Machine Learning Group

Machine Learning Group Coordinators

Dr. Jennifer Erway

Professor of Mathematics
Office: Manchester 130
Email: erwayjb ‘at’ wfu.edu

WFU Homepage

Professional Website
Research interests: Numerical optimization, computational PDEs, numerical linear algebra, scientific computation

Dr. Mostafa Rezapour

Teacher Scholar Postdoctoral Fellow
Office: Manchester 348
Email: rezapom ‘at’ wfu.edu

WFU Homepage

Professional Website
Research interests: Optimization, computational science, Machine Learning and Deep Learning.

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The Machine Learning Reading Group welcomes faculty and students who are interested in doing research in optimization, machine learning, deep learning and related areas. If you are interested in receiving information from the Machine Learning Group, please enter your name, email address and leave “Subscribe to the Machine Learning Group’s email list” in the comment box.

Machine Learning Reading Group

 

The Machine Learning Reading Group (MLRG) consists of a weekly meeting. We welcome all faculty and students to participate.

Time: Thursdays 10am – 11am

Place: Manchester 384

Zoom Link: Please contact Dr. Erway or Dr. Rezapour

Learning Objectives and Outcomes:

The purpose of the group is to provide a platform for discussions on several topics in supervised and unsupervised machine learning. The MLRG has been started for the needs of faculty and students looking to use machine learning and deep learning in their research and grow a deep understanding of how to problem solve using the most relevant algorithms and techniques across machine learning and deep learning.

Fall 2021- Machine Learning Reading Group speakers

Speaker 1: Dr. Jordan Culp

About the speaker: Dr. Culp is currently a Postdoctoral  Associate and PIMS Post-Doctoral Fellow in the Department of Cellular Biology and Anatomy at the University of Calgary. He works under the supervision of Dr Wilten Nicola to develop numerical and analytical tools to help understand the dynamic regulation of large-scale brain networks in health and disease.

Title: An introduction to Python
Abstract: In this presentation we will cover introductory topics in python. This will include installation, common packages, variables, data structures, functions, loops, printing, and plotting.
Python tutorial : Link

Speaker 2: Dr. Mostafa Rezapour

About the speaker: Dr. Rezapour is currently a Teacher Scholar Postdoctoral Fellow in the Department of Mathematics and Statistics at Wake Forest University. He works under the supervision of Dr Jennifer Erway (research) and Dr. Jeremy Rouse (teaching). His research interests are at the intersection of Optimization and Machine Learning.


Title: An introduction to Machine Learning
Abstract: In the supervised learning, we train models to classify data or predict outcomes accurately. In this presentation we will cover introductory topics in supervised machine learning.

Speaker : Dr. Mostafa Rezapour

About the speaker: Dr. Rezapour is currently a Teacher Scholar Postdoctoral Fellow in the Department of Mathematics and Statistics at Wake Forest University. He works under the supervision of Dr Jennifer Erway (research) and Dr. Jeremy Rouse (teaching). His research interests are at the intersection of Optimization and Machine Learning.



Title: A Machine Learning Analysis of a Cancer Dataset.

Abstract: In this presentation, we apply all widely used machine learning predictors such as decision tree, k-nearest neighbors, naive Bayes classifier, logistic regression, support vector machines, perceptron, neural network, random forest and a few techniques such as bagging, boosting, cross-validation and ensembleing to find the best model for a data set. Also, we use different techniques for feature selection such as comparing the accuracy of a specific model before and after removing a feature or using information gain for different features.

Speaker : Dr. Mostafa Rezapour

About the speaker: Dr. Rezapour is currently a Teacher Scholar Postdoctoral Fellow in the Department of Mathematics and Statistics at Wake Forest University. He works under the supervision of Dr Jennifer Erway (research) and Dr. Jeremy Rouse (teaching). His research interests are at the intersection of Optimization and Machine Learning.



Title: Deep Neural Network as an Interpolator

Abstract: In the first part of this presentation, we discuss the structure of a deep neural network and its functionality. Moreover, we discuss how to use a deep neural network for solving ODEs and PDEs and discuss how the Finite Element Method and the deep learning solvers are related. Finally, we briefly discuss how to use tensorflow for implementing algorithms in Python.

