Harsh Nilesh Pathak

I am an AI researcher, lecturer, and tech lead at GoDaddy, driving enterprise-ready generative AI solutions. With 10+ years of experience, I’ve published 25+ papers and built projects that generate millions in enterprise value.

Work experiences have in-depth experience and delivered below projects with production quality and many AB test winners.

Expedia: SEM, NLP, Vision, Prediction Models, Contrastive Embeddings
GoDaddy: AI, LLMs Agents, Budget Optimization, Question Answering and Domain name Search.
PhD WPI: Deep Learning Optimization, Sparse Neural Networks, Curriculum Learning, Continuation Methods, GANs
Wirte Blogs: AI Agents planning > https://medium.com/me/stats/post/912c96424cca

Research Projects

[In Progress] Extending Continuation Methods on Complex Plan for Neural Networks

Image credits Bertini ; PhD Thesis: Study of Dynamical systems on Complex plane to get rid of singularites

Present

Bifurcation Analysis of Neural Network

PhD Thesis: To study the dynamical properties of the neural network training/optimization using continuation methods and bifurcation analysis. On the left is the classic example of bifurcating solutions of a logistic model.

Present

Principled Curriculum Learning using Paramter Continuation Methods

ICML 2021 Workshop on Beyond First ordrer methods in ML

Creator of Continuation-JAX an adaptive curriculum libraray

May 2021

Model Curriculum in Teacher Student Curriculum Learning

Research Proposal [Link] [Code]

Enhancing Research in "Teacher Student Curriculum Learning"

Dec 2020

NPACS: Optimizer for Deep Learning

Paper accepted at ICMLA (2019)

Extended Chapter on Springer (2020)

Designed a continuation method for the non-convex optimization of Deep Neural Networks. Result our method (NPACS: Natural Parameter Adaption Continuation with Secant) converged faster & better generalization error compared to ADAM for Auto-encoders.

Sep 2020

Continuation Activations

Homotopy Activation functions. Start Simple! We do homotopy from Linear to Non-linear activation. So that you can systematically track the non-convex behavior of deep learning objective functions.

Published at WPI Thesis Link

Oct 2018

Continuation (C)-SMOTE

Using Machine Learning to generate more training data points given the support of data distribution to accelerate training of Neural Networks. Result 5x speed up in GANs and better generative quality than Batch Normalization.

Published at WPI Thesis Link

Aug 2018

Step-Up GAN

Data Curriculum for GANs. We designed a Curriculum which allows GANs to converge faster than WGAN and better (less biased) than Batch Normalization.

Project Link1

Project Link2

May 2018

Industry Projects

Image Moderation at Scale

Replaced a slow and costly human moderation system with Deep Learning developed solutions.

Medium Blog

June 2019

Attention GAN for Super-Resolution

Paper: IEEE BigData (Dec 2018)

Blog: Medium Blog

Using Attentional Generative Adversarial Networks to scale images to 4k.

Jan 2019

Multilingual NLU Systems Development and Deployment

Accepted at IEEE BigData 2020 [Video]

Multilingual user intent understanding via Text content. We classify, perform NER and also employ Learning to rank frameworks to coordinate with our business needs.

Jan 2020

Google Sites

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