Published on: Mar 3, 2016
Transcripts - NagaKarthikReddy_veerabhadra
NAGA KARTHIK REDDY VEERABHADRA
105 Bennington Hills Court,West Henrietta, NY 14586 | C: 5854654339 | firstname.lastname@example.org
I'm a Computer Vision and Machine Learning Graduate Researcher looking for Intern/Co-op/Full Time opportunities.
Master of Science:Electrical Engineering, GPA: 3.88/4 Exp. May’16
Rochester Institute of Technology Rochester, NY,USA
Coursework: Digital Image Processing, Machine Learning, Pattern Recognition, Image and Video compression, Digital
Signal Processing, Adaptive Signal Processing, Probability and Stochastic Processes.
Bachelor of Engineering: Electrical Engineering, GPA: 8.32/10 May’14
K.L University AP,India
Graduate Research Assistant Aug’15 - Present
Rochester Institute of Technology
Hierarchical decomposition of Large Deep Networks: Rochester, NY
Developed a new deep neural network architecture to classify arbitrary number of classes which selects a mini deep net
based on confusion between classes, and increased parallelism in the architecture while reducing number of computations
drastically and increased classification accuracy.
Data Logger Testing Intern Aug’13 - Dec’13
Efftronics Systems Pvt Ltd Vijayawada,AP
Worked on ARM (LPC23xx) Controller. Designed and involved in development of light Intensity controller. Hands on
experience on testing the Hardware on Printed Circuit Boards.
1. Rich feature hierarchies for accurate object detection using R-CNN features Jan’16
Instead of using block wise oriented histograms like SIFT and HOG, in this project Convolution neural networks are used to
extract the features of several regions of interest in an image. A fixed length feature vector is extracted for several regions in
an image using pre trained CNN and then classify each region with category specific linear SVMs.
2. Understanding Image Interpolation in the context of Convolutional Neural Networks Aug’15 – Dec’15
Using different interpolation techniques like Nearest Neighbor, Bilinear, Bicubic, Lanczos and comparing their results using
state of art convolutional neural network architectures. In this way the performance of interpolation is quantified.
3. Advanced capturing and interpolation of Color Information Aug’15 – Dec’15
Placed Bayer filter over the pixel sensors of an image sensor to capture color information. Use this information to interpolate
RGB at each pixel location.
4. Hand Written Digits Classification on MNIST Dataset May’15
A 10 class classification problem solved using both k-Nearest Neighbors and Neural Networks algorithms. Achieved 92%
accuracy using k-Nearest Neighbors & 88.5% using Neural Networks.
5. Study of Adaptive Cruise Control System Mar’14
Studied the application of adaptive cruise control systems and understand its importance in automotive industries. Studied
about various models, sensors and several aspects used while building the system.
6. LED Intensity Control Oct’13
Designed Test zig for LED intensity control with blue tooth. Using LPC2378 controller which works basically on the intensity
controlled by the user using his mobile. Simulator/compiler: keil U vision 4
MATLAB, C, C++, Caffe, Open CV, Embedded C Keil U vision 4, ARM EMULATOR, Linux