September 5, 2025
Subject: AI Research Scientist Career Path
Explored the career of an Artificial Intelligence Research Scientist, examining education requirements, job responsibilities, salary expectations, and the future of AI in various industries. Learned about the growing demand for AI skills and the diverse applications of AI technology.
September 12, 2025
Subject: Foundations of AI and Green Computing
Comprehensive analysis of six research sources covering green AI, AI's role in climate change, future AI predictions, privacy concerns, and medical applications. Gained foundational understanding of AI's environmental impact and the paradox of using energy-intensive technology to solve sustainability problems.
September 19, 2025
Subject: RAPFLO and Climate Resilience
Analysis of "Deep Learning Rapid Flood Risk Predictions for Climate Resilience Planning" by Yosri et al. Explored how hierarchical deep neural networks can predict floods more efficiently than traditional machine learning, using less energy while maintaining accuracy above 80% for 25-year predictions.
September 26, 2025
Subject: Machine Learning in Environmental Monitoring
Examined "Air Pollution Prediction with Machine Learning: A Case Study of Indian Cities" by Kumar & Pande. Learned how machine learning models can predict air quality patterns and the challenges of data variation due to events like COVID-19. Compared energy requirements of different AI model types.
October 2, 2025
Subject: Deep Learning in Environmental Applications
Proposed focusing ISM research on artificial intelligence and deep learning models for environmental hazard prediction. Outlined personal coding experience, mathematical background, and passion for combining AI with sustainability. Established goals for creating impactful AI models that can save lives.
Marking Period 2, 2025
Subject: Green AI and Sustainability Research
Comprehensive presentation synthesizing research on green AI, energy consumption of large language models, and sustainable computing practices. Presented findings to class and professionals, demonstrating understanding of AI's environmental impact and potential solutions.
January 16, 2026
Subject: LLM-Orchestrated AI Systems
Analysis of "Energy-Aware Data-Driven Model Selection in LLM-Orchestrated AI Systems" by Smirnova et al. Discovered how current AI orchestration frameworks waste energy through popularity-based model selection and qualitative data processing. Learned about implementing energy caps and quantitative metrics for sustainable AI.
January 21, 2026
Subject: Original Work Review & Mentorship
Second interview with Ms. Marta Adamska where she agreed to become my mentor. Discussed improvements to my original work website, explored the research paper on AI orchestration systems, and received guidance on making my prompt optimizer more accessible through GitHub Pages.
January 30, 2026
Subject: DVFS Settings and Performance
Analysis of "Investigating Energy Efficiency and Performance Trade-Offs in LLM Inference across Tasks and DVFS Settings" by Maliakel et al. Learned about Dynamic Voltage and Frequency Scaling (DVFS) for limiting model energy consumption. Discovered that Llama model was most energy-efficient across all tasks despite being less popular than GPT.
February 6, 2026
Subject: Final Product Planning
Discussed final product idea with mentor: creating a green AI orchestration framework. Ms. Adamska validated the project's feasibility and impact potential. Decided to make framework open-source for developer accessibility and consider building GreenInfer chatbot as front-end demonstration.
February 11, 2026
Subject: Framework Architecture & Timeline
Finalized project scope with mentor. Confirmed green orchestration framework is achievable within timeline. Will prioritize framework development first, then create GreenInfer chatbot interface if time permits. Discussed implementing energy caps, real-time tracking, and automated energy profiling tools.