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MIDAS announces winners of 2018 poster competition

By | Educational, General Interest, Happenings, Research

The Michigan Institute for Data Science (MIDAS) is pleased to announce the winners of its 2018 poster competition, which is held in conjunction with the MIDAS annual symposium.

The symposium was held on Oct. 9-10, 2018, and the student poster competition had more than 60 entries. The winners, judged by a panel of faculty members, received cash prizes.

Best Overall

Arthur Endsley, “Comparing and timing business cycles and land development trends in U.S. metropolitan housing markets”

Most likely health impact

  • Yehu Chen, Yingsi Jian, Qiucheng Wu, Yichen Yang, “Compressive Big Data Analytics – CBDA: Applications to Biomedical and Health Studies”
  • Jinghui Liu, “An Information Retrieval System with an Iterative Pattern for TREC Precision Medicine”

Most likely transformative science impact

  • Prashant Rajaram, “Bingeability and Ad Tolerance: New Metrics for the Streaming Media Age”
  • Mike Ion, “Learning About the Norms of Teaching Practice: How Can Machine Learning Help Analyze Teachers’ Reactions to Scenarios?”

Most interesting methodological advancement

  • Nina Zhou and Qiucheng Wu, “DataSifter: Statistical Obfuscation of Electronic Health Records and Other Sensitive Datasets”
  • Aniket Deshmukh, “Simple Regret Minimization for Contextual Bandits”

Most likely societal impact

  • Ece Sanci, “Optimization of Food Pantry Locations to Address Food Scarcity in Toledo, OH”
  • Rohail Syed, “Human Perception of Surprise: A User Study”

Most innovative use of data

  • Lan Luo, “Renewable Estimation and Incremental Inference in Generalized Linear Models with Streaming Datasets”
  • Danaja  Maldeniya, “Psychological Response of Communities affected by Natural Disasters in Social Media”

MICDE to provide data analysis and dissemination support for $18 million tobacco research center

By | General Interest, Happenings, News, Research

The University of Michigan School of Public Health will house a new, multi-institutional center focusing on modeling and predicting the impact of tobacco regulation, funded with an $18 million federal grant from the National Institutes of Health and the Food and Drug Administration.

The Center for the Assessment of the Public Health Impact of Tobacco Regulations will be part of the NIH and FDA’s Tobacco Centers of Regulatory Science, the centerpiece of an ongoing partnership formed in 2013 to generate critical research that informs the regulation of tobacco products.

The Michigan Institute for Computational Discovery and Engineering (MICDE) will support the center’s Data Analysis and Dissemination core by collecting national and regional survey data, conducting analysis of the use of tobacco products including vaping and e-cigarettes, and disseminate the resulting tobacco modeling parameters to other research centers and the Food and Drug Administration.

The center is led by MICDE affiliated faculty member Rafael Meza, associate professor of Epidemiology, and David Levy, professor of Oncology at Georgetown University.

For more on the center, see the press release from the U-M School of Public Health: https://sph.umich.edu/news/2018posts/tcors-091718.html

MDST group wins KDD best paper award

By | General Interest, Happenings, MDSTPosts, Research

A paper by members and faculty leaders of the Michigan Data Science Team (co-authors: Jacob Abernethy, Alex Chojnacki, Arya Farahi, Eric Schwartz, and Jared Webb) won the Best Student Paper award in the Applied Data Science track at the KDD 2018 conference in August in London.

The paper, ActiveRemediation: The Search for Lead Pipes in Flint, Michigan, details the group’s ongoing work in Flint to detect pipes made of lead and other hazardous material.

For more on the team’s work, see this recent U-M press release.

U-M part of new software institute on high-energy physics

By | General Interest, Happenings, News, Research

The University of Michigan is part of an NSF-supported 17-university coalition dedicated to creating next-generation computing power to support high-energy physics research.

Led by Princeton University, the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) will focus on developing software and expertise to enable a new era of discovery at the Large Hadron Collider (LHC) at CERN in Geneva, Switzerland.

Shawn McKee, Research Scientist in the U-M Department of Physics, is a co-PI of the institute. His His work will focus on integrating and extending the Open Storage Grid networking activities with similar efforts at the LHC.

For more information, see Princeton’s press release, and the NSF’s announcement.

