Zhenqi Liu (刘桢琦)

Senior undergraduate applying for a Ph.D. degree in Computational Neuroscience

Cover letter

I am a final year undergraduate student from Xi'an Jiaotong University, China, majoring in Automation, preparing for Ph.D. application of 2018 Fall. I take comprehensive curriculum with strong EECS background, keep top ranked with cGPA ~89/100 and multiple research experiences, hoping to join a comprehensive, interdisciplinary neuroscience Ph.D. program with computational emphasis. I am willing to practice both theoretical and experimental approaches from a neuroscientist's perspective in future research.

My major in Automation Science is a top program and the most suitable undergraduate training for neuroscience research with a computational approach in China. Equipped with advanced maths, statistics and physics, General Chem/Bio/Psych courses, CS-related practical programming skills, EE-related engineering theories like signal processing and pattern recognition, I also take graduate courses of Computational Neuroscience, Machine Learning, and Quantum Computing and get high grades during my exchange semester, where I was motivated to study neuroscience. My previous research experience covers aspects of pattern recognition and deep learning, while recently interned in a brain research lab in Chinese Academy of Science, studying criticality with collective behavioral approach while modeling neural activities and complex systems.

The goal for me to study neuroscience is to explain the gap between physical facts and intelligence, understand the brain computationally and improve computation techniques, which requires an in-depth understanding of neuroscience. My undergraduate training in Automation basically provides skill sets for analyzing and processing neural data and knowledge foundation for neuroinformatics. I am also a quick learner to familiar with the biology foundations. Personally, I hope to expose to both experimental and theoretical study. I am prepared (with passion) to do computational neuroscience research with practical experience and multiple-area-inspired approaches. I believe my experience in computation and modeling, my passion in exploring neuroscience and as a quick learner will certainly make contributions to the research.

Please see below for my CV.

Education


B.S. in Automation Science and Technology @ Xi'an Jiaotong University Sep. 2014 - est. June 2018

  • Honors Youth Program Class ( details below )
  • Special class with postgraduate recommendation
  • Top rank with cGPA ~89/100

Exchange Semester in Electrical Engineering @ National Tsing Hua University Feb. 2017 - June 2017

  • Take graduate courses with high grades

Honors Youth Program - Preparatory Year Two @ Xi'an Jiaotong University Sep. 2013 - Sep. 2014


Honors Youth Program - Preparatory Year One @ Suzhou High School Sep. 2012 - Sep. 2013

  • First Prize Winner of National High School Chemistry Competition

Supplement on the XJTU Honors Youth Program

XJTU Honors Youth Program ( aka. Special Class for the Gifted Young or the Juvenile Class, Link to Wikipedia ) is one of the only two programs of its kind in China. Approved by Ministry of Education in 1985, Xi'an Jiaotong University started to recruit students nationwide for the Program. The purpose is to pilot in innovation and quality education reformation, to choose juveniles with extraordinary intelligence, extensive and profound knowledge, good moral character and the spirit of innovation, and to produce excellent talents for world-class scientific research and innovation. Students in the Program take two years of preparatory class (one in selected high school, one in XJTU) instead of typical 3-year high school in China, and directly enter the university. There is rigorous mechanism of selection through competition.

Research Experiences


Undergraduate Thesis in Computational Neuroscience Mar. 2018 - est. June 2018
Brainnetome Center and National Laboratory of Pattern Recognition
Institute of Automation, Chinese Academy of Sciences (NLPR, CASIA)

  • Supervised by Prof. Shan Yu
  • Proposed title: Functional Significance of Long-Range Temporal Correlations in Neural Networks Activities and Its Application to Brain-inspired Computing
  • Doing background research on neural networks model, long-range temporal correlations, criticality, etc.
  • (In prog.) Learning modelling neural networks and processing of EEG signals by programming simulation.
  • (Planned) Trying to set up computing model based on artifical and biological-accurate neural networks.
  • (Planned) Trying to tune the model to various critical states and study the properties related to temporal information.
  • (Planned) Trying to analyze the functional significance of long-range tempotal correlations w.r.t. the models above.
  • The work is currently in progress.

Internship for Computational Neuroscience in XJTU-CASIA Elite Class Program Jul. 2017 - Dec. 2017
Brainnetome Center and National Laboratory of Pattern Recognition
Institute of Automation, Chinese Academy of Sciences (NLPR, CASIA)

  • Supervised by Prof. Shan Yu
  • Doing background research on criticality, collective intelligence, self-organized criticality(SOC).
  • Processing neural data including local field potential(LFP), whole-brain spiking of zebrafish, brain vessel fMRI
  • Trying to model neuron activities with collective intelligence model of bird flocks.
  • Using second-order model to discribe neural data and complex networks.
  • Studying second-order and higher-order relations in complex systems.

Undergraduate Researcher for Face Recognition and Tracking Sep. 2016 - Dec. 2016
National Engineering Laboratory for Visual Information Processing and Application, Xi'an Jiaotong University

  • Supervised by Prof. Jinjun Wang
  • Doing literature review on state-of-the-art results of face recognition with identifying features.
  • Exploring performance boost for face recognition and tracking.
  • To apply deep learning approach with caffe, building datasets.
  • Paused unexpectedly because of exchange term.

