Founder and CTO at Suchama AI || Founder Fellow at South Park Commons || Security Researcher at McAfee || B.Tech+MS, IIT Dharwad
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I am a researcher with 7 years of experience in industrial and academic research. I am passionate about building systems and have a keen interest in the area of Speech Processing, Machine Learning, and Deep Learning. Throughout my career, I have learned and gained experience in various fields but most importantly I have learned that you need to be determined and be modest if you want to make a big impact.
Indian Institute of Technology, Dharwad, India
MS by Research, Speech processing, Machine Learning, Deep Learning.
January 1, 2020 – January 1, 2022
Indian Institute of Technology, Dharwad, India
BTech - Bachelor of Technology, Electrical Engineering
January 1, 2016 – January 1, 2020
South Park Commons
Fellow
September 1, 2025 – Present
San Francisco Bay Area
Suchama AI
Founder and Chief Technology Officer
August 1, 2023 – Present
Bengaluru, Karnataka, India
McAfee
Security Researcher
November 1, 2022 – August 1, 2023
Bengaluru, Karnataka, India
Norwegian University of Science and Technology (NTNU)
Research And Development Intern
August 1, 2022 – November 1, 2022
Gjovik Norway
McAfee
Research Intern
January 1, 2022 – July 1, 2022
India
Ministry of Electronics and Information Technology
Research Fellow
January 1, 2022 – June 1, 2022
India
Indian Institute of Technology Dharwad
Teaching Assistant
August 1, 2021 – December 1, 2021
Dharwad, Karnataka, India
Indian Institute of Technology Dharwad
Teaching Assistant
January 1, 2021 – April 1, 2021
Dharwad, Karnataka, India
Indian Institute of Technology Dharwad
Teaching Assistant
September 1, 2020 – December 1, 2020
Dharwad, Karnataka, India
Indian Institute of Technology, Bombay
Research Intern
May 1, 2019 – July 1, 2019
Indian Institute of Technology, Bombay
Teaching Assistant
May 1, 2019 – June 1, 2019
HCLTech
Technical Intern
May 1, 2018 – July 1, 2018
Hubli Area, India · On-site
IIT Dharwad
Teaching Assistant
January 1, 2017 – April 1, 2017
Dharward, Karnataka, India
Voice Conversion
January 1, 2021 – April 1, 2021
Objective is to convert source speaker voice to target speaker voice. First the baseline system was generated using Gaussian Mixture Model. Further Bi-LSTM model was used to improve the performance. At the end the voice conversion was successful implemented
VOP detection under variable speech rate condition
January 1, 2020 – May 1, 2020
Vowel Onset Point (VOP) is the location of the beginning of the vowel. Most of the existing methods assume that the speech is produced at a normal speech rate. All the parameters for smoothing speech signal evidence as well as hypothesizing VOPs are set accordingly. These parameter settings may not work well for variable speech rate conditions. This work proposes a dynamic first-order Gaussian differentiator (FOGD) window-based approach to overcome this issue.
Comparative study of epoch extraction from speech
August 1, 2019 – November 1, 2019
Implemented all prominent and state of art algorithms for epoch extraction on clean and various noisy speech. Algorithms compared were Zero Frequency Filter, Zero Band Filter, FIR based epoch extraction and Zero Phase ZFR.
Machine Learning based Digital Modulation scheme recognition in Communication
August 1, 2019 – November 1, 2019
Classification of type of modulation (BPSK QPSK, 16QAM, 64QAM) from the data set. Concept used SVM, Neural Networks. Prediction of type of modulation from an unlabeled data. Concept used Gaussian Mixture Model (GMM)
Higher Order Time-Frequency Methods and its Applications
May 1, 2019 – August 1, 2019
Implemented and simulated Locally Optimised Spectrogram (LOS) on generated chirp signal and real bat echolocation. Implemented and simulated Adaptive Generalised Fractional Spectrogram (AGFS) on generated chirp signal.
Speech encryption and decryption
February 1, 2019 – April 1, 2019
Speech encryption was done by corrupting the speech by convolution of speech by AWGN noise. Decryption can be done by de-convolving the same corrupted signal by same AWGN noise. Concepts used were Convolution, Fast Fourier Transform, Inverse Fast Fourier Transform, Deconvolution, Sampling.
Active Noise Cancellation Headset
January 1, 2019 – May 1, 2019
Active Noise Cancellation involves the detection of external noise and generating an equal and opposite signal to cancel out this external noise via acoustic domain interference.
Bluetooth Low Energy
May 1, 2018 – July 1, 2018
• Simulated a working model of BLE with blocks of various modules on Cadence
Signal processing in a heart beat pulse counter
January 1, 2018 – April 1, 2018
Project was aimed to develop a system which collects signals of heart beat and process it to count heart beat/pulse in any situation.
Cultural Fit Analysis
The candidate's background is heavily skewed towards academic research, signal processing, machine learning, and deep learning, particularly in speech. While these skills are relevant to data analysis, the projects and experience do not explicitly demonstrate typical data analyst responsibilities such as data cleaning, visualization, SQL proficiency, A/B testing, or business intelligence. The candidate's profile suggests a strong fit for a research-oriented or advanced ML engineering role rather than a traditional Data Analyst position. The diversity of projects is high within the signal processing and ML domain, but lacks breadth in general data analysis tools and methodologies.
Soft Skills & Operational Fit
The candidate's experience as a Founder and CTO, along with multiple research and teaching assistant roles, suggests strong initiative, problem-solving abilities, and a capacity for independent work. The project descriptions, while technically detailed, lack explicit mention of collaboration or team-based achievements, making it difficult to fully assess team collaboration skills. The focus on research and academic projects indicates a strong analytical mindset.