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
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
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.
The workshop is supported by NSF grant#1925764.