Portrait of

Michael Clough

Featured mentor, FURI

Associate Teaching Professor, ENGR Academic & Student Affrs

Michael Clough is a senior lecturer in the School of Computing Augmented Intelligence at Arizona State University. His research interests include revenue management, airline operations engineering, product portfolio analysis, econometrics, linear and non-linear optimization, and optimal pricing policies.

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Featured Mentor Q&A

Michael Clough is an associate teaching professor of industrial engineering on the Academic and Student Affairs team in the Ira A. Fulton Schools of Engineering at Arizona State University. An expert in applied math and statistical analysis, Clough’s interests include revenue management, airline operations engineering, product portfolio analysis, econometrics, linear and non-linear optimization and optimal pricing policies. With years of experience in FURI mentorship, he’s happy to help students explore the world of engineering research. Read more

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Summer 2025

Alex Yepiz

Industrial engineering

Comparing LSTMs and GRUs with and without Reinforcement Learning for Stock Price Prediction and Trading Decisions

Comparing GRU and LSTM models with and without RL will advance knowledge of machine learning applications in quantitative finance.

Program: FURI

Spring 2025

Alex Yepiz

Industrial engineering

Comparing LSTMs with and without Reinforcement Learning for Stock Price Prediction and Trading Decisions

Quantifying reinforcement learning’s impact on machine learning trading models to assess effects on stock forecasts and trading decisions.

Program: FURI

Amishi Agrawal

Computer science

Using Machine Learning to Assess the Impact of Tires on Carbon Footprint

Studying the carbon footprint of tires will help understand environmental impacts of electric vehicles that are assumed to be sustainable.

Program: FURI

Spring 2021

Robert Wenli Dodge

Industrial engineering

The Impact of External Factors on Multiple-Choice Exams

Studying the impact of exam composition on how students perform will allow educators to design exams with greater efficacy.

Program: FURI

Spring 2019

Victoria Eugenia Gonzalez

Industrial engineering

Development of a Multiple Linear Regression Model to Reduce the Appearance of High-energy Consuming Regressors in Manufacturing Environments

Identifying the most energy consuming factors in manufacturing will enable the development of guidelines to make factories more sustainable.

Program: FURI