About me

An analytical thinker with a passion for technology and experience in diverse fields of science. A fast-learner who is curious, cooperative and conscientious. Proficient in statistics, machine learning and deep learning. I see myself working in a role with a steep learning curve where I can use my skills to provide actionable inputs.



Aug. 2021  - May 2023

Georgia Institute of Technology USA

MS in Computational Science and Engineering with a focus on Machine Learning and Data Science   Atlanta, Georgia

Aug. 2017  - May 2021    

Indian Institute of Technology India

BTech. in Engineering Physics   Hyderabad, TelanganaBTech. in Electrical Engineering


May 2022 - Aug. 2022

Lark Health

Data Science Intern
Sept. 2021  - May 2022

Georgia Institute of Technology - AdityaLab

Graduate Student AssistantGraduate Research Assistant
Aug. 2020 -Jan. 2021


Computer Vision Intern
Jan. 2019  - Dec. 2020

Indian Institute of Technology, Hyderabad

Undergraduate Research AssistantUndergraduate Teaching Assistant



Python C C++ Julia Bash FORTRAN


MySQL PostgreSQL


 Databricks    MATLAB    LaTeX    Tableau Grafana Sisense


Pytorch Pandas MPI OpenCV Scikit-learn    PySpark

Version Control 



AWS (ML tools)    Snowflake


July 6, 2022

A Primer to Gaussian Processes for Regression

Talk at Lark Health - Mountain view, California 

The motivation for this talk was to discuss effective data imputation strategies. Electronic Healthcare Records (EHRs) are inherently sparse in nature due to inconsistent sampling of parameters (like Weight, Hemoglobin level etc). Gaussian processes (GPs) are effective in making such data rich. This can have a strong effect on subsequent analyses (Sickness forecasting etc.). GPs have the utility of providing confidence bounds on prediction in addition to being more effective for smaller data. The original work by C. E. Rasmussen & C. K. I. Williams was summarized with the help of an intuitive guide by Jie Wang. 

April 22, 2022

Deep learning of contagion dynamics on complex networks

Talk at AdityaLab - Georgia Institute of Technology, College of Computing, Atlanta, Georgia

Work by Murpy et al. Forecasting the evolution of contagion dynamics is still an open problem to which mechanistic models only offer a partial answer. To remain mathematically or computationally tractable, these models must rely on simplifying assumptions, thereby limiting the quantitative accuracy of their predictions and the complexity of the dynamics they can model. The paper proposes a complementary approach based on deep learning where effective local mechanisms governing a dynamic on a network are learned from time series data. Proposed GNN architecture makes very few assumptions about the dynamics, and they demonstrate its accuracy using different contagion dynamics of increasing complexity. By allowing simulations on arbitrary network structures, their approach makes it possible to explore the properties of the learned dynamics beyond the training data. Finally, they illustrate the applicability of our approach using real data of the COVID-19 outbreak in Spain. The results demonstrate how deep learning offers a new and complementary perspective to build effective models of contagion dynamics on networks.

November 12, 2021

A Systematic Survey on Deep Generative Models for Graph Generation

Talk at AdityaLab - Georgia Institute of Technology, College of Computing, Atlanta, Georgia

Work by Guo et al. Graphs are important data representations for describing objects and their relationships, which appear in a wide diversity of real-world scenarios. As one critical problem in this area, graph generation considers learning the distributions of given graphs and generating more novel graphs. Owing to its wide range of applications, generative models for graphs have a rich history, which, however, are traditionally hand-crafted and only capable of modeling a few statistical properties of graphs. Recent advances in deep generative models for graph generation is an important step towards improving the fidelity of generated graphs and paves the way for new kinds of applications. This article provides an extensive overview of the literature in the field of deep generative models for graph generation.

February 18, 2021

Generalized Lomb-Scargle analysis of I and Tc decay rate measurements

Talk at the 39th meeting of the Astronomical Society of India, Hyderabad, India

Work by Gururajan et al. Poster presentation at the 39th ASI conference. Abstract is as follows : We apply the generalized Lomb-Scargle periodogram to the ^{123}I and ^{99m}Tc decay rate measurements based on data taken at the Bronson Methodist Hospital. The aim of this exercise was to carry out an independent search for sinusoidal modulation for these radio-nuclei (to complement the analysis in Borrello et al) at frequencies for which other radio-nuclei have shown periodicities. We do not find evidence for such a modulation at any frequencies, including annual modulation or at frequencies associated with solar rotation. Our analysis codes and datasets have been made publicly available.


Generalized Lomb–Scargle analysis of 123I and 99mTc decay rate measurements

G. Gururajan and S. Desai, The European Physical Journal C volume 80, Article number: 1071 (2020)