Academy Overview
The instructors of this academy are the postdoctoral scholars in the Eric and Wendy Schmidt AI in Science postdoctoral program and the Michigan Data Science Fellows program. The participants were exposed to advanced topics in data science and AI methods and their applications in science and engineering research.
Topics
- Bayesian inference through exact and approximate techniques.
- Real world applications and explanations of Natural Language Processing.
- Causal inference and its application in science and engineering research.
- Introduction to probabilistic programming languages.
- Explainable AI techniques and their applications.
Academy Details
- Skills in explainable AI, Bayesian inferencing, natural language processing, causal inferencing and real world applications of each
- Networking with instructors and fellow participants
- Certificate of completion
- A set of resources for continual improvement in the various technical domains
We sent payment instructions along with acceptance decisions.
- $1,000 for external participants (30% discount for U-M Alums)
- Thanks to support from the University, we are able to offer a reduced price of $50 for U-M personnel and students
>14 days before the first day: full refund minus $50 processing fee
Cancellation between 7 and 14 days of the first day: 50% refund
Less than 7 days: no refund
This academy workshop is open to U-M researchers and trainees, and those from the public and private sector who are interested in learning about incorporating data science and AI into their research.
- College-level math or statistics and beginner to intermediate understanding of machine learning techniques.
- Some coding experience is recommended.
- Students are expected to bring a laptop for programming components of the workshop.
Weiser Hall 10th floor – 500 Church St. Ann Arbor, MI 48109
There is no designated parking. The parking structure on Church street is available for those with blue, gold or industry passes, or the forest avenue parking garage if you don’t have a parking pass (though this costs per hour).
Academy Modules and Instructors
Yossi Cohen
Schmidt AI in Science Fellow, MIDAS
Explainable AI (XAI) has recently become a major field of research to address the need for better interpretability of data-driven models. The opaqueness of elaborate black-box learning models has impeded trust and acceptance, particularly in industries where high-stakes decision-making is common. This workshop will focus on equipping users with a set of tools to help them explain model behavior, with particular emphasis placed on techniques to obtain model-agnostic explanations for predictions.
This workshop will cover a range of topics related to XAI methodology, including inherently interpretable modeling, anomaly detection and interpretation, counterfactual explanations, model-specific explanations (e.g. TreeSHAP), and model-agnostic explanation techniques (e.g. LIME, SHAP). Attendees of this tutorial will gain hands-on experience applying XAI algorithms and visualizations to various datasets, imparting practical knowledge to enable and encourage users to implement these tools for their own needs.
Nathan Fox
Schmidt AI in Science Fellow, MIDAS
Natural Language Processing (NLP) is an Artificial Intelligence branch focused on enabling computers to understand, interpret, and generate human language. This field has garnered increasing attention due to recent advances in technology, including accessible chatbots such as ChatGPT. NLP has a broad range of scientific applications, such as language translation, sentiment analysis, speech recognition, information retrieval, and text summarization.
This workshop aims to equip participants with foundational NLP knowledge to enable coding for textual data analysis. It will primarily focus on practical applications of NLP models, utilizing big data sets scraped from social media websites such as Twitter. Throughout the workshop, participants will learn a range of methodologies and techniques that underlie NLP model development, culminating in sentiment analysis of data sets containing public attitudes and opinions on current environmental issues.
Bernardo Modenesi
Data Science Fellow, MIDAS
Causal Inference (CI) is the area of study that involves identifying and measuring cause-effect relationships among different variables. In order to do this, CI employs a statistical framework, with various inference methods and their respective assumptions with which it can disentangle correlation from causation. Given the interdisciplinary roots of CI, this workshop will cover methods developed from statistics, computer science, biostatistics, economics, etc., all of which are applicable in various fields of science. The workshop will cover a broad range of topics, including counterfactual inference, randomized experiments, natural experiments, quasi-experimental methods, and causal graphs. Participants will learn how to evaluate the validity of causal inference claims and how to address issues such as selection bias, confounding variables, and measurement error. By the end of this workshop, participants will be able to grasp the main principles of CI and will be able to apply those methods to real-world problems in science and engineering.
Maria Han Veiga
Data Science Fellow, MIDAS
Bayesian methods are a set of statistical techniques that use prior knowledge and probability theory to update beliefs and make predictions about uncertain events. These techniques are increasingly important in scientific computing where uncertainties and incomplete information play a role. This mini-tutorial will walk participants through the basic probabilistic concepts to give the understanding of how parameter estimation problems can be recast in the form of Bayesian inference, as well as equip them with hands-on tools for tackling such problems.
We will explain two main paradigms for posterior estimation: exact Bayesian inference and approximate techniques. We will also introduce probabilistic programming languages are in the modern landscape, demonstrating abilities of one of such easy-to-use software packages.
Lead Coordinator
Ken Reid
Data Science Fellow, MIDAS
Academy Schedule
8:30am – 9:00am : Session 1: Welcome, Coffee & Light Breakfast
9:00am – 10:30am : Session 2: Introduction to Probabilistic Programming (Maria Han Veiga)
10:30am – 10:45am : Break
10:45am – 12:00pm : Session 3: Hands-on with Probabilistic Programming (Maria Han Veiga)
12:00pm – 1:00pm : Networking Lunch
1:00pm – 2:15pm : Session 4: Introduction to Natural Language Processing (Nathan Fox)
2:15pm – 2:30pm : Break
2:30pm – 4:30pm : Session 5: Hands-on with Transformer based models (Nathan Fox)
4:30pm – 4:45pm : Conclusion
8:30am – 9:00am : Session 1: Welcome, Coffee & Light Breakfast
9:00am – 10:30am : Session 2: Introduction to Explainable AI (Yossi Cohen)
10:30am – 10:45am : Break
10:45am – 12:00pm : Session 3: Hands-on with Explainable AI (Yossi Cohen)
12:00pm – 1:00pm : Networking Lunch
1:00pm – 2:30pm : Session 4: Introduction to Causal Inference (Bernardo Modenesi)
2:30pm – 2:45pm : Break
2:45pm – 4:30pm : Session 5: Hands-on with Causal Inference estimation (Bernardo Modenesi)
4:30pm – 4:45pm : Conclusion
Questions? Contact Us.
For questions, please contact MIDAS Data Scientist and session coordinator Ken Reid (kenreid@umich.edu).