2021 Industrial and Systems Engineering magazine

2021

INDUSTRIAL & SYSTEMS

ENGINEERING INSIDE: Industrial Engineering for Public Good 7 Data Modeling (Reinvented) 9 The Importance of Information Design 11 Chasing Olympic Aspirations 15

CONTENTS Department of Industrial and Systems Engineering 100 Union Street SE, Minneapolis, MN 55455 Email: isye@umn.edu Phone: 612-624-1582 www.isye.umn.edu

Department Head Saif Benjaafar Director of Graduate Studies William L. Cooper Director of Undergraduate Studies Lisa Miller Director of Faculty and Academic Affairs Jean-Philippe Richard Department Administrator Hongna Bystrom Editor and Designer Sam Schaust

About the Front Cover In this magazine, we highlight the stories of diversity and social good coming from the students and faculty of the Department of Industrial and Systems Engineering at the University of Minnesota. Our front cover features the faces of graduates from the Industrial and Systems Engineering Class of 2021. A collage of the entire graduating class can be found on page 17.

Left to right starting at the top: Jorell Beltran, Aisha Omar, Zachary Drew, Abigail Clarisse Boyer, Jillian Litos, Elique Vaughn, Irene Jensen, Matthew Wiege, Kyle Young, Itzel Fimbres Huerta, Ryan Frommelt, Katherine Mueller, Fouad Omerabi, and Katherine Jin.

Features

departments

Evening the Odds

Faculty

ISyE Assistant Professor Nick Arnosti sees single lottery systems as a tool for good that could improve the lives of students and people living in public housing.

Message from the Department Head ISyE Faculty Listings

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Data Modeling (Reinvented) ISyE Assistant Professor Ying Cui wants to help the world make better use of Big Data.

9 Hitting the Information Sweet Spot In the fast-growing field of information design, ISyE Associate Professor Krishnamurthy Iyer examines how revealing the right information at the right time yields better outcomes.

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Hitch a Ride ISyE and Carlson School of Management researchers are teaming up with Hitch Health to determine how hospitals can cut down no-show rates for appointments.

3 Undergraduate Program Student Spotlight Undergraduate Student Highlights Undergraduate Senior Design Projects

15 Graduate Program Analytics Capstone Projects Graduate Student Higlights

21 Alumni Alumni Spotlight Department News Recent Publications and Recent Seminars

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Table of Contents

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Message from the Department Head The academic year 2020-2021 was without a doubt a challenging year.

However, it was also a year of tremendous growth and accomplishments for the ISyE department. Here are a few highlights.

• We graduated one of our largest undergraduate classes, with a job placement rate of over 97 percent.

Starting average salaries reached a record high at nearly $70,000 (with the highest salary over $110,000).

• Our graduate students continue to be sought after, with many placements in leading companies and academic institutions.

• Enrollment in ISyE courses reached an all-time high, despite the challenges of the pandemic and despite an overall drop in enrollments across the university.

Saif Benjaafar, Distinguished McKnight University Professor and ISyE Department Head

• The ISyE graduate program rose to 23 in the latest U.S. News and World Report ranking, marking a rise of

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Our faculty and students are tackling some of society’s most vexing problems, from improving access to health and housing to harnessing the power of big data to make decisions that benefit everyone.

—Saif Benjaafar

nine positions in three years. This is a confirmation of the growing recognition of the quality of our research and educational programs.

• We have successfully recruited three new faculty members: Alex Estes joining in Fall 2020, Nicholas Arnosti in Fall 2021 (read more about Nick’s research on page 7), and Martin Zubeldia in Fall 2022.

We are about to embark again on a new faculty search.

• In collaboration with the Department of Computer Science and Engineering and the Department of Statistics, we launched a new and innovative undergraduate major in Data Science.

• With our recently established committee on diversity, equity, and inclusion, we are exploring ways to increase diversity in the department, improving the experience and outcomes for under-represented students, and engaging with

communities of color.

• With support from generous donors, we established a community scholarship that recognizes students who contribute to diversity in the department. The first three recipients of this scholarship were recently selected (read about them on page 17).

• Construction has begun on Lind Hall, the future home of ISyE. The $33 million renovation will transform Lind Hall into a facility with state-of-the-art spaces for learning, research, and collaboration.

As you can see a lot is happening in ISyE. However, this is only the tip of the iceberg. In the pages of this magazine, you can read about some of the many successes of our faculty, students and alumni. You will get to know ways in which our students and alumni are impacting industry and the community. You will also get a glimpse into the exciting research led by our

talented faculty—from improving access to health and housing to harnessing the power of big data to make decisions that benefit everyone.

The growth and success of ISyE would not be possible without the tremendous support from the college (under the extraordinary leadership of Dean Mos Kaveh), our fantastic staff, our committed advisory board members, our many partners from industry and state and local government, and our alumni. A big thank you to all of you and to the many other friends of the department!

As usual, I will end with a request: please get and stay in touch. Are you inspired by something you read here?

Do you have an idea for a project?

Would you like to support student scholarships or faculty research?

Please send me an email or give me a call.

Message from the Department Head

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Faculty

Nick Arnosti Assistant Professor Ph.D., Stanford, 2016 Market design, emphasis on giving away social goods such as affordable housing and public school seats

Saif Benjaafar Distinguished McKnight University Professor and ISyE Department Head Ph.D., Purdue, 1992 Operations management, supply chains, service systems, sharing economy, sustainability

William Cooper Director of Graduate Studies and Professor Ph.D., Georgia Tech, 1999 Stochastic modeling, pricing, revenue management, applied probability

Ying Cui

Sherwin Doroudi

Darin England

Assistant Professor

Assistant Professor

Teaching Associate Professor

Ph.D., National University of Singapore, 2016 Optimization, stochastic programming, operations research

Ph.D., Carnegie Mellon, 2016 Stochastic modeling, queuing systems, computer security

Ph.D., University of Minnesota, 2006 Optimization, simulation, machine learning

Alex Estes

Krishnamurthy Iyer

Kevin Leder

Assistant Professor

Associate Professor

Associate Professor

Ph.D., University of Maryland, 2018 Combinatorial optimization, air traffic management, machine learning

Ph.D., Stanford, 2012 Game theory, applied probability, economics and computation, stochastic modeling

Ph.D., Brown, 2008 Stochastic modeling, cancer evolution, probability theory

Zhaosong Lu

Ankur Mani

Professor

Assistant Professor

Ph.D., Georgia Tech, 2005 Continuous optimization, statistics, data analtyics, machine learning, image proceessing

Lisa Miller Distinguished Teaching Professor and Director of Undergraduate Studies

Ph.D., Massachusetts Institute of Tech, 2014 Peer and network interactions, pricing, matching and mechanism design

Ph.D., Georgia Tech, 2002 Optimization, analytics, operations research

Jean-Philippe Richard

Shuzhong Zhang

Yiling Zhang

Professor

Professor

Assistant Professor

Ph.D., Georgia Tech, 2002 Mathematical optimization, healthcare, transportation, infrastructure

Ph.D., Erasmus University, 1991 Nonlinear optimization, game theory, signal processing, risk management

Ph.D., University of Michigan, 2019 Stochastic, integer and nonlinear programming, energy systems, healthcare, transportation

Tony Haitao Cui

Karen Donohue

Mingyi Hong

Alireza Khani

Affiliated Faculty

Affiliated Faculty

Affiliated Faculty

Affiliated Faculty

Carlson School of Management, UMN Deputy Associate Dean for Global DBA, Professor

Carlson School of Management, UMN Board of Overseers Professor

Department of Electrical and Computer Engineering, UMN Assistant Professor

Department of Civil, Environmental, and Geo-Engineering, UMN Assistant Professor

Faculty

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Evening the

Odds

ISyE Assistant Professor Nick Arnosti sees single lottery systems as a tool for good that could improve the lives of students and people living in public housing.