Internship for Neural Machine Translation in XJTU-CASIA Elite Class Program Jul. 2016 - Aug. 2016
National Laboratory of Pattern Recognition
Institute of Automation, Chinese Academy of Sciences (NLPR, CASIA)

  • Supervised by Prof. Jiajun Zhang and Prof. Chengqin Zong(Lab Director)
  • Learned the frontier deep learning techniques used in NLP area, especially for NMT (Neural Machine Translation).
  • Analyzed a complete CPU-based NMT system written in C++ code (seq2seq, LSTM, Encoder-Decoder).
  • Reviewed newer leading papers and added features to the system sequentially (Bidirectional RNN, Attention, Feed-Input, Blackout), under the guidance of the supervisor.
  • Modified the system while debugging it throughout the process.
  • Rewarded with program scholarship

Undergraduate Researcher for Human Recognition and Tracking in Sports Videos in ITP (XJTU Information-technology Talent Program) Jan. 2016 - June. 2016
National Engineering Laboratory for Visual Information Processing and Application, Xi'an Jiaotong University

  • Supervised by Prof. Jinjun Wang
  • Did a literature review on state-of-the-art human recognition and tracking researches.
  • Processed the code and conducted the experiment.}
  • Built datasets from scratch.
  • Practiced coding techniques related to video processing.

Accomplishments


Rewards In-Class

  • 2017 Outstanding Student of Xi'an Jiaotong University
  • 2017 Siyuan Scholarship of Xi'an Jiaotong University
  • 2016 Outstanding Student of Xi'an Jiaotong University
  • 2016 Siyuan Scholarship of Xi'an Jiaotong University
  • 2016 Program Scholarship of XJTU - CASIA Automation Elite Class Program
  • 2016 Accepted by XJTU (Xi'an Jiaotong University) - CASIA (Chinese Academy of Sciences) Automation Elite Class Program
  • 2016 Accepted by ITP ( Information-technology Talent Program ) of the School
  • 2015 Siyuan Scholarship of Xi'an Jiaotong University

Coursework

Maths
Mathematical Analysis for Engineering, Linear Algebra, Probabilities and Statistics, Functions of a Complex Variable and Integral Transforms, Equations of Mathematical Physics (Differential Equations),
EE Core
Analogy and Digital Circuit, Signal and System, Digital Signal Processing, Electromagnetic Fields and Waves,
CS Core
Data Structure and Alogorithm, Discrete Mathematics, Artificial Intelligence, Programming of C# and C++, Computer Security, Data Mining,
Automation Core
Control Theory, System modeling, Operational Research, Electric Machinery Control, Robot Control,
Graduate-level
Machine Learning, Computational Neuroscience, Quantum Computing, with high grades,
College Core
Chemistry (general, organic, experiments), Biology (general with experiments), Psychology, Sociology,
Coursera
Machine Learning
by Stanford University on Coursera. Certificate earned on July 2, 2016
Neural Networks and Deep Learning
by deeplearning.ai on Coursera. Certificate earned on September 4, 2017
Improving Deep Neural Networks
by deeplearning.ai on Coursera. Certificate earned on October 3, 2017
Introduction to Big Data
by University of California, San Diego on Coursera. Certificate earned on September 25, 2016
Data Visualization
by University of Illinois at Urbana-Champaign on Coursera. Certificate earned on September 23, 2016

Rewards Extracurricular

  • 2015 Member "The 30th Anniversary of The Juvenile Class" Volunteer
  • 2008 Amateur level-10 (top level) Certificate of Piano

Skills


Programming
C/C++, C#, Python, MATLAB, LaTeX
Web
HTML, CSS
Multimedia
MS Office, Adobe PS/PR/AU
Dev. Env.
Win, Linux, Git
Languages
Chinese (native), English (fluent)

Standardized Tests


TOEFL iBT
108 (R29 L30 S23 W26)
GRE
V156 Q170 AW4.0 (Sept. 2017) V154 Q170 AW3.5 (Aug. 2017)
CET-4/6
655(top 1% at 2014.6)/605(top 10% at 2015.6)

Research Interests


Neuroscience with Computational or Experimental Approach
MAIN AREA TO FOLLOW

  • Learning the foundations,
  • Using computational and engineering approaches to quantify experiments,
  • Interested in systems, behavioural and cognitive neurosience,
  • Interested in theoretical analysis or engineering realization(simulation),
  • Hoping to understand the brain computationally and improve computation techniques.

Theories and applications of Machine Learning and Artificial Intelligence
MAIN METHODS TO USE

  • Explaining the gap between physical facts and intelligence,
  • Having experience and interests in deep learning,
  • Interested in Neuroscience-inspired computing.

Interdisciplinary areas
WITH COMPUTER SCIENCE AND ELECTRICAL ENGINEERING

  • Possible improvements to machine learning or any computing techniques inspired by biology or social sciences,
  • Unique approaches with advanced computational techniques in these subjects.