LCIT-AISC-T3-S25/Group4
May 2025 – Jul 2025
3 Months active
Languages Used
CSSHTMLJupyter NotebookPythonSQLJavaScript
Technical Skills
CSS StylingData VisualizationDeep LearningExplainable AI (XAI)Frontend DevelopmentImage Analysis
19Preet
PROFILE
19preet
Over three months, Kalpesh Preet contributed to the LCIT-AISC-T3-S25/Group4 repository by developing end-to-end features spanning data visualization, model interpretability, and generative AI services. He implemented standardized JupyterLab theming using HTML and CSS, and integrated D3.js with Lodash for interactive charting. Kalpesh built reproducible model evaluation notebooks in Python, leveraging Keras and Scikit-learn for sentiment analysis and VGG16 comparisons. He also delivered a FastAPI-based image generation service using ONNX Runtime, and introduced frameworks for reinforcement learning agents and hyperparameter tuning. His work demonstrated depth in backend and frontend integration, supporting rapid experimentation and robust analytics without reported bugs.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
14Total
Bugs
0
Commits
14
Features
9
Lines of code
247,237
Activity Months3
Your Network
13 peopleSame Organization
@loyalistcollege.com4
Work History
July 2025
7 Commits • 5 Features
Jul 1, 2025July 2025 monthly summary for LCIT-AISC-T3-S25/Group4 focused on delivering end-to-end capabilities that accelerate analytics, ML experimentation, and AI-powered deployment. Key features delivered across the month include: 1) Data Visualization and Utility Library Integration — integrated core data visualization components with D3.js and Lodash utilities to enable comprehensive charting and data manipulation capabilities across the project. 2) Variational Autoencoder (VAE) for Image Generation — implemented a VAE model with training, evaluation (Inception Score, FID), and saving generated samples. 3) AI Model Tuning Experiments — added notebooks for hyperparameter tuning and optimization of AI models, including PEFT-based biomedical chatbot tuning and latent diffusion model tuning, with multiple rounds and evaluation setups. 4) Image Generation Service — launched an image generation service using FastAPI and ONNX Runtime with an endpoint to generate images from seeds and save outputs. 5) Reinforcement Learning Agents Framework — introduced a Python script framework to initialize, train, and test multiple RL agents (PPO and A2C) for prompt optimization, with callbacks and demonstration runs. No major bugs reported this month. Overall impact includes accelerated analytics workflows, scalable generative image capabilities, faster ML experimentation cycles, and a reusable RL experimentation framework. Technologies demonstrated include D3.js, Lodash, VAE, Inception Score, FID, PEFT, latent diffusion models, FastAPI, ONNX Runtime, PPO, and A2C.
7 Commits • 5 Features
Jul 1, 2025July 2025 monthly summary for LCIT-AISC-T3-S25/Group4 focused on delivering end-to-end capabilities that accelerate analytics, ML experimentation, and AI-powered deployment. Key features delivered across the month include: 1) Data Visualization and Utility Library Integration — integrated core data visualization components with D3.js and Lodash utilities to enable comprehensive charting and data manipulation capabilities across the project. 2) Variational Autoencoder (VAE) for Image Generation — implemented a VAE model with training, evaluation (Inception Score, FID), and saving generated samples. 3) AI Model Tuning Experiments — added notebooks for hyperparameter tuning and optimization of AI models, including PEFT-based biomedical chatbot tuning and latent diffusion model tuning, with multiple rounds and evaluation setups. 4) Image Generation Service — launched an image generation service using FastAPI and ONNX Runtime with an endpoint to generate images from seeds and save outputs. 5) Reinforcement Learning Agents Framework — introduced a Python script framework to initialize, train, and test multiple RL agents (PPO and A2C) for prompt optimization, with callbacks and demonstration runs. No major bugs reported this month. Overall impact includes accelerated analytics workflows, scalable generative image capabilities, faster ML experimentation cycles, and a reusable RL experimentation framework. Technologies demonstrated include D3.js, Lodash, VAE, Inception Score, FID, PEFT, latent diffusion models, FastAPI, ONNX Runtime, PPO, and A2C.
