Pitt | Swanson Engineering


Industrial engineering (IE) is about choices - it is the engineering discipline that offers the most wide-ranging array of opportunities in terms of employment, and it is distinguished by its flexibility. While other engineering disciplines tend to apply skills to very specific areas, Industrial Engineers may be found working everywhere: from traditional manufacturing companies to airlines, from distribution companies to financial institutions, from major medical establishments to consulting companies, from high-tech corporations to companies in the food industry.

View our Spring term 2018-2019 course schedule for undergraduate and graduate students.

View our Fall 2018 course schedule for undergraduate and graduate students.

The BS in industrial engineering program is accredited by the Engineering Accreditation Commission of ABET (http://www.abet.org). To learn more about Industrial Engineering’s Undergraduate Program ABET Accreditation, click here

Our department is the proud home of Pitt's Center for Industry Studies, which supports multidisciplinary research that links scholars to some of the most important and challenging problems faced by modern industry.



Pitt Researcher Uses Video Games to Unlock New Levels of A.I.


PITTSBURGH (November 5, 2018) … Expectations for artificial intelligences are very real and very high. An analysis in Forbes projects revenues from A.I. will skyrocket from $1.62 billion in 2018 to $31.2 billion in 2025. The report also included a survey revealing 84 percent of enterprises believe investing in A.I. will lead to competitive advantages.“It is exciting to see the tremendous successes and progress made in recent years,” says Daniel Jiang, assistant professor of industrial engineering at the University of Pittsburgh Swanson School of Engineering. “To continue this trend, we are looking to develop more sophisticated methods for algorithms to learn strategies for optimal decision making.” Dr. Jiang designs algorithms that learn decision strategies in complex and uncertain environments. By testing algorithms in simulated environments, they can learn from their mistakes while discovering and reinforcing strategies for success. To perfect this process, Dr. Jiang and many researchers in his field require simulations that mirror the real world.“As industrial engineers, we typically work on problems with an operational focus. For example, transportation, logistics and supply chains, energy systems and health care are several important areas,” he says. “All of those problems are high-stakes operations with real-world consequences. They don’t make the best environments for trying out experimental technologies, especially when many of our algorithms can be thought of as clever ways of repeated ‘trial and error’ over all possible actions.”One strategy for preparing advanced A.I. to take on real-world scenarios and complications is to use historical data. For instance, algorithms could run through decades’ worth of data to find which decisions were effective and which led to less than optimal results. However, researchers have found it difficult to test algorithms that are designed to learn adaptive behaviors using only data from the past.Dr. Jiang explains, “Historical data can be a problem because people’s actions fix the consequences and don’t present alternative possibilities. In other words, it is difficult for an algorithm to ask the question ‘how would things be different if I chose door B instead of door A?’ In historical data, all we can see are the consequences of door A.”Video games, as an alternative, offer rich testing environments full of complex decision making without the dangers of putting an immature A.I. fully in charge. Unlike the real world, they provide a safe way for an algorithm to learn from its mistakes.“Video game designers aren’t building games with the goal to test models or simulations,” Dr. Jiang says. “They’re often designing games with a two-fold mission: to create environments that mimic the real world and to challenge players to make difficult decisions. These goals happen to align with what we are looking for as well. Also, games are much faster. In a few hours of real time, we can evaluate the results of hundreds of thousands of gameplay decisions.”To test his algorithm, Dr. Jiang used a genre of video games called Multiplayer Online Battle Arena or MOBA. Games such as League of Legends or Heroes of the Storm are popular MOBAs in which players control one of several “hero” characters and try to destroy opponents’ bases while protecting their own. A successful algorithm for training a gameplay A.I. must overcome several challenges, such as real-time decision making and long decision horizons—a mathematical term for when the consequences of some decisions are not known until much later.“We designed the algorithm to evaluate 41 pieces of information and then output one of 22 different actions, including movement, attacks and special moves,” says Dr. Jiang. “We compared different training methods against one another. The most successful player used a method called Monte Carlo tree search to generate data, which is then fed into a neural network.”Monte Carlo tree search is a strategy for decision making in which the player moves randomly through a simulation or a video game. The algorithm then analyzes the game results to give more weight to more successful actions. Over time and multiple iterations of the game, the more successful actions persist, and the player becomes better at winning the game.“Our research also gave some theoretical results to show that Monte Carlo tree search is an effective strategy for training an agent to succeed at making difficult decisions in real-time, even when operating in an uncertain world,” Dr. Jiang explains. Dr. Jiang published his research in a paper co-authored with Emmanuel Ekwedike and Han Liu and presented the results at the 2018 International Conference on Machine Learning in Stockholm, Sweden this past summer. At the University of Pittsburgh, he continues to work in the area of sequential decision making with Ph.D. students Yijia Wang and Ibrahim El-Shar. The team focuses on problems related to ride-sharing, energy markets, and public health. As industries prepare to put A.I. in charge of critical responsibilities, Dr. Jiang ensures the underlying algorithms stay at the top of their game. ###
Matt Cichowicz, Communications Writer