By Sam Schaust

I n the United States, more than 10 million peo-

ple rely on federal rental assistance for their housing, according to the Center on Budget and Policy Priorities. Nearly 70 percent of these Americans are seniors, children, or people with disabilities who would otherwise be homeless if it weren’t for public agencies overseeing the allocation of affordable housing. However, the demand for affordable housing in America far outstrips supply. In an effort to fairly distribute the limited apartments and homes, public agencies in cities across the country have turned to lotteries and wait-lists.

“Every lottery and wait-list has slightly different rules about who’s eligible, how you apply, and how the lottery is run,” says ISyE Assistant Professor Nick Arnosti. “No two systems are alike; there really are a million variations. But rather than getting lost in the details, I think the important question to ask is: Are we giving people choice?”

For years, Arnosti has been studying housing allocation systems in the United States, partic-

ularly in New York City, to understand which types of lotteries or wait-lists generate better outcomes for more people. His interest began in 2016, the year he moved to New York City for an assistant professor position at Columbia University. Witnessing how deeply the issue impacted so many people led Arnosti to partner with Peng Shi, an assistant professor at the University of Southern California.

Arnosti and Shi discovered there was no perfect, one-size-fits-all lottery or wait-list system for cities to adopt. “Instead, cities implementing their own allocation systems need to watch out for implicitly penalizing applicants for being selective, as selectivity in housing allocation is a good thing and leads to a higher quality of matches,” says Shi. Generally, people who qualify for affordable housing are given options based on their need.

Need is determined by one’s income and job, among other factors. Agencies attempt to give priority to those with the greatest need, which is known as targeting. Arnosti and Shi believe

that more attention should be paid to a second objective: matching applicants to their ideal residence.

“It’s extremely rare for applicants to be asked about their preferences,” says Arnosti. Currently, agencies consider family size, preventing a family of four from landing in a one-bedroom apartment. Yet, Arnosti believes much more should be taken into account. “It is uncommon for applicants to have the chance to say where they’d like to live,” he says, “and that’s where I think there could be a lot of gains. One way to do that would be to show candidates in advance some housing options that might become available within the next six months, and then ask: Which ones look interesting?”

Although public housing agencies have historically emphasized targeting, Edward Kaplan believes Arnosti and Shi’s research has breathed new life into the debate over the value of matching. “The tradeoff between matching and targeting is really interesting and important,” says Kaplan, who studies public housing alloca-

tion as a professor of engineering at Yale University. “Trying to get better matching results could contribute meaningfully to individuals’ feelings of security, along with other aspects of preference. Arnosti and Shi’s work suggests [new] ways to think about paying more attention to the matching aspects of the problem.”

In the quest for fairness, Kaplan has found academics tend to favor lotteries over wait-lists.

“They are easy to implement and often have built-in fairness properties,” says Kaplan. However, Arnosti and Shi are interested in finding ways to improve both systems.

For example, outside of the United States, wait-lists for public housing look much different. Arnosti points to Amsterdam as a potential model for local agencies to follow. There, roughly 40 percent of the population relies on public housing, and residents keep their position in the waitlist until matched to an apartment they find suitable. “But in St. Paul, for example, it’s a take-it-or-leave-it offer,” says Arnosti.

“If you say no, you lose your spot on the list.”

When applicants have to wait many years for their next offer, most will accept the first offer they receive, even if it isn’t a great match.

For cities that allocate housing using lotteries, rather than wait-lists, Arnosti and Shi argue that using a single priority list for many buildings would give people more choice. “Right now, most lottery systems are done independently for each new building,” says Arnosti. “As a result, most people will win at most one lottery, and therefore not have much choice. Using a single lottery that applies to many or all buildings enhances choice: There will still be losers, but this way, the winners are allowed to choose among many buildings.”

Additionally, Shi suggests increased collaboration between academics and policymakers as a solution. As it stands today, “it may be too much to ask for policymakers to understand all

“I think the important question to ask is: Are we giving people choice?”

—Nick Arnosti, ISyE Assistant Professor

the nuances of the [lottery] design details,” says Shi. However housing is allocated, Arnosti believes the power of choice has the greatest chance to improve outcomes and people’s lives. “There are tradeoffs involved in the choice between a lottery and a wait-list, but either system can work well,” says Arnosti. “My goal is to identify opportunities for win-win changes that offer people more say in where they’re going to live.”

Improving Lotteries Everywhere When demand outweighs supply, lotteries can provide an equal opportunity for everyone. Arnosti believes improvements could be made to many existing systems that use lotteries, including:

Rolling the dice on school choice Many schools have long been experimenting with lotteries. As teenagers finish junior high and enter high school—or if they simply wish to switch schools—certain education systems are designing their own lottery process to determine student admission. Much like public housing allocation, many different systems are in use.

Arnosti spent years studying New York City schools and their experience running separate lotteries for each school, also known as independent lotteries. When applying, students are encouraged to rank up to 12 schools they like, leaving them with a choice between the ones where they were admitted. While this may sound fair on paper, Arnosti says, “you’re actually screwing some unfortunate people who draw bad lottery numbers at every single school at the same time.”

It turned out independent lotteries weren’t generating the best outcomes for the largest number of students. Due to the randomness in lotteries, students who ranked a school fourth or fifth on their list were oftentimes admitted over other students who ranked the same school first on their list. Meanwhile, swapping schools with another student so both received their first choices was not allowed. These unsatisfying outcomes prompted school administrators in New York City to reevaluate. Using student preferences, they simulated multiple lottery models to see how they could improve results for their entire student population.

“In the end, they realized they were getting a better match—basically more students getting their top choices—when they used a single lottery,” says Arnosti, “even though that wouldn’t necessarily be your first instinct.”

Unlike independent lotteries, single-lottery systems combine all of the schools in an area or district into one lottery. As with affordable housing, this ensures that people with good lottery numbers have many options to choose from. “It’s a simple solution and it works pretty well,” says Arnosti. “But there are consequences

Ticketing for entertainment events

Sales of popular new technology

Hunting and hiking permits

Vaccine allocation to a single lottery system. My work shows that using a single lottery often results in more students being administratively assigned to some school they didn’t list. This finding provides administrators with more information about how lottery design affects the final outcome.”

Of course, admissions are not solely determined by lottery. Schools often prioritize siblings of current students, or children who live nearby.

They also may grant priority to students with special needs. Arnosti says that school choice algorithms help ensure that priorities are stated transparently and applied consistently.

“At the end of the day, however, there is no perfect system: So long as there is a shortage of high-quality schools and affordable housing, some people will be left with bad options,” says Arnosti. “But some systems are better than others. The worst thing you can do with something that has high demand and limited supply is to give it to someone who doesn’t really value it and would have much preferred something else.”

Data Modeling (Reinvented)

ISyE Assistant Professor Ying Cui wants to help the world make better use of Big Data.

By Susan Maas

W

hile stuck at home during the Covid19 lockdown, Ying Cui saw a silver lining to her situation. Cui, who joined the ISyE Department as an Assistant Professor just weeks before lockdowns began, used the time to complete an 800page book with her postdoctoral advisor, Jong-Shi Pang from the University of Southern California. Researchers in a wide range of disciplines have already taken notice as Cui’s book can be used to learn how to make accurate predictions from oceans of data.

“In order to solve an optimization problem, most likely you need numerical algorithms,” Cui says.

These algorithms, she explains, could and should be as adaptive as available data permit. Currently, researchers and analysts use simple linear models or heuristic algorithms to capture information from data. But, Cui says, “the world is not linear.” Which is why she intends to use her book and ongoing research to help others make more sophisticated use of “Big Data.”

