Hi, I’m Abhijit!
I’m a Master’s student in Computer Science at UMass Amherst and a Graduate Research Assistant at Argonne National Laboratory, working on federated learning systems and ML infrastructure for APPFL, a DOE federated learning platform. There, I built and open-sourced HiveWatch, an observability toolkit for distributed ML training, and engineered the platform’s Kubernetes-native provisioning system on NERSC Spin.
Before that, I worked on scalable federated learning systems, including Flotilla, a modular and resilient framework I helped develop at the Indian Institute of Science, and FedProj, an algorithm addressing catastrophic forgetting under non-IID data, published in TMLR. Working on these systems got me interested in the challenges of heterogeneous infrastructure, privacy-preserving deployment, and making federated systems production-ready using trusted execution environments. My broader interests span distributed systems, generative modeling, and robotics.
Experience
- Graduate Research Assistant
Argonne National Laboratory (2026-Present) - 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, flow-matching world models, and custom dataloaders for FL frameworks
Distributed training and GPU programming for large-scale ML on NERSC/Polaris HPC
Deployed Confidential Containers and Kubernetes-native provisioning systems (APPFL on NERSC Spin) with runtime attestation
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
Experiment tracking, observability (HiveWatch), and distributed training tooling across APPFL
Fine-tuned BERT, RoBERTa, and clinical language models with LoRA and prompt-based methods
Systems scripting and automation for heterogeneous cluster deployments
Deployed a full ROS autonomy stack (SLAM, AMCL, move_base) on real hardware for an autonomous delivery robot
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
- AI Alignment
- Advanced NLP
- Advanced ML
- Distributed Systems
Undergraduate degree in Computer Science with focus on machine learning, distributed systems, and software engineering.
Experience
- Engineered a Kubernetes-native provisioning system on NERSC Spin for APPFL, a DOE federated learning platform, dynamically spinning up org-scoped, oauth2-proxy-authenticated Jupyter environments with Keycloak SSO and an invite-based FastAPI/PostgreSQL onboarding pipeline.
- Built and open-sourced HiveWatch, a framework-agnostic observability toolkit for distributed ML training with real-time metric dashboards, geographic client visualization, and experiment tracking via WandB and MLflow, adopted across APPFL.
- Engineered federated LoRA fine-tuning on the Evo-2 model pipeline for genomic AI training across institutions, reducing cross-site data transfer to 20 MB (down 74%) on Polaris/NERSC HPC.
- Designed and implemented FedProj, a gradient-projection algorithm for federated learning that prevents catastrophic forgetting on non-IID data, achieving 2–9% accuracy gains over state-of-the-art FL baselines across 100+ clients.
- Orchestrated 70+ large-scale training runs across CV (CIFAR-10/100, CINIC-10) and NLP (MNLI, SST-2, MARC) benchmarks; work published in TMLR.
- 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.