Making a Transparent Flexible Material of Silk and Nanotubes


PITTSBURGH (October 30, 2018) … The silk fibers produced by Bombyx mori, the domestic silkworm, has been prized for millennia as a strong yet lightweight and luxurious material. Although synthetic polymers like nylon and polyester are less costly, they do not compare to silk’s natural qualities and mechanical properties. And according to research from the University of Pittsburgh’s Swanson School of Engineering, silk combined with carbon nanotubes may lead to a new generation of biomedical devices and so-called transient, biodegradable electronics.The study, “Promoting Helix-Rich Structure in Silk Fibroin Films through Molecular Interactions with Carbon Nanotubes and Selective Heating for Transparent Biodegradable Devices” (DOI: 10.1021/acsanm.8b00784), was featured on the Oct. 26 cover of the American Chemistry Society journal Applied Nano Materials. “Silk is a very interesting material. It is made of natural fibers that humans have been using for thousands of years to make high quality textiles, but we as engineers have recently started to appreciate silk’s potential for many emerging applications such as flexible bioelectronics due to its unique biocompatibility, biodegradability and mechanical flexibility,” noted Mostafa Bedewy, assistant professor of industrial engineering at the Swanson School and lead author of the paper. “The issue is that if we want to use silk for such applications, we don’t want it to be in the form of fibers. Rather, we want to regenerate silk proteins, called fibroins, in the form of films that exhibit desired optical, mechanical and chemical properties.” As explained by the authors in the video below, these regenerated silk fibroins (RSFs) however typically are chemically unstable in water and suffer from inferior mechanical properties, owing to the difficulty in precisely controlling the molecular structure of the fibroin proteins in RSF films.  Bedewy and his NanoProduct Lab group, which also work extensively on carbon nanotubes (CNTs), thought that perhaps the molecular interactions between nanotubes and fibroins could enable “tuning” the structure of RSF proteins. “One of the interesting aspects of CNTs is that, when they are dispersed in a polymer matrix and exposed to microwave radiation, they locally heat up,” Dr. Bedewy explained. “So we wondered whether we could leverage this unique phenomenon to create desired transformations in the fibroin structure around the CNTs in an “RSF-CNT” composite.”According to Dr. Bedewy, the microwave irradiation, coupled with a solvent vapor treatment, provided a unique control mechanism for the protein structure and resulted in a flexible and transparent film comparable to synthetic polymers but one that could be both more sustainable and degradable. These RSF-CNT films have potential for use in flexible electronics, biomedical devices and transient electronics such as sensors that would be used for a desired period inside the body ranging from hours to weeks, and then naturally dissolve. “We are excited about advancing this work further in the future, as we are looking forward to developing the science and technology aspects of these unique functional materials,” Dr. Bedewy said. “ From a scientific perspective, there is still a lot more to understand about the molecular interactions between the functionalization on nanotube surfaces and protein molecules. From an engineering perspective, we want to develop scalable manufacturing processes for taking cocoons of natural silk and transforming them into functional thin films for next generation wearable and implantable electronic devices.” ### About the NanoProduct LabThe NanoProduct Lab (nanoproductlab.org), also known as the Bedewy Research Group, focuses on fundamental experimental research at the interface between nanoscience, biotechnology, and manufacturing engineering. The group explores basic scientific discoveries and applied technological developments in the broad area of advanced manufacturing at multiple length scales, creating solutions that impact major societal challenges in energy, healthcare, and the environment. This work was supported by the Mascaro Center for Sustainable Innovation (MCSI) at the University of Pittsburgh. M.B. is grateful for the Ralph E. Powe Junior Faculty Enhancement Award from Oak Ridge Associated Universities (ORAU). Fabrication and characterization were performed, in part, at the Nanoscale Fabrication and Characterization Facility, a laboratory of the Gertrude E. and John M. Petersen Institute of NanoScience and Engineering, housed at the University of Pittsburgh. The work was also supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1A4A1025169). Image below, left: ACS Applied Nano Materials cover. Image below, right: Schematic diagram illustrating the structural changes of RSF-CNT composite film exhibited during microwave- and vapor-treatment.


