Heng Ji is pursuing a Master of Science in Electrical Engineering at Columbia University. He received his Bachelor of Science in Applied Physics from the University of Science and Technology of China, where he received several awards for his academic and research accomplishments. His research at Columbia involves internet of things, big data analysis, and machine learning algorithms applied to sensor data collection and processing. Previously at USTC, he conducted research in gas electron multiplier electronic readout design and control of strontium titanate nanostructures.

1. HENG JI
530 Riverside Dr.#6A1, New York, NY 10027/ (646)-209-9847 / hj2436@columbia.edu
EDUCATION
Columbia University in the City of New York New York, NY
Master of Science in Electrical Engineering Expected Dec. 2017
l Awards: Tesla Scholarship (Top 15% of all admitted master students in Electrical Engineering department)
l Relevant Course works: Machine Learning, Big Data Analytic, Computer Networks, Internet of Things
University of Science and Technology of China (USTC) Hefei, China
Bachelor of Science in Applied Physics Sep. 2012-Jun. 2016
l Awards: The First-class Scholarship, GPA: 3.59, Rank:8/63
l Outstanding Research Project in USTC (Top 10% of 100 projects in School of Physical Science)
l First Prize in the 2014’s university-level research essay contest (Top 3% of 800 students)
RESEARCH PROJECTS
Internet of things and Big Data Analyze, Columbia University Sep. 2016-Present
l Investigated Microprocessor’s control method [Python], and optimized the connection of the whole
hardware system which included microprocessor, sensor, screen, vibration motor, accelerometer, and LED
l Uploaded the information from Microprocessor/sensor to cloud serves and applied machine learning
algorithm [Hadoop, Spark] for data processing.
l Learnt the method of using app development platform and design our software application on Android.
Machine Learning Programing, Columbia University Sep.2016-Present
l Recognized handwrite numbers by 1-NN classifier with 10,000 training samples and test error rate with
6,000 test data points [Python]
l Analyzed the type of articles by Naive Bayes classifier, and simplified the algorithm into binary classifier
under specific binary label conditions for decreasing calculation complexity [Python]
l Predicted the attitude of clients from their comment by applying different featuring method with online
perceptron learning method/SVM and AdaBoost.
The Design of Gas Electron Multiplier’s Electronic Readout Board, USTC Sep. 2014-Nov. 2015
l Proposed mathematical encoding method to reshape hardware compositions, and achieved 80% reduction
of electrical circuit’s consumption
l Implemented Euler’s shortest path theory, which is a basic mathematical graph solution, on hardware
design
l Developed decoding code [C] to transform digital numbers into physical information
l Produced a printed circuit board (PCB) and completed relevant PCB system testing
l Publication: “A Novel Method of Encoded Multiplexing Readout for Micro-pattern Gas Detectors,”
Chinese physics C, Vol.40, No.5, (2016): 056102.
The Control of SrTiO3’s Nanostructures, USTC Sep. 2014-Jan. 2015
l Designed experiment process including the choice of pre-corrosion acid, heating environment and time,
observing method
l Checked and modified experimental method based on the test result of each stage
l Produced, observed and adjusted the samples’ morphologies with the aid of Atomic Force Microscope
WORK EXPERIENCE
Shanghai Institute of Satellite Engineering, China Jul. 2015-Aug. 2015
l Participated in the thermal vacuum test of the Dark Matter Particle Explorer (DAMPE) Satellite
developed by USTC
l Monitored operation condition of DAMPE by analyzing the fluctuation of temperature and current
TECHNICAL SKILLS
l Programing languages: Python (Proficient), C, C++, Java, DOS
l Applications Systems: Hadoop, Pig, Hive, HBase, Mahout, Apache Spark, AWS, Google Cloud API&CE.