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@ReproAnalytics

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martinorkuma/README.md

👋 Hi, I’m Martin Orkuma

Data Scientist with an active Secret Clearance and over 4 years of experience turning data into clear insights and actionable strategies for making data-driven solutions.
My work focuses on building reproducible, data-driven pipelines that translate complex datasets into actionable insights.

I am currently pursuing a Master’s degree in Biological Data Science and have experience in statistical modeling, machine learning, and cross-platform analytics workflows.

📫 Connect with me on LinkedIn: https://www.linkedin.com/in/martin-orkuma/

🚀 Technical Toolkit

  • Programming & Scripting: Python, R, Bash (WSL/Linux)

  • Machine Learning & Analytics: Regression, Classification, Clustering, PCA, Model Evaluation, Cross Validation, TensorFlow, and PyTorch.

  • Statistical Methods: Hypothesis Testing, ANOVA, Experimental Design, Inferential Statistics, Longitudinal Data Analysis

  • Python: Pandas, NumPy, scikit-learn, scikit-image, statsmodels, , virtual environments (venv)

  • Data Management: SQL (Joins, Common Table Expressions, Window Functions), data cleaning and validation

  • Data Visualization: Matplotlib, Seaborn, Tableau, Power BI, and ggplot

  • Computer Vision / Imaging: OpenSlide, QuPath, whole-slide image (WSI) tiling, HSV-based tissue segmentation

  • Platforms & Tools: Git/GitHub, Jupyter, RStudio, WSL, Azure, Virtual environments


📂Featured Repositories

  • Naked Mole-Rat Ovarian Follicle Machine-Learning Project
    End-to-end machine learning pipeline for automated ovarian follicle segmentation and counting in naked mole rat histological images, integrating reproducible preprocessing, annotation, model training, and evaluation workflows.
  • Human Accelerated Regions Comparative Genomic Project
    Human Accelerated Regions (HARs) in Comparative Genomics: Association of Human-Lineage Accelerated Noncoding Regions with Brain Development and Function.
  • Anolis Ecomorph Classification
    An end-to-end biological data science project that uses Bash and Python to automate data ingestion, cleaning, exploratory analysis, feature engineering, and machine learning on Anolis lizard trait data to study evolutionary patterns and build a reproducible ML pipeline.
  • Comparative Fetal Overaian Reserve Analysis
    Comparative analysis of fetal ovarian reserve formation across three mammalian species using histological and biological datasets.

🎯 Interests & Focus Areas

  • Computational biology
  • Computer vision
  • Machine learning
  • Population health outcomes

Pinned Loading

  1. ReproAnalytics/nmr-ovarian-follicle-mlReproAnalytics/nmr-ovarian-follicle-mlPublic

    This project develops a machine-learning pipeline for the automated identification, segmentation, and quantification of ovarian follicles in histological images of naked mole rats.

    Jupyter Notebook 2

  2. fetal-ovarian-reserve-comparativefetal-ovarian-reserve-comparativePublic

    This project explores the formation of fetal ovarian reserve using comparative histology-derived metadata across mammalian species. Through structured exploratory analysis and visualization, it hig…

    R 1

  3. rodent-ovarian-follicle-mlrodent-ovarian-follicle-mlPublic

    Python 1

  4. har-comparative-genomicshar-comparative-genomicsPublic

    Human Accelerated Regions (HARs) in Comparative Genomics: Association of Human-Lineage Accelerated Noncoding Regions with Brain Development and Function

    Python

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
View martinorkuma's full-sized avatar
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followers
 · 16
following

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Achievement: QuickdrawAchievement: Pull Shark
Achievements
Achievement: QuickdrawAchievement: Pull Shark
Organizations
@ReproAnalytics

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martinorkuma/README.md
👋 Hi, I’m Martin Orkuma
Data Scientist with an active Secret Clearance and over 4 years of experience turning data into clear insights and actionable strategies for making data-driven solutions. 

