CV

Teaching Assistant

Northeastern University

Reviewed material, graded assignments, and conducted office hours for the courses: Introduction to Data Mining and Machine Learning in Fall 2023 and Foundations of AI in Spring 2024 semesters.

• Data Mining
• Reinforcement Learning
• Deep Learning
• Search and Optimization
• AI Ethics

Research intern

Indian Institute of Science (India Urban Data Exchange)

-Developed a cutting-edge privacy-preserving mean estimation algorithm, ensuring user-level privacy in a non-IID setting. Achieved exceptional results, surpassing existing approaches with a significant 20% increase in utility and an outstanding 36% enhancement in privacy.
-Designed a privacy-preserving approach for generative models based on quantization of latent space and exponential mechanism with differential privacy guarantees.

• Data Mining
• Reinforcement Learning
• Deep Learning
• Search and Optimization
• AI Ethics

Federated Learning Consultant

DynamoFL

-Implemented a pipeline for federated neural collaborative filtering from scratch for recommendation systems.
-Integrated federated learning optimizers and aggregators for models like Xgboost and K-Means clustering.
Undertook the development of end-to-end solutions with the dynamofl framework for diverse client use cases.

• Federated Learning
• Recommendation Systems
• XGBoost
• Clustering

Research Intern

National Institute of Technology, Kurukshetra (Security and AI Laboratory)

-Created a data poisoning attack for collaborative learning settings, which resulted in an adversarial success rate that was over 3 times improvement over the existing error-generic attacks.
- Investigated the use of encoder based models in distributed learning to achieve cross-domain generalization of network intrusion in a multi-client setting.

• Federated Learning
• Adversarial Machine Learning
• Instrusion Detection

MS

Northeastern University

Artificial Intelligence

• Unsupervised Machine Learning
• Foundations of AI
• Algorithms
• Program Design Paradigms

BTech

Vellore Institute of Technology

Computer Science and Engineering

• Machine Learning
• Natural Langugage Processing
• Parallel and Distributed Computing
• Data Privacy

Raman Research Award

Vellore Institute of Technology

Award for the publication 'A Novel Data Poisoning Attack in Federated Learning based on Inverted Loss Function'

Mean Estimation with User-Level Privacy for Spatio-Temporal IoT Datasets

arXiv preprint

A Novel Data Poisoning Attack in Federated Learning based on Inverted Loss Function

Elsevier Computers & Security

Applications of IoT and Cloud Computing: A COVID-19 Disaster Perspective

Springer New Frontiers in Cloud Computing and Internet of Things

A study of gene characteristics and their applications using deep learning

Springer Handbook of Machine Learning Applications for Genomics

Early diagnosis of retinal blood vessel damage via deep learning-powered collective intelligence models

Hindawi Computational and Mathematical Methods in Medicine

DAIDNet IDS

Conceptualized and implemented a novel deep learning model for the two-stage classification of network intrusions. DAIDNet leverages multiple autoencoders to learn patterns from distinct data subsets and has demonstrated state-of-the-art (SOTA) or near-SOTA performance on benchmark datasets.

• Autoencoders
• Tensorflow

FedPartial

A framework to enable collaborative learning across multiple hospitals, each using a different model architecture. The results demonstrated a remarkable 66% gain in communication efficiency, ensuring comparable utility to baseline models for a chest x-ray image classification task using the EfficientNet family of models.

• Federated Learning
• Tensorflow