‘The world is not linear’ It’s human nature, Cui acknowledges, to want to “simplify reality. But when we do that, we really lose some accuracy.” Her book provides mathematical foundations for nondifferentiable and nonconvex optimization problems. It embraces data with lots of variability, which contain sharp points or corners. She uses the letter “M” in the University of Minnesota’s logo as a metaphor for such models.

“You deal with some changing points; it goes up and down,” says Cui. “If you imagine data doing this same up-and-down, you have to imagine that you cannot manage it like a straight line. You have to take a different approach.” Using nonconvex and nondifferentiable methods to make meaning from raw data is daunting and complex stuff—computational ease is sacrificed for realism—and capable of yielding more accurate, precise predictions. And consequently, Cui says, “resulting in better policies and decisions.”

Much of Cui’s early career was focused

on mathematics for mathematics’ sake.

“When I was younger, I knew I liked to study mathematics,” Cui says. It wasn’t until her post-doctoral work began that “I realized all this mathematical stuff I worked on before could be useful in the real world.” Cui is excited to help people in a range of fields— from civil engineering to health care to economics—apply her work to advance their own research.

Last fall, the mathematical models Cui developed allowed one of her students to develop optimal substance and timing of government responses to the pandemic using real data from the state of Minnesota. Cui’s book and her expertise helped Anthony Zhenhuan Zhang complete his final Ph.D. project—which yielded invaluable potential guidance for policymakers—while earning runner-up for best paper from the Production and Operations Management Society’s College of Humanitarian Operations and Crisis Management.

“The reason I sought Ying’s help with this paper is because I specifically wanted to leverage [her] optimization techniques

“I have students from different fields and backgrounds, and some of them are talking about new applications for my research that I have never considered.”

—Ying Cui, ISyE Assistant Professor

to solving these problems,” Zhang says.

Responding effectively to Covid-19, he adds, “is a research question that everyone cares about, and we were able to provide strong theory support with these techniques.”

Cui found the collaboration highly satisfying.

“We tried to model the disease dynamic, and to understand how governments [might] affect the progress of the disease,” she says. They explored the cost effectiveness of various government policies—lockdowns, mask mandates, social distancing— considering factors like timing, unintended consequences, and how people might react to those policies.

The goal was to discern which actions— and at which times—might effectively slow disease spread with the least economic disruption. Zhang and Cui, with their fellow researchers, discovered that in Minnesota social distancing policies to be more effective than lockdowns. Additionally, they learned it is critical to implement social distancing policies at the post-pandemic-peak as more infectious variants become dominant.

Manifold applications The fast-growing field of personalized medical treatment, known as “precision medicine,” is one of many areas that stand to benefit from Cui’s nonconvex and nondifferentiable data models. Data detailing a patient’s unique medical background could be modeled in new ways to create better outcomes.

But that’s not all. Housing costs and utilities pricing can also be more accurately predicted with Cui’s approach. She expects to see myriad other applications in numerous disciplines, many of which she’s discovered through connections with colleagues in other colleges and departments at the University of Minnesota. “We can learn from each other and benefit from each other’s work,” Cui says.

ISyE Professor Zhaosong Lu has known Cui for a few years. He was one of the reviewers for her dissertation and is also on Cui’s mentoring committee in the ISyE Department.

Lu forsees that Cui’s work will have enormous implications in a wide array of fields.

“This kind of research crosses different [disciplinary boundaries]—computer science, electrical engineering, machine learning and artificial intelligence, and statistical analysis,” Lu says. “She’s a rising star, and she has all these great characteristics to make her successful here.”

Last spring, Cui offered a Ph.D. course based on her new book and asked students to work on projects relevant to nonconvex optimization. She found their ideas exhilarating. “I have students from different fields and backgrounds, and some of them are talking about new applications that I have never considered,” Cui says. “Talking with them, understanding the problems [they want to solve], feeling their energy around all these things—it’s really rewarding!

“It motivates me and helps me to understand what is important in the applied area.

I want to understand more applications, that way I know what else has not been developed mathematically and I can work on that next.”

Hitting the Information Sweet Sp ot

In the fast-growing field of information design, ISyE Associate Professor Krishnamurthy Iyer examines how revealing the right information at the right time yields better outcomes.

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“There is a great deal of work to be done in this area,” says Iyer. “Many models have a lot of assumptions, and a lot of potential applications for information design have yet to be investigated. Our task is figuring out how to share information when you don’t know exactly how the user will respond.”

Online sharing platforms such as Etsy and YouTube also struggle with incomplete information. When new content is added to these platforms, they don’t initially know the quality or relevance of the content. This issue became the focus of Iyer’s research with ISyE doctoral student You Zu. “Instead of knowing the quality and relevance of new content, YouTube has to learn the quality over time and decide whether or not to recommend it,” says Zu. “We proposed an efficient algorithm for this type of incomplete information that can provide benefits including increased sales, fewer returns, and improved user satisfaction.”

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Much of Iyer’s work in information design aims to provide a framework for mathematical models. These models are what platforms and services can use to discover what the correct balance between too much and too little information looks like.

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“Sharing information may not always bring higher welfare if the information is not released in a careful way,” says Iyer. “If a football game is about to finish and Uber informs every available driver, too many drivers may flood the area and many of them will fail to find a customer due to oversupply. It might

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gic approach. One where well-crafted information design reveals the correct amount of data to benefit everyone. Iyer’s recent work examining information design in spatial resource competition examines this problem head-on.

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While understanding the need to selectively release information may be simple, the nuances of deciding which information to share can be extremely complex. In the scenario where Uber notifies drivers about a football game ending, drivers may rush to the stadium and affect the entire network. Each driver’s decision about what action to take could be influenced by many factors, including the dynamics at each pick-up location, the driver’s beliefs about the state of the system, and more.

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be wiser for the platform to only inform a particular group of drivers to better match supply and demand.”

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he rapid growth of big data, e-commerce, and computing power has led to questions of what, when, and how much data companies and organizations should share with users. The inelegant solution is often an “all or nothing” approach in which information is either fully disclosed or kept hidden.

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Better Business with Information Design

Consider this example: It’s a busy Saturday at the local urgent care when two potential patients walk through the door; the first is a young girl and her mother—the girl has a deep cut on her forehead that will likely need stitches—and the second is a man with an itchy rash on his arm. In the lobby, they see a digital screen displaying a wait of 30 to 45 minutes to see a doctor. The mother knows her daughter’s cut is urgent and opts to wait, while the man decides he has better things to do and will call his family doctor on Monday.

Retailers such as Amazon tell customers an exact number of items remaining when inventory is low, while others only indicate “low stock” to encourage customers to buy sooner.

Recently, Iyer worked on a project to determine how retailers can credibly communicate inventory and demand information to customers in a way that maximizes their revenue. He considered questions such as: How are sales affected by all information shared versus no information? Are retailers better off sharing information privately with specific users or publicly? Analyzing questions like these led Iyer to develop a model that could benefit all types of online retailers.

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“With good information design we’re able to go beyond the individual user and shape a positive effect for the network as a whole,” says Iyer. “The result is that in the end, everyone is better off.”

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Iyer believes information design has the potential to shape a better future: one with less waiting in line, less traffic congestion, greater profits for businesses, better matching of service providers to service users, and greater access to services for those with the greatest need.

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—Krishnamurthy Iyer, ISyE Associate Professor

Based on their findings, Iyer and his research partner Vahideh Manshadi, a professor at Yale School of Management, believe information can play a promising role in improving congested social services.

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With information design, we’re able to go beyond the individual user and shape a positive effect for the network as a whole.

“When done strategically, displaying wait times in an emergency clinic is a great example of effective information design,” says Iyer.

“Upon arriving, users are deciding whether to wait for a service or leave and seek an outside option. By displaying wait times, the clinic can reduce congestion and improve outcomes by persuading more low-need users to seek outside options and therefore better serve high-need users.”

“Often, retailers want to sell their inventory before the price of an item drops,” says Iyer.

