Virtual Applied Data Science Training Institute (VADSTI)
AI/ML for Transforming Biomedical & Health Disparity Research
January 31, 2024 – March 28, 2024
About VADSTI
Technological advancements and efficient use of computational tools have made it possible to generate and store large amounts of heterogeneous and complex datasets in many disciplines, including public health, clinical, biomedical, and genomics. There is therefore increased demand for data analytics capabilities including artificial intelligence including (AI), and Machine learning (ML) to look at trends, predict outcomes, and make better clinical and health policy decisions. The Howard University Research Centers in Minority Institutions, AIM-AHEAD Data Science Training Core, and the Public Health Informatics Technology for District of Columbia (PHIT4DC) program is pleased to announce VADSTI 3.0, Spring 2024 Training Series to the Howard University community of researchers and beyond. The goal is to enhance data science capability and application by providing training in the foundations of programming and critical data analytic skills for planning and conducting research involving big data pertinent to biomedical and minority health and health disparities research. This Spring Training Series is project-based and will cover topics including AI and ML, Bias in AI/ML, Data Exploration and Visualization, Natural Language Processing and Large Language Models.
To register, click the following link. Register Now
For questions, contact VADSTI at vadsti@howard.edu or John Kwagyan, Ph.D. at jkwagyan@howard.edu
Program Objectives & Competencies
The primary objective of the 2024 VADSTI Spring Training Series is to provide training in data science fundamentals and cloud computing skills with hands-on application to minority health and health disparity datasets. Over the course of the training program, participants will:
- Be introduced to the foundations of AI and ML.
- Learn about various classification methods for ML.
- Learn about fairness and biases in AI and ML
- Learn about data exploration, and visualization using Power BI.
- Learn about natural language processing.
- Be introduced to large language models for healthcare data.
- Learn about security in the cloud environment.
Digital Certificate of Completion: Participants who complete all the modules and submit their projects in the VADSTI GibHub Data Science Project Portfolio will receive a verified digital certificate of completion.
Evaluation: At the end of each training module, you will be requested to complete electronic feedback forms on the extent to which expectations and objectives were met.
Registration & Fees: No fees for participation, but registration is required to attend.
VADSTI Training Program Schedule
No prerequisite for research knowledge topics. Basic undergraduate knowledge of algebra and
probability recommended for content knowledge topics. The training series consists of the
following modules.
Past Training Recordings
Participants are encouraged to review the lecture recordings of topics from 2023 Spring Training series.
Module 1
Introduction to AI and ML
Wednesday, January 31, & Thursday, February 1, 2024
11:00 AM – 2:00 PM EST
Module 2
Classification Models in Machine Learning
Wednesday, February 7, & Thursday, February 8, 2024
11:00 AM – 2:00 PM EST
Module 3A
Data Exploration and Visualization Using Power BI I
Wednesday, February 14, & Thursday, February 15, 2024
11:00 AM – 2:00 PM EST
Module 3B
Data Exploration Using Power BI II
Thursday, February 21 & Friday, February 22, 2024
11:00 AM – 2:00 PM EST
Module 4
Seminal Presentation on Bias in AI and ML
Thursday, February 28 & Friday, February 29, 2024
11:00 AM – 2:00 PM EST
Module 5
AI in Cybersecurity: The Good, the Bad, and the In-Between
Wednesday, March 13, & Thursday, March 14, 2024
11:00 AM – 2:00 PM EST
Module 6
Natural Language Processing
Wednesday, March 21 & Thursday, March 22, 2024
11:00 AM – 2:00 PM EST
Module 7
Large Language Models Application in HealthCare Data
Wednesday, March 28 & Thursday, March 29, 2024
11:00 AM – 2:00 PM EST
VADSTI Training Program Curriculum
Here are details for each of the modules
Wednesday, January 31, & Thursday, February 1, 2024
11:00 AM – 2:00 PM EST
INSTRUCTOR – Ebelechukwu Nwafor, Ph.D
This module aims to introduce students to the foundational concepts, methods, and applications of artificial intelligence (AI) and machine learning (ML) models applied within the healthcare domain. It seeks to convey the transformative potential of AI-driven innovations in healthcare, while addressing the theoretical and practical aspects. Upon completion of this module, you will be able to:
- Outline the difference between AI and ML
- Understand the foundations of AI and ML and their significance in healthcare.
