Informatics Faculty

Capturing the moon

Scientists around the world dream of being part of a NASA science mission. Few projects carry the vigor and prestige of exploring scientific questions that can be answered only with a view from and into space.

The Lunar Reconnaissance Orbiter, or LRO, is one of the more than 90 operating NASA missions, and is currently orbiting the moon with the primary objective of making fundamental discoveries about our closest celestial neighbor.

Launched in June 2009, the LRO’s primary mission is making fundamental scientific discoveries about the moon. Its original exploration mission was to support the extension of human presence throughout the solar system by identifying sites close to potential resources with high scientific value, favorable terrain and the environment necessary for safe future robotic and human lunar missions.

The LRO’s exploration mission was completed on September 15, 2010, when responsibility to begin the next LRO mission was transferred to NASA’s Science Mission Directorate.

The LRO has been equipped with seven instruments, one of which is the Lunar Reconnaissance Orbiter Camera, or LROC. This three-camera system is mounted on the LRO to capture the moon’s surface in high-resolution black and white images and moderate-resolution, multi-spectral images allowing scientists to see beyond what is visible to the human eye.

The Lunar Reconnaissance Orbiter Camera Science Operations Center, or LROC SOC, is housed at Arizona State University as part of the university’s School of Earth and Space Exploration. This proximity has created an incredible opportunity for students from the Ira A. Fulton Schools of Engineering to gain insight into space exploration without ever leaving campus. The variety of learning opportunities available to students help prepare them for various roles after graduation.

“I think students who can say they’ve been a part of an active spacecraft mission, collecting or processing data from a NASA satellite moving around the moon, stand out that much more to potential employers,” says Nick Estes, the LROC SOC manager at ASU.

Fulton Schools students operate in different areas of the center, performing tasks related to software, modeling and image creation from data.

Read more on Full Circle

Fixing the finding of faults

Imagine a factory filled with manufacturing equipment. Workers efficiently orchestrate the whir and hum of daily operations, but then an alarm sounds and the production line shuts down.

What happened? Did a faulty new device unleash the problem when it was brought into use for the first time? Or did a component suddenly fail within a machine already running at the heart of the plant?

Computer scientists ask similar questions when they seek to find and fix “bugs” or lapses in the integrity of software maintaining vital communications, infrastructure and security systems. Can the issue be identified by static analysis, perusing configurations of code? Or does the situation require dynamic analysis of programs while they are executing code and driving processes?

“Dynamic approaches dominate the field of binary analysis because of the actionability of results. If they tell you there’s a bug, there’s definitely a bug,” says Yan Shoshitaishvili, an assistant professor of computer science in the Ira A. Fulton Schools of Engineering at Arizona State University. “But they work only for the sections of software you observe running, and the burden of testing all the parts of complex applications would be untenable. In fact, the code coverage [or the portion of executed code that can be examined] for dynamic analysis is normally just 60%.”

This limitation means almost half of the space in which software bugs might be hidden, or occluded, remains unsearched by any given method of dynamic analysis. Alternatively, static analysis can reveal bugs that evade dynamic approaches; but it faces limits in terms of precision, scalability and reproducibility. So, neither method seems capable of fully revealing hidden vulnerabilities.

“That’s why my colleagues and I started exploring whether we can combine these two approaches,” Shoshitaishvili says. “One of our ideas uses a form of static analysis to identify potential bugs and also where within code they may be located. Doing so enables the extraction of specific pieces of software for subsequent dynamic testing, which is much more effective than trying to test entire applications.”

Shoshitaishvili and his team have completed preliminary work on this novel system, which they call Resin — a reference to the material used to plug holes in boat hulls. They now need to determine how their approach can be applied to yield maximum impact for keeping vital software shipshape.

The potential of this innovative work has captured the attention of the Defense Advanced Research Projects Agency, or DARPA, a U.S. Department of Defense entity that fosters the development of breakthrough technologies to enhance national and global security.

DARPA has chosen Shoshitaishvili for a 2022 Young Faculty Award, which identifies him as a rising star among university scientists, engineers and mathematicians. Awardees receive funding, mentorship and professional contacts that support their research and its application to defense and security issues. Shoshitaishvili’s award includes $500,000 distributed across two years, with the possibility of another $500,000 for a third year.

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Emerging voice

Experts say it’s certain that advances in artificial intelligence, or AI, and machine learning will not only improve existing technologies but also be a springboard to inventing new high-tech smart devices and systems.

That outlook for these fast-emerging fields bodes well for Jay Shah.

A graduate research assistant in the School of Computing and Augmented Intelligence, one of the seven Ira A. Fulton Schools of Engineering at Arizona State University, Shah is on track to earn a doctoral degree in computer science and embark on a career in which AI and machine learning will be key tools of his trade.

 

Shah is already gaining attention as one the emerging broadcast voices in the AI and machine learning community. The “Machine Learning Podcast with Jay Shah” has steadily been gaining listeners and drawing accolades since its debut more than three years ago.

Audiences are drawn by Shah’s interviews and discussions with scientists, engineers, professors and researchers whose work focuses on AI and machine learning. Shah has also been expanding the conversation specifically to educate and advise students about these complex and still relatively nascent areas of the tech universe.

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ASU earns best finish ever in cybersecurity competition

Cybercriminals look to exploit gaps in intelligence and information security networks to steal what they are after. Their methods are continually evolving and so too must the efforts of cyber defense teams.

Each year the Collegiate Cyber Defense Competition, or CCDC, provides college students across the country an opportunity to flex their cyber skills in a competitive environment. It also highlights the students’ competency in managing the challenges that come with protecting corporate network infrastructure and business information systems.

The CCDC features a national competition preceded by nine regional competitions around the country. As part of the Western Regional CCDC event, students from the Ira A. Fulton Schools of Engineering at Arizona State University compete against teams from Arizona, California and Nevada.