MIDAS researchers’ papers accepted at ACM KDD data science conference in London

By | General Interest, Happenings, News, Research

Several U-M faculty affiliated with MIDAS will participate in the KDD2018 Conference in London in August. The meeting is held by the Associate for Computing Machinery’s Special Interest Group in Knowledge Discovery and Data Mining (KDD).

U-M researchers had the following papers accepted:

Learning Adversarial Networks for Semi-Supervised Text Classification via Policy Gradient
Yan Li (U-M); Jieping Ye (U-M)

TINET: Learning Invariant Networks via Knowledge Transfer
Chen Luo (Rice University); Zhengzhang Chen (NEC Laboratories America); Lu-An Tang (NEC Laboratories America); Anshumali Shrivastava (Rice University); Zhichun Li (NEC Laboratories America); Haifeng Chen (NEC Laboratories America); Jieping Ye (U-M)

Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
Jiaqi Ma(U-M); Zhe Zhao (Google); Xinyang Yi (Google); Jilin Chen (Google); Lichan Hong (Google); Ed Chi (Google)

Learning Credible Models
Jiaxuan Wang (U-M); Jeeheh Oh (U-M); Haozhu Wang (U-M); Jenna Wiens (U-M)

Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories
Ian Fox (U-M); Lynn Ang (U-M); Mamta Jaiswal (U-M); Rodica Pop-Busui (U-M); Jenna Wiens (U-M)

ActiveRemediation: The Search for Lead Pipes in Flint, Michigan
Jacob Abernethy (Georgia Institute of Technology); Alex Chojnacki (U-M); Arya Farahi (U-M); Eric Schwartz (U-M); Jared Webb (Brigham Young University)

Career Transitions and Trajectories: A Case Study in Computing
Tara Safavi (U-M); Maryam Davoodi (Purdue University); Danai Koutra (U-M)

In addition, U-M Professor Jieping Ye will present at the event’s Artificial Intelligence in Transportation tutorial, and U-M Assistant Professor Qiaozhu Mei will speak as part of Deep Learning Day.

MICDE awards seven Catalyst Grants

By | General Interest, Happenings, News, Research

The Michigan Institute for Computational Discovery and Engineering has awarded its second round of Catalyst Grants, providing between $80,000 and $90,000 each to seven innovative projects in computational science. The proposals were judged on novelty, likelihood of success at catalyzing larger programs and potential to leverage ARC’s computing resources.

The funded projects are:

Title: Exploring Quantum Embedding Methods for Quantum Computing
Researchers: Emanuel Gull, Physics; Dominika Zgid, Chemistry.
Description: The research team will design quantum embedding algorithms that can be early adopters of quantum computers on development of advanced materials for possible applications in modern batteries, next-generation oxide electronics, or high-temperature superconducting power cables.

Title: Teaching autonomous soft machines to swim
Researchers: Silas Alben, Mathematics; Robert Deegan, Physics, Alex Gorodetsky, Aerospace Engineering
Description: Self-oscillating gels are polymeric materials that change shape, driven by chemical reactions occurring entirely within the gel. The research team will develop a computational and machine learning program to discover how to configure self-oscillating gels so that they undergo deformations that result in swimming. The long term goal is to develop a general framework for controlling autonomous soft machines.

Title: Urban Flood Modeling at “Human Action” Scale: Harnessing the Power of Reduced-Order Approaches and Uncertainty Quantification
Researchers: Valeriy Ivanov, Civil and Environmental Engineering; Nikolaos Katopodes, Civil and Environmental Engineering; Darren McKague Climate and Space Sciences and Engineering; Khachik Sargsyan, Sandia National Labs.
Description: The research team will demonstrate urban flood monitoring and prediction capabilities using NASA Cyclone Global Navigation Satellite System (CYGNSS) data and relying on state-of-the-science uncertainty quantification tools in a proof-of-concept urban flooding problem of high complexity.

Title: Advancing the Computational Frontiers of Solution-Adaptive, Scale-Aware Climate Models
Researchers: Christiane Jablonowski, Climate and Space Sciences and Engineering; Hans Johansen, Lawrence Berkeley National Lab.
Description: Researchers will further develop a 3-D mesh adaptation model for climate modeling, allowing computational resources to be focused on phenomena of interest such as tropical cyclones or other extreme weather events. The project will also introduce data-driven machine learning paradigms into modeling of clouds and precipitation.

