About Me
Hi, I’m Abhinay. I’m a researcher at Purdue interested in computer vision, graphics, and NLP.
Right now, I spend most of my time on generative models and representation learning. I care about outputs that are not just high-quality, but also structured, controllable, and consistent. I’m especially drawn to problems where models must respect objects, boundaries, and geometry instead of treating everything as one texture.
My master’s thesis was in medical imaging generation. I built a hybrid pipeline that combined self-supervised representations with diffusion and GAN-based synthesis to work in low-data settings. That experience made me care a lot about practical constraints like limited data, noisy signals, and the gap between something that looks good and something you can trust.
Currently, I’m exploring segmentation-aware conditioning for generation, using signals from models like SAM and Mask2Former to build per-instance conditioning that improves local structure and boundaries. In parallel, I work on adversarial robustness for NLP, focusing on query-efficient black-box attacks and evaluation tools for real deployed classifiers.
I enjoy collaborating with people who run careful experiments, move fast, and stay honest about what works and what doesn’t. If you’re working on generative modeling, robust NLP, or anything adjacent, feel free to reach out.
Technical Skills
Deep Learning Frameworks
- • PyTorch
- • TensorFlow
- • Keras
Libraries
- • NumPy
- • pandas
- • Matplotlib
- • scikit-learn
- • cuDNN
High-Performance Computing
- • GPU Computing (CUDA)
- • Parallel Processing
- • HPC Clusters
- • Bash Scripting
- • SLRUM
Programming Languages
- • Python
- • Java
- • JavaScript
Web Technologies
- • React
- • Node.js
- • HTML/CSS
Cloud and DevOps
- • AWS (EC2, ECS, EFS, S3, CloudWatch, VPC)
- • Git
- • Docker
Current Research
Computer Vision
Ongoing: Beyond Representation Sampling: Segmentation-Aware Conditioning for Generative Models — Combining SAM and Mask2Former to build per-instance conditioning vectors for controllable, fully unsupervised image synthesis.
Early experiments show improved FID and gains in precision and recall. Experiments are ongoing toward an ICML 2026 submission.
Natural Language Processing (NLP)
Submitted: CONQUEST: An Efficient Attack for the Constrained Hard-label Setting — designing query-efficient adversarial attacks when only top-1 labels are available, with strict perturbation and budget constraints.
The method combines adaptive search over discrete token edits with constraint-aware scoring, significantly reducing query counts compared to existing hard-label baselines on text classification benchmarks. The work is currently under review.
Publications & Research
Overcoming Black-box Attack Inefficiency with Hybrid and Dynamic N-nary Algorithms
EMNLP 2025
Novel approach to improve model robustness using score-based feedback for adversarial perturbations in NLP models.
Addressing Data Scarcity in Medical Imaging: A Hybrid IJEPA + Stable Diffusion + GAN Approach
Master's Thesis
Hybrid pipeline combining IJEPA, Stable Diffusion, and GANs for realistic synthetic medical image generation to address data scarcity.
Beyond Representation Sampling: Segmentation-Aware Conditioning for Generative Models
Research Project (Ongoing)
Exploring segmentation-aware conditioning techniques to achieve complete unsupervised image generation.
CONQUEST: An Efficient Attack for the Constrained Hard-label Setting
Research Project (Submitted)
Developing novel algorithms for adversarial attacks in scenarios where model confidence scores are unavailable.
Projects
Personality Prediction - 2.0
Jan 2024 - Apr 2024
Developed a platform using Django and React.js to provide high school students with MBTI personality predictions, leveraging NLP and machine learning models.
Key Highlights:
- • Implemented state-of-the-art transformer-based models to analyze textual data
- • Integrated MongoDB for scalable data storage
- • Optimized backend performance using Python and Django
Abstractive Text Summarization
Jan 2024 - Apr 2024
Built an advanced text summarization system using deep learning techniques to generate concise and coherent summaries from long-form content.
