IBM/materials
Nov 2024 – Apr 2026
2 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
Data LoadingData VisualizationMachine LearningMachine Learning ExamplesPythonchemoinformatics
Lisa Hamada
PROFILE
Lisa Hamada
Worked on the IBM/materials repository, delivering enhancements to data pipelines, model assets, and experiment workflows. Integrated Mordred and MorganFingerprint molecular descriptors with improved data handling and NaN robustness, strengthening model training and evaluation. Updated the FM4M notebook with expanded matplotlib-based visualizations and clearer documentation, while streamlining asset management by replacing obsolete pickle files with binary representations. Refactored the TDiMS project structure, reorganizing files and updating experiment scripts to support new datasets and output directories. Leveraged Python, pandas, and Jupyter Notebook to improve maintainability, reproducibility, and onboarding, enabling faster iteration and more reliable machine learning workflows for chemoinformatics applications.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
10Total
Bugs
0
Commits
10
Features
5
Lines of code
8,974
Activity Months2
Your Network
1090 peopleSame Organization
@ibm.com1079
Arun VMember
Aayush-PanchalMember
Aayush KumarMember
Abdallah AbdelaalMember
Abhijit DeokarMember
Abhina SreeMember
Abhishek KumarMember
Abhishek Kr SrivastavMember
Abraham Lara GranadosMember
Work History
April 2026
2 Commits • 1 Features
Apr 1, 2026April 2026: Delivered key refactor and workflow enhancements for IBM/materials (TDiMS), improving maintainability, usability, and experiment reproducibility. Reorganized repository structure, added new example scripts and notebooks, updated documentation and the experiment workflow, and refined run_nested_cv_experiment to accommodate a different dataset and output directory. Updated README with notebook dependencies to streamline setup. These changes reduce onboarding time, minimize setup friction, and enable faster iteration of experiments while ensuring reproducibility.
2 Commits • 1 Features
Apr 1, 2026April 2026: Delivered key refactor and workflow enhancements for IBM/materials (TDiMS), improving maintainability, usability, and experiment reproducibility. Reorganized repository structure, added new example scripts and notebooks, updated documentation and the experiment workflow, and refined run_nested_cv_experiment to accommodate a different dataset and output directory. Updated README with notebook dependencies to streamline setup. These changes reduce onboarding time, minimize setup friction, and enable faster iteration of experiments while ensuring reproducibility.
April 2026
November 2024
8 Commits • 4 Features
Nov 1, 2024Concise monthly summary for IBM/materials (2024-11): Key features delivered and improvements: - Mordred and MorganFingerprint descriptor integration with enhanced data handling, initialization, and NaN robustness for model training/evaluation, plus clearer model descriptions. - Representation model assets enhancement: added new binary representation model files and removed obsolete pickle files to streamline assets and reduce clutter. - FM4M notebook documentation and visualization improvements: updated example notebook to include matplotlib usage and documentation sections for Architecture and Workflow with placeholder visuals. - Demo/app usability and model options expansion: removed private-launch parameter share and enabled more models in the demo, improving usability and model selection for users. Major bugs fixed: - Implemented exclusion of rows containing NaN values in preprocessing, improving data cleanliness and model reliability. - General robustness enhancements for Mordred/MorganFingerprint functions and data handling to reduce edge-case failures. Overall impact and accomplishments: - Strengthened data pipeline reliability and model interpretability by integrating robust descriptors and clearer model descriptions. - Reduced asset clutter, improving maintainability and deployment readiness. - Expanded user-facing capabilities (more models in demo) and enhanced documentation for easier adoption and onboarding. Technologies/skills demonstrated: - Molecular descriptors: Mordred, MorganFingerprint - Data preprocessing and NaN handling - Python-based data pipelines and feature engineering - Notebook documentation, visualization (matplotlib) and Workflow/Architecture documentation - Asset management and repo hygiene (replacing old pickle assets with binaries)
November 2024
8 Commits • 4 Features
Nov 1, 2024Concise monthly summary for IBM/materials (2024-11): Key features delivered and improvements: - Mordred and MorganFingerprint descriptor integration with enhanced data handling, initialization, and NaN robustness for model training/evaluation, plus clearer model descriptions. - Representation model assets enhancement: added new binary representation model files and removed obsolete pickle files to streamline assets and reduce clutter. - FM4M notebook documentation and visualization improvements: updated example notebook to include matplotlib usage and documentation sections for Architecture and Workflow with placeholder visuals. - Demo/app usability and model options expansion: removed private-launch parameter share and enabled more models in the demo, improving usability and model selection for users. Major bugs fixed: - Implemented exclusion of rows containing NaN values in preprocessing, improving data cleanliness and model reliability. - General robustness enhancements for Mordred/MorganFingerprint functions and data handling to reduce edge-case failures. Overall impact and accomplishments: - Strengthened data pipeline reliability and model interpretability by integrating robust descriptors and clearer model descriptions. - Reduced asset clutter, improving maintainability and deployment readiness. - Expanded user-facing capabilities (more models in demo) and enhanced documentation for easier adoption and onboarding. Technologies/skills demonstrated: - Molecular descriptors: Mordred, MorganFingerprint - Data preprocessing and NaN handling - Python-based data pipelines and feature engineering - Notebook documentation, visualization (matplotlib) and Workflow/Architecture documentation - Asset management and repo hygiene (replacing old pickle assets with binaries)
Activity
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Quality Metrics
Correctness94.0%
Maintainability88.0%
Architecture88.0%
Performance90.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data LoadingData VisualizationMachine LearningMachine Learning ExamplesPythonPython scriptingchemoinformaticsdata analysisdata managementdata modelingdata processingdata representationdata sciencefile handlingmachine learning
Repositories Contributed To
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