Machine Learning Engineer
Resume:
JENNY
CHOU
MACHINE LEARNING
ENGINEER
EDUCATION
University of California, San Diego
M.S. in Electrical Engineering
**** – 2017
San Diego, California
Specialized in intelligent system and focused on text generation with LSTM using MATLAB and TensorFlow v1.0.
University of California, San Diego
B.S. in Electrical Engineering
2013 – 2016
San Diego, California
Specialized in intelligent and control system and focused on image detection with R-CNN using Caffe.
PROFILE
Aspiring machine learning engineer
with hands-on experience in building
ML algorithms with Python, scikit-
learn, and TensorFlow and industry
experience developing embedded
software in C++ and Object-Oriented
Programming. I have great passion for
driving innovation using machine
learning and deep learning, and I’m a
great collaborator with cross
functional teams.
CONTACT
*************@*****.***
San Diego, California
linkedin.com/in/jenny-ty-chou
github.com/jenny-chou
CERTIFICATION
TensorFlow Developer Certificate
AUG2020
Demonstrated ability to design
TensorFlow neural network models to
PROJECTS
Omdena: Improve Food Security in Senegal Link
Junior ML engineer
NOV2020 – Current
San Diego, California
• Deploys novel ML techniques to reduce food insecurity and increase crop yield in Senegal rural area.
• Predicts drought and flood and provides early climate hazard warning using historical weather data and CNN- LSTM module.
• Predicts crop yield using Google Earth Engine collected imageries and transfer learning with VGG16.
Kaggle: Ultrasound Nerve Segmentation Link
NOV2020
San Diego, California
• Simplifies pain relief technique by identifying the nerve system in ultrasound images for more efficient catheter placement in patient’s neck.
• Built state-of-the-art U-Net with TensorFlow.
• Final model highlights 62% of core nerve system, and it captures nerve structure while ultrasound probe moves, which are not always captured in ground truth masks. Kaggle: COVID Tweet Sentiment Analysis Link
OCT2020
San Diego, California
• Automates process of classifying COVID tweets into 3 sentiments and 2 intensity levels.
solve image classification, NLP, and
time series problems.
AWARD
Becton Dickinson MMS R&D WOW
Award – FY2020 Q1 Collaboration
JAN2020
For demonstrating noteworthy efforts,
above and beyond usual job duties
collaborating within software team
and other functional teams for the
new generation Alaris Plus infusion
pumps.
• Cleaned text by removing stop words, hashtags, and name tags and built embedding NLP model with
bidirectional LSTM and GRU in TensorFlow.
• Achieves up to 90% accuracy in sentiment classification and 78% accuracy in intensity level classification. Salary Prediction Link
SEP2020
San Diego, California
• Engineers a predictor for HR to determine proper salary based on job descriptions to draw more potential
candidates.
• Cleaned and explored 1M job descriptions and
engineered new features. Fed processed data to
Random Forest and Gradient Boosting.
• Lifted the performance by 78% compare to baseline estimator.
Kaggle: Titanic Link
SEP2020
San Diego, California
• Predicts if passengers survived the shipwreck given 900 Titanic passengers’ boarding information.
• Automated ML model development using pipeline and model fine tuning using grid search with scikit-learn.
• XGBoost model scored 77% accuracy on Kaggle
leadership board.
WORK EXPERIENCE
Becton Dickinson
Embedded Software Engineer, R&D
JUL 2016–APR 2020
San Diego, California
Coordinated embedded team effectively with cross functional teams (product managers, software quality engineers, DevOps engineers, electrical engineers, global sales, etc.) on bedside infusion pumps development, and delivered new releases on a timely fashion.
Initiated multiple field issue investigations and successfully deployed solutions.
Initiated and designed multiple new features and upgrades in UNIX/Linux environment inspired from user feedbacks.
Contact this candidate