“A common example of this is fashion, where summer items are sold on clearance after fall arrives. The model we constructed shows that sending a publicly visible signal such as a low inventory indicator results in a substantial increase in revenue compared to the full-information or no-information scenarios.”

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mation about the congestion level not only improves the welfare of high-need users, but also that of low-need ones by inducing a belief that the congestion level might be high in times of moderate congestion and thereby persuading away users with less severe needs,” says Manshadi. “This work is the first of its kind and will provide important managerial insights into designing more effective information-sharing platforms, such as dashboard programs.”

Social services such as public housing, freeways, and emergency rooms can also benefit immensely from well-designed information systems.

Retail businesses are particularly interested in information design as a way to maximize their profits. Providing online shoppers access to more data can oftentimes inform their purchasing decisions. However, the amount of information retailers provide varies widely.

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Disaster management

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Information design has many possible uses, including…

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Using Information Design

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Serving the Greater Good

ISyE and Carlson School of Management researchers are teaming up with Hitch Health to see how hospitals can cut down no-show rates for appointments.

By Sam Schaust

Will free transportation incentivize low-income patients to show up for hospital appointments? This is one of the questions ISyE Ph.D. student Nate Witte and postdoctoral fellow Shihong Xiao have been considering.

Missed healthcare appointments in the United States are estimated to cost hospitals and clinics $150 billion every year, with nearly one out of every five appointments resulting in a no-show. There are multiple reasons for patient no-shows, but recent studies indicate that chief among them is lack of access to reliable transportation.

“Transportation is an impediment that hits low-income communities particularly hard,” says ISyE Professor Saif Benjaafar who has been working with Witte and Xiao on this project. “Access to healthcare has always been a challenge [for low-income commu-

nities], but we did not realize the extent to which it was exacerbated by access to transportation.”

This realization is what led to the inception of Hitch Health, a Minneapolis-based startup, which offers free transportation for low-income patients via a ride-hailing service. Because low-income patients often do not own a smartphone, Hitch Health’s automated service sends an SMS text to qualifying patients who need a ride. If the patient opts in, the patient is picked up shortly before their appointment and then picked up afterwards and dropped off at a destination of their choosing, all free of charge.

After a nearly two-year pilot—in collaboration with Hennepin County—that involved providing thousands of free rides to patients throughout the metro region, Hitch Health reached out to Benjaafar to ask for help

How Hitch Health Works Step 1 Hitch Health schedules rides through SMS messages. Patients respond and receive free transportation. (Photos by Hitch Health)

Benjaafar, Witte, and Xiao teamed up with Carlson School of Management professor Jason Chan, an expert in information systems who specializes in empirical research. The team carried out a comprehensive analysis of the available appointment and show-up data across multiple clinics, before and after the launch of the pilot.

Can we further refine our prediction of which patients are most at risk for dropping out?

Can we target these at-risk patients better?

—Nate Witte, ISyE Ph.D. student

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To assess the effectiveness of the service, the research team carried out what is known as a difference in differences (DID) study that contrasts treated and control patients.

“Among the eligible patients, some utilized the ride-sharing service, while others did not,” says Xiao. “This allowed us to contrast the ‘takers’ with the ‘non-takers.’ The analysis revealed several interesting findings.”

Although the program reduced patient no-show rates by approximately 10 percent, the team found that this effect is short-lived.

“For many patients, the effect is fleeting,” says Benjaafar. “They eventually stop using the service all together.”

To understand what might be causing this, the team carried out separate analysis for patients based on their frequency of making appointments. “We found that the service has a significant initial impact on the no-show rates for all patient groups,” says Chan. “But the impact is only long-lasting in patients with low appointment frequency.” A plausible explanation is that patients with frequent

appointments likely suffer from poor health, which prevents them from using the service.

“Understanding how poor health stands in the way of using the service will be important for increasing its reach,” says Xiao.

The research team also found that patient demographics can influence the impact on no-shows. “The service is more effective in reducing no-shows for younger and English-speaking patients,” says Witte.

“Language can be a barrier for using the service,” adds Xiao. “To improve user retention, offering multiple language options, providing translation services, or matching a patient with a driver who speaks the same language could be considered.”

Witte is also eager to search for solutions to better meet the needs of all potential users.

“I hope to dig a little deeper into the ‘why,’” he says. “Why do patients stop using the service? Can we further refine our prediction of which patients are most at risk for dropping out? Can we target these at-risk patients better?”

The research team also sees new opportunities for a service like Hitch Health beyond healthcare. “The same intervention could improve access to other social services,” says Witte. Jepson agrees; she wants to one day see Hitch Health offering users a ride to work, grocery stores, the pharmacy, the courthouse, and even to polling places to vote.

Benjaafar, Chan, Jepson, Witte, and Xiao feel equally optimistic about exploring these and other ways transportation access can level the playing field for everyone.

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By providing free transportation to low-income individuals, we are protecting their rights to equal access to healthcare, voting, and their civil rights.

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assessing the effectiveness of the service, as well as for a deeper look into their data. “We wanted Saif and his team to analyze and validate the data we have and the results from our original assessment,” says Susan Jepson, co-founder of Hitch Health.

—Shihong Xiao, ISyE postdoctoral fellow

The patient schedules a clinic appointment. Hitch Health enters this into the electronic health record used by the hospital or health system.

Step 2 A CSV file is automatically created daily with patient appointment data. The file is automatically uploaded onto a secure server where Hitch Health software retrieves it for filtering.

Step 3 Hitch Health uses provider criteria to filter and identify appointments eligible for rides.

Step 4 Hitch Health proactively and automatically sends patient ride offers via SMS text when the appointment data meets the criteria.

Step 5 After the patient opts in to the program and accepts the ride offer, Hitch Health dispatches the ride on the day of the appointment.

Step 6 When a patient is ready to go home they text “ready.”

Hitch Health immediately dispatches a ride to pick the patient up from the clinic.

Chasing Gold Tom Donker moved from Zambia to the U.S. to compete in NCAA swimming competitions. Next, he has his sights set on the Olympic stage.

Despite holding a Dutch passport, Tom Donker did not grow up in the Netherlands—or any European country. Three years before he was born, Donker’s parents relocated to Malawi, a landlocked country in Sub-Saharen Africa. With less than 20 million residents, Malawi is the third-poorest country in the world, based on gross domestic product per capita. “Being exposed to that on a regular basis was very eye-opening,” says Donker. “You

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I’m very grateful for how I grew up and to have this experience of traveling the world. You don’t really find many European people who grew up in Africa and now go to college in the States.

—Tom Donker

can’t really compare it [to living anywhere else] because of how different it is.”

But not long after Donker turned 15, his dad changed jobs and the family moved again—now to the neighboring country of Zambia. Unlike his parents who spent their early years in the lush flatlands of the Netherlands, Donker was brought up surrounded

by Zambia’s diverse wildlife and Malawi’s nature reserves. It was a new environment for the entire family and “we really enjoyed it,” says Donker.

When Donker wasn’t in school or with friends in Lusaka, the capital city of Zambia, he spent his time competing and excelling in swimming competitions. Eventually, when it came time to apply to colleges, Donker had no shortage of athletic scholarship offers.

“I remember making big Excel sheets with a bunch of columns with different things that I found important,” says Donker. “For example: Was the campus near a big city? What’s the weather like? Everything you need to take into consideration when picking a college.”

During recruitment, Donker recalls having an instant connection with the University of Minnesota’s swim team

coaches. While this sparked his interest in moving to Minnesota, it was the state’s four-season climate and the university’s academic reputation that sold Donker. “The university here— particularly the College of Science and Engineering—is known all over the world,” he says. “But I came into college undecided [on my major]. I couldn’t really put my finger on a specific career path—mechanical engineering, civil engineering, or anything like that.”