My work focuses on building reproducible, data-driven pipelines that translate complex datasets into actionable insights.
I am currently pursuing a Master’s degree in Biological Data Science and have experience in statistical modeling, machine learning, and cross-platform analytics workflows.
📫 Connect with me on LinkedIn: https://www.linkedin.com/in/martin-orkuma/
🚀 Technical Toolkit
  • Programming & Scripting: Python, R, Bash (WSL/Linux)
  • Machine Learning & Analytics: Regression, Classification, Clustering, PCA, Model Evaluation, Cross Validation, TensorFlow, and PyTorch.
  • Statistical Methods: Hypothesis Testing, ANOVA, Experimental Design, Inferential Statistics, Longitudinal Data Analysis
  • Python: Pandas, NumPy, scikit-learn, scikit-image, statsmodels, , virtual environments (venv)
  • Data Management: SQL (Joins, Common Table Expressions, Window Functions), data cleaning and validation
  • Data Visualization: Matplotlib, Seaborn, Tableau, Power BI, and ggplot
  • Computer Vision / Imaging: OpenSlide, QuPath, whole-slide image (WSI) tiling, HSV-based tissue segmentation
  • Platforms & Tools: Git/GitHub, Jupyter, RStudio, WSL, Azure, Virtual environments

📂Featured Repositories
  • Naked Mole-Rat Ovarian Follicle Machine-Learning Project
    
End-to-end machine learning pipeline for automated ovarian follicle segmentation and counting in naked mole rat histological images, integrating reproducible preprocessing, annotation, model training, and evaluation workflows.
  • Human Accelerated Regions Comparative Genomic Project
    
Human Accelerated Regions (HARs) in Comparative Genomics: Association of Human-Lineage Accelerated Noncoding Regions with Brain Development and Function.
  • Anolis Ecomorph Classification
    
An end-to-end biological data science project that uses Bash and Python to automate data ingestion, cleaning, exploratory analysis, feature engineering, and machine learning on Anolis lizard trait data to study evolutionary patterns and build a reproducible ML pipeline.
  • Comparative Fetal Overaian Reserve Analysis
    
Comparative analysis of fetal ovarian reserve formation across three mammalian species using histological and biological datasets.

🎯 Interests & Focus Areas
  • Computational biology
  • Computer vision
  • Machine learning
  • Population health outcomes

Pinned
Loading
  1. ReproAnalytics/nmr-ovarian-follicle-mlReproAnalytics/nmr-ovarian-follicle-mlPublic
    
This project develops a machine-learning pipeline for the automated identification, segmentation, and quantification of ovarian follicles in histological images of naked mole rats.

    Jupyter Notebook
2

  2. fetal-ovarian-reserve-comparativefetal-ovarian-reserve-comparativePublic
    
This project explores the formation of fetal ovarian reserve using comparative histology-derived metadata across mammalian species. Through structured exploratory analysis and visualization, it hig…

    R
1

  3. rodent-ovarian-follicle-mlrodent-ovarian-follicle-mlPublic
    Python
1

  4. har-comparative-genomicshar-comparative-genomicsPublic
    
Human Accelerated Regions (HARs) in Comparative Genomics: Association of Human-Lineage Accelerated Noncoding Regions with Brain Development and Function

    Python

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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3
followers
 · 16
following

Achievements
Achievement: QuickdrawAchievement: Pull Shark
Achievements
Achievement: QuickdrawAchievement: Pull Shark
Organizations
@ReproAnalytics

Block or report martinorkuma

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martinorkuma/README.md
👋 Hi, I’m Martin Orkuma
Data Scientist with an active Secret Clearance and over 4 years of experience turning data into clear insights and actionable strategies for making data-driven solutions. 

My work focuses on building reproducible, data-driven pipelines that translate complex datasets into actionable insights.
I am currently pursuing a Master’s degree in Biological Data Science and have experience in statistical modeling, machine learning, and cross-platform analytics workflows.
📫 Connect with me on LinkedIn: https://www.linkedin.com/in/martin-orkuma/
🚀 Technical Toolkit
  • Programming & Scripting: Python, R, Bash (WSL/Linux)
  • Machine Learning & Analytics: Regression, Classification, Clustering, PCA, Model Evaluation, Cross Validation, TensorFlow, and PyTorch.
  • Statistical Methods: Hypothesis Testing, ANOVA, Experimental Design, Inferential Statistics, Longitudinal Data Analysis
  • Python: Pandas, NumPy, scikit-learn, scikit-image, statsmodels, , virtual environments (venv)
  • Data Management: SQL (Joins, Common Table Expressions, Window Functions), data cleaning and validation
  • Data Visualization: Matplotlib, Seaborn, Tableau, Power BI, and ggplot
  • Computer Vision / Imaging: OpenSlide, QuPath, whole-slide image (WSI) tiling, HSV-based tissue segmentation
  • Platforms & Tools: Git/GitHub, Jupyter, RStudio, WSL, Azure, Virtual environments