The competition is meant to simulate what a security team within a business setting would experience while monitoring their environments and during live attacks. The scenarios enacted over the course of the event are a good example of what a high-stress environment could look like in cybersecurity, and they demonstrate the need for teamwork and collaboration.

This year, led by captain Leilani Sears, a computer science major in the School of Computing and Augmented Intelligence, one of the seven Fulton Schools, the ASU team had their best finish ever in the Western Regional. They won third place overall and first place in the defense category. ASU has competed annually in the competition since 2015.

“The competition is centered around a simulation of a business environment that is undergoing live attacks throughout the duration of the competition,” Sears says. “Ultimately, the purpose is incident response, cyber defense and monitoring a diverse infrastructure while completing business tasks such as configuring systems, providing comprehensive reports and risk analysis of vulnerable systems.”

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Supporting Human-Artificial Intelligence Collaborations with Visual Interfaces

SCAI Summer ’22 Seminar Series Invited Speaker Dr. Sungahn Ko presents “Supporting Human-Artificial Intelligence Collaborations with Visual Interfaces”

Title – “Supporting Human-Artificial Intelligence Collaborations with Visual Interfaces”
Name – Sungahn Ko
Date – 6/1/22
Time – 11:00 a.m. – 12:00 p.m. AZ Time
Location – BYENG 210

Closing the gap for real-time data-intensive intelligence

 

The online world fills databases with immense amounts of data. Your local grocery stores, your financial institutions, your streaming services and even your medical providers all maintain vast arrays of information across multiple databases.

Managing all this data is a significant challenge. And the process of applying artificial intelligence to make inferences, or apply logical rules or interpret information, on such data can be urgent, especially when delays known as latencies are also a major issue. Applications such as supply chain prediction, credit card fraud detection, customer service chatbot provision, emergency service response and health care consulting all require real-time inferences from data being managed in a database.

The current lack of support for machine learning inference in existing databases means that a separate process and system is needed, and is particularly critical for the certain applications, like the ones mentioned above. The data transfer between two systems significantly increases latency and this delay makes it challenging to meet the time constraints of interactive applications looking for real-time results.

Jia Zou, an assistant professor of computer science and engineering in the Ira A. Fulton Schools of Engineering at Arizona State University, and her team of researchers are proposing a solution, that, if successful, will greatly reduce the end-to-end latency for all-scale model serving on data that is managed by a relational database.

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ASU entrepreneurial program expands research impact

Technological advances made through engineering research can improve many aspects of people’s lives. To make a real impact, however, researchers need to expand their work beyond the lab, such as through entrepreneurship.

The Ira A. Fulton Schools of Engineering at Arizona State University provides many opportunities for faculty members and students to create startups based on their research, and supports them through programs including the Fulton Entrepreneurial Professorship Program.

This competitive two-year professorship program provides tenured or tenure-track faculty members with time and $200,000 in resources to accelerate their ASU ventures and increase their impact. The professors can defer teaching and lab responsibilities to focus on their entrepreneurial activities, which include working with patent attorneys, attending Small Business Innovation Research workshops and meeting with investors and commercialization experts.

Yezhou “YZ” Yang, an assistant professor of computer science and engineering, and Hamid Marvi, an associate professor of mechanical and aerospace engineering, were selected among nine pitch competition finalists to be named the 2022 Fulton Entrepreneurial Professors.

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Exact Algorithms for Distributionally Risk-Receptive Programs and Camera View-Frame Placement Problems: Applications in Homeland Security

Manish Bansal, Assistant Professor
Grado Department of Industrial and Systems Engineering, Virginia Tech
Friday, April 29, 2022, 12 p.m. MST
BYENG 209 (Tempe)
Via Zoom: https://asu.zoom.us/j/8120740932

IE Decision Systems Engineering Seminar Series Spring 2022
Hosted by: Dr. Adolfo Escobedo

 

Abstract

For many security applications, it is critical to make strategic long-term planning decisions with uncertain input data parameters that also allow adjustments based on risk-appetite of a decision maker. In the first half of this talk, we consider a distributionally risk-receptive network interdiction problem (DRR-NIP) where a leader maximizes a follower’s minimal expected objective value for the best-case probability distribution belonging to a given set of distributions (referred to as ambiguity set). The DRR-NIP is applicable for network vulnerability analysis where a network user seeks to identify vulnerabilities in the network against potential disruptions by an adversary who is receptive to risk for improving the expected objective values. We present exact and approximation algorithms for solving DRR-NIP with a general ambiguity set. We also provide conditions for which these approaches are finitely convergent, along with results of our extensive computational experiments.

In the second half of the talk, we introduce a combinatorial optimization problem pertinent to network-based telerobotic cameras that enable decision makers to interact with a remote physical environment using shared resources. Specifically, we consider a system of p networked robotic cameras that receives rectangular subregions as requests from multiple users for monitoring. Each subregion (or request) has an associated reward rate that depends on the importance level associated with monitoring that subregion. Our goal is to select the best view frame (pan, tilt, and zoom) for the cameras with discrete or continuous resolutions to maximize the total reward from the captured parts of the requested subregions.

We develop exact and approximation algorithms for solving this NP-hard problem. These optimal or near-optimal solutions provide information to decision makers to conduct surveillance and reconnaissance in environments where it is tedious for humans to collect information. We also present results of our computational experiments conducted to evaluate the performance of these algorithms.

