class KaranKendre:
def __init__(self):
self.name = "Karan Kendre"
self.location = "San Jose, CA 🌉"
self.education = "MS Computer Science @ Northeastern University"
self.gpa = "4.0 / 4.0 ⭐"
self.current_role = "Research Assistant - Explainable AI"
self.interests = [
"LLMs & Agentic AI",
"Distributed Systems",
"Deep Learning & Computer Vision",
"Cloud Architecture",
"Quantum Computing"
]
def say_hi(self):
print("Thanks for dropping by! Let's build something amazing together 🚀")
me = KaranKendre()
me.say_hi()
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XAI: Summarization Assistant for Research
|
|
timeline
title Career Timeline
2021-2022 : TrakLabs (Refactor Academy)
: Full Stack Developer Intern
: Angular, Node.js, SPA Development
2022-2024 : CloudBloom Systems
: Software Developer
: Java Spring Boot, K8s, Microservices
: Golang CLI, 5G Systems, Event-Driven Architecture
2024-2026 : Northeastern University
: MS Computer Science (GPA 4.0)
2025-Present : Research Assistant
: Explainable AI, Transformers, NLP
|
RAG-powered AI agent converting user requirements into production-ready GCP infrastructure via Terraform with agentic orchestration, real-time knowledge retrieval, and human oversight. |
LLM-driven web reconstruction using Gemini API with FastAPI backend (<200ms response) and Next.js frontend. 85% structural accuracy in HTML/CSS generation. |
|
Intelligent tool for detecting circular dependencies in codebases using graph algorithms. Identifies cyclic imports to maintain clean architecture. |
U-Net based road segmentation achieving IoU scores of 0.78-0.81 with custom-curated dataset for dynamic road environments. |
|
CNN-based autoencoder reconstructing clean 5-qubit states from noisy density matrices using Google's Cirq. Achieved 0.47 average fidelity improvement. |
|
| Difficulty | Solved | Beats |
|---|---|---|
| 🟢 Easy | 124/918 | 93.18% |
| 🟡 Medium | 229/1978 | 94.95% |
| 🔴 Hard | 45/896 | 90.34% |
Machine Learning & Artificial Intelligence Honors
Savitribai Phule Pune University — Recognized for excellence in ML/AI during undergraduate studies
K. Kendre — arXiv preprint arXiv:2509.16242, 2025
| Certification | Provider |
|---|---|
| Generative AI with LLMs |  |
| Deep Learning Specialization | |
| ML Specialization | |
| Quantum Error Correction |  |
┌──────────────────────────────────────────────────────────────────────────┐
│ │
│ 🤖 LLMs & AGENTIC AI → RAG, Fine-tuning, Prompt Engineering │
│ 🏗️ SCALABLE SYSTEMS → Microservices, Event-Driven, K8s │
│ 🧠 DEEP LEARNING → Transformers, CNNs, Computer Vision │
│ ⚡ PERFORMANCE → 50-66% latency reduction achievements │
│ 🌐 CLOUD NATIVE → GCP, AWS, Terraform, Docker │
│ 🔮 QUANTUM COMPUTING → Qiskit, Cirq, Noise Reduction │
│ │
└──────────────────────────────────────────────────────────────────────────┘
"The best way to predict the future is to create it."
Open to collaborations on LLMs, AI/ML research, distributed systems, and more!
