1. RAGHU JAGADEESHA

807 College Avenue, Apt
7, Clemson, SC-29631
 raghu743@gmail.com
 (864)-650-3630
 raghujagadeesha
 RaghuJagadeesha
EDUCATION
Clemson University
MS Computer Engineering
January 2017 GPA: 3.53/4.0
BMS College Of
Engineering
BE Instrumentation Technology
July 2011 GPA: 76.20%
SKILLS
C++
C
Java
Python
Matlab
CUDA
Blepo(OpenCV)
Hadoop(Map-reduce)
Machine Learning
Deep Learning
RELEVANT COURSES
Introduction to Computer Vision
Image Processing
Pattern Recognition
Design and Analysis of Algorithms
Data-Structures
Software Design
Information Retrieval
Artificial Neural Networks
Machine Learning (Coursera)
Convolutional Neural Networks for
Visual Recognition
EMPLOYMENT
SAMSUNG R&D INSTITUTE INDIA-BANGALORE (SRIB) Bangalore
Senior Software Engineer Mar 2012 to Jul 2014
Senior Software Engineer in Kernel Development Team. Technologies: C, C++
• Implemented display drivers at kernel and Boot loader level for Samsung
smartphones powered by Qualcomm’s Snapdragon (MSM) chipsets.
• Independently handled android OS upgrade activities of display modules in multiple
variants and chipsets, fixed several critical display issues at kernel, Boat loader and
HAL (Hardware abstraction layer) in various smartphones.
• Developed a Ram Dump parser tool for debugging the display issues with features
like graphical representation of different function calls. This saved approximately 30%
of time in fixing several complex issues.
• Worked on display bring up on new line of Qualcomm chipsets used in latest models
of Samsung smart phones and tablets at Samsung HQ in Seoul-South Korea.
CLEMSON UNIVERSITY Clemson, South Carolina
Graduate Research Assistant Jan 2016 to Current
Study on detection and classification of epileptic transients in the EEG signals from
Hilbert Huang Transform derived features and multilayer RBF neural network
classifiers.
PROJECTS
INFORMATION RETRIEVAL
Implemented text classification search engine on Reuter's news data. Implemented
different indexing and retrieval techniques in Hadoop (Map-reduce) to reduce latency
in processing large data.
2D GAME DESIGN
Designed and developed data driven 2d game engine using C++ and SDL libraries. The
game was designed with object oriented approach incorporating few design patterns
like singleton, abstract factory, flyweight, observer and object pool patterns.
CUDA-MPI
Implemented canny edge detector and color histogram calculation using the CUDA
MPI hybrid model to optimize computation time of feature extraction, achieved a
speed up of 20 folds.
MACHINE LEARNING
Implemented (supervised learning) Naïve Bayes classifier, K-Nearest Neighbor, hyper-
planes (Ho-Kashyap), SVM, Softmax classifiers and (unsupervised learning) K-means
clustering, PCA. Built a basic spam classifier using SVM. Implemented a basic movie
rating recommender system using collaborative filtering.
COMPUTER VISION AND IMAGE PROCESSING
Implemented fruit detection using morphological operations and moment derived
features, edge detection algorithms like Sobel and Canny edge detection, Watershed
segmentation and range image segmentation, stereo vision on rectified images, Lucas
Kanade feature tracking, optical character recognition and Active contours.
NEURAL NETWORKS AND DEEP LEARNING
Implemented K-Nearest neighbor, Softmax, SVM, feedforward and Convolutional
neural networks to classify CIFAR-10 data. Implemented batch normalization, dropouts
for deep neural networks. Implemented Image captioning using RNN, LSTMs.
Implemented feature inversion and simple deep dream. Implemented Hopfield nets to
solve map coloring problem (NP-Hard).