bigolhonkey/portfolio

Suhas Gupta's Portfolio

Suhas Gupta Portfolio

(www.linkedin.com/in/guptasuhas)

Lead electrical engineer at Apple Inc. designing state of the art hardware for Macbook Pros.

• Delivered three new MacBook Pro products in 2018 by leading cross functional hardware development. Efficiently managed design and operations of failure analysis and directed engineering personnel to meet tight project deadlines and successfully deliver products to market.
• Lead cross-functional effort to automate image analysis for display defect detection through computer vision and machine learning algorithm development. This effort is intended to improve the display product development efficiency by 5x through elimination of qualitative binning of display performance by human operators. Proficient in developing code in a team environment using Git.
• Developed a portable automation library for data processing and analysis in MATLAB and Python that improved electrical module validation efficiency by 3x.

Design Engineer and Semiconductor Technologist @ Intel Corporation (previous)

• Led design of semiconductor devices and circuits for mixed signal IP development delivering three novel process nodes and enabled 2x manufacturing cost scaling per year.
• Drove technology definition through complex wafer data analysis consisting of defect signatures from more than a billion on-chip transistors and interconnect segments.
• Developed simulation models through OLS regression on high volume silicon data achieving ±5% silicon-to-model correlation across 3𝞂 sample distribution.
• Developed innovative circuit using gradient descent regression analysis to enable 20% silicon area scaling in 3D multi CPU chip architectures.

Student of Master's in Information and Data Science (MIDS Class of 2020) at UC Berkeley, CA

Previous education:

• Masters in Electrical and Computer Engineering (University of Florida, Gainesville)

MIDS@UC Berkeley

Projects

Project Title	Brief Description
Regression Modeling of County Crime Data	Regression modeling and statistical inference on crime data to assist a political campaign
Digit Classification	Developed an image recognition system for classifying digits in MNIST database
Text Classification	Trained classifiers to distinguish between news topics based on the texts in 20 newgroup posts
San Francisco Crime Classification	Kaggle competition for crime category classification given crime location and time

Courses

• Research design and applications for data and analysis (W201)
• Statistics and probability for data science (W203)
• Fundamentals of data engineering (W205)
• Applied machine learning (W207)