Chicago, IL University of Illinois at Chicago (UIC) Aug 2017 – May 2019

• Master of Science in Computer Science. (GPA-3.72)

• Coursework: Data Science, Machine Learning, Data Mining & Text Mining, Information Retrieval, Object Oriented Languages, Distributed Systems, Algorithms, Software Engineering, Advanced Software Engineering Hyderabad, India Chaitanya Bharathi Institute of Tech (CBIT) Aug 2012 - May 2016

• Bachelor of Engineering – Electrical and Electronics (GPA-3.5) Languages, OS and Technologies

• C, Python, Java, MATLAB, HTML, CSS, JavaScript, SQL, Hadoop, Solidity

• Windows, Mac

Academic Student Projects University of Illinois at Chicago Aug 2017 - Present Ethereum Blockchain (Solidity, Truffle, Ganache, Web3.js, HTML) github//CS-Courses-Blockchain

• Created an Ethereum blockchain containing CS course information in each block.

• Created a front-end web application with an option to add courses to the blockchain using Web3.js. Extended Vector Clock (Java, Multithreading, Arithmetic Precision) github//Encoded-Vector-Clock

• Identified how fast the EVC grows as a function of number of events executed by n processes in a distributed environment.

• Used Arithmetic Precision Library to store EVC and found errors introduced due to limited precision. Java Program Generator (Java) github//Java-Program-generator

• Programmed a generator that generates a syntactically correct but semantically meaningless java application.

• Created production rules that generate the application with input as a file that has parameters defined by the user. Mutation Testing (Java, Javassist) github//Mutation-Testing

• Manipulated Java bytecode leveraging structural reflection functionality provided by Javassist Library

• Experimented with concurrency and parallelism using the Java Executor framework. Chat Bot (Q&A) (Keras, Python, Pandas, NLTK) github//Chat-Bot-Q-A

• Created a Q&A chat bot which would give a YES/NO response given a short story and a question.

• Leveraged Facebook’s Babi dataset to train an RNN model using an LSTM layer.

• Trained the model using Google’s Tesla K80 GPU on 100 Epochs attaining an average accuracy of 82%. Aspect Term Analysis (Python, Scikit-Learn, Pandas, NLTK) github//Aspect-Term-Opinion-Target-Analysis

• Analyzed the Opinion (Sentiment) of the Aspect Term on review datasets using different classification techniques.

• Achieved average accuracy of 72.4% on Laptop review (Random Forrest) and 70.87% on Restaurant review (Linear SVC).

King County Real Estate Analysis (Python, Plotly, Pandas, Seaborn, Scikit-Learn) github//King-County-Real-Estate-Analysis

• Explored statistically significant features to analyze the real estate prices leveraging f-regression test on SelectKBest feature selection model.

• Attained 79% accuracy on price prediction models by fine tuning the hyperparameters using cross-validation. Election Party Prediction (Python, Plotly, Numpy, Pandas, Seaborn, Scikit-Learn) github//Election-Party-Prediction

• Pre-processed and explored the key features of State demographics and election result datasets.

• Leveraged classification and regression models to classify counties and predict votes. Spam E-mail Classification (MATLAB, MLE, Matrix Vectorization) github//Spam-Email-Classification

• Built a Logistic Regression model to find the maximum likelihood estimate of the sigmoidal spam probability function.

• Accomplished high test accuracies using gradient ascent optimization on MLE.

• Experimented with L2-regularization technique to get high test accuracies at the expense of low training accuracies Customer Churn Analysis (Python, Numpy, Pandas, Seaborn, Scikit-Learn) github//Customer-Churn

• Visualized correlation statistics of features responsible for customer churn using Seaborn plots.

• Achieved 86% F1-score using Random Forest classifier to classify if a customer would leave the service or not. Github: github.com/adarshjv20 LinkedIn: linkedin.com/in/adarshjv ADARSH JANAPAREDDY

ac8zts@r.postjobfree.com 773-***-****

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