📂Featured Repositories
  • Naked Mole-Rat Ovarian Follicle Machine-Learning Project
    
End-to-end machine learning pipeline for automated ovarian follicle segmentation and counting in naked mole rat histological images, integrating reproducible preprocessing, annotation, model training, and evaluation workflows.
  • Human Accelerated Regions Comparative Genomic Project
    
Human Accelerated Regions (HARs) in Comparative Genomics: Association of Human-Lineage Accelerated Noncoding Regions with Brain Development and Function.
  • Anolis Ecomorph Classification
    
An end-to-end biological data science project that uses Bash and Python to automate data ingestion, cleaning, exploratory analysis, feature engineering, and machine learning on Anolis lizard trait data to study evolutionary patterns and build a reproducible ML pipeline.
  • Comparative Fetal Overaian Reserve Analysis
    
Comparative analysis of fetal ovarian reserve formation across three mammalian species using histological and biological datasets.

🎯 Interests & Focus Areas
  • Computational biology
  • Computer vision
  • Machine learning
  • Population health outcomes

Pinned
Loading
  1. ReproAnalytics/nmr-ovarian-follicle-mlReproAnalytics/nmr-ovarian-follicle-mlPublic
    
This project develops a machine-learning pipeline for the automated identification, segmentation, and quantification of ovarian follicles in histological images of naked mole rats.

    Jupyter Notebook
2

  2. fetal-ovarian-reserve-comparativefetal-ovarian-reserve-comparativePublic
    
This project explores the formation of fetal ovarian reserve using comparative histology-derived metadata across mammalian species. Through structured exploratory analysis and visualization, it hig…

    R
1

  3. rodent-ovarian-follicle-mlrodent-ovarian-follicle-mlPublic
    Python
1

  4. har-comparative-genomicshar-comparative-genomicsPublic
    
Human Accelerated Regions (HARs) in Comparative Genomics: Association of Human-Lineage Accelerated Noncoding Regions with Brain Development and Function

    Python

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
View martinorkuma's full-sized avatar
3
followers
 · 16
following

Achievements
Achievement: QuickdrawAchievement: Pull Shark
Achievements
Achievement: QuickdrawAchievement: Pull Shark
Organizations
@ReproAnalytics

Block or report martinorkuma

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martinorkuma/README.md
👋 Hi, I’m Martin Orkuma
Data Scientist with an active Secret Clearance and over 4 years of experience turning data into clear insights and actionable strategies for making data-driven solutions. 

My work focuses on building reproducible, data-driven pipelines that translate complex datasets into actionable insights.
I am currently pursuing a Master’s degree in Biological Data Science and have experience in statistical modeling, machine learning, and cross-platform analytics workflows.
📫 Connect with me on LinkedIn: https://www.linkedin.com/in/martin-orkuma/
🚀 Technical Toolkit
  • Programming & Scripting: Python, R, Bash (WSL/Linux)
  • Machine Learning & Analytics: Regression, Classification, Clustering, PCA, Model Evaluation, Cross Validation, TensorFlow, and PyTorch.
  • Statistical Methods: Hypothesis Testing, ANOVA, Experimental Design, Inferential Statistics, Longitudinal Data Analysis
  • Python: Pandas, NumPy, scikit-learn, scikit-image, statsmodels, , virtual environments (venv)
  • Data Management: SQL (Joins, Common Table Expressions, Window Functions), data cleaning and validation
  • Data Visualization: Matplotlib, Seaborn, Tableau, Power BI, and ggplot
  • Computer Vision / Imaging: OpenSlide, QuPath, whole-slide image (WSI) tiling, HSV-based tissue segmentation
  • Platforms & Tools: Git/GitHub, Jupyter, RStudio, WSL, Azure, Virtual environments