Bio

Manish Bansal is an Assistant Professor with Grado Department of Industrial and Systems Engineering at Virginia Tech. He did Bachelors in Electrical Engineering from National Institute of Technology in India, and M.S. (with thesis) and Ph.D. from Department of Industrial and Systems Engineering at Texas A&M University. Prior to joining Virginia Tech, he was a postdoctoral fellow in Department of Industrial Engineering and Management Sciences at Northwestern University. He has served as president and vice-president of INFORMS Junior Faculty Interest Group during 2020-2022. He is serving as secretary of Engineering Faculty Organization at Virginia Tech. His research is focused on the theory of mixed integer programming, stochastic and distributionally robust optimization, and location science along with their applications in homeland security, logistics, and telerobotics. Currently, his research team has 4 PhD students and has received multiple grants from National Science Foundation and Department of Defense.

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Know thy enemy

As the software development landscape evolves, new security vulnerabilities are surfacing. Traditionally, a software’s source code could shed light on its vulnerabilities, but acquiring high-quality source code for the purpose of finding weaknesses can be difficult because of “compiling.”

Compiling refers to the process of transforming and optimizing a program’s source code to generate a final executable, which is a file that causes a computer to perform indicated tasks according to the encoded instructions. While an executable performs well and runs quickly on computers, it no longer has any information about the original source code.

Today, more and more software is developed in high-level programming languages, such as C++, Go and Rust, because of their many advantages, including higher development speed and better software engineering practices. Most importantly, programs written in high-level languages are compiled into machine code, the elemental language of computers, and will execute on computers at what is known as native speed. Executing at native speed allows for the fastest results.

Read more on Full Circle

Securing the microelectronics supply chain

Microelectronics enable all of the electronic systems we use today, from pacemakers, voting machines and cars to airplanes, missiles and supercomputers.

Also known as semiconductors, microelectronics have a uniquely complicated production supply chain. A typical semiconductor’s production occurs on multiple continents and takes three or more trips around the world, spanning 25,000 miles, according to a report by the Semiconductor Industry Association.

Over the course of this journey, many security challenges arise. Counterfeiting is a prevalent example. Between 2007 and 2010, U.S. Customs officials seized more than 5 million counterfeit microchips destined for commercial aviation and military applications. This problem has only increased due to supply chain issues and the resulting chip shortages during the COVID-19 pandemic.

Additional security issues include stolen intellectual property and design reverse-engineering. Along the supply chain, people can manipulate quality control information to pass lower-quality microchips as military-grade for a higher resale price. During production, malicious circuits or components could also be added and activated later to steal critical user information or disrupt the proper function of the device, such as a car failing to stop at a red light or a missile striking an unintended location.

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Using AI to battle Alzheimer’s

It’s estimated that more than six million Americans — and about 24 million people worldwide — are living with the degenerative brain disease called Alzheimer’s, a progressive mental deterioration that is the fifth leading cause of death in the United States among people who are 65 and older.

The Alzheimer’s Association and other sources report that medical experts estimate the number of people with this form of dementia more than doubling within the next three decades.

That reality is leading to more research aimed at deepening knowledge about the causes and progression of the disease to help in developing effective treatments.

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Ingenuity in teaching, research and leadership earns top honor

Two Ira A. Fulton Schools of Engineering faculty members were among those named 2022 President’s Professors by Arizona State University President Michael Crow. Andréa Richa, a professor in the School of Computing and Augmented Intelligence and Thomas Sugar, a professor in The Polytechnic School, two of the seven Fulton Schools, were acknowledged for their enthusiasm and innovation in teaching, the ability to inspire original and creative work by students, mastery of subject matter and scholarly contributions.

Richa’s and Sugar’s trailblazing teaching and research efforts in two growing areas of engineering are generating national attention through earning various awards and work on economic development initiatives.

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Student groups help women thrive in engineering

When women don’t see themselves represented in engineering and technology classrooms, research, careers and leadership, it can make succeeding in those fields a struggle.

As STEM fields diversify and become more inclusive, female students in the Ira A. Fulton Schools of Engineering at Arizona State University can find a place for support in student organizations that bring women and allies together to thrive.

Some of those organizations are the ASU section of the Society of Women Engineers, known as SWE; Women in Aviation, or WAI; Women in Computer Science, called WiCS; and Women in Science and Engineering, or WISE.

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Frontiers in Reliability Engineering Research and Applications

E. A. Elsayed, Professor
Industrial and Systems Engineering, Rutgers University
Friday, February 11, 2022, 12 p.m. MST
Via Zoom: https://asu.zoom.us/j/7274630791

IE Decision Systems Engineering Seminar Series Spring 2022
Hosted by: Ashif Iquebal

Abstract 
The pace of the developments of new materials, advances in manufacturing processes and sensors technology have resulted in the introduction of new products with unique designs and configuration. The human health care systems and the need for closer integration of engineering into the medical field have been greatly affected by such developments. New medical devices, human body parts replacements with “artificial” ones, their manufacturing, quality and reliability pose challenging research issues. This presentation addresses key challenges in reliability modeling, estimation, prediction and testing approaches for such products and highlights key challenges that require investigation by the reliability engineering community.

Bio 
E.A. Elsayed is Distinguished Professor of the Department of Industrial and Systems Engineering, Rutgers University. He is also the Director of the NSF/Industry/University Co-operative Research Center for Quality and Reliability Engineering. He was the Chair of ISE, Rutgers University from 1983 to 2001. His research interests are in the areas of quality and reliability engineering and Production Planning and Control. He is a co-author of Quality Engineering in Production Systems, McGraw Hill Book Company, 1989. He is also the author of Reliability Engineering, Addison-Wesley, 1996. These two books received the 1990 and 1997 IIE Joint Publishers Book-of-the-Year Award respectively. his recent book Reliability Engineering 2nd Edition, Wiley 2012 received the 2012 Outstanding Publications of IIE. The third edition of this book by Wiley in 2021 was selected to be included in the Best Industrial Management eBooks of All Time. Dr. Elsayed has received many awards and honors and was the keynote speaker of many international conferences.

