About Me
Tech Lead & Innovator In Computer Aided Design for Integrated Circuits and Systems.
Tech Lead & Innovator In Computer Aided Design for Integrated Circuits and Systems.
"To see a World in a Grain of Sand, And a Heaven in a Wild Flower,
Hold Infinity in the palm of your hand, And Eternity in an hour."
-William Blake
In 2007, my PhD supervisor Prof. David Z. Pan introduced to me the fantastic world of Electronic Design Automation, and it has never ceased to amaze. Throughout the years of grabbling with NP Complete problems in the EDA realm, I came to realize the bigger picture of these techniques outside EDA, as well as the value of bringing in new methods to the classic topics.
My current focus in EDA includes physical design, physical verification and custom design automation flows at the cutting edge process nodes.
An enthusiast in AI and deep learning, I also apply learning algorithms to solve difficult problems with new angles.
Major Awards:
Best Ph.D. Dissertation Award in EDA, awarded by ACM SIGDA, 2013
Best Paper Award, Asia and South Pacific Design Automation Conference (ASPDAC), 2012
Best Paper Award Finalist, International Conference on Computer-Aided Design (ICCAD), 2011
Best Student Paper Award, International Conference on IC Design and Technology (ICICDT), 2009
Industry Experience
Senior Staff Software Engineer (CAD), Samsung Austin Research Center (SARC), 2017 - Present
Principal Hardware Engineer (CAD), Oracle Corp (Formerly SUN Microsystems Inc.), 2011-2017
CAD/LFD Intern, Siemens EDA (Formerly Mentor Graphics Corp.), 2009
Professional Services
Invited Technical Program Committee member of Design Automation Conference in Physical Design and DFM tracks (2018-Present)
Education
M.S.E., Ph.D. in EECE, The University of Texas at Austin, 2006 - 2011
Software
Proficient: C++, Python, Common Lisp / Cadence SKILL, SKILL++
Past experience: Java, Ruby, Clojure, Tcl/Tk, Perl, R
Training/Certificates
Andrew Ng's Deep Learning Specialization on Coursera.org
Completed a 17-week deep learning specialization by Andrew Ng, covering 5 major topics: Neural Networks and Deep Learning, Improving Neural Networks, Structuring Machine Learning Projects, Convolutional Neural Netwoks, and Sequence Models.
A movable feast in deep learning, this specialization features great breath and depth in the state-of-the-arts of modern AI, with lectures and programming assignments in calculus, deep NN (cross-entropy loss, back-propagation algorithms - SGD, RMSProp, Adam, regularization, hyper parameter tuning), convNets such as ResNet, VGGNet, Inception network, Residual network, Siamese network, art style transfer network, and recurrent networks such as RNN, GRU, LSTM, cascaded and bi-directional LSTM, etc.
I especially enjoyed the project assignments in word embedding (Word2vec, GloVe) of natural language processing, as well as using attention models for speech recognition. Multi-object classification for autonomous vehicle (YOLO/YOLO2) and art style transfer network are also great re-implementations of the original publications.
DataCamp's Data Scientist With Python Certificate
Completed a grand 4-month journey in DataCamp's Data Scientist With Python, a full-blown curriculum taught by top experts in the field.
This career track features 26 courses and 100+ hours of materials and projects, covering statistics, machine learning, data manipulation and visualization, graph algorithms, SQL, Spark, natural language processing, deep learning and various python packages such as Keras, TensorFlow, Scikit-learn, Cython, NLTK, Pandas, pySpark, Networkx, etc.
Highly recommend as I enjoyed every minute of it!