105 Bennington Hills Court,West Henrietta, NY 14586 | C: 5854654339 | naga.karthik555@gmail...
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Published on: Mar 3, 2016

Transcripts - NagaKarthikReddy_veerabhadra

  • 1. NAGA KARTHIK REDDY VEERABHADRA 105 Bennington Hills Court,West Henrietta, NY 14586 | C: 5854654339 | Objective I'm a Computer Vision and Machine Learning Graduate Researcher looking for Intern/Co-op/Full Time opportunities. Education 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 Professional Experience 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. Relevant Projects 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 Skills MATLAB, C, C++, Caffe, Open CV, Embedded C Keil U vision 4, ARM EMULATOR, Linux

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