Donker’s favorite subjects growing up were math and physics, “so I knew I was going to go the engineering route,” he says. It was Donker’s uncle from Holland who introduced him to studying industrial and systems engineering. “As I started looking deeper into the industrial engineering program, I thought ‘wow, okay, this is actually really interesting,’” says Donker. “I get the business side, a little bit of supply

Tom Donker competing at a swim meet for the University of Minnesota. (Photo provided by Tom Donker)

chain, some inventory management, but then also math.”

Officially this spring, Donker received his diploma; but a unique opportunity has given him the chance to earn his master’s, too. “In 2019, at the start of the season, I broke my elbow and was out for three months,” says Donker. His coaches suggested he take a “redshirt” year.

“As a college student athlete, you have five years to complete four seasons,” says Donker. “You see it normally in football: People will redshirt their first year which means they can train with the team but not compete. It’s not very common in swimming, but when given the opportunity to compete for another year after the first four, I took it with both hands.”

With the extra time to train and compete, Donker believes he may have

what it takes to qualify for the 2024 Olympics in Paris. In fact, Donker is already planning his path to swim against the best in the world. “I have an opportunity to go to the Netherlands and train with the Dutch team,” says Donker. “That’s something I am really considering after my next year.”

On top of his Olympic aspirations, Donker will also be pursuing his master’s in analytics through a new integrated degree program offered by the ISyE Department. If all goes as planned, Donker will receive his second diploma in the same calendar year he finishes his collegiate athletic career. “I already knew I’d be pretty well set coming out of the University of Minnesota with an industrial and systems engineering degree,” says Donker.

“But never did I imagine I would be leaving with a bachelor’s and master’s in five years.”

Stat Sheet: Tom Donker Class: Redshirt senior Positions: Backstroke and butterfly Records: Dutch national record for 18-year-olds in 100-meter backstroke (56.02 seconds) Favorite swimmer: Adam Peaty (Great Britain)

Student Spotlight 16

Three ISyE Students Earn Scholarships for Diversity, Equality, Community Work Hassen Jama, Governess Simpson, and Grace Hansen were selected as the first-ever recipients of the ISyE Community Scholarship.

The award provides $2,000 to students whose actions help create a more diverse and inclusive environment in the ISyE Department, as well as pave the way for broader representation in industrial engineering and, more generally, in STEM-related fields.

Each student shared their reaction upon hearing they had been chosen as an ISyE Community Scholarship recipient.

The Industrial and Systems Engineering graduating class of 2021.

“It has been challenging to pursue higher education at the University of Minnesota while paying for college and raising four children. This scholarship signifies an acknowledgement that student demographics and their needs are changing constantly. This scholarship will help me cover part of my tuition fees, which in turn means not having to work as much during the academic year and alleviating financial hardship in my family.”

-Hassen Jama “Receiving this scholarship is an honor, as well as a stepping stone to further diversify STEM-related fields; I hope to continue representing Industrial Engineering and the limitless opportunities available because of the program.”

-Governess Simpson “Increasing diversity in STEM fields has been a passion of mine since I joined robotics in elementary school. I never thought it would earn me scholarship money. ISyE at the University of Minnesota is doing a wonderful job of starting new programs and initiatives like this that are making our degree program a more inclusive environment.”

-Grace Hansen

school, Hansen and her fellow members saw new potential for Green Girls STEM Foundation to support women and other underrepresented groups interested in STEM. “I knew that it can be intimidating being the only woman in the room,” says Hansen. “I watched as my female friends and classmates lost interest in science and technology due to harmful stereotypes. I came to understand that this loss of interest would only be combatted when underrepresented groups were involved in STEM at an earlier age so that they would be more confident about their abilities.”

Grace Hansen (second from right) awarding a scholarship. (Photo courtesy of Grace Hansen)

Supporting Underrepresented STEM Students Junior Grace Hansen was awarded a 2021 ISyE Community Scholarship for her nonprofit work and outreach toward diverse communities and students.

If you are interested in learning more about Green Girls STEM Foundation or donating to their cause, please visit ggstemfoundation.com.

As an elementary school student, Grace Hansen gravitated towards her school’s robotics club. Her fascination with remote-controlled and autonomous machines has continued all through her grade school years and to this day. Now, as a junior majoring in industrial and systems engineering, Hansen is the Director of Programs for the University of Minnesota Robotics student group. But this isn’t the only science, technology, engineering, and math (STEM) organization where Hansen holds a leading role.

Hansen is also the president and cofounder of a nonprofit called Green Girls STEM Foundation. Along with her high school robotics team, Hansen launched the organization in 2015 to “help students fundraise for competition fees, encourage diversity, and provide support to other local robotics teams,” Hansen says. But after high

This realization pushed Hansen to change the mission of Green Girls STEM Foundation, starting with its outreach and inclusion efforts. “We created outreach programs to teach students with disabilities, members of the LGBTQ+ community, and foster children about STEM using affordable activities made from household items,” says Hansen. Then, during the pandemic, Hansen and her nonprofit members produced a series of educational videos with activities young students could try at home. “We made episodes that focused on boats and buoyancy; Newton’s laws and marble roller coasters; and renewable sources of energy,” says Hansen.

But perhaps most impressive has been the scholarship support from Green Girls STEM Foundation. Over the past four years, the nonprofit has awarded $10,000 in scholarships and grants to underprivileged students and robotics teams. “In five years, I would like to distribute ten $1,000 scholarships a year to students seeking undergraduate degrees in STEM,” says Hansen.

“We need people from different backgrounds working together on our hardest engineering projects because the solutions need to serve the entire population, not just the majority.”

Undergraduate Student Highlights 18

Seniors Modernize a University Pharmacy For their senior design project, a group of ISyE students assisted the University of Minnesota Medical Center in its pharmacy redesign.

Thirteen organizations with a mix of local and global operations sponsored 17 senior design projects this spring 2021 semester. Project teams of four to five seniors worked to address important issues affecting these businesses—from distribution center redesigns to planning the distribution of COVID-19 vaccines. Teams worked closely with their sponsors to collect data, develop models, perform analysis and, in certain cases, conduct site visits. The work they completed provided their sponsors with fresh perspectives and insights.

Simultaneously, students practiced hands-on approaches to solving problems in a real-world setting.

One group worked with the University of Minnesota Medical Center (UMMC) to help transition its pharmacy on the East Bank campus from a manual inventory management system to an automated carousel system.

UMMC faced several shortfalls due

to its older system. The on-hand inventory of medications were not being tracked and a lack of storage space led to a high volume of expired medications. Furthermore, the manual workflows to restock medication bins and fill prescriptions were time-consuming, slowing down the pharmacy’s operations. Seniors Drew Bennett, Ying Xin Cheh, Zach Drew, Britta Gantert, and Jill Litos developed a solution they believed would create a more efficient operation process for UMMC, while also saving them money.

The group started by devising a minmax inventory model. Using this model, UMMC could determine a more acurate timing and quantity for individual medication re-orders.

Switching to this model can shrink UMMC’s on-hand inventory by at least 10 percent, the group found, as well as save at least $195,000 each year. Additionally, the group outlined a plan for organizing the more than

2,000 medications held in UMMC’s East Bank pharmacy. Aside from assorting medications into alphabetical clusters, the group developed an optimization model that prioritizes fast-moving medications in the first few rows of the carousel and placing commonly used medications near each other. The group simulated this new system and found it improved workflows by reducing the maximum processing time by more than two hours for batch orders.

Other senior design teams developed improved medical clinic check-in process flows, optimized shared workspace layouts, and global supply chain risk mitigation plans, among others. Whether the work was performed on-site with safety precautions due to the COVID-19 pandemic, or was performed remotely, the teams provided novel industrial engineering solutions with lasting value to their sponsors.

Seniors Ying Xin Cheh, Zach Drew, and Drew Bennett (middle to right) learn about medications with their UMMC sponsor at the East Bank pharmacy.