Speaker : Dr. Rachel Minster


About the speaker: Dr. Minster is currently a Postdoctoral Fellow in the Department of Computer Science at Wake Forest University. He works under the supervision of Dr. Grey Ballard in numerical linear algebra, specifically in the areas of randomized algorithms and tensor decompositions.
Title: Tensors in Machine Learning
Abstract: In this talk, we will give an introduction to tensors, or multidimensional arrays, focusing on the context of machine learning. We will provide an overview of tensor operations and tensor decompositions, as well as applications where the use of tensors can be particularly beneficial.

Speaker : Dr. Mohsen Bahrami


About the speaker: Dr. Bahrami is currently a faculty member in the Department of Radiology at Wake Forest School of Medicine. His research is mainly focused on developing statistical and machine learning tools for analyzing brain network data. He works with Dr. Paul Laurienti and Dr. Sean Simpson at the Laboratory for Complex Brain Networks (LCBN).


Title: Low Dimensional Manifolds and Dynamic Brain Networks
Abstract: In this presentation, I will have a brief introduction to using machine learning methods, particularly recent data reduction methods, to project dynamics of brain networks into lower dimensional manifolds. The rich spatio temporal information provided by such lower dimensional manifolds has great potential for visualization, analysis, and interpretation of dynamic brain networks in health and disease.

Speaker : Dr. Seda Camalan

About the speaker: Dr. Camalan is currently a postdoctoral associate in the Department of Computer Science at Wake Forest University. Her research is mainly focused on developing deep learning applications on images. Her previous publications related to biomedical images and now working on satellite images.
Title: Deep Learning Applications and CNN
Abstract: In this presentation, we will have a brief introduction to deep learning, Convolutional Neural Network (CNN), and their applications. We will define the layers, functions, optimizations, and normalizations of deep learning.

Speaker : Dr. Seda Camalan

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Speaker : Dr. Yasin Wahid Rabby

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Speaker : Dr. Seda Camalan

About the speaker: Coming Soon! Title: Coming Soon! Abstract: Coming Soon!

Speaker : Ramesh Sah

About the speaker: Coming Soon! Title: Coming Soon! Abstract: Coming Soon!

Speaker: Dr. Jordan Culp

About the speaker: Dr. Culp is currently a Postdoctoral  Associate and PIMS Post-Doctoral Fellow in the Department of Cellular Biology and Anatomy at the University of Calgary. He works under the supervision of Dr Wilten Nicola to develop numerical and analytical tools to help understand the dynamic regulation of large-scale brain networks in health and disease.
Title: Coming Soon!
Abstract: Coming Soon!
Thanksgiving Break

Speakers : Lucas Hansen and Dr. Mostafa Rezapour    


About speaker 1: Lucas Hansen is currently a senior at Wake Forest University double majoring in Economics and Mathematics with a minor in Statistics. Upon graduation, he will be working in New York City for Swiss Re Alternative Capital Partners. His interests in machine learning are illuminating black box algorithms and Bayesian hyperparameter optimization.  




About speaker 2: Dr. Rezapour is currently a Teacher Scholar Postdoctoral Fellow in the Department of Mathematics and Statistics at Wake Forest University. He works under the supervision of Dr Jennifer Erway (research) and Dr. Jeremy Rouse (teaching). His research interests are at the intersection of Optimization and Machine Learning.



  Title: A Machine Learning Analysis of Covid-19 Mental Health Data
Abstract: In this presentation, we utilized survey data from the Inter-university Consortium for Political and Social Research and constructed several statistical models to analyze the impacts the COVID-19 pandemic has had on the mental heath of frontline workers in the United States. The results indicate that the top predictors of mental health were the Healthcare role a person is in (Nurse, Psychologist, Emergency Room Staff, etc.), the amount of alcohol consumed, and how many hours a person has slept.

Machine Learning Research Group

Welcome to the Machine Learning Research Group. We are an active group of researchers working on optimization algorithms, machine learning, deep learning and some applications.

Machine Learning Research Group current members (chronological order)

1. Dr. Jennifer Erway 
2. Dr. Mostafa Rezapour
3. Dr. Seda Camalan
4. Dr. Rachel Minster

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