Optimization, Machine Learning and High Performance Computing

Group is directed by:
Professor Oliveira and  Professor Stewart

Fall 2022-Spring 2023 Talks: (Fridays)
January 27: TBD
February 10: TBD
February 24: TBD
March 10: TBD
March 24: TBD
April 7: TBD
April 21: TBD
May 5: TBD

Fall 2021- Spring 2022 Talks:
September 9th - Jamil Gafur
September 16th - Katie Elmmert
October, 7th  - Tarun Roy
October 21st - Violet Tiema
November 11th - Ibrahim Emirahmetoglu
December 9th- Cory Kromer-Edwards

We focus on applications with large amounts of data where new optimization or parallel algorithms are essential.

Recent projects include: Machine Learning and Genetics, Neural Networks and Educational data, Theoretical Foundations of Neural Networks, Gerrymandering

          
We study classical optimization methods, stochastic gradient descent, and metaheuristics for machine learning and their parallelization. Above, see simulations of gradient descent and a metaheuristic algorithm (images provided by the PhD students).

Current PhD students working on this Group

• David Aitens - GNN, Oliveira
  
• Umesh Arampath - ML for chemistry and pharmacy, Oliveira
• Yilin Zhu -  Optimization for ML, Stewart
  
• Ibrahim Emirahmetoglu  - Optimization for Machine Learning, Stewart
  
• Tarun Roy  -  Data Science and Machine Learning, Oliveira
  
• Jamil Gafur  - Optimization and HPC for Machine Learning, Oliveira
  
• Cory Kromer-Edwards  - Machine Learning for Genetic Data Problem, Oliveira
  

Some Recent PhD Students:

• Geoff Converse - (2021, FineTune Learning), Neural Network Methods for Application in Educational Measurement
  
• Michael Kratochvil - (2022, Swell Energy), Stochastic optimization problems for energy distribution
  
• Violet Tiema - (2022, Rockwell College), Extreme Learning Machine approaches to feature selection
  
• Jared Grove  (2022, John Deere),  Gerrymandering and Markov chains
• Jeffrey Hajewski (2020, SAP software company), New Parallel Algorithms for Support Vector Machine and New Architecture Search
• Thamer Alsulaiman (2019, Oracle), Detecting Complex Genetic Mutations in Large Human Genome Data
• Rahil Sharma (2016, Cadence),  Shared and Distributed Memory Parallel Algorithms to Solve Problems in Biological, Social Networks, and Spatial Domain Applications
• Cole Stiegler (2018, Target Corp.), Efficient Local Optimization for Low-Rank Large-Scale Instances of the Quadratic Assignment Problem
• Mario Barela (2016, BAE Systems), A complementarity approach to modeling dynamic electric circuits
  
• Benjamin Dill (2016, Westminster College, Utah), Numerical simulation of the impact of a steel ball with a rigid foundation
  more under our webpages
  

2015-2016 Kyumok Lee, Wyatt Bettis, Yue Bin, Kenong Su

2016-2017 Kenong Su, Jing Chen, Jingrong Li, Benjamin Jacobs

2017-2018 Yash Chauhan, Anthony Pizzimenti, Joseph Constant, Yunyi Li, Sangguan Wang

2018-2019 Mathias Sader, Chunquing Cao, Katherine Wasmer, Vanessa Chen, Kayla Gibson, Laura Weiler.

Summer 2015 Gracé Ndovia, Mitchell Penningroth, Jacob Smith, Mathias Sader, Loius Hasley, T'shilyn Harrington, Brian Schweer

Summer 2016 Rzwan Didhu, Liam Hagan, Daniel Kelly, Dillon Kock, Lucas Zach, Nolan Dewitte, Jiahua Zhang, Anthony Pizzimenti, Austin Russ, Derek Choi, Katherine Chace

Summer 2017 Carlos Zapata, Kelvin Encarnacao, Abigail Williams-Yee, Carles Mascardo, Natasha Brown-Joel,Jacob Howard, Robert Gerritsen, Minhan Lin, Biao Liu

Summer 2018 Kevin Liu, Ethan St John, Gus Johnson, Ethan Trepka, Moly Mcdermott, Steve Garcia, Rachel Hammel, Brandon Koch, Prateek Sudhanshu Raikwar, Jeremy Feguson, Yinguy Zhou, Pranav Sateesh, Andy Hou, Drew Graves

2021 REU Mentor:

• Loreen Peregrine (St Ambrose University, Iowa)
• Julian Rodriguez (Pell College, Iowa).

on neural networks for personality trait assessment.