Data Science Fellow.
Fall 2024
Warren Alpert Medical School
Data Science Fellow.
Fall 2024
Warren Alpert Medical School
Data Science Fellow.
Fall 2024
Warren Alpert Medical School
Data Science Fellowship
Data Science Fellowship
Data Science Fellowship
Bridging the gap between data science, artificial intelligence, and clinical research.
Role
Role
Role
AI/ML Fellow
AI/ML Fellow
AI/ML Fellow
Duration
Duration
Duration
Aug 2024 - Jan 2025
Aug 2024 - Jan 2025
Aug 2024 - Jan 2025
Visit Site
Visit Site
Visit Site
Team
Team
Team
Fellows:
Dave (🙋🏻), Peter Graham
Fellows:
Dave (🙋🏻), Peter Graham
Fellows:
Dave (🙋🏻), Peter Graham
Fellowship Advisor:
Dr. Linda Clark
Fellowship Advisor:
Dr. Linda Clark
Fellowship Advisor:
Dr. Linda Clark
Faculty Partner:
Dr. Thanujaa Subramaniam
Faculty Partner:
Dr. Thanujaa Subramaniam
Faculty Partner:
Dr. Thanujaa Subramaniam
Disciplines
Disciplines
Disciplines
Artificial Intelligence
Machine Learning
Data Science
Education
Overview
Overview
Overview
Each year, the Data Science Institute and the Sheridan Center for Teaching & Learning select around 15 students to work with faculty members from a variety of disciplines to enhance course curricula, assignment structures, and supporting materials in response to the increasing demand for data-driven research. As a Data Science Fellow, I led the development of an educational conference on Data Science, AI, and ML in collaboration with Dr. Thanujaa Subramaniam from the Warren Alpert Medical School.
During my fellowship, I:
Learned about various teaching techniques and theories.
Led the development of interactive, educational modules for clinical researchers.
Hosted an 8-hour-long conference.
Each year, the Data Science Institute and the Sheridan Center for Teaching & Learning select around 15 students to work with faculty members from a variety of disciplines to enhance course curricula, assignment structures, and supporting materials in response to the increasing demand for data-driven research. As a Data Science Fellow, I led the development of an educational conference on Data Science, AI, and ML in collaboration with Dr. Thanujaa Subramaniam from the Warren Alpert Medical School.
During my fellowship, I:
Learned about various teaching techniques and theories.
Led the development of interactive, educational modules for clinical researchers.
Hosted an 8-hour-long conference.
Each year, the Data Science Institute and the Sheridan Center for Teaching & Learning select around 15 students to work with faculty members from a variety of disciplines to enhance course curricula, assignment structures, and supporting materials in response to the increasing demand for data-driven research. As a Data Science Fellow, I led the development of an educational conference on Data Science, AI, and ML in collaboration with Dr. Thanujaa Subramaniam from the Warren Alpert Medical School.
During my fellowship, I:
Learned about various teaching techniques and theories.
Led the development of interactive, educational modules for clinical researchers.
Hosted an 8-hour-long conference.
The Problem
The lack of a shared understanding between data scientists and clinical researchers hinders effective collaboration in clinical research.
The lack of a shared understanding between data scientists and clinical researchers hinders effective collaboration in clinical research.
The lack of a shared understanding between data scientists and clinical researchers hinders effective collaboration in clinical research.
Through a series of meetings and interviews, the fellows identified an increasing demand for technical knowledge among clinical researchers to enable proactive collaboration with data science experts during clinical research. However, current clinical researchers and medical students lack access to additional resources for comprehensive reviews of technical concepts, which hinders effective communication between researchers and technical experts in critical areas such as data gathering, model selection, and result evaluation.
Through a series of meetings and interviews, the fellows identified an increasing demand for technical knowledge among clinical researchers to enable proactive collaboration with data science experts during clinical research. However, current clinical researchers and medical students lack access to additional resources for comprehensive reviews of technical concepts, which hinders effective communication between researchers and technical experts in critical areas such as data gathering, model selection, and result evaluation.