📂Featured Repositories
  • Naked Mole-Rat Ovarian Follicle Machine-Learning Project
    
End-to-end machine learning pipeline for automated ovarian follicle segmentation and counting in naked mole rat histological images, integrating reproducible preprocessing, annotation, model training, and evaluation workflows.
  • Human Accelerated Regions Comparative Genomic Project
    
Human Accelerated Regions (HARs) in Comparative Genomics: Association of Human-Lineage Accelerated Noncoding Regions with Brain Development and Function.
  • Anolis Ecomorph Classification
    
An end-to-end biological data science project that uses Bash and Python to automate data ingestion, cleaning, exploratory analysis, feature engineering, and machine learning on Anolis lizard trait data to study evolutionary patterns and build a reproducible ML pipeline.
  • Comparative Fetal Overaian Reserve Analysis
    
Comparative analysis of fetal ovarian reserve formation across three mammalian species using histological and biological datasets.

🎯 Interests & Focus Areas
  • Computational biology
  • Computer vision
  • Machine learning
  • Population health outcomes

Pinned
Loading
  1. ReproAnalytics/nmr-ovarian-follicle-mlReproAnalytics/nmr-ovarian-follicle-mlPublic
    
This project develops a machine-learning pipeline for the automated identification, segmentation, and quantification of ovarian follicles in histological images of naked mole rats.

    Jupyter Notebook
2

  2. fetal-ovarian-reserve-comparativefetal-ovarian-reserve-comparativePublic
    
This project explores the formation of fetal ovarian reserve using comparative histology-derived metadata across mammalian species. Through structured exploratory analysis and visualization, it hig…

    R
1

  3. rodent-ovarian-follicle-mlrodent-ovarian-follicle-mlPublic
    Python
1

  4. har-comparative-genomicshar-comparative-genomicsPublic
    
Human Accelerated Regions (HARs) in Comparative Genomics: Association of Human-Lineage Accelerated Noncoding Regions with Brain Development and Function

    Python

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
View martinorkuma's full-sized avatar
3
followers
 · 16
following

Achievements
Achievement: QuickdrawAchievement: Pull Shark
Achievements
Achievement: QuickdrawAchievement: Pull Shark
Organizations
@ReproAnalytics

Block or report martinorkuma

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martinorkuma/README.md
👋 Hi, I’m Martin Orkuma
Data Scientist with an active Secret Clearance and over 4 years of experience turning data into clear insights and actionable strategies for making data-driven solutions. 

My work focuses on building reproducible, data-driven pipelines that translate complex datasets into actionable insights.
I am currently pursuing a Master’s degree in Biological Data Science and have experience in statistical modeling, machine learning, and cross-platform analytics workflows.
📫 Connect with me on LinkedIn: https://www.linkedin.com/in/martin-orkuma/
🚀 Technical Toolkit
  • Programming & Scripting: Python, R, Bash (WSL/Linux)
  • Machine Learning & Analytics: Regression, Classification, Clustering, PCA, Model Evaluation, Cross Validation, TensorFlow, and PyTorch.
  • Statistical Methods: Hypothesis Testing, ANOVA, Experimental Design, Inferential Statistics, Longitudinal Data Analysis
  • Python: Pandas, NumPy, scikit-learn, scikit-image, statsmodels, , virtual environments (venv)
  • Data Management: SQL (Joins, Common Table Expressions, Window Functions), data cleaning and validation
  • Data Visualization: Matplotlib, Seaborn, Tableau, Power BI, and ggplot
  • Computer Vision / Imaging: OpenSlide, QuPath, whole-slide image (WSI) tiling, HSV-based tissue segmentation
  • Platforms & Tools: Git/GitHub, Jupyter, RStudio, WSL, Azure, Virtual environments

📂Featured Repositories
  • Naked Mole-Rat Ovarian Follicle Machine-Learning Project
    
End-to-end machine learning pipeline for automated ovarian follicle segmentation and counting in naked mole rat histological images, integrating reproducible preprocessing, annotation, model training, and evaluation workflows.
  • Human Accelerated Regions Comparative Genomic Project
    
Human Accelerated Regions (HARs) in Comparative Genomics: Association of Human-Lineage Accelerated Noncoding Regions with Brain Development and Function.
  • Anolis Ecomorph Classification
    
An end-to-end biological data science project that uses Bash and Python to automate data ingestion, cleaning, exploratory analysis, feature engineering, and machine learning on Anolis lizard trait data to study evolutionary patterns and build a reproducible ML pipeline.
  • Comparative Fetal Overaian Reserve Analysis
    
Comparative analysis of fetal ovarian reserve formation across three mammalian species using histological and biological datasets.