Sponsors of 2021 Projects Andersen Corporation Best Buy Boston Scientific Carver County Daikin Applied Hennepin County M Health Fairview Minneapolis Veterans Affairs Health Care System Minnetronix Medical Stratasys Target University of Minnesota Medical Center West Monroe Partners

Middle: Seniors Britta Gantert (left) and Jill Litos (middle) reviewing the current inventory management system.

Undergraduate Senior Design Projects 20

Analytics Capstone Projects Every fall, students in the Analytics master’s program put their skills to the test in a realworld setting by completing an exciting capstone project. These projects are proposed by businesses and the results carry the potential to have both local and global ramifications.

In small teams of two or three, students interact closely with their industry sponsor and a faculty advisor as they use data-driven methodologies to build actionable models and, at the end of the semester, communicate their results and recommendations.

What follows are two of the standout projects from the fall 2020 semester.

Discovering Outliers in Aerospace Manufacturing At the Collins Aerospace manufacturing facility in Burnsville, assembly line workers construct thousands of parts used to assemble airplanes of all speeds and sizes.

“One of the challenges with our particular business is that we have so many different products,” says David Potasek, an engineer at Collins Aerospace. “We are an aerospace company, so every engine or plane we support will typically need a custom solution. We have to manage multiple assembly lines to accommodate all of our products.”

This low-volume, high-mix operational structure can lead to labor differences, such as more time spent producing a specific airplane sensor as compared to similar sensors on other planes. For their project, Potasek challenged students Tony Roberts, Luke Riveness, and Vijay Varadarajan with analyzing Collins Aerospace’s assembly line data and identifying variations, or outliers.

Collins Aerospace could then take

those standout parts or procedures and determine if a new assembly process or more training was needed.

Very quickly Potasek was impressed by the group’s ability to make sense of the data—despite never getting the chance to visit the company’s Burnsville site due to COVID-19 restrictions. “The students actually identi-

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This is real-world data; it’s not perfect. And they embraced the imperfections of that and turned it on its head.

—David Potasek, project sponsor

fied some metrics that I never would have thought about,” he says. “One of them was ‘missing labor entries,’ which are typically ignored. If you see greater clusters of the omissions of data associated with different product lines or people, that’s also a clue that there could be a potential issue there.

“This is real-world data; it’s not perfect. And they embraced the imperfections of that and turned it on its head. I was most impressed with that: turning that negative into a positive.”

The group presented their findings, along with a prototype to identify future outliers, to managers and product line groups at Collins Aerospace. “Some very high-level people were at those presentations and they did an excellent job,” says Potasek.

“It’s a challenge to identify where to put your resources when you work in this sort of environment. But the students gave us more insight into our operations.”

M.S. Analytics students partnered with an aerospace parts manufacturer and an HVAC company as part of their capstone projects. (Photo from Daikin Applied)

Designing Efficient Shipping Strategies Daikin Applied, a global leader in commercial and industrial air conditioning systems, is facing an issue that is affecting many other retailers in the United States: the expansion of online sales has given rise to a broad and geographically dispersed base of customers.

“The parcel and [Less than Truck Load, or LTL] freight space used to be largely commercial,” says Ed Blackford, vice president of parts and logistics at Daikin Applied. “In the last several years, however, the volume of goods shipped directly to homes via parcel and LTL is up 300 percent because of online purchases. Carriers are taking the freight despite staffing challenges, and a lack of trucks and trailer capacity. Plus, there’s been more damage in transit, lost orders and the like.” Daikin is facing questions about how to adjust its shipping strategies in response to this new industry landscape.

Like many other commercial enterprises, Daikin is now serving a spectrum of customers—from building

contractors to household owners.

One customer may buy several hundreds of Daikin’s HVAC parts and products, while another customer may buy only one. “We’re shifting our freight modes away from LTL to [Truckload, or TL] to help avoid rising costs, network delays and handling concerns,” says Blackford, “but there is a law of diminishing returns here. At some point, shipping a single pallet in one truck puts us at a financial disadvantage. We need to ship full truckloads or nearly full.”

Blackford and his colleagues at Daikin turned to students Aparajita Kar, Seungyoon Lee, and Weiqi Wang to find a solution to this shipping dilemma. Specifically, Daikin was interested in creating a new system that would minimize costs and the chance of damaged shipments by packing trucks as full as possible with the fewest number of stops possible.

The group received Daikin’s forecasted production schedule and used it to build an algorithm that determines shipping patterns for orders

headed to the same geographical location. “These students created an algorithm that did a better job than what we had been doing manually,” says Blackford. “What the students did such a nice job of was understanding our requirements. Taking into account zip codes, weights, length, width, height, and then cubing out the truckload. This in turn would allow us to tender the load of goods to customers.”

Kar, Lee, and Wang also developed a dashboard to show possible consolidations of shipping orders that Daikin could use in the future. “They built a whole tool where you could play around with forecast numbers for the next week,” says Blackford.

“The students used regression analysis and optimization to find the best routes, and clustering to identify which products should go on what trucks. Those are the things they learned in the [Analytics M.S.] program and they applied it here beautifully. They really hit it out of the park.”

Analytics Capstone Projects 22

Beating Cancer with Math “The ultimate goal is a future of personalized medicine,” says Einar Bjarki Gunnarsson, “where knowledge of each individual patient’s biology can be used to find the best treatment for that individual.”

ISyE Ph.D. student Einar Bjarki Gunnarsson was chosen as a recipient of the 2021-2022 Doctoral Dissertation Fellowship. The award, which is bestowed by the University of Minnesota Graduate School, is highly competitive among Ph.D. candidates entering their final year before graduation.

Each $25,000 fellowship provides students with the opportunity to commit their full-time efforts to a research project of their choice.

Gunnarsson, who hails from Reykjavik, Iceland, is pursuing both a Ph.D. in the Department of Industrial Systems and Engineering and a master’s degree in the School of Mathematics at the University of Minnesota. His advisors are Kevin Leder, an Associate Professor of ISyE, and Jasmine Foo, a Professor in the School of Mathematics.

“A lot of factors went into my decision to study at the University of Minnesota,

Einar Gunnarsson was recently awarded the Doctoral Dissertation Fellowship. (Photo from Einar Gunnarsson)

including a flexible and interdisciplinary Ph.D. program, and professors who are both leading academics and nice people,” says Gunnarsson. “I’ve appreciated having the ability to carve my own way through the program, keeping one foot in ISyE and the other in the math department, with plenty of support and encouragement from my advisors and other mentors along the way.”

In his graduate research, Gunnarsson has been studying and developing mathematical models of cancer evolution. Every cancer patient has a fairly unique biology; therefore, drug treatment plans do not affect all patients equally. The complexity of this problem greatly interests Gunnarsson and is something he believes is well suited for his industrial engineering mindset.

“Broadly speaking, we are trained to think about how to model complex systems, and to use the models to investi-

gate how the systems can be designed or controlled to achieve a desired outcome,” says Gunnarsson. “In these abstract terms, the problem of treating cancer can be thought of as the problem of controlling a complex system.

Framing the problem this way enables you to use tools from probability and optimization to make predictions about treatment outcomes and to search for mathematically optimal treatments, which I think is a useful perspective.”

However, Gunnarsson believes that the problem of identifying the right treatment plan for each cancer patient will require a collaborative effort. “I think the future of cancer research will very much be interdisciplinary teams,” he says, “where people from fields such as biology, medicine, mathematics, physics, engineering, and data science combine their insights and perspectives to attack this problem from as many directions as possible.”

Kang Earns Top Teaching Assistant Award ISyE Ph.D. student Kang Kang was the 2021 recipient of the John Bowers Excellence in Teaching Assistance Award.

Established in 2003 by alumnus John Bowers (Physics '76), this award honors an outstanding teaching assistant who has demonstrated exceptional interest and commitment to the teaching of students in the College of Science and Engineering at the University of Minnesota.

