update with new pubs and completed courses

_pages/about.md

@@ -9,10 +9,10 @@ I'm a data scientist, bioinformatics scientist, and neuroscientist.
 
 I studied biochemistry at UCLA, earned my Ph.D. in neuroscience at Yale University, and performed postdoctoral research at the Salk Institute. My research on neural plasticity was fueled by my interests in learning. I investigated signaling mechanisms in a neurogenic niche and the molecular changes that occur within neurons in response to an organism having new experiences. A list of my publications can be found [here](https://scholar.google.com/citations?user=wGG8V78AAAAJ&hl=en).
 
-My academic work led to life science industry positions. I initially worked at Fluidigm Corporation as a product applications scientist, a multi-faceted role that included generating customer-facing data analyses, educating internal staff and customers, and serving as a technical liasion for product management. From there, I served as a bioinformatics scientist, supporting R&D efforts. Examples of external-facing data content I generated can be found [here](https://benslack19.github.io/projects/5_professional_projects/). This was followed by a fellowship at [Insight Data Science](https://www.insightdatascience.com) and another at UCSF and UC Berkeley as a [Johnson & Johnson Data Science Health Innovation Fellow](https://innovateforhealth.berkeley.edu), researching social determinants of health. I am currently a Staff Machine Learning Researcher at [Seer](https://seer.bio) where I analyze mass spectrometry proteomics data from patient cohorts.
+My academic work led to life science industry positions. I initially worked at Fluidigm Corporation as a product applications scientist, a multi-faceted role that included generating customer-facing data analyses, educating internal staff and customers, and serving as a technical liasion for product management. From there, I served as a bioinformatics scientist, supporting R&D efforts. Examples of external-facing data content I generated can be found [here](https://benslack19.github.io/projects/5_professional_projects/). This was followed by fellowships at [Insight Data Science](https://www.insightdatascience.com) and UCSF/UC Berkeley as a [Johnson & Johnson Data Science Health Innovation Fellow](https://innovateforhealth.berkeley.edu), researching social determinants of health. During my time as a Staff Machine Learning Researcher at [Seer](https://seer.bio), I analyzed mass spectrometry proteomics data from patient cohorts, including a [study](https://www.biorxiv.org/content/10.1101/2024.01.05.574446v1) on Alzheimer's disease patients.
 
-I have extensive experience performing independent research, analyzing and interpreting complex data sets, optimizing data analysis, and communicating my results through oral and written presentations. A glance over my shoulder and you'll likely see a Linux terminal, a Python Jupyter notebook, or RStudio.
+I have extensive experience performing independent research, analyzing and interpreting complex data sets, optimizing data analysis, and communicating my results through oral and written presentations.
 
 In parallel to my work experiences, I have been engaged in various educational efforts. While I was an undergraduate, I served as an AmericaReads tutor. During graduate school, I mentored elementary and middle-school age children on a weekly basis, in addition to my teaching fellow responsibilities. When I was in my postdoc, I served as a consultant to educators interested in knowing more about the brain. I then started to guest lecture on neural stem cells in an online course. Finally, my first biotech role included instruction in classroom, lab, and online formats.
 
-In my spare time, I can be found surfing, hiking, or eating Mexican food with my family somewhere in California. I can be reached at ben.lacar AT gmail.com.
+In my spare time, I can be found surfing, hiking, or eating Mexican food with my family somewhere in California. I can be reached at ben.lacar AT gmail.com.

_pages/ds-courses.md

@@ -5,28 +5,29 @@ author_profile: false
 permalink: /ds-courses/
 ---
 
-This is a list of courses I've taken as I've delved deeper into data science and statistics.
+This is a list of courses I've taken as I've delved deeper into coding, data science, and statistics.
 
