About
Machine Learning Engineer & Researcher
Computer Science graduate student with expertise in Deep Learning, Reinforcement Learning, and Cloud Computing.
Currently pursuing my Masters in Computer Science at Arizona State University, I'm passionate about solving complex problems in machine learning and analytics. My research focuses on applications of Reinforcement Learning.
Arizona State University
VIT Bhopal University
Technical Expertise
Machine Learning
Programming
Technologies & Frameworks
Experience
Professional Experience
Research Assistant
Knowledge Discovery & Data Mining Group
March 2025 - Present
Arizona State University, Tempe, AZ, USA
- Conducting research on the applications of Reinforcement Learning for feature transformation, causal discovery, and privacy-preserving data reprogramming in real-world tabular datasets.
- Co-authored DELTA, a two-phase generative disentangled learning framework for Privacy-Preserving Data Reprogramming (PPDR) that balances predictive performance with reduced sensitive attribute inferability.
Associate Analytics Engineer
Shell PLC
Aug 2022 - Aug 2024
Bangalore, Karnataka, India
- Worked on large complex financial data using Alteryx and SQL databases to automate workflows which resulted in 40% reduction in the workload of the team. Built an interactive dashboard to provide a holistic view of the finances.
- Collaborated with a global team on a deep learning-based image analysis project for lube monitoring. It involved developing a python-based model for ROI detection and classification. Worked with Linux clusters to enhance workflow efficiency.
- Automated geo-mechanical experiments with python which lead to a drastic reduction in the time to generate reports by 50% per lab technician.
- Developed a process automation framework for business-critical deployments for disaster recovery involving more than 100 applications. This led to a reduction in the workload of 3 business days per team member.
- Developed an interactive dashboard to track 150+ business continuity services, including disaster recovery and enterprise recovery for business-critical applications.
- Collaborated with 20+ application owners, stakeholders, and vendors to ensure the seamless execution of the disaster recovery process being a disaster recovery focal point.
Internships
Software Development Engineer (Intern)
HabitUp
March 2022 – June 2022
Remote
- Developed a habit tracking android application using the flutter framework.
- Successfully published the app on the google play store.
- Enabled over 100,000 users to successfully track their habits through the app.
Machine Learning Engineer (Intern)
Technocolabs
Feb 2021 - Apr 2021
Remote
Worked on Machine Learning algorithms and models using python.
Lead a team of 5 members for the major project.
- The minor project (web app), an ML model trained to classify comments into different categories.
- Hosted on 'Heroku'
- Technology & Frameworks: Python, Flask, HTML, CSS
- Links: GitHub Repository
Toxic Comments Classification
- The major project (web app), an ML model trained to predict movie ratings.
- Hosted on 'Heroku'
- Technology & Frameworks: Python, Flask, HTML, CSS
- Links: Github Repository
Movie Rating Prediction
Publications
My recent research publications in peer-reviewed conferences and journals.
DELTA: Variational Disentangled Learning for Privacy-Preserving Data Reprogramming
ICDM - International Conference on Data Mining, 2025
This research introduces DELTA, a novel approach to privacy-preserving machine learning that enables effective data reprogramming while maintaining data confidentiality through variational disentanglement.
Under Review
Advanced Deepfake Detection Using Inception-ResNet-v2
International Conference on Communication and Intelligent Systems, 2023
This paper presents a novel approach to deepfake detection using advanced neural network architectures. Our model achieves state-of-the-art accuracy in identifying manipulated media content.
Patents
Intellectual property from my research and development work.
MITRA: Marine Inspection Tool for Rating and Assessment
Shell PLC | Filed: 2024
An innovative solution in lube monitoring that uses deep learning for image classification with 90% accuracy. This technology enables more efficient and reliable assessment of marine equipments. The patent covers the methodology, algorithms, and application of this technology in industrial settings.
Patent Pending
Projects
Machine Learning & AI
Reinforcement Learning for Assembly Planning with the Burr Puzzle
January 2025 - Present
• Developed an offline reinforcement learning pipeline to solve complex mechanical assembly tasks using the Burr Puzzle as a proxy for multi-part object assembly under physical constraints.
• Designed a novel vertex-based action space enabling collision-free, gravity-aware assembly moves, and implemented a pruned n-step lookahead with greedy rollout for efficient planning.
• Accelerated planning via Assembly-by-Disassembly strategy, reducing search complexity and achieving successful six-piece puzzle assembly with up to 35% fewer stages compared to traditional one-arm methods.
Implementation of Flash Attention in CUDA
January 2025 - Present
• Implementing Flash Attention for large language models (Llama 3), reducing inference latency and memory bandwidth through operator fusion and SoftMax tiling.