Key Highlights:
- • Developed CNN-based model for text processing
- • Leveraged TensorFlow, Keras, and scikit-learn
- • Demonstrated expertise in deep learning for NLP
Image Coloring
Sept 2023 - Dec 2023
Developed a CNN-based model using TensorFlow and Keras to predict and generate color details for grayscale images, enhancing their visual quality.
Key Highlights:
- • Built CNN architecture for image colorization
- • Used deep learning for image processing
- • Enhanced visual quality of grayscale images
Purdue Marketplace (Telescope)
Sept 2023 - Dec 2023
Created Purdue e-commerce platform utilizing React, Node.js, HTML, CSS for seamless user experience with full-stack functionality.
Key Highlights:
- • Full-stack e-commerce platform development
- • Managed GitHub repository for collaboration
- • Implemented robust functionality and intuitive interface
Experience
Graduate Research Assistant
Purdue University, Indiana, Fort Wayne
- • Master's Thesis: Investigating a hybrid IJEPA + Stable Diffusion + GAN pipeline to address data scarcity in medical imaging. Leveraging GPU-based HPC clusters for high-volume synthetic image generation.
- • NLP Research: Transitioning from score-based adversarial attacks (EMNLP) to hard-labeled black-box scenarios, developing novel N-nary attack algorithms without model feedback.
- • Hands-on experience with High-Performance Computing (HPC), training deep learning models on multi-node clusters.
Software Engineer
Infosys Limited, Bangalore, India
- • Built Spring Boot REST APIs integrated with Finacle Script for LMS–VAM (menu validation, redirects, SQL CRUD).
- • Refactored modules for modularity and reusability, separating orchestration from Finacle business rules.
- • Containerized microservices (fnhttp-va, lm) with Docker for reproducible, versioned releases.
- • Coordinated rollouts with infrastructure across staging and production to minimize downtime.
- • Monitored and optimized containers on AWS ECS, EC2, EFS, S3, and CloudWatch to improve scalability and reliability.
- • Streamlined release cycles and service reliability through standardized containers and modular API architecture.
Machine Learning Engineer Intern
The People's Corp, Bangalore, India
- • Architected an enterprise RAG platform using OpenAI GPT-4, LangChain, and Pinecone to enable semantic search across 100k+ documents for cross-functional knowledge management.
- • Implemented Cohere ReRank integration to improve retrieval precision, reducing irrelevant responses by ~40% and enhancing answer quality for business-critical queries.
- • Deployed containerized RAG services on Kubernetes with a Redis caching layer, supporting high-concurrency workloads and reducing query latency by ~60% for 500+ daily users.
- • Collaborated with product and operations teams to build an AI-powered knowledge hub, streamlining access to internal documentation and enabling interactive querying of policies and procedures.
Student & Assistant Teacher Intern
JSpiders Institute, Bangalore, India
- • Trained and certified in Java Full-Stack Development, Core and Advanced Java (J2EE), SQL, and PL/SQL.
Education
Master's in Computer Science
Purdue University Fort Wayne
Fort Wayne, Indiana
Focus: Deep Learning, High-Performance Computing, AI Research
Bachelor of Science in Mechanical Engineering
Amrita Vishwa Vidyapeetham University
Bangalore, India
Focus: Engineering Fundamentals, Problem Solving
Certifications
Build Better Generative Adversarial Networks (GANs)
DeepLearning.AI
June 2024
Natural Language Processing with Classification and Vector Spaces
DeepLearning.AI
July 2024
Supervised Machine Learning: Regression and Classification
Stanford Online
July 2024
Convolutional Neural Networks
DeepLearning.AI
Feb 2024
Neural Networks and Deep Learning
DeepLearning.AI
Jan 2024
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
DeepLearning.AI
Jan 2024
Get in Touch
Interested in collaborating on research, discussing AI innovations, or exploring exciting opportunities? Let's connect!
Contact Information
Phone
(260) 515-4640