Kjellström International Conference on Learning Representations (ICLR), 2013

Multi-Class Detection and Segmentation of Objects in Depth

C. Zhang and H. Kjellström http://arxiv.org/pdf/1301.5582v1.pdf

Grant & Awards Stiftelsen Promobilia PhD Research Grant; Principal Investigator (PI) Grant period: Mar. 2016 - Mar. 2018 Received 1 200 000 SEK ( around 147 700 USD) grant for research on efficient and robust

inference algorithms for computer vision with big data.

Vinnova Grant “Challenge-driven innovation - Step 1”; Co-PI Grant period: Nov. 2015 - Aug. 2016 Received 444 000 SEK (around 54 600 USD) grant from Vinnova for machine learning on

pain diagnoses together with MD.PhD. Bo Christer Bertilson (KI).

Stiftelsen Promobilia Research Project Grant; Principal Investigator (PI) Grant period: Aug. 2015 - Aug. 2017 Received 150 000 SEK (around 18 500 USD) grant from Stiftelsen Promobilia for

automatic object and scene understanding. NVIDIA Hardware Grant; Jan. 2015

Winner of KTH innovation competition; Oct. 2014 Received 25 000SEK (around 3 070 USD) award.

Member of Winning Team of Best Hackathon project; Jul. 2014 One Week Hackathon; Microsoft Research Cambridge

Excellent Student in Study Scholarship, BJTU; Sep. 2007 Excellent Student in Social Activities Scholarship, BJTU; Sep. 2006

Talks Microsoft Research, Cambridge; Sep. 2017 University of Cambridge; Sep. 2017 University of California, Santa Barbara (UCSB); May. 2017 HiQ Stockholm; Dec. 2016 IDA Machine Learning Seminars, Linköping University; Nov. 2016 Disney Research Pittsburgh; Aug. 2016 Swedish Defence Research Agency (FOI); Jan. 2016

Stanford University; Dec. 2015 The Royal Swedish Academy of Engineering Sciences (IVA); Nov. 2015 KTH Innovation & founder.org; Dec. 2014 Microsoft Research Cambridge; Sep. 2014 The University of Sheffield; Sep. 2013

Stockholm University; June. 2013 Extracurricular Activity Served as reviewer for:

The Journal of Machine Learning Research (JMLR)IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

EES Pattern Recognition International Conference on Machine Learning (ICML) International Conference on Learning Representations (ICLR) Conference on Neural Information Processing Systems (NIPS) European Conference on Computer Vision (ECCV) Conference on Computer Vision and Pattern Recognition (CVPR) International Conference on Computer Vision (ICCV) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) IEEE International Conference on Robotics and Automation (ICRA)

Teaching Assistant for: Image Analysis and Computer Vision, KTH; 2011 – 2016

Applied Programming and Computer Science, KTH; 2014 Advanced Machine Learning, KTH; 2015 Decision Support System, KTH; 2016 Image Based Recognition and Classification, KTH; 2016 Machine Learning Reading group (co-organizer), KTH; 2015-2016 PhD student supervision: Marcus Klasson (co-supervisior; main-supervisor: Prof. Hedvig Kjellström) Internship project supervision: David Wang (co-supervisior; main-supervisor: Stephan Mandt)

Tao Ding (co-supervisior; main-supervisor: Maarten Bos) Judith Butepage (co-supervisior; main-supervisor: Stephan Mandt) Marcus Klasson (co-supervisior; main-supervisor: Rafael Tena) Tianfan Fu (co-supervisior; main-supervisor: Stephan Mandt)

Master and Bachelor thesis supervision: Tong Sheng (co-supervisior; main-supervisor: Prof. Hedvig Kjellström) Saad Eddin (main supervisor) Lan Wang (main supervisor) John Pertoft (co-supervisior; main-supervisor: Prof. Hedvig Kjellström) Kerry Zhang (co-supervisior; main-supervisor: Prof. Jussi Karlgren) An Qu (main supervisor) Charles Hamesse (main supervisor; examinar: Prof. Hedvig Kjellström) Ruibo Tu (main supervisor; examinar: Prof. Hedvig Kjellström)

Language Skill Mandarin: Native speaker English: Fluent business English spoken/written Swedish: Basic communication

IT Skill Skilled in C++, Matlab, Bash script, Python and C#

Interests Painting: Have learned painting for 7 years. Draw as a hobby. Sport: Regular physical exercise. Have finished races, e.g. Stockholm half marathon,

Pittsburgh half marathon.