• Using Triton compiler to create attention modules, minimizing HBM transfers and enhancing memory access, resulting in improved throughput.
Metamorphosis Automation
Aug 2020 - May 2021
• Developed a deep learning model using ResNet-34 and CNNs to detect car accidents from real-world CCTV surveillance footage with high accuracy.
• Built a complete pipeline integrating ML inference, a RESTful API, and Android/web dashboards to deliver real-time accident alerts with geo-tagged evidence.
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Google-Colab link
Computer Vision & Medical Imaging
Self-Supervised Dense Point Tracking in Turbulent Videos
August 2024 - December 2024
• Developed a benchmark for point tracking in videos with induced atmospheric turbulence, enhancing the DINO-Tracker with RAFT-based optical flow refinement.
• Diagnosed robustness gaps in DINO-Tracker, revealing a 32% average-Jaccard drop under moderate turbulence and mapping failure modes in occluded, high-blur frames.
• Estimated the strength of turbulence using the trajectories of points in the turbulent videos.
Swin Transformer for Vision Tasks
August 2024 - December 2024
• Used Swin Transformer for classification, localization, and segmentation on ChestXRay14, NODE21, and ChexMask datasets to optimize pipelines and improve performance in medical image analysis.
• Achieved 72.64% accuracy training a model from scratch and 81.78% accuracy fine-tuning on ImageNet for ChestXRay14 classification.
Comparative Study of Video Retrieval Methods
August 2024 - December 2024
• Implemented dimensionality reduction techniques (PCA, SVD, LDA, K-Means) on ResNet, HOG, HOF, and Color Histogram feature spaces to rank and retrieve videos based on similarity.
• Developed centroid-based ranking and label prediction models, achieving improved retrieval accuracy and demonstrating SVD's effectiveness in preserving latent structures.
Cloud & Edge Computing
Edge-Based Face Recognition Pipeline using AWS IoT Greengrass
April 2025 - May 2025
• Designed and deployed a distributed face recognition pipeline by integrating AWS IoT Greengrass, MQTT, Lambda, and SQS for low-latency edge processing.
• Implemented real-time face detection on simulated IoT edge devices using MTCNN and Python components deployed via custom Greengrass components.
• Enabled secure device-to-cloud communication by configuring AWS IoT Core policies, certificates, and MQTT bridges for message exchange.
• Triggered cloud-based FaceNet-based face recognition via SQS queues and Lambda functions, maintaining a privacy-preserving and scalable architecture.
Serverless Face Recognition Pipeline with AWS Lambda & ECR
March 2025 - April 2025
• Developed a face recognition service using AWS Lambda, Docker, and Elastic Container Registry (ECR) to enable serverless ML inference on streaming video frames.
• Built and containerized custom Lambda functions for face detection (MTCNN) and recognition (FaceNet) with PyTorch models, deployed via ECR.
• Integrated AWS SQS for decoupled communication between detection and recognition stages, ensuring scalability and low latency.
Cloud-Based Face Recognition System Architecture and Optimization
February 2025 - March 2025
• Designed a scalable cloud-based face recognition system on AWS using S3, SQS, and dynamic EC2 instances for efficient image processing.
• Built a responsive Python web tier to handle HTTP requests, manage cloud storage, and coordinate communication between the UI and processing layer.
• Engineered the application tier for deep learning inference with PyTorch, processing images from cloud storage and returning results via asynchronous message queues.
• Developed an auto-scaling controller to dynamically provision resources, optimizing costs and ensuring system responsiveness with zero idle instances.
Mobile & Web Development
mShare
August 2020
• Developed and published a file-sharing mobile application on the Google Play Store.
• Enabled high-speed offline sharing via hotspot and QR code with support for large files including photos videos, apps, and folders.
• Implemented advanced features such as pause/resume transfers, multi-device sharing, and automatic reconnection on network changes.
• Designed a lightweight, speed-optimized UI for reliable performance on low-end Android devices.
• Integrated network speed measurement and dynamic connection switching to enhance user experience.
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GitHub Repository
COVFeed
Jun 2020
• Built a mobile application using the Flutter framework to serve as a semi-news platform for healthcare professionals.
• Designed for doctors, nurses, and medical staff to share real-time regional health updates during the COVID-19 pandemic.
• Developed in a team of two as part of the International Flutter Hackathon – Hack’20.
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GitHub Repository
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YouTube Video
Contact
Feel free to reach out to me if you have any questions, opportunities, or just want to connect!
Location:
Tempe, AZ, USA