AI/ML Engineer & Researcher — from vision robustness evaluation to production LLM systems 🚀
class SarthakChauhan:
def __init__(self):
self.role = "AI/ML Engineer & Researcher"
self.education = "B.Tech CSE (AI/ML) @ Bennett University"
self.achievements = "CGPA: 9.42/10.0 | Dean's List (Top 10%)"
self.location = "India 🇮🇳"
def current_work(self):
return [
"🔬 Vision model robustness: benchmarking 12 architectures across IN-Val/V2/R/A (ECE, NLL, per-class dispersion)",
"🚗 Fog-highway dehazing benchmark: 10 architectures, 15–20 dB PSNR gap finding (DICCT 2026)",
"🏫 Production RAG pipeline @ Cograd: 50+ teachers, 6 schools, 42% prep-time reduction",
"💬 Hinglish abuse detection: XLM-R + BiGRU, F1 0.866 on 700K posts (IEEE AICAPS 2026)"
]
def skills(self):
return {
"AI/ML": ["Deep Learning", "NLP", "Computer Vision", "RAG", "PINNs"],
"LLM Stack": ["LangChain", "LlamaIndex", "CrewAI", "AutoGen", "LangGraph"],
"Frameworks": ["PyTorch", "TensorFlow", "Hugging Face", "FastAPI"],
"MLOps": ["Docker", "MLflow", "W&B", "ONNX", "TensorRT"]
}
def fun_fact(self):
return "I think my GPU works harder than I do 😄"|
Distribution Shift & Model Calibration Investigating how natural and rendition-based shifts expose calibration failures in vision models. Found training recipe dominates over architecture family: ResNet-50-V1 (ECE=0.039) vs V2 (ECE=0.410) at comparable Top-1 accuracy. |
Evaluation Beyond Average Accuracy Building benchmarking frameworks that measure worst-group robustness, per-class dispersion, ECE, and NLL across architecture families (ResNets, ViTs, Swin-T, ConvNeXt, MaxViT) on IN-Val, IN-V2, IN-R, IN-A. |
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Text-to-SQL System with Multi-Agent Orchestration 🗃️ Handles 200+ table databases with GPT-4o + LangChain
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Real-time AI Doubt Clustering for Live Classes ⚡ 6-stage async pipeline with dedicated Redis workers per stage
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Enterprise RAG System @ Cograd (Team Project) 🏫 Deployed across 50+ teachers in 6 schools
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AI Jewelry Design Studio 💎 Fine-tuned SDXL via LoRA (FP16, 10K steps) on self-curated 6,157-image dataset
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📌 Ongoing Research: Vision Model Robustness Evaluation — benchmarking 12 architectures (ResNets, ViTs, Swin-T, ConvNeXt, MaxViT) across IN-Val, IN-V2, IN-R, IN-A · Measuring ECE, NLL & per-class dispersion · W&B Report ↗ · PyTorch · In Progress
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| 🥇 Hackathons & Competitions | 🎓 Academic | 📜 Certifications |
|---|---|---|
| Amazon ML Challenge 2024 Top 0.5% (409/74,823) |
Dean's List Award Top 10% |
IBM Machine Learning |
| IIT Bombay Convolve Top 50/4,189 Teams |
CGPA: 9.42/10.0 | Deep Learning Specialization (Andrew Ng) |
| Kharagpur Data Science Semi-finalist |
Published @ IC3SE 2025 | GenAI with LLMs |
| AI Agents Intensive — Google × Kaggle 2025 |
📄 "Hinglish Abusive Comment Detection Using Transformer-Based Models" (First Author) Accepted at AICAPS 2026, IEEE Kerala Section — XLM-R + BiGRU, F1 0.866 on 700K+ code-mixed posts
📄 "Image and Video Dehazing for Dense-Fog Indian Highway Scenarios" (First Author) Accepted at DICCT 2026 — Benchmarked 10 dehazing methods; identified 15–20 dB PSNR gap between synthetic benchmarks and real dense-fog conditions
📄 "Deep Learning-Based Brain Tumour Identification" (Second Author) Accepted & Presented at IC3SE 2025, IEEE UP Section — Residual CNN, 97.10% accuracy at 5M parameters
I'm always excited to collaborate on innovative AI/ML projects!
💼 Open to: Research Collaborations | Open Source | AI/ML Internships
📧 Reach me at: sarthak4156@gmail.com