YASH KHAIRNAR
[ 001 / ABOUT ]
Hi, I'm a Master's student in Computer Science at San José State University.
I currently work as a Graduate Researcher with the SJSU Research Foundation with Dr. Tshukudu and collaborate with Dr. Yuejiang Liu at Stanford University. My research focuses on self-improving, action-conditioned world models and long-horizon planning for intelligent agents. In Summer 2026, I also served as an AI/ML Instructor for Stanford AI4ALL, working with students on artificial intelligence, machine learning, and robotics.
My broader interests span Large Language Models, Agentic AI, Reinforcement Learning, Computer Vision, Robotics, and Autonomous Systems. Prior to graduate school, I worked on industry projects involving the development, training, fine-tuning, optimization, and deployment of machine learning models. My experience spans AI/ML engineering, computer vision, backend systems, agent-based architectures, and autonomous systems.
I particularly enjoy working at the intersection of world models, agents, and embodied intelligence, while continuing to explore how learning-based systems can become more adaptive, capable, and autonomous.
I'm always interested in connecting with researchers, engineers, and organizations working on AI, machine learning, robotics, world models, and intelligent systems.
Currently seeking New Grad 2027 full-time opportunities in AI/ML, Machine Learning Engineering, and Software Engineering.
DOWNLOAD RÉSUMÉ PDFCURRENT INTERESTS
01World Models
02Core ML/DL, Computer Vision, LLMs, RL and Multimodal systems
03Agentic AI Systems
PAPER REPRODUCTIONS & LABSPaper Reproductions & LabsA collection of my implementations and reproductions of various AI/ML research papers.EXPLORE LABS[ 002 / EXPERIENCE ]
August 2026 - Present
San Jose State University
Working on a JEPA-inspired, action-conditioned learner world model that predicts student knowledge-state transitions from programming trajectories and supports adaptive instructional planning.
June 2026 - July 2026
Stanford University
Mentored 30+ students in AI/ML concepts, Python programming, and project development through hands-on workshops and technical mentorship.
July 2024 - June 2025
Accurate Industrial Controls Pvt. Ltd.
Engineered an end-to-end predictive maintenance system for industrial generators using anomaly detection and RUL prediction models, achieving 93% accuracy across 500+ hours of telemetry data.
August 2023 - November 2023
Accurate Industrial Controls Pvt. Ltd.
Built an anomaly detection system for copper coil inspection using PatchCore and YOLO, integrating object tracking and image compression to improve inference efficiency.
November 2022 - February 2023
ResoluteAI Software
Developed a facial recognition attendance system using MTCNN for detection and a custom ANN classifier for embeddings, achieving 90% accuracy with less than 2% FPR.
[ 003 / PROJECTS ]
FIG. 012026 / EXPERIMENT 01
RAG application that indexes technical documentation URLs and answers questions with retrieved context.
Python, FastAPI, LangChain, RAG
FIG. 022026 / EXPERIMENT 02
Cafe discovery platform with personalized recommendations, location-aware search, and a mobile/web product surface.
Next.js, React Native, AWS, collaborative filtering
FIG. 032025 / EXPERIMENT 03
EEG-to-text translation system using VQ-VAE, BART Transformer, and LSTM Seq2Seq with attention.
PyTorch, Hugging Face, Flask, Next.js
FIG. 042025 / EXPERIMENT 04
Serverless email marketing platform with analytics, AI-assisted content generation, and multi-region AWS deployment.
AWS Lambda, S3, API Gateway, DynamoDB, SQS, SNS, SES, Terraform
FIG. 052025 / EXPERIMENT 05
Classified malware into 12 families using DenseNet121 CNNs on 10K+ samples, reaching 95% accuracy.
PyTorch, DenseNet121, SVM, grid search
FIG. 062025 / EXPERIMENT 06
Multi-agent collaborative IDE where humans and AI agents can edit the same codebase with CRDT-backed sync.
CrewAI, Gemini orchestration, Automerge, React[ 004 / EDUCATION ]
San Jose, CA
San Jose State University
Graduate study focused on AI/ML systems, distributed systems, and applied software engineering.
Pune, India
Savitribai Phule Pune University
Undergraduate foundation in computer engineering, algorithms, databases, systems, and applied machine learning.
[ 005 / WRITING ]
MEDIUM / AI NOTES
Deep dive into the transformer architecture that revolutionized machine learning, from attention mechanisms to practical implementations.
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ESSAY 02MEDIUM / AI NOTES
Exploring how the RNNSearch mechanism solved the information bottleneck in sequence-to-sequence models for translation tasks.
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ESSAY 03MEDIUM / AI NOTES
Comparative study using Google Speech Commands dataset. Understanding the trade-offs between architectural choices in recurrent networks.
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ESSAY 04MEDIUM / AI NOTES
On model efficiency, pruning strategies, and the importance of optimization for production ML systems and edge deployment.
READ ARTICLE[ 006 / CONTACT ]