Noopur Nishikant Zambare

Noopur Nishikant Zambare

Undergraduate Student at Indian Institute of Technology

Posts by Collection

experience

Undergraduate ML Researcher at IIT Jodhpur

Research Internship | Remote | Ongoing
  • Project: Healthcare Analytics using GANs
  • Developing an algorithm for robust classification of chewing moments.
  • Working on utilizing flexibility offered by Liquid Neural Networks
  • Tech stack: Liquid Neural Network, Machine Learning, Deep Learning

MITACS Globalink Research Internship At University of Calgary

Research Internship | Calgary, Canada | May 2023 - August 2023
  • Project: Return on Investment (ROI) of Data Analytics
  • Used NLP, Active Learning and requirements dependency extraction to construct a full framework to estimate ROI
  • Hosted the application on AWS EC2 and implemented CodePipeline to automate the deployment procedure
  • Business Impact: Highlighted possible ROI under several circumstances, potentially helping the business to manage resources and prioritise data-driven plans.
  • Tech stack: Deep Learning, Machine Learning, Data Science, NLP, Cloud Computing (AWS), React JS, Flask

Amazon ML Summer School’22

Amazon | Apprenticeship | July 2022
  • Competed with the engineering students in India in programming skills, logical reasoning, mathematics and machine learning assessments and was selected among the top few students for this training session
  • Hands-on: Deep Neural Networks, Sequential Models, Unsupervised Learning, Causal Inference & Reinforcement Learnin

AutomationEdge, India

Machine Learning Internship | India | June 2022 - July 2022
  • Employed Curriculum Learning to progressively present training samples for effective learning.
  • Enhanced comprehension of textual customer complaints by implementing NER and POS tagging methods
  • Impact: Improved text analytics and Intent Recognition Algorithms
  • Tech stack: Machine Learning, Deep learning, NLP, Spacy, Textacy

AutomationEdge, India

Data Science Internship | India | Dec 2021 - Jan 2022
  • Formulated predictive models for IT ticket volumes using Deep Neural Networks and worked on Resource Allocation.
  • Leveraged NLP techniques to enhance the functionality of ChatBots built with BOT Framework Composer integrating knowledge base with Microsoft Azure.
  • Impact: Automation strategies established the groundwork for customer service operations innovation.
  • Tech stack: Machine Learning, Deep Learning, Power BI, Microsoft Azure, Bot Composer Framework

projects

Liquid Neural Networks for Enhanced Decision-Making

A decision-making algorithm based on Federated Co-Learning : Bachelor’s Thesis [Ongoing]
  • Developing an algorithm to incorporate Co-Training into Federated Learning such that data is decentralized yet benefiting both models mutually.
  • Utilizing Liquid Neural Networks as the base model in Federated Learning Network.

Machine Unlearning in Large Language Models using a Neuroscientific Cognitive Approach

Optimizing Unlearning LLMs with advanced neuro-inspired algorithms
  • Developed and improved a state-of-the-art unlearning algorithm for Large Language Models by adding extensive representations of the memory system, modulations of the attention mechanism, and other cognitive processes motivated by neuroscience.
  • Created an adaptive threshold mechanism for the reconsolidation window to optimise unlearning timing and requirement depending on error severity, concept usage frequency, and emotional connotations.

Q&A: System Based on Google Palm LLM and Langchain

LLM-powered Q&A system using Langchain + Google Palm
  • Developed an LLM-powered Q&A system (Langchain + Google Palm) within Streamlit UI to reduce workload for e-learning company.
  • Spearheaded the implementation of FAISS vector database for fast retrieval of relevant answers, optimizing system performance and user satisfaction.
  • This innovative approach enables real-time response and seamless access to knowledge within the e-learning platform.

Bayesian Meta-Learning

Framework for adaptive learning in constrained data
  • Used Bayesian Meta-Learning to design a framework that adapts fast and performs efficiently with limited data.
  • Designed a Few-Shot Learning paradigm which achieved 80% accuracy in the classification tasks.

Meta Learning with GANs for Text Classification

ML framework for Text Classification using Meta-Learning with GANs
  • Applied a cutting-edge Meta-Learning framework to substantially improve model performance and facilitate rapid adaptation to dynamically changing environments and tasks.
  • Developed GAN for data augmentation consisting of a multi-layered generator and discriminator.address Catastrophic Forgetting during Meta-Learning.
  • Achieved an accuracy of 95%, compared to 87% for baseline models.

Bias Detection in Federated Learning and Swarm Learning

Technique to detect biases in Distributed ML Algorithms
  • Designed 5 clients and 5 agents system for distributed learning.
  • Explored Differential Privacy techniques to add noise to the aggregated model updates, ensuring privacy in Federated Learning.
  • Analyzed security in Swarm Learning using Decentralized Identity Management.

AI For Healthcare

Framework for harnessing ML to create Wearable Medical Technology
  • Carried out clinical studies with the goal of categorising mandibular motions during eating.
  • Real-time sensor data acquisition and transmission enabled by the HC-05 Bluetooth Module.
  • Compared computational efficiencies of LNN and LSTM for sequential data.

Vehicle Detection Through Transfer Learning

A Transfer Learning framework for detecting vehicles in aerial images
  • Utilised the potential of transfer learning with VGG16 network architecture.
  • Attained quicker convergence on vehicle detection dataset.
  • Conduced trials on grey and coloured images and obtained model accuracy of 60%.

research

Incorporating Deep Q - Network with Multiclass Classification Algorithms

IJCRT Issue 11, Volume 8 2023

In this study, we investigate the potential of Deep Q-Network (DQN) to enhance multiclass classification algorithms, with a specific focus on predicting financial distress in companies and broader applications in fields such as finance and risk management. Key algorithm used - Reinforcement Learning.

View here

talks