Python, C/C++, Machine Learning, Deep Learning, Keras, TensorFlow

TANMAY GUPTA

********@***.*** +1-480-***-**** Tempe, AZ linkedin.com/in/tanmay-gupta182

SUMMARY

Master’s graduate experienced in developing algorithmic Signal Processing and Machine Learning based solutions using Python, SQL, R, C/C++, and MATLAB. Experienced in implementing Natural Language Processing (NLP) and Deep Learning applications. Professional experience as an analyst with a demonstrated history of synthesizing insights from data. Seeking full-time opportunities starting July 2020.

EDUCATION

Master of Science in Engineering, Electrical Engineering GPA: 3.88/4

Arizona State University, Tempe, AZ May 2020

Courses: Statistical Machine Learning, Artificial Neural Computation, Random Signal Theory, Digital Signal Processing, Computer Vision.

Bachelor of Engineering, Instrumentation and Control Engineering GPA: 3.62/4

Netaji Subhas Institute of Technology (University of Delhi), New Delhi, India May 2017

TECHNICAL SKILLS

Programming/Engineering tools: Python, MATLAB, C++, Jupyter Notebook, Java, Embedded C, Visual Studio, Android Studio

Frameworks: Spark, TensorFlow, Keras, PyTorch, Scikit-Learn, NumPy Pandas, Hadoop, OpenCV

Others: SQL, R, Alteryx, Jira, Tableau, Git, Microsoft Office Suite, TI MSP430

PROFESSIONAL EXPERIENCE

Research Data Analyst Aide, Office of the University Provost - ASU, Tempe, AZ Dec 2019 – May 2020

•Responsible to analyze the impact of strategies aimed at improving the success rate of Arizona State University students.

•Consolidated, cleaned, and transformed data to prepare it for reporting and analysis.

•Analyzed the engagement effectiveness of “Sunny” (third-party SMS chatbot) with over 20,000 Arizona State University freshmen. Independently identified and explored promising areas of research using chatbot data to measure the impact of Sunny.

•Performed sentiment analysis on Sunny’s SMS conversations with users using Natural Language Toolkit (NLTK) Vader sentiment analysis tool in Python to identify the user acceptance of Sunny and its services.

•Pulled and processed the student demographic data from the ASU database using Oracle SQL Developer and Alteryx.

Data Analytics Consultant, Fractal Analytics, Gurugram, India Jun 2017 – Jul 2018

•Developed and deployed machine learning based models to produce data-driven solutions to the clients’ business needs.

•Built an unsupervised modeling pipeline to create clusters of high-risk targets by processing multiple databases of 300 million individuals. Clusters were created based on existing and derived behavioral metrics using Spark SQL and PySpark.

•Optimized the investment strategy by building a multivariate linear regression model on revenue versus marketing activity for a US-based Fortune 500 client in collaboration with their Data Science team. The new strategy yielded an increase of 23.5 million USD (9%) in 2018 Quarter 4 revenues versus 2017. Built the regression model on R.

Machine Learning Research Assistant, Netaji Subhas Institute of Technology, New Delhi, India Oct 2016 – May 2017

•Implemented a power spectrum feature extraction algorithm from publicly available electromyogram (EMG) signals data of multiple movement classes being performed by amputees. Derived the features using MATLAB.

•Built a Deep Neural Network classifier for movement classification on MATLAB. Achieved an accuracy of 94.7%.

•“EMG Pattern Classification using Neural Networks”. Published in October 2017. First author among a four-person team. [LINK]

ACADEMIC PROJECTS

Deep CNN based HDR Image Reconstruction using Single Exposure Image Spring 2020

•Executed a Deep Convolutional Neural Network based algorithm to reconstruct a high dynamic range (HDR) image from a single exposure low dynamic range (LDR) image of the scene instead of a stack of multiple exposure images.

•Trained the network on HDR-LDR image pairs using TensorFlow, TensorLayer and OpenCV libraries in Python.

•Performed data augmentation (random cropping/flipping) using C++ to increase size of training HDR-LDR image pairs.

Phoneme Classification and Speaker Recognition Fall 2019

•Derived MFCC features for individual phonemes from the TIMIT speech corpus using MATLAB.

•Built a Deep Neural Network classifier using Keras on Python to classify the phonemes and speakers.

•Obtained 91% phoneme classification accuracy for 6 broad phoneme classes and 87% speaker recognition accuracy.

Double Moon Classification Problem Fall 2019

•Solved this binary classification problem composed of non-linearly separable classes using Logistic Regression, Neural Network, and Support Vector Machine (SVM) classifiers using Scikit-learn library to compare their classification accuracy.

Study and Implementation of Self-Driving Car using Reinforcement Learning Spring 2019

•Implemented a self-driving car using PyTorch based Deep Q-Learning for Reinforcement Learning framework.

•The car could navigate user-generated paths of varying complexities to reach its assigned destination.

Stock Price Prediction using Long Short-Term Memory (LSTM) Model Spring 2019

•Modelled an LSTM network using Keras on Python to predict the future value of the stock price of a company.

•Achieved a prediction R2 score of 99%. Used stock price data of a real organization obtained from Kaggle.

Adaptive Noise Cancellation System for Speech Enhancement Fall 2018

•Coded an Adaptive Noise Cancellation filter system on MATLAB to obtain intelligible speech from a pre-recorded real-life noisy conversation. The system improved SNR by 22 decibels and resulted in interpretable speech.

•Digital FIR Filter coefficients are updated using the Least Mean Squares algorithm, a gradient descent optimization method.

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