Sign in

Electrical Engineering Data

New York, New York, United States
May 19, 2017

Contact this candidate


Tell: 929-***-****, Email:


A motivated, quick-learning individual seeking to contribute strong problem solving, data analysis, model building skills as well as interdisciplinary backgrounds (software and hardware) in industry. EDUCATION

M.S in Data Science (GPA: 3.96/4.0) May 2017

University of Rochester, Rochester, NY

Course: Deep Learning and graph model, Data mining, DataBase Systems, Statistical Methods, Random Process, Probabilistic Model Inference...

M.S in Electrical Engineering (EDA (Electronic Design Automation) Lab) 2014 National Taiwan University, Taipei, Taiwan(ROC)

B.A in Electrical Engineering 2009

National Taiwan University, Taipei, Taiwan(ROC)


Parade Technologies, Ltd Sept 2014 - May 2016

Analog Circuit Design Research and Development

Analyze and preprocess large-scale transmission data.

Auto-acquisition,analysis and prediction of signal data, stochastic analysis of data.

Design and develop analog circuits, i.e high speed data drivers, bandgap, LDO, OSC.

De ne models and methodologies to increase accuracy, productivity and quality.

Evaluate and optimize performance through analysis, simulation and veri cation. SKILLS AND INTERESTS

Expertise in python, R, MATLAB and packages Tensorow, sklearn for preprocessing, analysis and prediction.

Decent in database programming languages (Spark, SQL, Hive, Hadoop, Mongodb) for data processing.

Pro cient in data mining and advanced machine learning skills.

Comfortable with analyzing large datasets and provide quantitative trends.

Experienced in developing mathematical models to predict patterns.

Expertize in hardware design, simulation, veri cation and production.

Strong communication and written skills.

Outstanding problem-solving skills to help bypass technical and business issues. PROJECTS

Time Series Prediction Based on Dynamic Probabilistic Models (Python, R): Auto-separate time sequential data into multiple Probabilistic Models changing with time to predict Time Sequential data

Deep Learning on Music Genre Prediction and Generation(Python, Tensor

ow): CNN and bidirectional LSTM to predict and generate music genre

TSEWSI (TW Index) Prediction Based on Deep Learning-Parallel LSTM (Python, Tensor


Online Retail Products Database Systems (SQL, PHP, Spark): A web interface for online shopping allowing users and administrators to access and retrieve product information in MySQL.

Soleo Industry Project on Phone Ads Prediction (Python, Tableau): Used time and space features to recommend Ads to customers and predict their categories. Achieves 6 times better.

Abnormal Electricity Usage Detection Based on Time-Series (Python, Tableau): Extracted over 200 time-related features to predict abnormal electricity usage of an identity with accuracy over 0.85.

Human Action Recognition via Mobile Sensors (Python, R, Tableau): Used mobile sensor signals to identify behavior. Accuracy over 0.85

Parade Tech Commercial Product on Driver Integrated Systems (in Parade Tech): Participated in design of high-speed drivers, IOs and power regulators of Parade Tech commercial projects and products.

MOS Subthreshold Mismatch Models (in Parade Tech):

Operational Ampli er Phase-Inversion Model (in Parade Tech):

Dissertations (Taped-Out): "An Area E cient High-Speed SAR ADC in 0.18um"

Contact this candidate