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Gracenote
Data Scientist
June 26, 2026 – Present
saaki
June 25, 2025 – Present
SA-AKI Mortality Prediction — Survival analysis & binary classification for Sepsis-Associated Acute Kidney Injury using MIMIC-IV. CatBoost, LightGBM, XGBoost, Logistic Regression. AUROC ~0.80+
View Projectopenrouter-mcp-multimodal
March 26, 2025 – Present
MCP server for OpenRouter — chat with 300+ LLMs (Claude, Gemini, GPT), analyze images / audio / video, generate images / speech / music / video (Veo 3.1, Sora, Seedance, Wan) from Claude Desktop, Cursor, Kiro, VS Code.
View ProjectVirtual-Self-Driving-Car-AI-using-Kivy-and-Reinforcement-Learning
February 15, 2018 – Present
I implemented a car which is built using Kivy framework and then used 3 sensors and trained it using Reinforcement Learning
View ProjectGANs-using-pyTorch-from-Scratch
February 15, 2018 – Present
My favourite project till now . In this GAN I implemented a simple generator which generates Some samples based on a dataset and gets creative after a few Epochs
View ProjectRecurrent-Neural-Networks-to-predict-Google-Stock-Price
February 15, 2018 – Present
I tried to predict google stock price using LSTMs
View ProjectLSTM-using-pyTorch
February 15, 2018 – Present
I implemented a challenging LSTM network using pyTorch on the MNIST datase
View ProjectLinear-Discriminant-Analysis
January 16, 2018 – Present
We used LDA in this project to expand the capabilities of our Logistic Regression Classifier in both Python and R
View ProjectUpper-Confidence-Bounds
January 16, 2018 – Present
I implemented the reinforcement learning based model Upper Confidence Bound in both Python and R
View ProjectPolynomial-Regression
January 16, 2018 – Present
Implementation of Polynomial Regression Model using both Python and R
View ProjectMultiple-Linear-Regression
January 16, 2018 – Present
Implementation of Multiple Linear Regression both in Python and R
View ProjectCultural Fit Analysis
The candidate's projects show a strong personal interest in machine learning and data science, aligning with a data-driven culture. The diversity of projects, from traditional ML to deep learning and reinforcement learning, indicates a proactive and curious mindset. However, the lack of team projects or professional experience details makes it difficult to fully assess cultural fit beyond technical alignment.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions are concise, but there's no information on collaboration, problem-solving approach, or communication style in a team setting.