CTO, Inventive.ai (RFP + AI) | YC | Stanford | IIT
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Building Inventive AI (YC S23): AI-first RFP automation platform. Love working on hard technical problems. In the trenches working with a small, tight-knit and hardworking team.
Stanford University
Master of Science - MS, Applied Math and Engineering - Machine Learning / Computer Vision
N/A – Present
Indian Institute of Technology, Madras
Dual Degree (BS + MS)
N/A – Present
MES Kishora Kendra
High School
N/A – Present
Inventive AI
Co-Founder and CTO
January 1, 2023 – Present
Mountain View, California, United States · On-site
Packt
Author
January 1, 2022 – January 1, 2022
Ambient.ai
Applied Research Scientist
January 1, 2019 – January 1, 2023
SLB
ML Scientist
January 1, 2017 – January 1, 2019
Stanford University
Research Assistant
September 1, 2016 – April 1, 2017
Stanford, California
Matroid
ML Engineer
March 1, 2016 – December 1, 2016
3239 El Camino Real #310, Palo Alto, CA 94306, United States
Stanford University
Teaching Assistant
January 1, 2016 – June 1, 2016
MPI-CBG - Max Planck Institute of Molecular Cell Biology and Genetics
Summer Research Intern
May 1, 2013 – July 1, 2013
Dresden, Germany
The University of Queensland
Summer Intern
December 1, 2012 – January 1, 2013
Brisban
Deep Reinforcement Learning on Atari games
September 1, 2016 – Present
Currently working on implementing Deep Reinforcement Algorithms on Atari Breakout. Expected to finish it by December.
FusionNet: 3D Model Classification Using Multiple Data Representations
January 1, 2016 – Present
- Trained a novel convolutional neural network for classifying 3D CAD models. Name it FusionNet - At the time of submission, we obtained leading results on the Princeton ModelNet challenge. - An article about it was featured on the O'Reilly website: https://www.oreilly.com/ideas/from-image-recognition-to-object-recognition
Deep Learning for 3D model classification (Deep3D)
January 1, 2016 – March 1, 2016
Conventional convolutional neural networks use pixel images as input. We designed and implemented a custom deep neural network for classifying 3D CAD models with input as voxels. Visualized the network's final fully connected layer using t-SNE. Tools used: Caffe and Python
Convolutional Neural Networks for Image Classification
January 1, 2016 – February 1, 2016
Built forward and back-propagation routines for convolution, ReLU, dropout, batch-normalisation and softmax in python using Numpy. Used it to train a three layer neural network for image classification.
Predicting ALS disease progression
September 1, 2015 – December 1, 2015
Worked with missing data to find and use the most relevant features for predicting the progression of Amyotrophic Lateral Sclerosis (ALS). This was a Kaggle dataset. We ranked 8th out of 90 teams at Stanford in the Kaggle in-class competition. Used several standard statistical ML techniques including random forests, bagging and AdaBoost in the process. Language used: R
Solving heat equation using CG method
September 1, 2015 – December 1, 2015
I built a sparse matrix solver in C++ using the conjugate gradient method. Used the solver to find a stable state temperature configuration of a pipe carrying hot fluids. Languages used: C++ and Python.
A Novel Low cost automatic warning system for high speed winds
August 1, 2008 – December 1, 2008
Analysed and prototyped a community scale warning system for high-speed winds and tornados. It won the CSIR invention award from the Government of India.
A Novel Low cost design of Tic-Tac-Toe board game for the visually challenged
June 1, 2007 – May 1, 2008
This is one of my dearest projects I have worked on so far. I started working on it in the beginning of 10th grade and spent almost a year perfecting a low cost electro mechanical device for visually impaired people to play tic-tac-toe. It involved simple geometrical and complementary patterns on the circuit board to identify whether an 'X' or an 'O' is placed. This project won the National (Indian) Science Fair 2007 and was displayed at the International Science and Engineering Fair.
Data Engineering on Google Cloud Platform Specialization
Coursera
June 23, 2026 – Present
Cultural Fit Analysis
The candidate demonstrates a strong inclination towards research and development in AI/ML, with a history of working on challenging, innovative projects. The co-founder role and authorship of a technical book suggest an entrepreneurial spirit and a commitment to continuous learning and contribution. The diversity of projects, from academic research to practical applications and even early-stage inventions, indicates a broad interest and adaptability. While the target role is 'Data Analyst', the candidate's profile leans heavily towards advanced ML/AI research and engineering, which might be an overqualification or a mismatch if the Data Analyst role is purely focused on descriptive analytics and reporting. However, if the Data Analyst role involves advanced predictive modeling, feature engineering, or data science, there could be a strong fit.
Soft Skills & Operational Fit
The candidate's project descriptions and career progression suggest strong problem-solving abilities, initiative (Co-Founder role, personal projects), and a drive for innovation. The teaching assistant role indicates communication and mentorship skills. The diverse project portfolio, including award-winning initiatives, points to a proactive and results-oriented individual. However, without specific psychometric test results, a detailed assessment of work attitude, stress handling, and team collaboration is not possible.