July 2025
June 2025
2 Commits • 2 Features
Jun 1, 2025June 2025 monthly work summary for LCIT-AISC-T3-S25/Group4 focused on delivering reproducible model evaluation notebooks to accelerate experimentation and enable data-driven decision-making. No explicit bug fixes were recorded for this period; the emphasis was on feature development and establishing robust evaluation pipelines that deliver actionable insights for model selection and performance tracking.
June 2025
2 Commits • 2 Features
Jun 1, 2025June 2025 monthly work summary for LCIT-AISC-T3-S25/Group4 focused on delivering reproducible model evaluation notebooks to accelerate experimentation and enable data-driven decision-making. No explicit bug fixes were recorded for this period; the emphasis was on feature development and establishing robust evaluation pipelines that deliver actionable insights for model selection and performance tracking.
May 2025
5 Commits • 2 Features
May 1, 2025Summary for 2025-05: Delivered two major features in LCIT-AISC-T3-S25/Group4: 1) JupyterLab UI Styling and Theming — introduced a standardized HTML/CSS styling file to unify icons, themes, toolbars, dialogs, and overall UI appearance, improving consistency and usability. Commit: 40a81224ceaee2fe56da29ea6dfce20bc158f10e. 2) Image Model Interpretability with LIME/SHAP (Notebooks & Code) — added end-to-end support for interpretability, including model loading, preprocessing, and SHAP/LIME visual explanations to indicate influential regions. Commits: c2454d404b814958187d3874864e7b89e30522d1; 8a440f376489aef006309df806bdb753dfbc9918; d68ccaa3f757035988e81414ee99c4bbb0219fcf; 709e5f249552dd76fee5d9da7ac456831757ef61. Major bugs fixed: none reported this month. Overall impact: enhances UI consistency, strengthens model interpretability, enabling faster decision-making and greater user trust. Technologies/skills demonstrated: HTML/CSS styling, JupyterLab integration, Python-based data/model preprocessing, LIME/SHAP interpretability, notebooks and code delivery.
5 Commits • 2 Features
May 1, 2025Summary for 2025-05: Delivered two major features in LCIT-AISC-T3-S25/Group4: 1) JupyterLab UI Styling and Theming — introduced a standardized HTML/CSS styling file to unify icons, themes, toolbars, dialogs, and overall UI appearance, improving consistency and usability. Commit: 40a81224ceaee2fe56da29ea6dfce20bc158f10e. 2) Image Model Interpretability with LIME/SHAP (Notebooks & Code) — added end-to-end support for interpretability, including model loading, preprocessing, and SHAP/LIME visual explanations to indicate influential regions. Commits: c2454d404b814958187d3874864e7b89e30522d1; 8a440f376489aef006309df806bdb753dfbc9918; d68ccaa3f757035988e81414ee99c4bbb0219fcf; 709e5f249552dd76fee5d9da7ac456831757ef61. Major bugs fixed: none reported this month. Overall impact: enhances UI consistency, strengthens model interpretability, enabling faster decision-making and greater user trust. Technologies/skills demonstrated: HTML/CSS styling, JupyterLab integration, Python-based data/model preprocessing, LIME/SHAP interpretability, notebooks and code delivery.
May 2025
Activity
Loading activity data...
Quality Metrics
Correctness74.4%
Maintainability75.8%
Architecture74.4%
Performance68.6%
AI Usage42.8%
Skills & Technologies
Programming Languages
CSSHTMLJavaScriptJupyter NotebookPythonSQL
Technical Skills
Agent ManagementBERTScoreBackend DevelopmentCSS StylingD3.jsData LoadingData PreprocessingData VisualizationDeep LearningExplainable AI (XAI)FastAPIFrontend DevelopmentHyperparameter TuningImage AnalysisImage Classification
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline