MTech AI · IISc Bangalore

Rishav
Saha

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AI researcher at the intersection of computer vision, model security, and responsible autonomous systems. Currently at Mercedes-Benz R&D India.

Currently

AI Research Scientist at MBRDI — autonomous driving perception, fairness evaluation, LLM security.

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Publication

ACM ASIACCS 2026

SMAUG-SCA.
First power side-channel attack on a post-quantum standard.

Conducted the first power side-channel attack on SMAUG-T, the Korean post-quantum cryptography KEM standard, using ChipWhisperer-based trace acquisition and leakage analysis on embedded implementations.

Demonstrated key extraction with >99.9% success probability using fewer than 10 power traces — a result with direct implications for real-world deployment of KpqC-standard devices.


Experience

Mercedes-Benz R&D India Present
AI Research Scientist
  • Benchmarked CLRNet and open-source lane detection models on CARLA-generated datasets — 96.64% daylight accuracy, maintaining above 90% under rain, fog, dawn, and night.
  • Architected an end-to-end synthetic data pipeline combining CARLA simulator scenarios with Gemini Flash for scalable autonomous driving training data.
  • Developed a fairness metric quantifying model consistency across weather conditions, enabling objective performance-parity assessment.
  • Contributed to the Evalanche LLM evaluation framework — model accuracy, fairness, and adversarial robustness assessments.
Scaler Jun 2025 – Dec 2025
Data Science Intern
  • Built a CatBoost-based lead conversion model achieving 88.67% validation accuracy, improving lead targeting efficiency by 8.87%.
  • Engineered features from raw CRM data; optimized through cross-validation and hyperparameter tuning to reduce noise in downstream predictions.

Projects

SLM Finetuning

Compressed Multi-Task Qwen3 for Code Intelligence

Fine-tuned Qwen3-0.6B with Unsloth and LoRA on CodeXGLUE tasks — defect detection, clone detection, summarization. Merged task-specific adapters into a unified multi-task model, then compressed via Wanda pruning, post-training quantization, and SVD-based FFN decomposition.

Search

Semantic Search over 300K MS MARCO Passages

Hybrid retrieval pipeline combining BM25, FAISS-based dense vector search, and Reciprocal Rank Fusion (RRF). 300K 768-dim embeddings via Sentence Transformers. Best NDCG@10: 0.9356. Best MRR: 0.9167.

LLM

Multi-Turn Text-to-SQL with Phi-3 & LoRA

Conversational Text-to-SQL assistant fine-tuned on Phi-3 Mini 4K using LoRA and Unsloth. 94.0% Exact Match Accuracy. 91.22% Average Token F1. SQLGlot-based validation achieving 100% syntactically valid SQL.

LLM Evaluation

Long-Context QA on the NovelQA Benchmark

Evaluated InternLM2-Chat on NovelQA (200K+ token contexts). Implemented RAG, Multi-hop Event Graph Reasoning, and HyDE retrieval strategies. Improved accuracy from 45.45% → 54.54%.


Skills

Languages

PythonC++C

Frameworks & Tools

PyTorchHugging FaceLangchainFastAPIDockerGit

Libraries

NumPyPandasScikit-LearnMatplotlibFAISS

Education

Aug 2023 – Jun 2025Bangalore, India

MTech in Artificial Intelligence

Indian Institute of Science (IISc)

Aug 2019 – Jun 2023Kolkata, India

BTech in Computer Science & Engineering

University of Engineering and Management


Blog


Let's connect.

* IISC BANGALORE · AI RESEARCH SCIENTIST · MBRDI