AI-NSF workshop

Food is provided so, please, RSVP here.

What is the AI-NSF workshop?

This workshop (Day 1) is designed to introduce and give tips and tricks to those who are planning on submitted proposals to NSF.  Come ask an NSF Program Manager your burning questions about how the NSF functions, how proposals get reviewed, and more.  This workshop (Day 2) will focus more on AI applications across disciplines and RedCap, an NIH sponsored/funded surveying software that will keep your data secure, gives your collaborators only the access they need (no more, no less), and will ease your survey-related IRB woes.  Current Tentative Schedule

RSVP here

Who:  Students, Faculty, Staff

When:  February 27th & 28th from 9a-5p; come and go as needed

Where: DACC Workforce Training Center Rm 121; 2345 Nevada Ave, Las Cruces, NM 88001

Parking: Free in front of the building

What:  Current Tentative Schedule

Speakers:

Dr. Andruid Kerne: NSF Programs survey and discussion AND NSF Proposal Tactics: Unofficial Symposium

Dr. Vinitha Subburaj (WTAMU): (TALK 1) I will be talking about my experience working on a project using machine learning algorithms to predict patterns in the grid data collected from the Distributed Energy Resources (DER) at a local electrical engineering company. The main research objective was to increase the net value of the overall grid systems by minimizing uncertain systems failures, infrastructure investments, better planning and improved resilience of clean energy. Predictive framework was developed using python modules to 1) preprocess the data, 2) classify the test and training datasets, and 3) apply different machine learning algorithms like Support Vector Machines (SVM),  logistic  regression, and decision  trees on the data. I will be also sharing the challenges faced while working on this project along with the experience of involving undergraduate students in this research.

(TALK 2) This talk will focus on where to find AI CFP’s. In this presentation, I will go over the process, timeline, and challenges involved in writing an interdisciplinary AI proposal to local industries and to other bigger venues like (NSF, DOE, etc…).

Dr. Ramyaa (NMTech): (TALK 1)  Use ML in nutrition: phenotypically categorizing people based on their relationship between diet, exercise and body weight.  We use machine learning algorithms to predict body weight from diet and exercise. Then we use clustering algorithms to phenotype people and show that the predictions are better within each group.

(TALK 2)  Abstraction based learning: Learning a task directly is harder than learning the correct abstraction and then learning the task. For instance, learning the capital English alphabet is much easier if lines and semicircles are already learned. Further, such a system would be resistant to adversarial attacks, generalize better, and allow for transfer. We propose a method to learn abstractions and learn a task over them. We demonstrate learning with examples from very different domains.

 

The workshop is supported by NSF grant#1925764.

RSVP here