Title: Deciphering the meaning of human brain rhythms using novel algorithms and massive, rare datasets
Researchers: Omar Ahmed, Psychology, Neuroscience and Biomedical Engineering
Description: The team will develop a set of algorithms for use on high performance computers to analyze de-identified brain data from patients in order to better understand what electrical oscillations tell us about rapidly changing behavioral and pathological brain states.

Title: Embedded Machine Learning Systems To Sense and Understand Pollinator Behavior
Researchers: Robert Dick, Electrical Engineering and Computer Science; Fernanda Valdovinos Ecology and Evolutionary Biology, Center for Complex Systems; Paul Glaum, Ecology and Evolutionary Biology.
Description: To understand the mechanisms driving the population dynamics of pollinators, the research team will develop technologies for deeply embedded hardware/software learning systems capable of remote, long term, autonomous operation; and will analyze the resulting new data to better understand pollinator activity.

Title: Deep Learning for Phylogenetic Inference
Researchers: Jianzhi Zhang, Ecology and Evolutionary Biology; Yuanfang Guan, Computational Medicine and Bioinformatics.
Description: The research team will use deep neural networks to infer molecular phylogenies and extract phylogenetically useful patterns from amino acid or nucleotide sequences, which will help understand evolutionary mechanisms and build evolutionary models for a variety of analyses.

For more on the Catalyst Grants, see http://micde.umich.edu/catalyst/.

MIDAS Data Science for Music Challenge Initiative announces funded projects

By | Data, General Interest, Happenings, News, Research

From digital analysis of Bach sonatas to mining data from crowdsourced compositions, researchers at the University of Michigan are using modern big data techniques to transform how we understand, create and interact with music.

Four U-M research teams will receive support for projects that apply data science tools like machine learning and data mining to the study of music theory, performance, social media-based music making, and the connection between words and music. The funding is provided under the Data Science for Music Challenge Initiative through the Michigan Institute for Data Science (MIDAS).

“MIDAS is excited to catalyze innovative, interdisciplinary research at the intersection of data science and music,” said Alfred Hero, co-director of MIDAS and the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science. “The four proposals selected will apply and demonstrate some of the most powerful state-of-the-art machine learning and data mining methods to empirical music theory, automated musical accompaniment of text and data-driven analysis of music performance.”

Jason Corey, associate dean for graduate studies and research at the School of Music, Theatre & Dance, added: “These new collaborations between our music faculty and engineers, mathematicians and computer scientists will help broaden and deepen our understanding of the complexities of music composition and performance.”

The four projects represent the beginning of MIDAS’ support for the emerging Data Science for Music research. The long-term goal is to build a critical mass of interdisciplinary researchers for sustained development of this research area, which demonstrates the power of data science to transform traditional research disciplines.

Each project will receive $75,000 over a year. The projects are:

Understanding and Mining Patterns of Audience Engagement and Creative Collaboration in Large-Scale Crowdsourced Music Performances

Investigators: Danai Koutra and Walter Lasecki, both assistant professors of computer science and engineering

Summary: The project will develop a platform for crowdsourced music making and performance, and use data mining techniques to discover patterns in audience engagement and participation. The results can be applied to other interactive settings as well, including developing new educational tools.

Understanding How the Brain Processes Music Through the Bach Trio Sonatas
Investigators: Daniel Forger, professor of mathematics and computational medicine and bioinformatics; James Kibbie, professor and chair of organ and university organist

Summary: The project will develop and analyze a library of digitized performances of Bach’s Trio Sonatas, applying novel algorithms to study the music structure from a data science perspective. The team’s analysis will compare different performances to determine features that make performances artistic, as well as the common mistakes performers make. Findings will be integrated into courses both on organ performance and on data science.

The Sound of Text
Investigators: Rada Mihalcea, professor of electrical engineering and computer science; Anıl Çamcı, assistant professor of performing arts technology

Summary: The project will develop a data science framework that will connect language and music, developing tools that can produce musical interpretations of texts based on content and emotion. The resulting tool will be able to translate any text—poetry, prose, or even research papers—into music.

A Computational Study of Patterned Melodic Structures Across Musical Cultures
Investigators: Somangshu Mukherji, assistant professor of music theory; Xuanlong Nguyen, associate professor of statistics

Summary: This project will combine music theory and computational analysis to compare the melodies of music across six cultures—including Indian and Irish songs, as well as Bach and Mozart—to identify commonalities in how music is structured cross-culturally.