Engineering Student Athletes: Craig Bair

Industrial, Student Profiles

Craig Bair Sport: Soccer Position: Outside Back Major: Industrial Engineering and Economics Class: Senior Hometown: Brecksville, Ohio “Being a student-athlete has taught me to make the most of my opportunities and most importantly how to respond to failure. It also teaches you how to manage stress and turn it into a motivator for your success. I set aside certain days, look a week ahead, and use my time wisely to stay on top of my work. There’s no set routine. You just have to plan ahead and learn to balance everything at once to perform at your best.” “In the Top Tier” By the time Craig Bair was a senior in high school, he knew he wanted to study engineering, he knew he wanted to play college soccer, and he knew he wanted to go to Pitt. “I applied to a few places at first,” he says, “but I didn’t look at many other schools once I knew what great opportunities I would have at Pitt. The University is top-tier academics and top-tier athletics at the same time.” During the fall of his first year at Pitt, Bair worked for the soccer team as student manager and by spring, had his chance to make the team. As a student, Bair’s love of math led him to major in industrial engineering and a double-major in economics because of the flexibility it would provide him after college. He says, “Nearly every company needs an industrial engineer. When I thought about the kind of work I’d like to do after college, the combination of industrial engineering and economics seems to open the door to any kind of opportunity I think I’d like to pursue.” Although he’s thought a lot about what he wants to do after graduation, Bair has his sights set firmly on helping his team win this season. “We have a team with a lot of potential to do something special this year. The ACC is the best conference in the country. Usually at least two teams from our conference are in the College Cup each year, so we’ll face a lot of talented teams but no one that we can’t compete with,” he says. Noteworthy ACC Honor Roll, 2014 - present ACC Top Six for Service Pitt Blue and Gold Student Athlete Engineering Dean's List Volunteer, Coach for College, Thuan Hung, Vietnam University Scholar Scholarship Richard Lombardi Scholarship A Typical Day 6:00 am: Wake up 7:00 am: Arrive for practice 8:30 - 10:30 am: Training 11:00 am - 12:00 pm: Ice bath, rehab, etc. 1:00 - 5:00 pm: Classes 6:00 - 8:00 pm: Film, homework or recovery session 9:00 pm: Reading 10:00 pm: Sleep Note: This is part one of a four-part series about student-athletes at the Swanson School of Engineering. Part two will appear on the SSOE website on October 24, 2018. ###
Matt Cichowicz, Communications Writer

