Sai Teja Duthuluri is a computer science graduate with a M.S. from Georgia Tech and B.Tech from National Institute of Technology Karnataka. He has work experience as a research assistant at Georgia Tech developing machine learning algorithms and as a software developer intern at Pindrop and Oracle India. His technical skills include Python, C/C++, Ruby, and machine learning techniques like PCA, LDA, and particle/Kalman filters. He has also completed projects in medical records, music recommendation, visual SLAM, and music genre classification.

1. Sai Teja Duthuluri
dsaiteja1908@gmail.com
(404) 953 – 9489
EDUCATION
M.S. in Computer Science Aug 2015 – Dec 2016
Georgia Institute of Technology CGPA: 3.87/4.0
B.Tech. in Computer Engineering Jul 2008 – May 2012
National Institute of Technology Karnataka, India CGPA: 8.23/10.0
TECHNICAL SKILLS
Advanced: Python, C, C++, Ruby
Proficient: Perl, Shell scripting, PHP
Technologies: REST, Django, Selenium, MySQL, Cucumber, RSpec
MACHINE LEARNING SKILLS
 Experienced in CV techniques like Harris corner detection, SIFT, HoG, RANSAC, Bag of Words, Viola-Jones face detection
 Experienced in applying PCA, LDA, NB-Classifier, Ensemble learning, clustering techniques towards image classification
 Implemented supervised learning and unsupervised learning methods, tested on public datasets
 Implemented particle filters, Kalman filters for robot localization and online learning algorithms for real-time data classification
WORK EXPERIENCE
Graduate Research Assistant Aug 2016 – Dec 2016
Georgia Institute of Technology
 Maintain and develop new features for an open-source Lab Information System, developed in PHP with MySQL and Apache
backend
 Currently deployed in multiple African countries
Software Developer Intern May 2016 – Jul 2016
Pindrop
 Developed a Ruby-based framework that enabled UI and back-end test automation
 Thoroughly followed Agile philosophy, Test-Driven Development, Behavior-Driven Development principles
Graduate Research Assistant Jan 2016 – May 2016
Georgia Institute of Technology
 Isolated SSVEP EEG brain signals using in-ear electrodes
 Developed end-to-end pipeline for signal acquisition, processing, peak detection and classification in Python
Software Developer II Jun 2012 – Jun 2015
Oracle India
 Developed test framework and tools for OVM for Sparc, a virtualization product for Oracle Sparc Enterprise Servers
 Developed test framework for power management features of the Oracle Sparc Enterprise Servers in C and Perl
Software Engineering Intern May 2011 – Jul 2011
Cisco India
 Prototyped a patch for SystemTap which enables placing hardware watchpoints for user-space variables, using the perf
subsystem in Linux
PROJECTS
ConCat – Electronic Patient Record System PHP, HTML
 Developed a PhoneGap application for android and a website for tracking medical records of congenital cataract patients
 Currently deployed in African hospitals
 Developed REST APIs and deployed on AWS
Music Recommendation System Python, PHP
 Aggregated low-level song features into song signature and compared songs using Earth Mover’s Distance (EMD)
 Developed the data processing, feature extraction and similarity metric pipeline for the system
Loop Detection in Visual SLAM Python, Caffe
 Fine-tuned existing models through a deep learning Siamese pipeline and custom robot image data
 Used the tuned model to build a binary classifier to detect loops in robot path using only the images captured (~90% accuracy)
Music Genre Classification MATLAB
 Classified of any audio song file into a musical genre (Rock, Pop, Classical, Jazz, and Metal) using only the low-level features
(~83% accuracy)
 Used unsupervised learning methods K-Means, Self-Organization Maps (SOM)
PUBLICATIONS
 Senthilnath, J., Karnwal, Nitin & Sai Teja, D. (2014). “Crop type classification based on clonal selection algorithm for high
resolution satellite image.” International Journal of Image, Graphics and Signal Processing, MECS, Vol. 6(9), pp. 11-19