🎯 Interests & Focus Areas
  • Computational biology
  • Computer vision
  • Machine learning
  • Population health outcomes

Pinned
Loading
  1. ReproAnalytics/nmr-ovarian-follicle-mlReproAnalytics/nmr-ovarian-follicle-mlPublic
    
This project develops a machine-learning pipeline for the automated identification, segmentation, and quantification of ovarian follicles in histological images of naked mole rats.

    Jupyter Notebook
2

  2. fetal-ovarian-reserve-comparativefetal-ovarian-reserve-comparativePublic
    
This project explores the formation of fetal ovarian reserve using comparative histology-derived metadata across mammalian species. Through structured exploratory analysis and visualization, it hig…

    R
1

  3. rodent-ovarian-follicle-mlrodent-ovarian-follicle-mlPublic
    Python
1

  4. har-comparative-genomicshar-comparative-genomicsPublic
    
Human Accelerated Regions (HARs) in Comparative Genomics: Association of Human-Lineage Accelerated Noncoding Regions with Brain Development and Function

    Python

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
View martinorkuma's full-sized avatar
3
followers
 · 16
following

Achievements
Achievement: QuickdrawAchievement: Pull Shark
Achievements
Achievement: QuickdrawAchievement: Pull Shark
Organizations
@ReproAnalytics

Block or report martinorkuma

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martinorkuma/README.md
👋 Hi, I’m Martin Orkuma
Data Scientist with an active Secret Clearance and over 4 years of experience turning data into clear insights and actionable strategies for making data-driven solutions. 

My work focuses on building reproducible, data-driven pipelines that translate complex datasets into actionable insights.
I am currently pursuing a Master’s degree in Biological Data Science and have experience in statistical modeling, machine learning, and cross-platform analytics workflows.
📫 Connect with me on LinkedIn: https://www.linkedin.com/in/martin-orkuma/
🚀 Technical Toolkit
  • Programming & Scripting: Python, R, Bash (WSL/Linux)
  • Machine Learning & Analytics: Regression, Classification, Clustering, PCA, Model Evaluation, Cross Validation, TensorFlow, and PyTorch.
  • Statistical Methods: Hypothesis Testing, ANOVA, Experimental Design, Inferential Statistics, Longitudinal Data Analysis
  • Python: Pandas, NumPy, scikit-learn, scikit-image, statsmodels, , virtual environments (venv)
  • Data Management: SQL (Joins, Common Table Expressions, Window Functions), data cleaning and validation
  • Data Visualization: Matplotlib, Seaborn, Tableau, Power BI, and ggplot
  • Computer Vision / Imaging: OpenSlide, QuPath, whole-slide image (WSI) tiling, HSV-based tissue segmentation
  • Platforms & Tools: Git/GitHub, Jupyter, RStudio, WSL, Azure, Virtual environments

📂Featured Repositories
  • Naked Mole-Rat Ovarian Follicle Machine-Learning Project
    
End-to-end machine learning pipeline for automated ovarian follicle segmentation and counting in naked mole rat histological images, integrating reproducible preprocessing, annotation, model training, and evaluation workflows.
  • Human Accelerated Regions Comparative Genomic Project
    
Human Accelerated Regions (HARs) in Comparative Genomics: Association of Human-Lineage Accelerated Noncoding Regions with Brain Development and Function.
  • Anolis Ecomorph Classification
    
An end-to-end biological data science project that uses Bash and Python to automate data ingestion, cleaning, exploratory analysis, feature engineering, and machine learning on Anolis lizard trait data to study evolutionary patterns and build a reproducible ML pipeline.
  • Comparative Fetal Overaian Reserve Analysis
    
Comparative analysis of fetal ovarian reserve formation across three mammalian species using histological and biological datasets.