NSF Awards Pitt Engineers $200K to Study the Impact of Reflection on Learning

Electrical & Computer, Industrial

PITTSBURGH (September 25, 2018) … University of Pittsburgh professors Samuel Dickerson and Renee Clark received an NSF grant to help students in the Swanson School of Engineering start to think about thinking. The two-year, $200,000 award will support a project to improve learning and development by promoting the frequent use of reflection and “metacognition” among students in the Department of Electrical and Computer Engineering. Dickerson, an assistant professor of electrical and computer engineering, believes that the Swanson School is perfect for this kind of project. “Engineering is different from other disciplines because this type of thought process isn’t inherent in our training,” he said. “Reflection and metacognition are not skills that are regularly cultivated or practiced in the engineering curriculum - in the classroom we are more focused on immediate problem-solving rather than pausing and looking at the big picture, which is more common in the engineering workplace.” They hope to change that standard at Pitt by first introducing these skills to electrical and computer engineering students in Dickerson’s ECE-0257 microelectronic circuits course. According to Clark, assistant professor of industrial engineering, it is easier for a student in a classroom environment to ask a professor or teaching assistant to help them solve a problem. Outside of college however, there may be fewer resources on which to rely. Dickerson and Clark want to encourage engineering students to develop lifelong learning skills that will help them independently learn how to find a solution and ultimately give them an advantage when they join the workforce. “When a student faces an obstacle in class or doesn’t perform to the level he/she should, we don’t typically ask them to critically reflect on how they got there, what they can do to solve it, or how they can perform better,” said Clark, who is  also director of assessment for the Engineering Education Research Center (EERC). “Our goal is to utilize frequent activities that prompt students to reflect and better understand their learning processes.” “Metacognition is a useful skill that helps students take a deeper look at their learning processes by simply thinking about their thinking,” said Dickerson. “Reflection is a closely related skill where students are asked to critically analyze something they have done. In this project, we want to encourage students to use both metacognition and reflection to guide their own learning during new tasks.” A unique aspect of their research is the use of SPICE simulation tools to drive students to analyze their work and gain insight into success as well as mistakes. “I will ask the students in my class to use engineering theory to complete a problem and then compare their answer to a computed result using SPICE, the standard simulation environment used by professionals to predict electronic circuit behavior,” explained Dickerson. “I want them to reflect on the gaps in their understanding, thereby taking a deeper look at their learning process and understanding.” Dickerson and Clark will examine the impact of frequent reflection using SPICE by looking at both quantitative and qualitative data. In addition to monitoring exam scores, they will distribute surveys, conduct interviews, and hold focus groups. They will be using a system to measure the depth of the students’ reflections and will evaluate the content to see if it is showing growth in students’ professional development. “The results we are looking for are not necessarily better exam scores,” said Clark. “We want to know if we have cultivated reflective and metacognitive skills in engineering students and if we have made an impact on their development.  We will be analyzing both the depth and content of their reflections using a systematic approach that has been working for us in our preliminary research.” With the use of these skills, Dickerson and Clark hope that ECE students will become better students, learners, and professionals by developing the ability to critically reflect on their own performance. These types of reflective activities are applicable across disciplines and can be easily implemented in any classroom at the University. Clark said, “We hope that these efforts will help our students develop lifelong learning skills that will make them better prepared for the professional world.” ###


NSF Awards IE’s Andrés Gómez $150K to Solve Widespread Optimization Problems in Computational Mathematics


PITTSBURGH (August 13, 2018) … Relationships between return and investment cost, profit and time, or cost and quality are important for decision-makers looking to optimize efficiency. If the possible choices faced by the decision-maker have a simple structure, then these tradeoff problems can be solved efficiently; however, in practice, the decisions are rarely simple and the existing computational approaches fail after complexity reaches a certain point.The National Science Foundation (NSF) Division of Mathematical Science awarded $150,000 to Andrés Gómez, assistant professor of industrial engineering at Pitt’s Swanson School of Engineering, to widen the computational boundaries of complex optimization problems involving such tradeoffs. The project titled “Advancing Fractional Combinatorial Optimization: Computation and Applications” (1818700) begins Sept. 1.“We will be working with a hard class of problems called single- and multiple-ratio fractional combinatorial optimization problems,” Dr. Gómez explains. “There are no adequate approaches to these kinds of problems if they involve many layers of complexity or variability. This project aims to develop computational approaches with solid underlying theoretical foundations to solve these problems.”Dr. Gómez’s research falls broadly into the field of “decision-making under uncertainty.” He studies ways to improve mathematical modeling to better understand problems in finance, statistics, machine learning, manufacturing, revenue management, and many other applications.“Our proposed approaches will contribute to our understanding of mathematical optimization, particularly conic, fractional and discrete optimization, combinatorics, and algebraic graph theory,” adds Dr. Gómez.Oleg Prokopyev, professor of industrial engineering at Pitt, will join Dr. Gómez as co-principal investigator of the study. ###
Matt Cichowicz, Communications Writer

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