“It’s a great honor to receive this award,” says Kang. “I feel grateful for all the professors and students I worked alongside. Without their guidance and endless support, this wouldn’t be possible.”

Over the past five years, Kang estimates he has been a teaching assistant for roughly a dozen classes—both undergraduate and graduate-level—helping more than 500 students.

“The most rewarding moment was when I received a Thank a Teacher note from one of my students, which said, ‘thank you so much for being the best T.A.!” says Kang. “It was a long time ago, in 2019, yet the memory feels like yesterday.”

Kang adds, “Nothing makes me happier than creating a positive impact towards others and helping them succeed.”

Zhang Wins Runner-Up in Best Paper Competition ISyE Ph.D. student Anthony Zhenhuan Zhang won runner-up at the Best Paper Award Competition held by the Production and Operations Management Society’s College of Humanitarian Operations and Crisis Management (HOCM).

Zhang’s paper, “No Panic in the Pandemic: The Impact of Individual Choice on Public Health Policy and Vaccine Priority,” studied public health interventions and their effects based on COVID-19 data from Minnesota. Zhang found lockdowns and social distancing policies to be more effective when the disease prevalence is not at its peak. Additionally, social distancing was determined to be more

effective than lockdowns. While giving vaccine priority to the elderly is most effective in reducing total deaths, it has to be accompanied with more stringent social distancing policies, Zhang found.

In their comments, HOCM judges found Zhang’s paper to be a “technically well done paper that is well written, and interesting,” while offering “excellent and unique methodological applications” that “attempts to answer a question on everybody’s mind today.”

Zhang’s paper was co-authored by ISyE Assistant Professor Ying Cui, Miao Bai (University of Connecticut), and Guangwen Kong (Temple University).

Graduate Student Highlights 24

Henrique Lispector wants to push data science in some unexpected directions, such as for cooking and music. (Photo provided by Henrique Lispector)

Achieving a Lifelong Dream Henrique Lispector founded his first company at a troubling time and was rewarded with immediate success.

Henrique Lispector grew up in Recife, Brazil with dreams of one day launching his own company. But he never imagined that day would come at the height of the COVID-19 pandemic.

“Last year, I was just so full of ideas and saw so many opportunities that I felt it was the right time to do it,” Lispector says about his consulting firm HENSLIP. Founded in September 2020, HENSLIP provides data analysis, process optimization, and automation solutions to clients and companies in a myriad of industries. The company specializes in optimizing repetitive processes and using data science to build reports, predictive models, web applications, and dashboards. “We mine data and deliver gold in the form of information,” says Lispector.

In less than a year, Lispector—the lone

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Were it not for my degree, I am not sure I would see the world the way I see it now. I am not sure I would feel as prepared and confident to take the jobs I took and to be starting my own company.

—Henrique Lispector, B.S. in ISyE (2016)

the population of Recife; aims to build linguistics and literature web application for Brazilian high school and college students; and plans to enter the law world serving Brazilian legal teams through jurimetrics, which is the application of statistics and probability to law. In the future, Lispector sees HENSLIP producing solutions that may apply to more unexpected fields, such as music and cooking.

employee of HENSLIP—has made tools that could be used by clients all over the world. “The majority of our work has been helping Brazilian clients organize their finances and create investment strategies,” he says. “We developed and built Aha, our own robot that scans all Brazilian funds available for investment as well as the entire Brazilian stock market and suggests investment strategies.” Lispector based the design of Aha on the “Modern Portfolio Theory,” which won Harry Markowitz the Nobel Memorial Prize in Economic Sciences in 1990.

Lispector’s expertise has been utilized across multiple industries and countries. HENSLIP recently partnered with clients from India and France to design financial modelling tools.

Lispector’s company also assisted Brazilian retailers with organizing and cleaning their data; developed a COVID-19 vaccine tracker website for

“I, personally, have a goal of pushing data analysis to fields that are very little or completely unexplored and unexpected,” says Lispector. “The amazing thing about the type of service we provide is that it can be done remotely.

Therefore, we can take on clients from anywhere in the world. [Recently] we were in talks with a potential client from Spain. Sometimes a product that is developed under a country’s circumstances can be quickly adapted to other countries—and that is what we are aiming for.”

This nimbleness is something that Lispector credits to his time as an international student majoring in industrial and systems engineering at the University of Minnesota. “ISyE allowed us to choose a path to focus on towards the end of the degree, and I chose data analytics with just a general idea of what it was,” says Lispector. “I fell in love with it and immediately realized that there were no boundaries to it. I could analyze anything and generate value immediately by finding patterns.

Sometimes, I could even predict the

future. Combining that with all the skills I had learned from ISyE, I knew I would be in a good position in the future to found my own company.”

Lispector continued his education back in São Paulo, Brazil, where he earned his M.B.A. At the same time, he built up industry experience at Brazil’s largest auto insurance company and at the most popular food delivery smartphone app in the country. Yet, looking back, Lispector continues to view his years as an ISyE student as the pivotal time that shaped him.

“I like to say that I think the way I do today because of my degree in Industrial and Systems Engineering,” says Lispector. “I was trained to find bottlenecks, cut waste, and optimize everything. It really became an obsession, in a good way, and it can be applied to anything, in any job you take, in any situation of your life.

“The way industrial engineers think is not something ordinary. Not every degree gives you this wide perspective about subjects and how to be continuously improving systems. My degree has opened my mind to many different areas—from finance to statistics, from marketing to entrepreneurship. Were it not for my degree, I am not sure I would see the world the way I see it now. I am not sure I would feel as prepared and confident to take the jobs I took and to be starting my own company.”

Alumni Spotlight 26

Saif Benjaafar was named the ISyE Department Head in 2018. (Photo by Rebecca Slater)

Benjaafar Awarded Prestigious Professorship Story by: Center for Transportation Studies at the University of Minnesota

Saif Benjaafar, a Distinguished McKnight University Professor and head of the Department of Industrial and Systems Engineering, has been named a McKnight Presidential Professor.

The McKnight Presidential Endowed Professorship is among the highest honors for faculty at the University of Minnesota. Recipients are recommended by their college dean and chosen at the discretion of the president based, in part, on their academic and research accomplishments and their contributions to advancing the University among its peers.

“I am humbled by this incredible honor,” Benjaafar says. “I am grateful to the University leadership and the leadership in the College of Science and Engineering for their support. This is a recognition of the contributions of many colleagues and many students

over many years.”

The endowment funding, Benjaafar says, “will allow us to expand the research footprint of our group and to undertake higher-risk and longer-term projects that may otherwise be difficult to pursue. In particular, I am excited about exploring innovative ways transportation services could be delivered to improve access. This work will leverage existing activities that have been generously supported by the Center for Transportation Studies over many years.”

The McKnight award recognizes the critical importance of the University’s most distinguished faculty across all disciplines, as well as the importance of strengthening University of Minnesota faculty for the future. Only four other College of Science and Engineering faculty members currently hold this honor.

Arnosti Joins the ISyE Faculty The Department of Industrial and Systems Engineering welcomes Nick Arnosti, who joined the ISyE faculty as an Assistant Professor.

Arnosti’s research focuses on market design, with particular emphasis on allocating social goods, such as affordable housing and public school seats.

He has also studied the allocation of hunting licenses, hiking permits, and discounted tickets to events.

Prior to joining the University of Minnesota, Arnosti was an Assistant Professor at Columbia Business School, where he taught the M.B.A. core class Operations Management, as well as a Ph.D. elective on Rationing Social Goods.

Arnosti received his Bachelor’s degree in Mathematics and Computer Science from Williams College. In 2016, he earned a Ph.D. in Operations Research from Stanford University.

