Resume/CV
Email | Linkedin | Website | Github
Education
September 2021 - May 2023
Carnegie Mellon University, Pittsburgh, USA
School of Computer Science
Master of Science in Computational Biology
August 2016 - May 2020
PES University, Bangalore, India
Bachelor of Technology in Biotechnology (Major)
Bachelor of Technology in Computer Science (Minor)
Skills
Programming Languages
Python, Go, C, Java, R, MATLAB
Frameworks
Pytorch, Tensorflow, Keras, Skorch, Scikit-learn
Web Development
HTML, Javascript, CSS
Database Management
PostgreSQL
Tools
Git, AWS Cloud
Experience
January 2022 - Present
Research Assistant
Lee Lab, Carnegie Mellon University, Pittsburgh, USA
- Analyzing macaque EEG data to evaluate evolution of neural firingrates, sparsity and noise correlations.
- Identifying the application of Community Structures on the data.
- Utilizing predictive coding to model spatiotemporal patterns in the brain.
June 2020 - July 2021
Project Assistant
Cognition Lab, Indian Institute of Science, Bangalore, India
- Designed and developed 2-ADC behavior tasks for investigating the effect of cognitive load on attention.
- Ran initial cohort of subjects to analyze and quantify behavior.
- This is still a work-in-progress project at the lab, and the developed task design and scripts are currently being extended upon by the related PhD student.
December 2019 - May 2020
Research Intern
Cognition Lab, Indian Institute of Science, Bangalore, India
- Worked with multiple PhD students to conduct experimental tasks and decode EEG signal data.
- Pioneered the use of web-based experiments to remotely conduct experiments during COVID-19.
- Worked on my undergraduate thesis titled “Selection in Visual Working Memory due to Exogenous Visuospatial Attention”, by employing a 4-AFC task.
June 2018 - August 2018
Software Developer, Intern
CGI, Bangalore, India
- Developed a spelling corrector for a ChatBoth platform using LSTM recurrent neural networks.
- Employed sequence-to-sequence and character-based encoder-decoder model.
Projects
March 2023 - Present
An Active Learning Approach to Predicting Potency of Small Molecules for Antibacterial Screening
Carnegie Mellon University, Pittsburgh, USA
- Predicting potency of small molecules on Burkholderia cenocepacia using active machine learning techniques, using SMILES data.
- Implementation of active machine learning techniques like Active Deep Reinforcement Learning, A2 algorithm, Expected Model Change and Query-by-committee selection strategies.
March 2023 - Present
A Comparative Study and Improvement of Models Predicting Transcription Start Sites
Carnegie Mellon University, Pittsburgh, USA
- Comparison and improvement of latest TSS prediction pipelines, for better annotation of transcripts.
- Implementation of techniques like ADAPT-CAGE, DeepTSS and DeeReCT-TSS and integrating multiple sequence data types like CAGE-seq, RNA-seq, ChIP-seq, and DNase-seq for each model.
- Usage of STAR, HISTAT2 and MACS during pre-procesing of RNA-seq and CAGE-seq data.
September 2022 - November 2022
A single cell RNA-seq based aging clock for human neurons
Carnegie Mellon University, Pittsburgh, USA
- Identified latent representations for efficient prediction of patient age.
- Developed Poisson Variational Autoencoder for efficient age prediction, which beat the baseline Multilayer Perceptron model and Standard VAE model performance with around 36% improvement.
October 2022 - November 2022
Common Signatures between Severe Asthma and Lung Cancer
Carnegie Mellon University, Pittsburgh, USA
- Implemented Gene Set Enrichment Analysis on data from GEO, to identify common signatures.
- Validated results by classifying a separate lung cancer dataset using Decision Trees.
September 2022 - November 2022
Simulating sequence evolution for in-silico experimentation
Carnegie Mellon University, Pittsburgh, USA
- Developed a simulation of bacterial transcription, translation, fitness, selection and mutation.
- This is a team project with five members, each responsible for one of the five steps mentioned above.
- Responsible for the mutation step, where a mutation is programmatically induced at random positions, sampled from a uniform or binomial distribution.
February 2022 - March 2022
Classification of Glioma
Carnegie Mellon University, Pittsburgh, USA
- Processed gene expression data from TCGA to classify glioma subtypes.
- Employed Gaussian Naive Bayes and Support Vector Machine to model the data
- Validated the results with a 5-fold cross-validation and achieved over 85% accuracy.
October 2021 - November 2021
Natural Selection Simulator
Carnegie Mellon University, Pittsburgh, USA
- Developed a graphical game-like simulator to observe selection over several generations.
March 2017 - April 2017
Robot arm using Arduino
PES University, Bangalore, India
- Equipped the robot arm with a camera, which was programmed to identify an object of a specific color and dimension with the help of OpenCV library.
- Upon detection, the arm grabs the object.
September 2016 - October 2016
Hit-and-score graphical game
PES University, Bangalore, India
- Designed the graphics of the game on Python using the Pygame library.
- Developed a shooting game that requires the player to shoot a moving object on the screen to increase the score.
Relevant Coursework
Carnegie Mellon University
10617: Intermediate Deep Learning
10725: Convex Optimization
02613: Algorithms & Advanced Data Structures
02620: Machine Learning
02750: Automation Science
03713: Bioinformatics Data Practicum
PES University
Data Structures
Design and Analysis of Algorithms
Database Management Systems
Operating Systems
Molecular Modeling and Simulation
