Rajkumar

Conjeevaram

Mohan

Data Scientist

As a graduate student in Data Science, I possess extensive knowledge of the sub- ject and have hands-on experience in implementing Statistical Machine Learning and Deep Learning models. Throughout my academic journey, I have undertaken various challenging projects covering domains such as finance, healthcare, and more. I leverage my expertise in programming languages like Python and R, along with my proficiency in SQL, to design and implement predictive models, conduct exploratory data analysis, and develop data-driven solutions for complex prob- lems. Apart from Machine Learning, I am also passionate about other branches of AI and constantly strive to learn more. I approach challenges with enthusiasm and take pride in my ability to adapt quickly to new technologies and environments. Rajkumar, who is passionate about the subject, and is self-driven, would prove to be an exceptional candidate for the Data Scientist position. Personal Info

Phone

202-***-****

E-mail

adzgzx@r.postjobfree.com

Date of birth

01-18-1990

Skills

R

Python

Deep Learning

PyTorch

TensorFlow

Numpy

Scikit-Learn

Tableau

AWS

GCP

Analytics

MySQL

MongoDB

Neo4j

Linux

Algorithms

Experience

Student Technical Support Specialist

George Washington University / 06/2022 - 12/2022

Served as a Data Analyst, whose primary role involved analyzing data that had complex relationships, cleansing it, and creating interactive visualizations that effectively conveyed important information about the inter-group relation- ships among militant organizations worldwide (e.g. how they evolved over time). Through my work, I improved the system's efficiency by approximately 30%, as measured by the latency in loading different visualizations. Complex visualization of the network was one of the primary objectives of the work. This was a United States Department of Defense project funded by the DHS. Education

MS Data Science

George Washington University / 12/2021 - Present

Washington, DC

Notable courses: Machine Learning, Time Series Analysis & Modeling, Deep Learning, and Natural Language Processing.

MS Artificial Intelligence

Imperial College London / 07/2015 - 10/2017

London

Notable courses: Advanced Statistical Machine Learning, Computer Vision, and Intelligent Data Analysis & Probabilistic Inferences. BSc (Hons) Computer Information Systems

University of Liverpool / 07/2010 - 06/2013

Liverpool

First Class Honors Degree

Certifications

TensorFlow Developer

Coursera / 10/2020 - Present

Natural Language Processing

Coursera / 12/2020 - Present

Practical Time Series Analysis

Coursera / 11/2021 - Present

Machine Learning

Statistics

NoSQL

Docker

Git

SciPy

NumPy

Jupyter

Pandas

Matplotlib

Seaborn

Excel

Keras

PySpark

Links

LinkedIn

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

GitHub

https://www.github.com/rajkumarcm

Technical Projects

Brain Tumor Segmentation

03/2023 - 04/2023

Washington, DC

Classified pixel-wise tumorous cells in the brain, and obtained 0.78 Jaccard score using 3D UResNet.

Challenges and Solutions:

1. High-dimensional images caused memory problems (solution: Grouped con- volution)

2. Low performance (solution: Residual blocks)

Brain Tumor Recognition

10/2022 - 11/2022

Washington, DC

Recognized presence of tumor in the brain using large scale Resnet, and achieved 94% F1-macro on test set.

US Air Pollution Prediction and Forecast

09/2022 - 11/2022

Washington, DC

Estimated the order of the AR, and MA processes to predict and forecast the Carbon Monoxide (CO) Air quality index, and achieved 68.11% R2 value using ARIMA, and Holt-Winters Seasonal Method.

Challenges and Solutions:

1. Missing Data - (Seasonal Naive method was used to replace the missing values) 2. No adequate information to predict CO AQI - (Used weather information to assist in predicting CO AQI)

3. Seasonal data - (Log transformation followed by 1st order differencing to make it stationary)

4. Mix of AR and MA processes - (Having only one of these makes it easier to trace the order of the process through its respective plot. However, when there is a mix of processes that generated the data, I had to resort to the GPAC method that help determine the mix order)

Credit Card Default Prediction

03/2022 - 04/2022

Washington, DC

Classified Taiwanese bank clients who have the potential to default, with 0.813 F1-score using Decision Tree.

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