New AI Courses

I introduced two new AI courses at UF, CAP4613 and CAP5404 , Deep Learning for Computer Graphics at the undergraduate and graduate level.  Both courses received approval in the Florida State-Wide numbering system effective Summer 2021, Spring 2022 respectively.

New Funding

Awarded: UF Informatics Institute, Seed Fund, Focus on AI Grant: Phenotyping a Rodent Model of Major Depressive Disorder: A Deep-Learning Approach. (start date: August 15th 2021)

Project Title #1: 3D Digitization, Printing and Exploration of Fossils

Department:
Computer and Information Sciences and Engineering
Faculty Mentor:
Corey Toler-Franklin, ctoler@cise.ufl.edu
Ph.D. Student Mentor(s):
Shashank Ranjan, shashankranjan@ufl.edu
Terms Available:
Fall, Spring
Student Level:
Junior, Senior, 2 students per semester
Prerequisites:
Programming fundamentals, knowledge of web technologies (javascript, jquery, html) and graphics libraries (OpenGL, WebGL) is a plus
Credit:
0-3 credits via EGN 4912
Stipend:
$10-15 per hour for up to 20 hours per week
Application Requirements:
Resume, UF unofficial transcripts, faculty interview, be available for weekly group meetings; email one pdf file with all application requirements to Corey Toler-Franklin, ctoler@cise.ufl.edu
Application Deadline:
End of add/drop period in each term
Website:
www.coreytoler.com
Project Description:
3D scanning technologies are powerful tools for digitizing and preserving fragile artifacts such as fossils and biological specimens. Our research introduces new algorithms for rendering and visualizing this 3D data. The goal of the project is to develop web applications which allow users to study and interact with 3D models in real-time.

Project Title #2: Simulating Biological Materials in 3D

Department:
Computer and Information Sciences and Engineering
Faculty Mentor:
Corey Toler-Franklin, ctoler@cise.ufl.edu
Ph.D. Student Mentor(s):
Shashank Ranjan, shashankranjan@ufl.edu
Terms Available:
Fall, Spring, Summer
Student Level:
Freshman, Sophomore, Junior, Senior; 3 students per semester
Prerequisites:
Programming fundamentals, experience with C++ and/or knowledge of web technologies (javascript, jquery, html, webgl)
Credit:
0-3 credits via EGN 4912
Stipend:
$12 per hour, flexible time
Application Requirements:
Resume, Faculty interview; Email one pdf file with all application requirements to Corey Toler-Franklin, ctoler@cise.ufl.edu
Application Deadline:
Rolling applications until filled
Website:
www.coreytoler.com
Project Description:
One goal of graphics and vision research is to create visually accurate representations of real objects from measured data. This project develops physically-based algorithms that simulate the complex interactions that occur naturally between light and biological materials (iridescence, florescence, translucency). Data will be captured using state-of-the-art imaging systems. Materials will be replicated in 3D using a customized multi-material stereolithography printing system.

Project Title #3: Convolutional Neural Networks for Identifying Cancer in Slide Scans

Department:
Computer and Information Sciences and Engineering
Faculty Mentor:
Corey Toler-Franklin, ctoler@cise.ufl.edu
Ph.D. Student Mentor(s):
Shashank Ranjan, shashankranjan@ufl.edu
Terms Available:
Fall, Spring, Summer
Student Level:
Freshman, Sophomore, Junior, Senior; 3 students per semester
Prerequisites:
Programming fundamentals, experience with C++ and/or knowledge of web technologies (javascript, jquery, html, webgl)
Credit:
0-3 credits via EGN 4912
Stipend:
None
Application Requirements:
Resume, Faculty interview; Email one pdf file with all application requirements to Corey Toler-Franklin, ctoler@cise.ufl.edu
Application Deadline:
Rolling applications until filled
Website:
www.coreytoler.com
Project Description:
This project applies deep learning algorithms for identifying and classifying cancerous regions in high resolution slide scans. Assistance is needed with interface development for viewing detection results and data collection and processing. Students will gain experience with computer vision topics related to signal processing and deep learning.