Machine Learning for Your Research

by PICSciE/Research Computing

Training/Workshop Research & Data Analysis

Wed, Mar 30, 2022

4 PM – 6 PM EDT (GMT-4)

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This workshop will give an overview of several modern supervised and unsupervised machine learning methods. We will discuss the advantages and limitations of each and explore what types of problems each is best suited to address.

Workshop format: Lecture and discussion

Target audience: This workshop will be most useful for people whose research has (or could have) at least some quantitative elements and who are interested in incorporating Machine Learning into their work. It might also be interesting for people not currently involved in such research but curious about how ML can be used in research more generally.

Knowledge prerequisites: A "big picture" concept of what Machine Learning entails, namely selecting an algorithm with a mathematically defined learning goal and then using data examples to adjust that algorithm's parameters in order to move towards this goal is very useful but not explicitly required as we will cover these topics at the beginning of the class. Participants should also have an understanding of what sorts of data exist in their field or project and what kinds of questions they might want to answer with ML.

Hardware/software prerequisites: None

Learning objectives: Attendees will leave with an understanding of common ML algorithms, the types of data they require, and what types of problems they are best suited for. If time allows, we will spend time discussing and brainstorming specific project ideas from participants’ individual research.


Savannah Thais's profile photo

Savannah Thais

Princeton University

Vineet Bansal's profile photo

Vineet Bansal

Princeton University

Vineet Bansal is a Senior Research Software Engineer who works in Research Computing and the Center for Statistics and Machine Learning (CSML). Vineet earned his MS in Computer Science from Michigan State University. His role at CSML is to productionize and optimize code for several research projects. Vineet has dabbled in many programming languages throughout his career, but is mostly focused on Python these days.

Jose Garrido Torres's profile photo

Jose Garrido Torres

Princeton University

Jose Garrido Torres is a data scientist at the Center for Statistics and Machine Learning at Princeton University. He is part of the Schmidt Data X Fund and Princeton Catalysis Initiative in the group of Professor Abigal G. Doyle. He focuses on developing computational methods for the exploration of chemical space using artificial intelligence.

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PICSciE/Research Computing | View More Events
Co-hosted with: GradFUTURES

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