Nikhil Madaan

Nikhil Madaan

Senior AI Research Engineer

Bloomberg AI

Biography

Hi, This is Nikhil Madaan. I’m a Senior AI Research Engineer at Bloomberg AI, focusing on multi-variate time-series modeling to build real-time pricing models.

Previously, I was a M.S. in Computer Engineering (AI-ML Concentration) student at Carnegie Mellon University. During my time at CMU, I was a part of MultiComp Lab, where I worked with Jianing “Jed” Yang and Prof. Louis-Philippe Morency on bias analysis in Multimodal QA datasets. I was also a part of Lion’s Research group, where I worked on Personalized Federated Learning with Dr. Taejin Kim and Prof. Carlee Joe-Wong.

During my master’s, I interned at Amazon as Applied Scientist, with the Media and ADs group, where I worked with Dr. Manisha Verma on multi-modal product headline generation. Prior to my master’s I worked as SWE-ML at Flipkart as a part of the Catalog Ingestion team, where I played a significant role in designing and implementing scalable AI systems.

I am particularly interested in the applications of Deep Learning in areas such as Natural Language Processing, 3d-Computer Vision, and Multimodal ML.

Interests
  • Multimodal ML
  • NLP
  • 3D Computer Vision
  • Robotics
Education
  • M.S. in Computer Eng. (AI/ ML Concentration), 2021-2022

    Carnegie Mellon University

  • B.E. Hons in Electrical and Electronics Eng., 2015-2019

    Birla Institute of Technology & Science, Pilani

Experience

 
 
 
 
 
Bloomberg AI
Senior AI Researcher
January 2023 – Present NYC
  • Working on leveraging ML models for multi-variate time-series modeling to build real-time pricing models for Fixed-Income securities.
 
 
 
 
 
Amazon - Media and Ads Group
Applied Scientist
Amazon - Media and Ads Group
May 2022 – August 2022 Seattle
  • Worked on generating headlines for products by factoring in multiple modalities such as Product Images and product attributes (Text); using SOTA multimodal fusion networks such as Flava, Mantis.
  • Employed contrastive learning to improve the diversity of the generated headlines and rouge, bleu score by 53.5% and 145% respectively, w.r.t unimodal models.
 
 
 
 
 
MultiComp Lab, CMU
Graduate Research Assistant
MultiComp Lab, CMU
January 2022 – May 2023 Seattle
  • Worked on analyzing QA bias in Multi-modal Question Answering Systems using fine-tuned language models.
 
 
 
 
 
Lions Research Lab, CMU
Graduate Research Assistant
Lions Research Lab, CMU
January 2022 – December 2022 Pittsburgh
  • Worked on the transferability of adversarial attacks in Personalized Federated Learning setup and trying to make the learnt model more robust to such attacks..
 
 
 
 
 
Walmart Grp (Flipkart)
Software Engineer - Machine Learning
Walmart Grp (Flipkart)
July 2019 – August 2022 Bangalore, India
  • Leveraged Image encoders such as ViT, ResNets, to generate embeddings of the product images. Indexed the generated embeddings, added multi-cluster support for reads and writes, and used the embeddings to support Product Deduplication.
  • Developed a prioritized distributed message processing xtension to the camel-Kafka component in Java, to support priority consumption of records and implemented various consumption strategies to support different use cases.

Recent Publications

LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an Agent
LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an Agent

Accepted at ICRA 2024 | LangRob @ CoRL(LangRob) 2023.

Contrastive Multimodal Text Generation for E-Commerce Brand Advertising
Contrastive Multimodal Text Generation for E-Commerce Brand Advertising

Accepted at KDD (Multimodal) 2023.

Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning
Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning

Accepted at Neurips 2023.

Characterizing Internal Evasion Attacks in Federated Learning
Characterizing Internal Evasion Attacks in Federated Learning

Accepted at AISTATS 2023