🎯 Interests & Focus Areas
  • Computational biology
  • Computer vision
  • Machine learning
  • Population health outcomes

Pinned
Loading
  1. ReproAnalytics/nmr-ovarian-follicle-mlReproAnalytics/nmr-ovarian-follicle-mlPublic
    
This project develops a machine-learning pipeline for the automated identification, segmentation, and quantification of ovarian follicles in histological images of naked mole rats.

    Jupyter Notebook
2

  2. fetal-ovarian-reserve-comparativefetal-ovarian-reserve-comparativePublic
    
This project explores the formation of fetal ovarian reserve using comparative histology-derived metadata across mammalian species. Through structured exploratory analysis and visualization, it hig…

    R
1

  3. rodent-ovarian-follicle-mlrodent-ovarian-follicle-mlPublic
    Python
1

  4. har-comparative-genomicshar-comparative-genomicsPublic
    
Human Accelerated Regions (HARs) in Comparative Genomics: Association of Human-Lineage Accelerated Noncoding Regions with Brain Development and Function

    Python

, 'i');
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injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Achievements
Achievement: QuickdrawAchievement: Pull Shark
Achievements
Achievement: QuickdrawAchievement: Pull Shark
Organizations
@ReproAnalytics

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martinorkuma/README.md
👋 Hi, I’m Martin Orkuma
Data Scientist with an active Secret Clearance and over 4 years of experience turning data into clear insights and actionable strategies for making data-driven solutions. 

My work focuses on building reproducible, data-driven pipelines that translate complex datasets into actionable insights.
I am currently pursuing a Master’s degree in Biological Data Science and have experience in statistical modeling, machine learning, and cross-platform analytics workflows.
📫 Connect with me on LinkedIn: https://www.linkedin.com/in/martin-orkuma/
🚀 Technical Toolkit
  • Programming & Scripting: Python, R, Bash (WSL/Linux)
  • Machine Learning & Analytics: Regression, Classification, Clustering, PCA, Model Evaluation, Cross Validation, TensorFlow, and PyTorch.
  • Statistical Methods: Hypothesis Testing, ANOVA, Experimental Design, Inferential Statistics, Longitudinal Data Analysis
  • Python: Pandas, NumPy, scikit-learn, scikit-image, statsmodels, , virtual environments (venv)
  • Data Management: SQL (Joins, Common Table Expressions, Window Functions), data cleaning and validation
  • Data Visualization: Matplotlib, Seaborn, Tableau, Power BI, and ggplot
  • Computer Vision / Imaging: OpenSlide, QuPath, whole-slide image (WSI) tiling, HSV-based tissue segmentation
  • Platforms & Tools: Git/GitHub, Jupyter, RStudio, WSL, Azure, Virtual environments

📂Featured Repositories
  • Naked Mole-Rat Ovarian Follicle Machine-Learning Project
    
End-to-end machine learning pipeline for automated ovarian follicle segmentation and counting in naked mole rat histological images, integrating reproducible preprocessing, annotation, model training, and evaluation workflows.
  • Human Accelerated Regions Comparative Genomic Project
    
Human Accelerated Regions (HARs) in Comparative Genomics: Association of Human-Lineage Accelerated Noncoding Regions with Brain Development and Function.
  • Anolis Ecomorph Classification
    
An end-to-end biological data science project that uses Bash and Python to automate data ingestion, cleaning, exploratory analysis, feature engineering, and machine learning on Anolis lizard trait data to study evolutionary patterns and build a reproducible ML pipeline.
  • Comparative Fetal Overaian Reserve Analysis
    
Comparative analysis of fetal ovarian reserve formation across three mammalian species using histological and biological datasets.

🎯 Interests & Focus Areas
  • Computational biology
  • Computer vision
  • Machine learning
  • Population health outcomes

Pinned
Loading
  1. ReproAnalytics/nmr-ovarian-follicle-mlReproAnalytics/nmr-ovarian-follicle-mlPublic
    
This project develops a machine-learning pipeline for the automated identification, segmentation, and quantification of ovarian follicles in histological images of naked mole rats.

