ADDRESS

*** ****** ***, #**, ********, NY,

*****

Joheun Kang

818-***-****

adce2p@r.postjobfree.com

adce2p@r.postjobfree.com

OBJECTIVE

Pursuing 2020

Summer Internship

INTERESTS

Visiting Auto shows, Soccer,

Coffee tour

ABOUT ME

Technical skills Python, TensorFlow, Spark, R, Hadoop, SQLite, C++, Latex, M.S Excel Languages Korean (Native speaker), English.

Certificate Big Data with Amazon Cloud, Hadoop/Spark and Docker (NYC Data Science Academy) EDUCATION

New York University

(09/2019 – 05/2021)

- M.S Electrical & Computer Engineering

University of California Santa Barbara

(09/2016 – 06/2019)

- Bachelor of Mathematics, B.S, 2019 - Minor: Applied Statistics

- Major GPA: 3.5 - Dean’s Honors

COURSES

New York University

(09/2019 – 05/2021)

- Machine Learning (Python) - Programming for Data Science (Python)

- Probability of Stochastic Processes - System Optimization

- Data Structure and Algorithms - Artificial Intelligence (Python) University of California Santa Barbara

(09/2016 – 06/2019)

- Computer Science (Python) - Programming for Computer Science (C++)

- Advanced Numerical Analysis (Python) - Advanced Linear Algebra

- Statistical Machine Learning (R) - Stochastic Process (Python)

- Regression Analysis (R) - Probability & Statistics PROJECT EXPERIENCE

Gradient Episodic Memory for Continual Learning (Google Collab, DNN, Object Detection)

(3/2020 – )

- In order to reduce catastrophic forgetting from Empirical Risk Minimization, implemented GEM method and compared with other learning methods.

- Using MNIST permutations, MNIST rotation, and CIFAL-100, figured out Average Accuracy, Backward Transfer, and Forward Transfer classification accuracy of single, Independent, Multimodal, EWC, and GEM

- Overall, GEM exhibit minimal forgetting and faster computation at each learning iteration. Kaggle Career Con 2019, Help Navigate Robot (Google Collab, Machine Learning)

(1/2020 – 3/2020)

- Using sensor data such as acceleration and velocity, predicted floor types using Random forest Algorithm.

- Accomplished effective dimensionality reduction using statistical analysis and feature engineering.

- Achieved 85% accuracy, which is same accuracy as the second place of the competition. Using Google Trends to Decipher Dark Figures of Crime (Python, Data Science)

(9/2019 – 12/2019)

- Evaluated the usefulness of extracting meaning from google searches, that could correlate with the unmeasured rates of certain criminal activities.

- Implemented a program called “GTKeyword” that successfully retrieves google search data for user inputted keywords, sorted by geographic location and time period.

- Visualized the correlation between GT searches (from potential victims) and the official (UCR) and unofficial (NCVS) crime statistics for rape, burglary, and vehicle theft in US metro areas. 2016 Election Prediction (R, Machine Learning)

(4/2019 – 6/2019)

- Predicted voter behaviors by collecting and analyzing data sets by using diverse machine learning algorithms.

- Visualized and analyzed data using functions and algorithms in R (Charts, Maps, Graphs). Airfoil Self-Noise Regression Analysis (R, Regression Analysis)

(1/2019 – 3/2019)

- Proposed a linear regression model for the dataset to develop a fundamental understanding of self- noise mechanisms.

- The regression model provides meaningful insight into decreasing airfoil self-noise for a variety of aerodynamic systems.

WORK EXPERIENCE

KW International (Mechanics Intern)

Fontana, CA (06/2015 – 09/2015)

- Organized various documentations including each driver's work hours and distance traveled.

- Assisted in coordinating supply transportation across multiple states through commercial trucks. Mathematics Tutor

La Crescenta, CA (09/2014 -06/2015)

- Helped high school students with their understanding of mathematics.

- Mentored a class of 7 high school students to assist in their mathematics.

Contact this candidate