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Navigating the nuance of negation

English and most other human languages apply a feature known as negation. For example, the sentence “Sarah never leaves her house in the morning without coffee” includes two negations: never and without. But the most common interpretation of this sentence is positive. People infer that Sarah does actually leave her house — after coffee.

“Roughly 20% of sentences in English use one or more negations. So, they are very common. Even young children understand and use them,” says Eduardo Blanco, an associate professor of computer science in the Ira A. Fulton Schools of Engineering at Arizona State University. “But computers find it incredibly challenging to comprehend negation in human or natural language. Even state-of-the-art machine translation systems experience substantial drops in performance when faced with negations in textual inputs.”

Blanco is an expert in natural language processing, a subfield of computer science that seeks to design algorithms and models that enable machines to better understand people. His research within the School of Computing and Augmented Intelligence, one of the seven Fulton Schools, centers on computational semantics or the construction of meaning representations from text such as human-generated questions.

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ASU cultivates new market opportunities for small farmers

American agriculture has been consolidating for decades. Farms have dwindled in number as they have grown in scale, and the same is true of businesses that process and distribute food. Forces driving this shift include technological advances that improve efficiencies and reduce costs, but those mainly benefit larger operations that can afford to adopt them.

Countering this broad trend, recent agricultural research suggests that emerging precision technologies offer hope for smaller operations. Novel systems that collect, analyze and apply detailed information to better connect farm production and markets for fresh produce could yield crucial benefits for small growers.

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Computing scenarios for defusing polarizing politics

Opposites may attract when it comes to personal relationships. In political affairs today, however, that claim is becoming more difficult to assert.

New research shows that common ground is shrinking in politics and people on opposite ends of the ideological spectrum are more entrenched in their divergent positions than at any time in recent history.

Those conclusions are not derived only from results of traditional opinion polls. In this era of big data and cutting-edge computational techniques, scientists are modeling patterns of various human interactions to examine how those relationships shape individuals’ viewpoints, as well as change the collective attitudes and behaviors of a population over time.

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Data DNA to provide security for generative modeling

What are the consequences if machines are intelligent but don’t have identities? What are the possible ways to hold machines, and their creators, accountable for their actions taken upon human society?

This is part of the work being explored by Yi “Max” Ren, an assistant professor of aerospace and mechanical engineering in the Ira A. Fulton Schools of Engineering at Arizona State University. Ren and a team of ASU researchers were recently awarded a National Science Foundation grant to study the attribution and secure training of generative models.

Generative models capture distributions of data from a range of high-dimensional, real-world content, such as the cat images that dominate the web, human speeches, driving behavior and material microstructures, among many other types of content. And by doing so, the models gain the ability to synthesize new content similar to the authentic content.

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Fulton Schools students named US finalists in Red Bull Basement global competition

Two Arizona State University students will represent the United States in Turkey in the Red Bull Basement Global Final on Dec. 13–15. Brinlee Kidd and Sylvia Lopez were selected from 182 applications across the country to pitch their automated note-taking tool, Jotted.

The Red Bull Basement program was created to empower student innovators in all areas of study to kick-start their ideas using technology to drive positive change.

“Our goal with Jotted is to empower students and prepare them for the academic challenges they will inevitably face,” Lopez says.

Like other popular note-taking apps, Jotted lets students type notes and organize them into digital notebooks. But it doesn’t stop there. The software automatically creates flash cards from the notes for future studying. It also includes a resource-finding feature. For example, if a student is confused during a lecture and the professor moves on too quickly, the student can mark that portion of the notes. Jotted will conduct a search and suggest additional resources to increase understanding. Before an exam, Jotted will even create practice tests based on the lecture notes. 

Read more at ASU News

Incentive Design for Promoting Ridesharing

Neda Masoud, Assistant Professor
Civil and Environmental Engineering, University of Michigan
Friday, November 12, 2021, 12 p.m. MST
Via Zoom: https://asu.zoom.us/j/8120740932

IE Decision Systems Engineering Seminar Series Fall 2021
Hosted by: Geunyeong Byeon

Abstract
Traffic congestion has become a serious issue around the globe, partly owing to single-occupancy commuter trips. Ridesharing can present a suitable alternative for serving commuter trips. However, there are several important obstacles that impede ridesharing systems from becoming a viable mode of transportation, including the lack of a guarantee for a ride back home as well as the difficulty of obtaining a critical mass of participants. This study addresses these obstacles by introducing a Traveler Incentive Program (TIP) to promote community-based ridesharing with a ride-back home guarantee among commuters. The TIP program allocates incentives to (1) directly subsidize a select set of ridesharing rides, and (2) encourage a few, carefully selected set of travelers to change their travel behavior. We formulate the underlying ride-matching problem as a budget-constrained min-cost flow problem, and present a Lagrangian Relaxation-based algorithm with a worst-case optimality bound to solve large-scale instances of this problem in polynomial time. We further propose a polynomial-time budget-balanced version of the problem. Numerical experiments suggest that allocating subsidies to change travel behavior is significantly more beneficial than directly subsidizing rides. Furthermore, using a flat tax rate as low as 1% can double the system’s social welfare in the budget-balanced variant of the incentive program.

Bio
Neda Masoud is an Assistant Professor of Civil and Environmental Engineering at the University of Michigan. She holds a Bachelor’s of Science degree in Industrial Engineering and a Master’s of Science degree in Physics. She received her PhD in Civil and Environmental Engineering from the University of California Irvine. Her research focuses on devising operational and planning tools to facilitate the transition into the next generation of mobility systems, which are envisioned to be connected, automated, electrified, and shared.

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Mapping the mind’s eye

Specific areas of the human brain process different functions, such as the auditory cortex for hearing and the olfactory cortex for smell. Among these functional areas, the single largest is devoted to vision. The dominance of the visual cortex may not be surprising given the importance of sight to the human species. But science still has a lot to learn about the way input from our eyes is represented by our brains.

