M N Aditya

📧 mnaditya05@gmail.com

📍 Chennai, India

I am a final-year B.Tech student in Electronics and Communication Engineering with a specialization in Data Science at SRMIST Kattankulathur. My research sits at the intersection of federated learning, medical image analysis, explainable AI (XAI), and TinyML — building AI systems that are efficient, trustworthy, and deployable in resource-constrained environments.

I have had the opportunity to work with research groups at IIT Kharagpur, IIT Jammu, NIT Kurukshetra, NIT Trichy, and IIIT Allahabad, as well as industry experience at SRM Technologies and Farmience AgroTech. My work spans federated learning for healthcare, document image forgery detection, dental X-ray segmentation, and financial distress prediction.

I have authored and co-authored 13 publications — including journal articles, conference papers, and book chapters — in venues spanning computer vision, biomedical engineering, and applied AI. I am actively seeking research opportunities (RA/MS/PhD) in federated learning, privacy-preserving ML, and efficient deep learning.

Research Interests: Federated Learning · Medical Image Analysis · Explainable AI · TinyML · Self-Supervised Learning · Agentic AI

selected publications

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Development of a Device Prototype for Orthodontic Debonding Force Measurement

Pandurangan Harikrishnan, Varadaraju Magesh, Moodur Narayanan Aditya, and 3 more authors

Trends in Biomaterials and Artificial Organs, 2026

Grant No: CRG/2022/005834

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Orthodontic bracket debonding requires controlled force application to avoid enamel damage, yet no standardised measurement device exists for clinical practice. This work presents a purpose-built prototype that quantifies debonding forces in real time, enabling practitioners to apply evidence-based force thresholds. The device is validated against conventional load-cell benchmarks and demonstrates sufficient accuracy and repeatability for clinical deployment, contributing a tangible tool to DST-SERB funded research on orthodontic biomechanics.
@article{aditya2026orthodontic, title = {Development of a Device Prototype for Orthodontic Debonding Force Measurement}, author = {Harikrishnan, Pandurangan and Magesh, Varadaraju and Aditya, Moodur Narayanan and Varun, Thoodur Venkatachalapathy and Srikrishnan, Vijay and Devulapalli, Rahul}, journal = {Trends in Biomaterials and Artificial Organs}, year = {2026}, note = {Grant No: CRG/2022/005834}, }
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FedDQN-Ctrl: Dynamic Client Compression for Federated Learning

Moodur Narayanan Aditya and Ghanapriya Singh

In SiMPaCT 2026, 2026

Forthcoming

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Communication overhead is a primary bottleneck in federated learning, particularly when clients operate under heterogeneous bandwidth constraints. FedDQN-Ctrl addresses this by training a Deep Q-Network controller that dynamically selects per-client compression ratios each round, balancing model accuracy against communication cost. The controller learns a policy that adapts to instantaneous network conditions and client data heterogeneity, achieving comparable accuracy to uncompressed baselines at a fraction of the communication budget.
@inproceedings{aditya2026federated, title = {{FedDQN-Ctrl}: Dynamic Client Compression for Federated Learning}, author = {Aditya, Moodur Narayanan and Singh, Ghanapriya}, booktitle = {SiMPaCT 2026}, year = {2026}, note = {Forthcoming} }
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TinyFedPrompt: Federated Prompt Tuning of BioMedCLIP for Privacy-Preserving Pneumonia Detection on Edge Hardware

Moodur Narayanan Aditya, Keerthivasan Krishnamoorthy, Kartikeya Aripirala, and 1 more author

In PACCAS 2026, 2026

Forthcoming

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Deploying medical AI on edge hardware while preserving patient privacy demands both computational efficiency and strong privacy guarantees. TinyFedPrompt replaces full fine-tuning of BioMedCLIP with federated prompt tuning: each client learns a small set of soft prompt tokens locally while the frozen backbone remains on-device. Raw patient images never leave the client, and only compact prompt updates are aggregated each round. The system achieves competitive pneumonia detection accuracy on Raspberry Pi-class hardware with under 5 MB of communication per client per round, demonstrating practical privacy-preserving medical AI at the edge.
@inproceedings{aditya2026tinyfed, title = {{TinyFedPrompt}: Federated Prompt Tuning of BioMedCLIP for Privacy-Preserving Pneumonia Detection on Edge Hardware}, author = {Aditya, Moodur Narayanan and Krishnamoorthy, Keerthivasan and Aripirala, Kartikeya and Jansi, R.}, booktitle = {PACCAS 2026}, year = {2026}, note = {Forthcoming} }