- Recognize various healthcare data types and their potential for AI/ML applications.
- Identify opportunities and challenges associated with deploying AI in healthcare.
- Apply basic AI/ML techniques to real-world healthcare datasets and scenarios.
Wednesday, February 7, & Thursday, February 8, 2024
11:00 AM – 2:00 PM EST
INSTRUCTOR – Moussa Doumbia, Ph.D
In this module, I will explore various machine learning classification models in-depth, dissecting their theoretical foundations and practical implementations. Subsequent sections will individually address prominent models such as Decision Trees, Random Forests, Naive Bayes, K-nearest neighbors, and Support Vector Machines, detailing their operational methodologies, strengths, and constraints. A comparative analysis will further enhance understanding, highlighting criteria for model selection tied to specific project demands and data configurations. The utility of these models for healthcare decisions will be emphasized.
Wednesday, February 14, & Thursday, February 15, 2024
11:00 AM – 2:00 PM EST
INSTRUCTOR – Samuel Tweneboah-Koduah, Ph.D
In the digital economy, data is said to be the currency, the power of which depends on how best data analysts can explore, extract knowledge, and discover and visualize patterns, relationships, and perhaps outliers within available datasets. In this module, participants will learn how to use Power BI to connect and visualize data (through multiple views of data), make inference to support data-centric healthcare. Upon completion, participants will (i). understanding the data mining (exploration) process, (ii) learn about data extraction and transformation decisions and communicate and interpret results through interactive graphs.
Wednesday, February 21, & Thursday, February 28, 2024
11:00 AM – 2:00 PM EST
INSTRUCTOR – Samuel Tweneboah-Koduah, Ph.D
We continue with Module 3A to provide recipes for data preparation, exploration, and visualization, which are critical steps in any data science project. We will (i) learn about model building and (ii) to perform data visualization, including designing reports, dashboards and tiles
Wednesday, February 28, & Thursday, February 29, 2024
11:00 AM – 2:00 PM EST
4.1: Fairness and Bias in AI | Wednesday, 11:00- 12:15 PM
Presenter: Aylin Caliskan, Ph.D
4.2: Algorithm Bias and Real-World Consequences | Wednesday, 12:30- 2:00 PM
Presenter: Habeeb Oluwofobi, Ph.D
4.3: Can AI be Bias Free? | Thursday, 11:00- 12:15 PM
Presenter: Assya Pascalev, Ph.D
4.4: Explainable AI (XAI) and Interpretability in Algorithm Decision Making | Thursday, 12:30-2:00 PM
Presenter: Habeeb Oluwofobi, Ph.D
Howard University Spring Break
March 2-9, 2024
Wednesday, March 13, & Thursday, March 14, 2024
11:00 AM – 2:00 PM EST
INSTRUCTOR – Omoche C. Agada, Ph.D
The growing popularity of AI has created both opportunities and challenges in many spheres of human endeavor. AI and in particular Large Language Models (LLMs) like ChatGPT have provided the opportunity for rapid code generation for application development and complex problem troubleshooting. The capability to generate complex codes from scratch with limited coding skills has also created an opportunity for armature threat actors popularly known as script kiddies to generate sophisticated attacks against some of the world’s most critical cyber infrastructure. This module will examine several opportunities as well as threats that advancements in AI bring to the world of cybersecurity. A major highlight will be an exercise demonstrating the application of AI in the development of an intrusion detection system, a machine leaning – based cybersecurity device.
Wednesday, March 21, & Thursday, March 22, 2024
11:00 AM – 2:00 PM EST
INSTRUCTOR – Anietie Andy, Ph.D
This module will provide introductory concepts to the field of Natural Language Processing (NLP). This module will focus on selected NLP topics and NLP tasks. Specific topics will include Prompt Engineering, Instruction Following, and GPT; Text Classification and Sentiment Analysis. On completion of this module, you will be familiar with various NLP tasks.
Wednesday, March 28, & Thursday, March 29, 2023
11:00 AM – 2:00 PM EST
INSTRUCTOR – Anietie Andy, Ph.D
In this module, we will discuss the application of large language models (LLM) to clinical such as data from electronic medical records and healthcare data from other sources. We explore the strengths and limitations of LLMs and their potential to improve the efficiency and effectiveness of clinical research. We will conduct several analyses in which large language models are applied to these data.