 | Course  | Example projects or notes |
 | ---  |  --- | 
-| Programming for Everybody ([Coursera](https://www.coursera.org/specializations/python))  | |
-| Python Data Structures ([Coursera](https://www.coursera.org/specializations/python))  | |
-| Using Python to Access Web Data ([Coursera](https://www.coursera.org/specializations/python))  | |
-| Using Databases with Python ([Coursera](https://www.coursera.org/specializations/python))  | |
-| Capstone: Retrieving, Processing, and Visualizing Data with Python ([Coursera](https://www.coursera.org/specializations/python))  | [Gene subpaneling project](https://github.com/benslack19/gene_subpanel) |
-| Introduction to Data Science in Python ([Coursera](https://www.coursera.org/learn/python-data-analysis))  | [Housing prices (final assignment)](https://github.com/benslack19/intro_ds_housing_prices) |
-| Applied Plotting, Charting & Data Representation in Python ([Coursera](https://www.coursera.org/learn/python-plotting))  | [Correlation of spending and winning (final assignment)](https://github.com/benslack19/applied_plotting_python_padres) |
-| Machine Learning ([Coursera](https://www.coursera.org/learn/machine-learning))  | [All assignments](https://github.com/benslack19/machine_learning_assignments) |
+| Programming for Everybody ([Coursera](https://www.coursera.org/specializations/python))  | Completed |
+| Python Data Structures ([Coursera](https://www.coursera.org/specializations/python))  | Completed |
+| Using Python to Access Web Data ([Coursera](https://www.coursera.org/specializations/python))  | Completed |
+| Using Databases with Python ([Coursera](https://www.coursera.org/specializations/python))  | Completed |
+| Capstone: Retrieving, Processing, and Visualizing Data with Python ([Coursera](https://www.coursera.org/specializations/python))  | Completed, [Gene subpaneling project](https://github.com/benslack19/gene_subpanel) |
+| Introduction to Data Science in Python ([Coursera](https://www.coursera.org/learn/python-data-analysis))  | Completed, [Housing prices (final assignment)](https://github.com/benslack19/intro_ds_housing_prices) |
+| Applied Plotting, Charting & Data Representation in Python ([Coursera](https://www.coursera.org/learn/python-plotting))  | Completed, [Correlation of spending and winning (final assignment)](https://github.com/benslack19/applied_plotting_python_padres) |
+| Machine Learning ([Coursera](https://www.coursera.org/learn/machine-learning))  | Completed all [assignments](https://github.com/benslack19/machine_learning_assignments) |
 | The Complete SQL Bootcamp ([Udemy](https://www.udemy.com/the-complete-sql-bootcamp/))  | |
 | A/B Testing by Google ([Udacity](https://www.udacity.com/course/ab-testing--ud257))  | |
 | Statistics and probability ([Khan Academy](https://www.khanacademy.org/math/statistics-probability))  | |
-| Bayesian Statistics: From Concept to Data Analysis ([Coursera](https://www.coursera.org/learn/bayesian-statistics))  | |
+| Bayesian Statistics: From Concept to Data Analysis ([Coursera](https://www.coursera.org/learn/bayesian-statistics))  | Completed |
 | Python for Data Structures, Algorithms, and Interviews! ([Udemy](https://www.udemy.com/course/python-for-data-structures-algorithms-and-interviews/))  | |
 | Intermediate SQL ([DataCamp](https://www.datacamp.com/courses/intermediate-sql)) |  |
-| Neural Networks and Deep Learning ([Coursera](https://www.coursera.org/learn/neural-networks-deep-learning)) |  |
-| Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization ([Coursera](https://www.coursera.org/learn/deep-neural-network)) |  |
-| Structuring Machine Learning Projects ([Coursera](https://www.coursera.org/learn/machine-learning-projects)) |  |
+| Neural Networks and Deep Learning ([Coursera](https://www.coursera.org/learn/neural-networks-deep-learning)) | Completed |
+| Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization ([Coursera](https://www.coursera.org/learn/deep-neural-network)) | Completed |
+| Structuring Machine Learning Projects ([Coursera](https://www.coursera.org/learn/machine-learning-projects)) | Completed |
 | Statistical Rethinking ([by Richard McElreath](https://xcelab.net/rm/statistical-rethinking/)) | Completed homework assignments and led informal discussion group |
+| Python Object-Oriented Programming ([LinkedIn Learning](https://www.linkedin.com/learning/python-object-oriented-programming-22888296/python-object-oriented-programming?u=185169545) | Completed |
 
 Tool development through projects.
 

_posts_drafts/2024-05-09-cause-and-effect.ipynb

@@ -51,7 +51,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# draw DAG with dustin's code"
+    "# draw DAG with dustin's code\n"
    ]
   },
   {

_projects/4_academic_projects.md

@@ -7,8 +7,11 @@ header:
 
 ---
 
-The following are selected examples of projects from my graduate neuroscience studies at Yale, post-doctoral neuroscience and molecular biology research at the Salk Institute, and research work as a data science fellow with UCSF and UC Berkeley.
+The following are selected examples of projects from my graduate neuroscience studies at Yale, post-doctoral neuroscience and molecular biology research at the Salk Institute, health informatics research as a data science fellow with UCSF and UC Berkeley, and as a machine learning researcher at Seer.
 
+- Identification of Novel Biomarkers for Alzheimer’s Disease and Related Dementias Using Unbiased Plasma Proteomics [(preprint)](https://www.biorxiv.org/content/10.1101/2024.01.05.574446v1)
+
+- Automatic extraction of social determinants of health from medical notes of chronic lower back pain patients [(link)](https://academic.oup.com/jamia/article/30/8/1438/7133957)
 
 - Identifying social risks from notes in the Electronic Health Record and evaluating associations with emergency department visits [(in prep, link)](https://github.com/benslack19/pediatric_oncology_EHR)