Tejas Bana
I am a Machine Learning Engineer at Albert Invent, where I build reliable, scalable, and real-time agentic AI systems to accelerate scientific R&D. My work focuses on creating LLM agents through multi-agent architectures, relational Retrieval-Augmented Generation (RAG), and prompting techniques.
Apart from my current work, I am interested in representation learning and diffusion models. My background includes a Master's from Carnegie Mellon University and a portfolio of projects spanning generative models, computer vision, and privacy-preserving ML.
LLM Agents & Applied AI
Agentic Systems for Scientific R&D
Leading the development of two core agentic systems:
- LLM Discovery: Created a chat-based search and recommendation engine for chemical formulations, scaling to millions of data points, improving data accessibility for scientists. Reduced typical search time from months to just 5 seconds.
- Agentic Interface: Built a production agent to simplify a complex UI, usings LLMs to translate natural language queries into concrete UI actions and data retrieval. Achieved 91% reliability, significantly simplifying the user experience and reducing onboarding time.
Generative Models & Representation Learning
GANs for Architectural Floorplan Generation
ML Intern at Uniworks Designs
Automated the generation of apartment floorplans and furniture layouts using a two-stage GAN pipeline (House-GAN and ArchiGAN). Deployed the model as a service on GCP, reducing initial design time for architects by up to 25%.
Generative Camouflage for the Indian Army
R&D ML Intern at College of Military Engineering
Developed software to generate pixelated, terrain-specific camouflage patterns. The system uses satellite imagery, segments terrain features with Mask R-CNN, and applies a generative algorithm to create 5K resolution patterns in under 60 seconds, significantly reducing vehicle visibility. Received "Best Intern" award for this work.
Learning-Based Image Synthesis (16-726)
Carnegie Mellon University
A deep dive into generative vision through hands-on projects:
Generative Vision Seminar (16-895)
Carnegie Mellon University
A seminar course exploring the nuances of modern generative models, from understanding their success to critiquing their capabilities for future research. Covered topics like Diffusion Models, VAEs, and Flow-based models.
Course PageSiamese Networks for Fashion Similarity
R&D ML Intern at IIT Bombay
Built a deep learning system to find visually similar clothing items from a massive dataset. Implemented a Siamese network with triplet loss and multi-class negative sampling to learn powerful image representations. Used Annoy for efficient, real-time nearest-neighbor search.
Publications
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Robust Recovery of Adversarial Samples
ICML-2021 Workshop on AML
Paper -
Privacy-Preserving Keystroke Analysis using Fully Homomorphic Encryption & Differential Privacy
International Conference on Cyberworlds (CW 2021)
Paper -
Vit - Inception - GAN for Image Colourizing
The Conference on Machine Learning, Optimization and Data science (LOD)
Paper
Relevant Coursework
- On-Device Machine Learning (11-767): Built efficient ML systems via quantization, pruning, and neural architecture search.
- Large Language Models (11-667): Studied methods and applications of modern LLMs.
- Advanced NLP (11-711): Audited course covering advanced topics in Natural Language Processing.
- Neural Signal Processing (18-698): Learned neural decoding, firing rate estimation, and spike sorting.
- Computational Models of Neural Systems (15-883): Explored computational approaches to understanding neural function.
- Introduction to Machine Learning (18-661): Core principles and algorithms of machine learning.