Hi, I’m Abhijit!
I am a first-year Master’s student in Computer Science at UMass Amherst, with a focus on ML systems and trustworthy AI. I have hands-on experience building scalable federated learning systems, including Flotilla, a modular and resilient federated learning framework developed during my time at the Indian Institute of Science, and FedProj, an algorithm addressing catastrophic forgetting under non-IID data distributions. Working on these systems made me deeply interested in privacy-preserving deployment, heterogeneous infrastructure, and making federated systems production-ready using Trusted Execution Environments and Confidential Containers.
I am currently learning CUDA and large-scale distributed training across multi-GPU setups. My broader interests span Generative Modeling, Reinforcement Learning, Robotics, and AI Alignment.
Experience
- Associate Software Engineer
IUDX-IISc (2024-2025) - FL Research Intern
DREAM:LAB, IISc (2023-2024) - NLP Research Intern
Carelon (2023)
Education
- MS CS, UMass Amherst
2025-Present - BTech CS, VIT
2020-2024
Interests
- Federated Learning and Secure ML
- Diffusion Models and World Models
- RL, Robotics, and AI Alignment
Skills
Primary language across research projects, federated systems, and ML experiments
Built CNNs, Transformers, diffusion pipelines, and custom dataloaders for FL frameworks
Deployed Confidential Containers with runtime attestation across multi-cloud infrastructure
Used heavily across research deployments and personal projects
Architected TEE-based federated learning deployments with cryptographic remote attestation
Scaled Flotilla to 1,024 clients and orchestrated large-scale FL experiments
Fine-tuned BERT, RoBERTa, and clinical language models with LoRA and prompt-based methods
Systems scripting and automation for heterogeneous cluster deployments
Explored through graduate robotics coursework at UMass Amherst
Used for data management and experiment logging across research projects
Data preprocessing, baselines, and statistical analysis of federated learning results
Applied in LLM reasoning and agentic framework experiments
Education
Graduate student focusing on Federated Learning, Generative Modeling, and Trustworthy AI.
Relevant Courses:
- Robotics
- Advanced Generative AI
- Advanced NLP
- Advanced ML
- Distributed Systems
Undergraduate degree in Computer Science with focus on machine learning, distributed systems, and software engineering.
Experience
- Architected confidential computing solutions using Multi-Party Computation, Differential Privacy, and AMD SEV-SNP TEEs with cryptographic remote attestation for healthcare federated learning deployments.
- Deployed Confidential Containers (Peer Pods) on Kubernetes with runtime attestation, enabling privacy-preserving ML across multi-cloud infrastructure while reducing setup time by 60% and attack surface by 85%.
- Co-developed Flotilla, an in-house FL framework surpassing Flower, FedML, and OpenFL in scalability and reliability.
- Executed 90+ experiments on heterogeneous clusters (46 Raspberry Pis, 12 Nvidia Jetsons) benchmarking IID and non-IID performance.
- Scaled Flotilla to 1,024 AWS clients, achieving 55% faster performance than Flower by eliminating communication bottlenecks.
- Designed CPU and memory visualizations contributing to a publication in Elsevier JPDC.
- Engineered a Memory Module into ClinicalBERT, DistilBERT, RoBERTa, and Clinical-Longformer for sentiment analysis, achieving 90% accuracy.
- Transitioned centralized models to federated learning, improving accuracy by 2% and reducing training time by 30% with LoRA, P-Tuning, and Prompt Tuning.
Selected Projects
Publications
A flexible and lightweight federated learning platform for edge environments with modular strategy support, asynchronous updates, and strong fault tolerance.