This specialized work within neuroscience is called retinotopy or retinotopic mapping, and a leading researcher in the field is Yalin Wang, an associate professor of computer science and engineering in the Ira A. Fulton Schools of Engineering at Arizona State University.

“Retinotopic maps depict the way neurons in the brain display our visual field, which is what we see or the way light stimulates our retinas,” Wang says. “Our current research examines a specific area in the brain called V1, which is one of 14 areas devoted to visual processing. So, it’s a very defined space, but our understanding has remained limited.”

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Rethinking Resilience

Professor David Alderson
Operations Research, Naval Postgraduate School
Friday, October 22, 2021, 12 p.m.
BYENG 210

IE Decisions Systems Engineering Fall ’21 Seminar Series
Hosted by: Geunyeong Byeon
For recording, contact geunyeong.byeon@asu.edu.

Abstract 

Operations Research has yielded remarkable progress in the planning, operation, and design of a variety of civilian and military systems, making it possible to do things faster, better, and cheaper. However, the growing complexity of the world is showing that increased efficiency often comes at a price—when things fail, they can do so catastrophically. The term “resilience” has recently become a popular buzzword for systems that can absorb, resist, and recover from disruptive events. However, much of the work to date on this topic is merely descriptive and not particularly informative about what to do to make systems more resilient. This talk takes a modern look at the concept of resilience as it applies to the ability of a system to continue to operate in the presence of disruptive events and/or surprise. We also describe the limitations of big data analytics for resilience when systems are challenged by fundamental surprises never conceived during model development. In these cases, adoption of big data analytics may prove either useless for decision support or harmful by increasing dangers during unprecedented events.

Bio

David Alderson is a Professor in the Operations Research Department and serves as Founding Director for the Center for Infrastructure Defense at the Naval Postgraduate School (NPS). Dr. Alderson’s research focuses on the function and operation of critical infrastructures, with particular emphasis on how to invest limited resources to ensure efficient and resilient performance in the face of accidents, failures, natural disasters, or deliberate attacks. His research explores tradeoffs between efficiency, complexity, and fragility in a wide variety of public and private cyber-physical systems. Dr. Alderson has been the Principal Investigator of sponsored research projects for the Navy, Army, Air Force, Marine Corps, and Coast Guard and a recipient of ACM SIGCOMM Test of Time Paper Award in 2016, Military Operations Research Society Richard H. Barchi Prize in 2014, and AIAA Homeland Security Award in 2007. Dr. Alderson received his doctorate from Stanford University and his undergraduate degree from Princeton University. 

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Sex, Drugs, and Radiomics of Brain Cancer

Vasek and Anna Maria Polak Professor, Dr. Kristin Swanson
Cancer Research, Mayo Clinic
Friday, October 8, 2021, 12 p.m.
Via Zoom: https://asu.zoom.us/j/7274630791

IE Decisions Systems Engineering Fall ’21 Seminar Series
Hosted by: Ashif Iquebal
For recording, contact aiquebal@asu.edu.

Abstract
Glioblastoma are notoriously aggressive, malignant primary brain tumors that have variable response to
treatment. This presentation will focus on the integrative role of 1) biological sex-differences, 2) heterogeneity in drugdelivery and 3) intra-tumoral molecular diversity (revealed by radiomics) in capturing and predicting this variable
response to treatment. Specifically, I will highlight burgeoning insights into sex differences in tumor incidence,
outcomes, propensity and response to therapy. I will further, quantify the degree to which heterogeneity in drug
delivery, even for drugs that are able to bypass the blood-brain barrier, contributes to differences in treatment
response. Lastly, I will propose an integrative role for spatially resolved MRI-based radiomics models to reveal the
intra-tumoral biological heterogeneity that can be used to guide treatment targeting and management.

Bio
Dr. Swanson is currently the Vasek and Anna Maria Polak Professor in Cancer Research
and also holds appointments as Professor of Radiation Oncology and Cancer biology at
Mayo Clinic, where she directs the Mathematical NeuroOncology Lab and co-directs the
Precision NeuroTherapeutics Innovation Program at Mayo Clinic. She also holds an
appointment as Professor of Mathematical and Statistical Sciences at Arizona State
University. Dr. Swanson received her BS in mathematics with a minor in physics in 1996
from Tulane University. She then earned her MS in 1998 and PhD in 1999 in mathematical
biology from the University of Washington. Dr. Swanson went on to a postdoctoral
fellowship in mathematical and computational medicine at the University of California, San
Francisco. She joined the faculty at the University of Washington in 2000, with
appointments in both neuropathology and applied mathematics. In 2015, she joined Mayo
Clinic in Arizona as Professor and Vice Chair of the department of neurological surgery.
Dr. Swanson is an internationally recognized mathematical oncologist focused on
delivering optimal treatment to patients with brain cancer. Her research lab is driven by
the motto that “every patient deserves their own equation.” As a mathematical oncologist,
Dr. Swanson’s research interests are in clinical trial design and predictive mathematical
modeling for the treatment of patients with brain cancer. Her laboratory group works to
generate patient-specific predictive models to effectively and accurately predict tumor
growth and response to therapy in individual patients. The group works with clinical and
research teams at Mayo Clinic to bring these innovations to the clinic while identifying new
predictive models. This work can also be used to inform novel therapy design, resulting in
better treatment and outcomes for patients.Dr. Swanson is recipient of the 2017 Mayo
Clinic Service Award for Diversity and Inclusion, the 2008 University of Washington Award
for Undergraduate Research Mentor of the Year. Her research efforts have been
supported through funding by the NIH, the Ivy Foundation, the James S McDonnell
Foundation, the James D. Murray Endowed Chair at the University of Washington, and the
Mayo Clinic.