    Jupyter Notebook
2

  2. fetal-ovarian-reserve-comparativefetal-ovarian-reserve-comparativePublic
    
This project explores the formation of fetal ovarian reserve using comparative histology-derived metadata across mammalian species. Through structured exploratory analysis and visualization, it hig…

    R
1

  3. rodent-ovarian-follicle-mlrodent-ovarian-follicle-mlPublic
    Python
1

  4. har-comparative-genomicshar-comparative-genomicsPublic
    
Human Accelerated Regions (HARs) in Comparative Genomics: Association of Human-Lineage Accelerated Noncoding Regions with Brain Development and Function

    Python

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
View martinorkuma's full-sized avatar
3
followers
 · 16
following

Achievements
Achievement: QuickdrawAchievement: Pull Shark
Achievements
Achievement: QuickdrawAchievement: Pull Shark
Organizations
@ReproAnalytics

Block or report martinorkuma

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martinorkuma/README.md
👋 Hi, I’m Martin Orkuma
Data Scientist with an active Secret Clearance and over 4 years of experience turning data into clear insights and actionable strategies for making data-driven solutions. 

My work focuses on building reproducible, data-driven pipelines that translate complex datasets into actionable insights.
I am currently pursuing a Master’s degree in Biological Data Science and have experience in statistical modeling, machine learning, and cross-platform analytics workflows.
📫 Connect with me on LinkedIn: https://www.linkedin.com/in/martin-orkuma/
🚀 Technical Toolkit
  • Programming & Scripting: Python, R, Bash (WSL/Linux)
  • Machine Learning & Analytics: Regression, Classification, Clustering, PCA, Model Evaluation, Cross Validation, TensorFlow, and PyTorch.
  • Statistical Methods: Hypothesis Testing, ANOVA, Experimental Design, Inferential Statistics, Longitudinal Data Analysis
  • Python: Pandas, NumPy, scikit-learn, scikit-image, statsmodels, , virtual environments (venv)
  • Data Management: SQL (Joins, Common Table Expressions, Window Functions), data cleaning and validation
  • Data Visualization: Matplotlib, Seaborn, Tableau, Power BI, and ggplot
  • Computer Vision / Imaging: OpenSlide, QuPath, whole-slide image (WSI) tiling, HSV-based tissue segmentation
  • Platforms & Tools: Git/GitHub, Jupyter, RStudio, WSL, Azure, Virtual environments

📂Featured Repositories
  • Naked Mole-Rat Ovarian Follicle Machine-Learning Project
    
End-to-end machine learning pipeline for automated ovarian follicle segmentation and counting in naked mole rat histological images, integrating reproducible preprocessing, annotation, model training, and evaluation workflows.
  • Human Accelerated Regions Comparative Genomic Project
    
Human Accelerated Regions (HARs) in Comparative Genomics: Association of Human-Lineage Accelerated Noncoding Regions with Brain Development and Function.
  • Anolis Ecomorph Classification
    
An end-to-end biological data science project that uses Bash and Python to automate data ingestion, cleaning, exploratory analysis, feature engineering, and machine learning on Anolis lizard trait data to study evolutionary patterns and build a reproducible ML pipeline.
  • Comparative Fetal Overaian Reserve Analysis
    
Comparative analysis of fetal ovarian reserve formation across three mammalian species using histological and biological datasets.

🎯 Interests & Focus Areas
  • Computational biology
  • Computer vision
  • Machine learning
  • Population health outcomes

Pinned
Loading
  1. ReproAnalytics/nmr-ovarian-follicle-mlReproAnalytics/nmr-ovarian-follicle-mlPublic
    
This project develops a machine-learning pipeline for the automated identification, segmentation, and quantification of ovarian follicles in histological images of naked mole rats.

    Jupyter Notebook
2

  2. fetal-ovarian-reserve-comparativefetal-ovarian-reserve-comparativePublic
    
This project explores the formation of fetal ovarian reserve using comparative histology-derived metadata across mammalian species. Through structured exploratory analysis and visualization, it hig…

    R
1

  3. rodent-ovarian-follicle-mlrodent-ovarian-follicle-mlPublic
    Python
1

  4. har-comparative-genomicshar-comparative-genomicsPublic
    
Human Accelerated Regions (HARs) in Comparative Genomics: Association of Human-Lineage Accelerated Noncoding Regions with Brain Development and Function

    Python