Through a series of meetings and interviews, the fellows identified an increasing demand for technical knowledge among clinical researchers to enable proactive collaboration with data science experts during clinical research. However, current clinical researchers and medical students lack access to additional resources for comprehensive reviews of technical concepts, which hinders effective communication between researchers and technical experts in critical areas such as data gathering, model selection, and result evaluation.
Solution
A tailored 1-day conference to help researchers at Warren Alpert Medical School.
A tailored 1-day conference to help researchers at Warren Alpert Medical School.
A tailored 1-day conference to help researchers at Warren Alpert Medical School.
Specifically, the conference focused on:
Python Primer
Data Science Concepts and Data Visualization
Introduction to Artificial Intelligence and Machine Learning
Specifically, the conference focused on:
Python Primer
Data Science Concepts and Data Visualization
Introduction to Artificial Intelligence and Machine Learning
Process
Developing Modules
Since our client — the Warren Alpert Medical School — had a clear request, and the fellows validated the need through interviews, we moved on to the development phase. The workflow involved four key aspects:
Mastering Technical Content
Effectively delivering technical concepts required a deep understanding of the material.
I spent most of my time reviewing technical materials and transforming them into digestible snippets of information.
Reference Studies
Examples were drawn from existing medical and clinical research publications.
I focused on researching commonly used data types and topics.
Communication
Conducted weekly mock presentations with Dr. Subramaniam to refine our delivery and ensure clarity.
Iteration
Continuously improved slide quality and the overall presentation through feedback and revisions.
Since our client — the Warren Alpert Medical School — had a clear request, and the fellows validated the need through interviews, we moved on to the development phase. The workflow involved four key aspects:
Mastering Technical Content
Effectively delivering technical concepts required a deep understanding of the material.
I spent most of my time reviewing technical materials and transforming them into digestible snippets of information.
Reference Studies
Examples were drawn from existing medical and clinical research publications.
I focused on researching commonly used data types and topics.
Communication
Conducted weekly mock presentations with Dr. Subramaniam to refine our delivery and ensure clarity.
Iteration
Continuously improved slide quality and the overall presentation through feedback and revisions.
Interactive Modules & Code Exercises
Interactive Modules & Code Exercises
Interactive Modules & Code Exercises
Slides and content customized for non-technical clinical researchers
Slides and content customized for non-technical clinical researchers
Final Deliverables
Educational Modules & Conference
The Data Science Fellows program is fundamentally about learning how to effectively share knowledge with the community. To achieve this, we dedicated significant time to studying teaching methods, cognitive load theories, and techniques for scaffolding complex ideas — tools I used to develop different sections of the conference.
When developing the modules, I incorporated:
Interactive coding exercises (2 CoLab notebooks)
Lecture slides (140+ slides)
Pre- and post-conference surveys
Group discussions
For the full conference structure and syllabus, click here.
The Data Science Fellows program is fundamentally about learning how to effectively share knowledge with the community. To achieve this, we dedicated significant time to studying teaching methods, cognitive load theories, and techniques for scaffolding complex ideas — tools I used to develop different sections of the conference.
When developing the modules, I incorporated:
Interactive coding exercises (2 CoLab notebooks)
Lecture slides (140+ slides)
Pre- and post-conference surveys
Group discussions
For the full conference structure and syllabus, click here.
Conclusion
Growth & Reflection
Helping clinicians better understand the tools at their disposal and dedicating our time to finding the best ways to support them was not only an educational experience but also a rewarding one.
While working as a data science fellow, I also learned:
How to apply principles from Flawless Consulting to gather context, information, and client needs effectively.
How to deliver technical concepts by leveraging different cognitive types, scaffolding techniques, and incorporating breaks for a non-technical audience.
How to pace myself for an 8-hour-long conference.
Helping clinicians better understand the tools at their disposal and dedicating our time to finding the best ways to support them was not only an educational experience but also a rewarding one.
While working as a data science fellow, I also learned:
How to apply principles from Flawless Consulting to gather context, information, and client needs effectively.
How to deliver technical concepts by leveraging different cognitive types, scaffolding techniques, and incorporating breaks for a non-technical audience.
How to pace myself for an 8-hour-long conference.