Solutions Cybersecurity competition challenges next generation of security experts

Every year, the gladiators of hacking meet to sharpen their skills and compete in the world’s most elite digital coliseum — DEF CON.

A pillar of the cybersecurity industry, DEF CON is one of the world’s largest hacking conventions, with its first event taking place in 1993. It offers hands-on hacking opportunities, workshops and presentations from government, industry and education experts in the field. Attendees included those interested in protecting software computer architecture, digital infrastructure and anything vulnerable to hacking.

Since 2018, faculty, students and staff with the ASU Global Security Initiative’s Center for Cybersecurity and Digital Forensics have organized DEF CON’s signature event, the Capture the Flag competition, which has multiple security challenges that competitors must identify and resolve. Hundreds of teams from all over the world compete each year to make the final round, with 16 teams emerging as finalists.

Read more on ASU News

Marrying Stochastic Gradient Descent with Bandits

Associate Professor Cong Shi
School of Industrial & Operations Engineering, University of Michigan
Friday, October 1, 2021, 12 p.m.
Via Zoom: https://asu.zoom.us/j/8120740932

IE Decisions Systems Engineering Fall ’21 Seminar Series
Hosted by: Geunyeong Byeon
For recording, contact geunyeong.byeon@asu.edu

Abstract
We consider a periodic-review single-product inventory system with fixed cost under censored demand. Under full demand distributional information, it is well-known that the celebrated $(s,S)$ policy is optimal. In this paper, we assume the firm does not know the demand distribution a priori, and makes adaptive inventory ordering decision in each period based only on the past sales (a.k.a. censored demand) data. The standard performance measure is regret, which is the cost difference between a feasible learning algorithm and the clairvoyant (full- information) benchmark. Compared with prior literature, the key difficulty of this problem lies in the loss of joint convexity of the objective function, due to the presence of fixed cost. We develop a nonparametric learning algorithm termed the $(\delta, S)$ policy that combines the powers of stochastic gradient descent, bandit controls, and simulation-based methods in a seamless and non-trivial fashion. We prove that the cumulative regret is $O(\log T\sqrt{T})$, which is provably tight up to a logarithmic factor. We also develop several technical results that are of independent interest. We believe that the framework developed could be widely applied to learning other important stochastic systems with partial convexity in the objectives.

Bio
Cong Shi is an associate professor of Industrial and Operations Engineering at University of Michigan. His research is focused on the design of efficient algorithms with theoretical performance guarantees for stochastic optimization models in operations management. Main areas of applications include inventory control, supply chain management, revenue management, and service operations. He received his Ph.D. in Operations Research at MIT in 2012, and his B.S. in Mathematics from the National University of Singapore in 2007. He won the first place in the INFORMS George Nicholson Student Paper Competition 2009, and the third place in the INFORMS Junior Faculty Interest Group (JFIG) Paper Competition 2017.

Autonomous Systems for Discoveries and Advanced Manufacturing

Mike and Sugar Barnes Professor Dr. Yu Ding
School of Industrial & Systems Engineering, Institute of Data Science, Texas A&M University
Friday, September 24, 2021, 12 p.m.
Via Zoom: https://asu.zoom.us/j/7274630791

IE Decisions Systems Engineering Fall ’21 Seminar Series
Hosted by: Ashif Iquebal
For recording, contact aiquebal@asu.edu.

Abstract
Discovering and manufacturing new materials is a laborious and time-consuming process. Historically this process takes countless time of material scientists and manufacturing engineers as they experiment with many different ingredient compositions and process conditions, in order to find the right combination leading to the material of desired properties. A team of researchers at Texas A&M University is contemplating the question— could an autonomous system be developed to replace the countless human hours and thus accelerate the discovery and manufacturing of advanced materials? In this talk, the speaker will review some of the existing platforms that inject certain degree of intelligence into a manufacturing system. The speaker will further discuss his team’s ongoing work of handling surprise observations and its implication in the broad effort of building such autonomous systems.

Bio
Dr. Yu Ding is the Mike and Sugar Barnes Professor of Industrial & Systems Engineering at Texas A&M University and Associate Director for Research Engagement of Texas A&M Institute of Data Science. Dr. Ding received his Ph.D. degree from the University of Michigan in 2001. His research interest is in data and quality science. Dr. Ding is a recipient of the 2018 Texas A&M Engineering Research Impact Award, the 2019 IISE Technical Innovation Award, the 2020 Texas A&M’s University-Level Distinguished Achievement Award in Research, and a Fellow of IISE and ASME. Dr. Ding is serving as the Editor-in-Chief for IISE Transactions for the term of 2021-2024.

Welcome, new faculty!

Join us in welcoming seven new faculty to SCAI. These talented professionals bring skills and insights from leading laboratories and industry innovators across the nation and the world.

Read more on Full Circle

Ronald Askin

Ronald Askin
Professor

Research interests
Logistics, manufacturing systems analysis, production planning and scheduling, operations research, applied statistics

Ron.Askin@asu.edu
(480) 965-2567
Tempe campus, BYENG 548

Janaka Balasooriya

Janaka Balasooriya
Associate Program Chair, Computer Systems Engineering
Senior Lecturer

Research interests 
Distributed, internet and grid computing, web service coordination primitives and system architectures, biological data integration and interoperability, middleware and embedded software

Janaka.Balasooriya@asu.edu
(480) 727-8593
Tempe campus, BYENG 578

Ayan Banerjee

Ayan Banerjee
Assistant Research Professor

Research interests
Power management in high performance data centers, cyber physical systems, medical control systems, data mining, mobile computing

Ayan.Banerjee@asu.edu 
Tempe campus, BYENG 517

Imon Banerjee

Imon Banerjee
Assistant Professor, Department of Biomedical Informatics, Department of Radiology, Emory University

Ajay Bansal

Ajay Bansal
Assistant Professor

Research interests
Logic programming, constraint programming, answer set programming, data mining, machine learning, semantic computing, service-oriented architecture.