Zhang Awarded Two Seed Grants ISyE Assistant Professor Yiling Zhang has been awarded two seed grants from the Center for Transportation Studies.

algorithms that could be adopted by businesses and other organizations around the world.

The grants, amounting to $100,000, will be dedicated to two separate studies.

The second study will evaluate electric transit, specifically the electric bus lines used by Metro Transit in the Twin Cities area. Zhang, along with a multidiciplinary team of experts from the University of Minnesota, will develop a blueprint that could be used

by Metro Transit to strategically place charging stations for buses along bus routes. For this project, too, Zhang anticipates the model developed by her and her team could be adopted by other transit operators in cities across the United States.

The first study will analyze crowdsourcing delivery drivers in the case of last-mile delivery, an aspect of the delivery process wherein goods are transported from a business, warehouse, or separate location to the customer’s residence. Zhang will collaborate with ISyE Professor Saif Benjaafar with the intention of developing last-mile delivery models and

Leder Receives Norway Research Grant The Research Council of Norway has awarded a four-year, $173,000 grant to ISyE Associate Professor Kevin Leder and a research team.

advanced courses analyzing how statistics and mathematical modeling can be used to implement personalized medicine.

The funds will be used in support of two goals. The first involves Leder’s research into the statistical questions surrounding personalized medicine.

The second will consist of developing

Leder is the co-principal investigator of this project. He will be collaborating with Arnoldo Frigessi, a professor at the University of Oslo, on the research and course development.

Department News 28

Recent Faculty Publications Arnosti, N., and S.M. Weinberg, “Bitcoin: A Natural Oligopoly,” forthcoming in Management Science, 2021.

Arnosti, N., and T. Randolph, “Parallel Lotteries: Insights from Alaskan Hunting Permit Allocation,” forthcoming in Management Science, 2021.

Benjaafar, S., S. Wu, H. Liu, and E. Gunnarson, “Dimensioning On-Demand Vehicle Systems,” forthcoming in Management Science, 2021.

Benjaafar, S., H. Bernhard, C. Courcoubetis, and M. Kanakakis, “Drivers, Riders, and Service Providers: The Impact of the Sharing Economy on Mobility,” forthcoming in Management Science, 2021.

Nosrat, F., W. L. Cooper, and Z. Wang, “Pricing for a Product with Network Effects and Mixed Logit Demand,” Naval Research Logistics, 68(2), 159-182, 2021.

Qi, Z., Y. Cui, Y. Liu, and J.S. Pang, “Statistical Analysis of Stationary Solutions of Coupled Nonconvex Nonsmooth Empirical Risk Minimization,’’ forthcoming in Mathematics of Operations Research, 2021.

Wang, S., T. Chang, Y. Cui, and J.S. Pang, “Clustering by Orthogonal NMF Model and Non-Convex Penalty Optimization,” forthcoming in IEEE Transactions on Signal Processing, 2021.

Doroudi, S., T. Avgerinos, and M. HarcholBalter, “To Clean or Not to Clean: Malware Removal Strategies for Servers Under Load,” European Journal of Operational Research, 292(2), 596-609, 2021.

Gardner, K., J. Abdul Jaleel, A. Wickeham, and S. Doroudi, “Scalable Load Balancing in the Presence of Heterogeneous Servers,” Performance Evaluation, 145, 1-19, 2021.

Estes, A.S., and M.O. Ball, “Monge Properties, Optimal Greedy Policies, and Policy Improvement for the Dynamic Stochastic Transportation Problem,” INFORMS Journal on Computing, 33(2), 2020.

Estes, A.S., M.O. Ball, and D.J. Lovell, “Data Exploration by Representative Region Selection: Axioms and Convergence,” Mathematics of Operations Research, 2021.

Ciocan, D.F., and Iyer, K., “Tractable Equilibria in Sponsored Search with Endogenous Budgets,” Operations Research, 69, 227-244, 2021.

Gorokh, A., S. Banerjee, and K. Iyer, “The Remarkable Robustness of the Repeated Fisher Market,” The Twenty-Second ACM Conference on Economics and Computation (EC), 2021.

Yu, P., T.K. Pong, and Z. Lu, “Convergence Rate Analysis of a Sequential Convex Programming Method with Line Search for a Class of Constrained Differenceof-Convex Optimization Problems,” forthcoming in SIAM Journal on Optimization, 2021.

Lu, Z., Z. Sun, and Z. Zhou, “Penalty and Augmented Lagrangian Methods for Constrained DC Programming,” forthcoming in Mathematics of Operations Research, 2021.

Huang, J., A. Mani, and Z. Wang, “The Value of Price Discrimination in Large Social Networks,” forthcoming in Management Science, 2021.

Mani, A., L.R. Varshney, and A. Pentland, “Quantization Games on Social Networks and Language Evolution,” IEEE Transactions on Signal Processing, 2021.

Nguyen, T., J.P. Richard, and M. Tawarmalani, “Convexification Techniques for Linear Complementarity Constraints,” Journal of Global Optimization, 80(2), 249286, 2021.

Zhang, J., M. Hong, and S. Zhang, “On Lower Iteration Complexity Bounds for the Saddle Point Problems,” forthcoming in Mathematical Programming, 2021.

Jiang, B., H. Wang, and S. Zhang, “An Optimal High-Order Tensor Method for Convex Optimization,” forthcoming in Mathematics of Operations Research, 2021.

Zhang, Y., M. Lu, and S. Shen, “On the Values of Vehicle-to-Grid Electricity Selling in Electric Vehicle Sharing,” Manufacturing & Service Operations Management, 23, 488-507, 2021.

Recent Seminars Fall 2020 September 9 Hayriye Ayhan Georgia Institute of Technology

November 4 Karla Hillier Trane Technologies

February 3 Lewis Ntaimo Texas A&M University

“Optimizing the Interaction between Residents and Attending Physicians”

“Solving Problems with Analytics and Machine Learning Across Industries”

“Expected Conditional Risk Measures for Risk-Averse Multistage Stochastic Programs”

September 16 Matthias Poloczek Uber AI

November 18 Luis Nunes Vicente Lehigh University

“Scalable Bayesian Optimization for High Dimensional Expensive Functions”

“Accuracy and Fairness Trade-offs in Machine Learning: A Stochastic MultiObjective Approach”

September 23 Radhika Kulkarni SAS Institute Inc. “Machine Learning, Artificial Intelligence and Optimization: Opportunities for Inter-Disciplinary Innovation”

September 30 Chrysanthos E. Gounaris Carnegie Mellon University “Decision-making Across Scales: From Supply Chains to Materials Nanostructure”

October 21 John Khawam Stitch Fix “Making the Most of Inventory: Advance Inventory Availability at Stitch Fix”

October 28 Vivek Saxena Advisory Aerospace OSC “Digital Technologies in Aerospace Manufacturing - A Perspective from the Industry”

December 2 Simge Küçükyavuz Northwestern University “Mixed-Integer Convex Programming for Statistical Learning”

November 13 Jamol Pender Cornell University “Queues with Delayed Information”

Spring 2021 January 20 Hai Wang Singapore Management University “Ride-Sourcing Systems & MultipleObjective Online Ride Matching”

January 27 Merve Bodur University of Toronto “Copositive Duality for Discrete Markets and Games”

February 17 David Simchi-Levi Massachusetts Institute of Technology “Statistical Learning in Operations: The Interplay Between Online and Offline Learning”

February 24 Ali Makhdoumi Duke University “Sequence-Submodularity and its Application to Online Advertising”

March 10 Hui Zou University of Minnesota “Sparse Composite Quantile Regression in Ultrahigh Dimensions”

March 31 John-Paul Clarke University of Texas at Austin “Towards Increasingly Autonomous Aircraft-Enabled Mobility”

April 28 Noshir Contractor Northwestern University “People Analytics: Using Digital Exhaust from the Web to Leverage Network Insights in the Workplace”

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