Skanda Sridharan Bharadwaj

Master of Science (Data Science)

West Lafayette, Indiana, US +1-765-***-****

adms30@r.postjobfree.com

https://www.linkedin.com/in/skandasbharadwaj/

EDUCATION

Master of Science (Major: Data Science)

Purdue University, West Lafayette GPA – 3.79/4 Graduation: May 2021 DATA SCIENTIST PROFILE

Offer unique combination of experience and knowledge in machine learning, deep learning, statistics and optimization to perform my duties as a Data Scientist at maximum efficiency. I am flexible and available to work just about any time starting immediately. SKILL EMPHASIS

• Deep Learning Algorithms (Convolutional

LSTM Neural Networks, Autoencoders,

Generative Adversarial Networks etc. using

Keras Python)

• Machine Learning (Linear and Logistic

Regression, Neural Networks, etc.)

• Statistics and Probability, Linear Algebra

• Machine learning Certification (Stanford)

• SQL for data science Certification

• Optimization techniques and Algorithms

• Data Imputation (MICE, GAN, KNN)

• Python, Python NumPy, Pandas Library

• AGILE Methodology

• MATLAB, Octave

Professional Experience and Projects

Data Mine Project at Caterpillar Inc. (CAT Digital) Aug 2020 – Apr 2021

• Worked on a project involving data imputation in a multi variate time series data frame and critical event prediction modelling using python.

• Was actively involved in data profiling and Exploratory Data Analysis (EDA), built correlation matrices for the entire data set.

• Implemented MICE, KNN and GAN to impute data using python in AWS.

• Worked on imputed model validation and finalized the model. Certification in Machine Learning (offered by STANFORD) May 2020 – July 2020

• Programmed one-vs-all logistic regression and neural networks to recognize hand-written digits. Similarly, implemented the back-propagation algorithm for neural networks and applied it to the task of hand-written digit recognition.

• Used support vector machines (SVMs) to build a spam classifier.

• Implemented the K-means clustering algorithm and applied it to compress an image.

• Implemented the anomaly detection algorithm and applied it to detect failing servers on a network.

• Scored an aggregate of 98% in the certification course. Deep Learning Projects Aug 2020 – Dec 2020

• Implemented a CNN to classify fashion articles from the Fashion-MNIST dataset.

• Implemented a CLDNN to classify the modulation of time-series data.

• Implemented various types of autoencoder neural networks using the MNIST Database of Handwritten Digits.

• Implemented a GAN capable of generating MNIST-like hand-written digits.

• Implemented attacks (Fast Gradient Sign Method, Projected Gradient Descent, Carlini & Wagner Attack and DeepFool) and defenses (Denoising Autoencoders, Adversarial Training, Dimensionality Reduction and Adversarial Detection) on a classifier trained to classify hand-written digits from the MNIST database and tested their accuracy.

Wing Optimization for DBF 2018 Competition Aircraft Aug 2019 – Dec 2019

• Optimized the wing of the DBF competition aircraft in MATLAB using multi-disciplinary optimization algorithms.

• Multi objective problem was solved using Goal attainment and sequential quadratic programming

(SQP).

• Pareto front was developed and the results obtained were verified with practical flight test results.