Ajay.Bansal@asu.edu
(480) 727-1647
Polytechnic campus, PRLTA 230V
Research website

Srividya Bansal

Srividya Bansal
Associate Professor
Program Chair, Software Engineering

Research interests
Semantic computing, big data integration, semantics-based solutions for outcome-based instruction design in STEM education, delivery models for software engineering education; web service description, discovery and composition.

Srividya.Bansal@asu.edu
(480) 727-5107
Polytechnic campus, PRLTA 230G
Research website

Dimitri Bertsekas

Dimitri Bertsekas

Dimitri Bertsekas
Fulton Chair of Computational Decision Making

Research interests
Reinforcement learning, artificial intelligence, optimization, linear and nonlinear programming, data communication networks, parallel and distributed computation

Chris Bryan

Chris Bryan
Assistant Professor

Research interests
Information visualization, visual analytics, virtual reality, data storytelling, data analysis, explainable machine learning and artificial intelligence; perception and cognition, human-computer Interaction, data privacy and security

Ariane Both

Ariane Both
Student Services Coordinator Associate

Focus
ASU Online campus
Advising support staff

Kevin Burger

Kevin Burger
Lecturer

Research interests
Embedded systems, introductory programming, data structures and algorithms, computer architecture and organization, web development

Burgerk@asu.edu
(480) 965-1493
Tempe campus, BYENG 516

John Caumban

John Caumban
Computer Science

Being a SCAI Mentor gives me the chance to welcome future Sun Devils and share knowledge that may help them exceed in their academic careers.

Michael Clough

Michael Clough
Lecturer

Research interests
Measurement of nano/microscale mechanisms underlying thermofluidic and interfacial phenomena for design of nanoengineered solutions that enhance macroscale performance of a variety of industrial applications including energy systems, thermal desalination, and transportation systems.

Mclough@asu.edu
(480) 965-2804
Tempe campus, BYENG 480

Sam Dicaro

Sam Dicaro
Computer Systems Engineering

Being a SCAI Mentor gives me a sense of purpose. I enjoy knowing that I might have helped someone on their journey to success.

Edmond Dong

Edmund Dong
Computer Science 4+1

As a SCAI Mentor, I have opportunities to provide the advice I wish I was given when I was starting college.

Adolfo Escobedo

Adolfo Escobedo
Assistant Professor

Research interests
Theory and application of linear and mixed integer programming; design, analysis, and efficient implementation of optimization algorithms; computational linear algebra; power systems operation and planning; circular economy.

Adres@asu.edu
(480) 965-5248
Tempe campus, BYENG 346

Georgios Fainekos

Georgios Fainekos
Associate Professor

Research interests
Cyber-physical systems: hybrid dynamical systems, real-time and embedded systems, formal methods with applications to automation & control: system testing and verification, formal languages and logic, motion planning in robotics, unmanned aerial vehicles (UAV)

Fainekos@asu.edu
(480) 965-8267
Tempe campus, CTRPT 203-17
Research website

Xuerong Feng

Xuerong Feng
Senior Lecturer

Research interests
Algorithm design and analysis, including network algorithms, Bioinformatics algorithms and parallel algorithms

Xuerong.Feng@asu.edu
(480) 965-2855
Tempe campus, BYENG 512

Aubrey Fox

Aubrey Fox
Academic Success Advisor, Sr.

Focus
Polytechnic campus
ASU Online campus
Undergraduate advising

Kevin Gary

Kevin Gary
Associate Professor

Research interests
Software architecture and design, open source software, agile methods, applications in healthcare and e-learning.

Kgary@asu.edu
(480) 727-1373
Polytechnic campus, PRLTA 230C

Andrew Garza

Andrew Garza
Computer Science 4+1

I really enjoy the fact that I’ve been able to make a really tiny impact on many different people. It’s not like I’m changing their life or anything, but I do find it very meaningful.

LJ Gomez

LJ Gomez
Industrial Engineering

To me, being a peer mentor means getting to welcome new Sun Devils and help them get comfortable at wonderful, sunny ASU!

Kirk Hagen

Kirk Hagen
Academic Success Advisor Sr.

Focus
Polytechnic campus
ASU Online campus
Undergraduate advising

Amy Hara

Amy Hara
Consultant and Chair, Department of Radiology, and Professor of Radiology, Mayo Clinic

Yoshihiro Kobayashi

Yoshihiro Kobayashi
Senior Lecturer

Research interests
Game design, game development, digital fabrication, AI in design, virtual reality, building information modeling, procedural modeling, computer graphics, PRISM Lab at ASU

Ykobaya@asu.edu
(480) 965-3708
Tempe campus, BYENG 354

Kelli Kreger

Kelli Kreger
Executive Administrative Support Specialist

Kelli.Kreger@asu.edu
(480) 965-8244
Tempe campus

Sue Lafond

Sue Lafond

Sue Lafond
Academic Success Advising Coordinator Sr.

