Software Engineer @ Warner Music Group
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Oklahoma State University
Master's Degree, electrical and computer engineering
January 1, 2014 – January 1, 2016
Dayananda Sagar College Of Engineering, Bangalore
Bachelor of Engineering (B.E.), instrumentation technology
January 1, 2009 – January 1, 2013
Warner Music Group
Software Engineer - III
October 1, 2025 – Present
Bengaluru, Karnataka, India
Amazon
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October 1, 2022 – September 1, 2024
Bengaluru, Karnataka, India
Soroco
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July 1, 2021 – October 1, 2022
Bengaluru, Karnataka, India
Gauss Surgical
Deep learning Engineer
March 1, 2019 – June 1, 2020
Menlo Park, California
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Deep learning Engineer
December 1, 2016 – January 1, 2019
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OTSAW Digital
Robotics software engineer
September 1, 2016 – November 1, 2016
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VI Solutions, Bangalore
Hardware Engineer- Intern
December 1, 2013 – May 1, 2014
Bengaluru Area, India
LEAST SIGNIFICANT BIT STEGANOGRAPHY and PIXEL GRID MAPPING
April 1, 2016 – Present
•Developed least significant bit replacement techniques(1-bit,2-bit and 3-bit) of steganography to hide an image into another and successively retrieving it back in MATLAB. •Watermarking of images to ensure the rightful ownership and quality of images under the threats of cyber security using D-sequences. *Introduced a new method called the grid pixel mapping to hide black and white images into black and white images. *The method splits the cover image pixels into grids and maps the grid pixels such that they form the majority of the bit that they hiding. *The method was benchmarked to successfully hide an image within cover image without being detected and also could be retrieved back without any distortion.
EXTREME LEARNING MACHINE
April 1, 2016 – Present
*New learning algorithm for single layer feed forward networks that works faster than the traditional backpropagation and with better generalization performance. *Extreme learning machine works faster due to the exclusion of the iterative optimization of weights and biases through gradient based methods. *ELM randomly assigns the input layer weights and biases and only the output layer biases are computed in accordance with the training samples through which the algorithm learns. *ELM was tested on various data sets to perform different tasks such as function approximation, regression, classification etc.
MONITORING THE HUMAN ACTIVITY IN A SMART HOME THROUGH NEURAL NETWORKS AND DATA ANALYSIS
March 1, 2016 – Present
• The main goal of the project was to monitor the location of a subject in real time by the using the data from various PIR sensors deployed in the smart home by designing supervised learning class of neural networks that can perform classification. • Fast classification neural networks was designed to perform classification operation under the supervised learning rule using the output data from the sensors on a data set in the order of 1000s, achieving high accuracy under the real-time demands.
Artificial Neural Networks
February 1, 2016 – Present
• Designed and implemented machine learning algorithms such as performance learning, perception rule, Widrow-Hoff rule and back-propagation algorithms on multilayer networks for regression, function approximation and classification applications. • Developed associative and competitive networks, including feature maps, radial basis networks and learning vector quantization under unsupervised learning to perform pattern recognition, clustering and prediction. • Analysis of neural network performance measures using Bayesian regularization and early stopping training methods, ensuring network generalization ability in MATLAB.
OPTIMIZATION APPLICATIONS
September 1, 2015 – Present
•Developed line search algorithms (golden section, newton secant, successive quadratic) and optimizers(leap frogging, incremental steepest descent, Hook-Jeeves, Levenberg-Marquardt) in VBA. •Compared and analyzed various optimizers, convergence criteria and objective functions spread over various applica-tions
MACHINE LEARNING and ARTIFICIAL INTELLIGENCE
April 1, 2015 – Present
• Implemented various supervised and unsupervised learning algorithms such as K-means clustering, Gaussian regression model and Bayesian non-parametric approach to determine the future state of a dynamic system. • Designed Reinforcement learning algorithms for artificially intelligent systems such as Markov Decision Processes, Value iteration, TBVI (Transition based Value Iteration), Q-learning, SARSA(state,action,reward,state,action) to determine the optimal path of a robot
DIGITAL SIGNAL PROCESSING
October 1, 2014 – Present
* Developing Butterworth,Chebyshev filters under given specifications in MATLAB. * Linear convolution of functions using DFT(digtial and fourier transform) and FFT(fast fourier transform) in MATLAB. * Determination of chirped frequency and aliasing of the signals and sampling rate conversion(up sampling and down sampling) in MATLAB.
DIGITAL AND AUTOMATIC CONTROL SYSTEMS
September 1, 2014 – Present
•Analysed the stability of a system through the methods of root locus, Bode plots, Nyquist plots and determined the controllability and observability of a system by applying Lyapunov stability analysis. •Designed the digital control systems using Root locus and Frequency based methods and pole placement by compen-sating for phase lead and phase lag by computing the transient and steady state response of a system.
VOICE MESSAGE PAGING FOR BLINDS
May 1, 2013 – Present
* The main goal of the project was to build a blind stick that page a voice message to the blind upon detection of obstacles and pits in their path. * IR sensors were used for sensing the obstacles and pits. * 8051 microcontroller was used as the mmain CPU of the system and voice was recorded and paged through speakers.
Machine Learning-Stanford university
Stanford University
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio is heavily focused on academic and personal research in AI, Machine Learning, and control systems, which aligns with a data-intensive role. However, the professional experience, while technical, is primarily in Software Development and Deep Learning Engineering, not explicitly Data Analyst roles. The diversity of projects showcases a strong technical curiosity and ability to learn, but the direct alignment with a 'Data Analyst' target role, which often requires strong business acumen, data visualization, and SQL/BI tool proficiency, is not explicitly demonstrated in the provided data. The projects are more research-oriented than business-application focused.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex, multi-faceted problems. The internship description mentions teamwork and time management, suggesting foundational soft skills. However, without specific behavioral assessment data or interview transcripts, a comprehensive evaluation of soft skills and operational fit is limited.