Yuhuang Hu
Zürich, Switzerland
H +41 76 519 95 11
B yuhuang.hu@ini.uzh.ch
Education
2017-present Ph.D. Student, Institute of Neuroinformatics, UZH/ETH Zürich, Zürich, Switzerland.
2016–2017 MScNSC, Institute of Neuroinformatics, UZH/ETH Zürich, Zürich, Switzerland.
2011–2015 BCompSc
, Department of Artificial Intelligence, Faculty of Comp Sci & Info Tech, University
of Malaya, Kuala Lumpur, Malaysia.
Skills
Academic
Skills
{ Algorithm design and implementation, Data analysis.
{ Professional in Deep Learning, Computer Vision, Image and Video Processing.
{ Familiar with Natural Language Processing, Acoustic Processing.
Programming
Skills
{ Professional in Python programming and development.
{ Proficient at PyTorch, Tensorflow, and modern Deep Learning tools.
{ Familiar with C/C++, Java, Matlab programming.
{ Familiar with modern VCS and CI/CD.
General
Skills
{ Quick learning and problem-solving under time constraints.
{ Critical thinking and eective communication.
Languages { Chinese: Native. English: Fluent.
Research Interests
My current research focuses on Deep Neural Networks, Self-supervised Learning,
Event-based Learning and Processing, and Computer Vision. I am also strongly
interested in Artificial General Intelligence, Decision Theory, Information Theory,
Computational Neuroscience and Theory of Everything.
Experience
Mar. 2018-
May. 2019
Teaching Assistant, D-ITET, ETH Zürich, Zürich, Switzerland.
Teaching assistant of Projects & Seminars module for bachelor students. Focused on Deep
Learning and Computer Vision using Raspberry Pi. (Spring semesters 2018, 2019)
Oct. 2016-
Sep. 2017
Technical Assistant, iniLabs GmbH, Zürich, Switzerland.
Part-time technical assistant on: Neuromorphic devices, maintenance, etc.
Nov. 2013 Trainer
, Transformative Robotic Education Towards Realization of National Education
Philosophy (TRANS-ROE).
Teaching: Robotics Education Training Workshop.
Oct. 2012-
Jul. 2015
Research Assistant, Advanced Robotic Lab, University of Malaya.
A Generalized Quantum-Inspired Decision Making Model, Deep Learning, Robotics.
Sep. 2012-
Dec. 2014
Demonstrator, Faculty of Comp Sci & Info Tech, University of Malaya.
TA for Programming I (WXES1116) and Data Structure (WXES1117).
Jan. 2011-
Sep. 2017
Trainer & Editor.
Provide L
A
T
E
X training course and document typesetting services.
Project Experience
2011 Clustering Algorithms for Scene Classification
2014 Improved Spatio-temporal Deep Network for Pattern Recognition (Bachelor Thesis)
2014
TeslaEye: A Deep Eye-mimicry Video Processor. (Granted Tesla K40 GPU by NVIDIA)
2016 Max-Pooling Operations in Deep Spiking Neural Networks
2017 Understanding Iterative Estimation in Gated Neural Networks (Master Thesis)
2018 Incremental Learning meets Reduced Precision Networks
2019 Learning to Exploit Multiple Vision Modalities by Using Grafted Networks
2020 DDD20: End-to-End Event Camera Driving Dataset
Awards & Activities
Nov. 2008-’09 Participant of National Olympiad in Informatics in Provinces.
Nov. 2012-’14 Participant of ACM-ICPC Malaysia al-Khaw
¯
arizm
¯
ı National Programming Contest.
Feb. 2013 Dean’s List for Semester I Session 2012/2013 (Faculty of CS & IT, University of Malaya).
Aug. 2013
Silver medal of HuroCup Marathon category in 18th FIRA RoboWorld Cup & Congress
2013, Kuala Lumpur, Malaysia.
Dec. 2013 My Robot, Cover story of Life & Times, New Straits Times (December 16).
Sep. 2014 Google Summer of Code 2014 (Sponsored by Google and OpenCog Organization).
Websites
http://dgyblog.com/
https://github.com/duguyue100
¯ https://www.linkedin.com/in/duguyue100
Selected Publications
[1] Y. Hu
, T. Delbruck, S-C. Liu, “Learning to Exploit Multiple Vision Modalities by Using
Grafted Networks” in The 16th European Conference on Computer Vision (ECCV), Online,
2020.
[2] Y. Hu
, J. Binas, D. Neil, S-C. Liu, T. Delbruck, “DDD20 End-to-End Event Camera
Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering
Prediction” in The 23rd IEEE International Conference on Intelligent Transportation Systems
(ITSC), Virtual, 2020.
[3]
Y. Gao, N.I. Nikolov,
Y. Hu
, R.H.R. Hahnloser, “Character-Level Translation with
Self-attention” in 2020 Annual Conference of the Association for Computational Linguistics
(ACL), Online, 2020.
[4]
S. Wang,
Y. Hu
, J. Burgués, S. Macro, S-C. Liu, “Prediction of Gas Concentration
Using Gated Recurrent Neural Networks” in 2020 2nd IEEE International Conference on
Artificial Intelligence Circuits and Systems (AICAS), Genoa, Italy, 2020.
[5] Y. Hu
, T. Delbruck, S-C. Liu “Incremental Learning Meets Reduced Precision Networks”
in 2019 IEEE International Symposium on Circuits and Systems (ISCAS), Sapporo, Japan,
2019.
[6]
N.I. Nikolov,
Y. Hu
, MX. Tan, R.H.R. Hahnloser, “Character-level Chinese-English
Translation through ASCII Encoding” in The Third Conference on Machine Translation
(WMT18), Brussels, Belgium, 2018.
[7]
B. Rueckauer, I-A. Lungu,
Y. Hu
, M. Pfeier, S-C. Liu, “Conversion of Continuous-
Valued Deep Networks to Ecient Event-Driven Networks for Image Classification”,
Frontiers in Neuroscience, 11:682, 2017.
[8] Y. Hu
, H. Liu, M. Pfeier, T. Delbruck, “DVS Benchmark Datasets for Object Tracking,
Action Recognition and Object Recognition”, Frontiers in Neuroscience, 10:405, 2016.
[9] Y. Hu
, C.K. Loo, “A Generalized Quantum-Inspired Decision Making Model for
Intelligent Robot”, Scientific World Journal, Recent Advances in Information Technology,
2014.
[10] Y. Hu
, W.L. Hoo, C.S. Chan, “Clustering Algorithms for Scene Classification A
Performance Comparison between K-means; Fuzzy c-means and GMM” in IIEEJ 3rd
Image Electronics and Visual Computing Workshop, Kuching, Malaysia, 2012.