Focus
Tempe campus
Undergraduate advising
NEST/SOAR focus

Joohyung Lee

Joohyung Lee
Associate Professor

Research interests
Knowledge representation and reasoning, computational logic, logic programming, logics in security, computational semantics of natural language

Joolee@asu.edu
(480) 965-2784
Tempe campus, BYENG 586
Research website

Yann-Hang Lee

Yann-Hang Lee
Professor

Research interests
Real-time computing, internet of things (IoT), embedded system and software, fault-tolerant computing, distributed computing, service-oriented computing, performance evaluation

Yhlee@asu.edu
(480) 727-7507
Tempe campus, CTRPT 203-23
Research website

Tim Lindquist

Tim Lindquist
Professor Emeritus

Research interests
Software engineering, distributed and mobile systems, web applications and programming languages

Tim.Lindquist@asu.edu
(480) 727-2783
Polytechnic campus, PRLTA 230
Research website

Ross Maciejewski

Ross Maciejewski
Co-Associate Director, School of Computing and Augmented Intelligence and Professor
Director, Center for Accelerating Operational Efficiency, Department of Homeland Security (DHS) Center of Excellence

Research interests
Information visualization, geographical visualization, computer graphics, syndromic surveillance, volume rendering, non-photorealistic rendering, decision support systems

Nick Martinez

Nick Martinez
Computer Science (Cybersecurity) 4+1

As a SCAI mentor, I can provide help and inspiration to up-and-coming students, especially by sharing tips that I have learned from struggles on my own path.

Ryan Meuth

Ryan Meuth
Lecturer

Research interests
Examining ways to improve engineering education, in particular the first year engineering experience

Rmeuth@asu.edu
(480) 727-6389
Tempe campus, BYENG 438

Katina Michael

Katina Michael
Professor
Joint appointment with School for the Future of Innovation in Society

Research interests
Emerging technologies, innovation, informatics, technology and society, policy and society, ubiquitous computing, digital media, wearable computing, embedded devices, big data, open data, drones, cybersecurity, IoT, robotics, automation

 

Katina.Michael@asu.edu
(480) 965-6316
Tempe campus
Research Website

Ariane Middel

Ariane Middel

Ariane Middel
Assistant Professor
Joint appointment with School of Arts, Media and Engineering

Research interests
Urban climate, heat mitigation, thermal comfort, human biometeorology, modeling and simulation, microclimate, local climate, climate-sensitive urban design, climate adaptation and mitigation, urban heat islands, urban climate informatics, geographic information systems, human-environment interaction, land use and land cover, sustainability, geovisualization

Ariane.Middel@asu.edu
(480) 967-2875
Tempe campus
Research Website

Pitu Mirchandani

Pitu Mirchandani
Professor, The Avnet Chair in Supply Chain Networks

Research interests
Optimization, decision-making under uncertainty, real-time control and logistics, application interests in urban service systems, transportation, and homeland security

Pitu@asu.edu
(480) 965-2758
Tempe campus, BYENG 332
Advanced Transportation and Logistics: Algorithms and Systems (ATLAS) Research Laboratory
Research Website

Peter Moore

Peter Moore

Peter Moore
Computer Science

Being a SCAI mentor is all about helping my fellow Sun Devils learn how to be a Sun Devil!

Rong Pan

Rong Pan
Associate Professor

Research interests
Industrial statistics, reliability analysis and time series modeling

Rong.Pan@asu.edu
(480) 965-4259
Tempe campus, BYENG 352

Anshuman Panda

Anshuman Panda
Division Education Director, Medical Physics, Department of Radiology, and Assistant Professor of Radiology, Mayo Clinic

Bhavik N. Patel

Bhavik N. Patel
Associate Professor
Co-director, ASU-Mayo Center for Innovative Imaging
Director of Artificial Intelligence, Mayo Clinic
Joint appointment with the School of Biological and Health Systems Engineering

Senior Associate Consultant and Associate Professor, Department of Radiology, and Chair, MCA Radiology AI, Machine Intelligence in Medicine and Imaging (MI•2) Center

Research interests
Artificial Intelligence, machine learning, deep learning, natural language processing

patel.bhavik@mayo.edu
480-342-0898
Mayo Clinic
Department of Radiology
13400 East Shea Blvd
Scottsdale, AZ

Ted Pavlic

Ted Pavlic
Assistant Professor

Research interests
Distributed algorithms, autonomous systems, decentralized decision making, complex adaptive systems, self organization, hybrid dynamical systems, sustainability in the built environment, behavioral ecology, behavioral economics, operations research, bio-mimicry and bio-inspiration, parallel computation, robotics,energy systems, intelligent control, optimization, game theory, resource allocation, collective behavior

Tpavlic@asu.edu
(480) 965-2899
Tempe campus, BYENG 314
Research website

Josue Quintero

Josue Quintero
Computer Science (Software Engineering)

Being a SCAI mentor allows me to diminish any uncertainty and hesitation students beginning their journey will have.

Fengbo Ren

Fengbo Ren
Associate Professor

Research interests
Hardware acceleration, embedded, reconfigurable, and parallel computing solutions for data analytics and information processing, energy-efficient computing, data-driven compressive sensing, hardware-friendly machine learning

Renfengbo@asu.edu
(480) 727-5793
Tempe campus
Research website

Andrea Richa

Andrea Richa
Co-Associate Director, School of Computing and Augmented Intelligence
President’s Professor

Research interests
Self-organizing particle systems, programmable matter, bio-inspired algorithms, distributed computing and algorithms, theory of wireless communication, graph, randomized, and approximation algorithms, self-stabilizing overlay networks, combinatorial optimization, distributed resource allocation

Aricha@asu.edu
(480) 965-7555
Tempe campus, BYENG 440
Research website

Shanee Rogers

Shanee Rogers
Student Services Coordinator Associate

Focus
Polytechnic campus
ASU Online campus
Advising support staff

Kevin Rudd

Kevin Rudd
Senior Computer Architecture & Computer Engineering Researcher, Laboratory for Physical Sciences (LPS) in the Advanced Computing Systems (ACS) Research Program

George Runger

George Runger
Professor
Program Chair, Industrial Engineering and Engineering Management
Graduate Program Chair, Industrial Engineering

Research interests
Statistical learning, process control, data mining for massive, multivariate data sets

Runger@asu.edu
(480) 965-3193
Mayo Clinic, Samuel C. Johnson Research Bldg 13212