The Data Science for Music program is the fifth challenge initiative funded by MIDAS to promote innovation in data science and cross-disciplinary collaboration, while building on existing expertise of U-M researchers. The other four are focused on transportation, health sciences, social sciences and learning analytics.

Hero said the confluence of music and data science was a natural extension.

“The University of Michigan’s combined strengths in data science methodology and music makes us an ideal crucible for discovery and innovation at this intersection,” he said.

Contact: Dan Meisler, Communications Manager, Advanced Research Computing
734-764-7414, dmeisler@umich.edu

Eric Parish, Aero Ph.D student, wins Von Neumann Fellowship from Sandia National Labs

By | Happenings, News, Research

Eric Parish

Eric Parish, who will graduate this spring with a Ph.D in Aerospace Engineering, is the 2018 recipient of the prestigious John von Neumann Postdoctoral Research Fellowship from Sandia National Laboratories (SNL). The highly competitive fellowship offers the opportunity to establish his own program at SNL to conduct innovative research in computational mathematics and scientific computing on advanced computing architectures.

Parish came to U-M from the University of Wyoming, and has developed innovative methodologies of computational math and physics with Prof. Karthik Duraisamy.

Parish said two of his accomplishments in his doctoral work have been developing data-driven solutions to computational physics problems using the NSF-funded ConFlux computing cluster, and bringing together ideas from statistical mechanics to develop efficient numerical solutions of complex partial differential equations.

“It was bridging a gap between communities,” he said of the latter effort.

“Eric came up with a particularly clever way of generalizing concepts from physics to develop a foundation to solve complex equations at a low cost in a mathematically rigorous fashion,” Duraisamy said. “He is one of the rare students who commands an exceptional grasp of applied mathematics, computing and physics, while being well-rounded in his organizational and communication skills. It has been a pleasure and a privilege to work with him.”

Parish said this research could eventually help usher the next generation of flight, for example, “hypersonic” aircraft that can travel at speeds of Mach 8-10. To help get there, his work moves the field toward a better understanding of the underlying physical phenomena via accurate numerical simulations.

At Sandia’s labs in Livermore, Calif., Parish said he plans to continue the work he started at U-M to develop “reduced order models”, which can process past simulation data to greatly reduce the computational cost of future simulations.

Parish said that conducting research at U-M, with the availability of high performance computing resources and a community of computational scientists to bounce ideas off of, helped push his research to a higher level.

“Within Aero, there are five or six very strong computational groups, which really helps me understand the fundamental aspects of what we’re doing, and what the addition of my small little delta means,” he said. “It’s very exciting to do computational research in that environment; it motivates me to come up with better code.”

In 2016, Parish received a $4,000 fellowship from the Michigan Institute for Computational Discovery and Engineering (MICDE). He used some of the funds to attend the International Workshop on Variational Multiscale Methods in Spain last year, where he met a few dozen people from around the world working on similar problems.

“It was fantastic to network and learn from them,” he said.

Parish grew up in Laramie, Wyo., before attending the University of Wyoming, where he played Division 1 golf. He said there was a small but active computational science community at U-W.

“For its size, there was a lot of good computational stuff there,” he said, adding that 10 years ago he would never have predicted the current direction of his career.

Golf played a significant role in his development as well, Parish said: “Being a successful student-athlete takes an extraordinary amount of work. The successes and failures I had … played an integral part in the development of my work ethic, time management skills, mental attitude, and overall growth as a person…I believe that the experience I gained as a student-athlete gave me a unique perspective and skill set that I was able to use to my advantage.”

As far as his future goes after Sandia, Parish said he plans to either continue in the national lab environment or to explore faculty positions so that he can teach and motivate students as his professors at Wyoming and Michigan did for him.

“I’m grateful for everyone’s help,” he said. “The doors that Michigan can open and the quality of people here are very apparent.”

A simulation of magnetohydrodynamic turbulence done on the ConFlux cluster with roughly 1 billion degree of freedom computation generating about 4TB of data.

Peers Health and U-M begin research partnership using disability and workers’ comp healthcare data

By | General Interest, Happenings, News, Research

Peers Health and the University of Michigan are starting a two-year research project that will apply advanced learning technologies to a proprietary global database of millions of de-identified disability and workers’ compensation cases. The goals of the project include developing a prescriptive modeling framework to facilitate development of optimal return-to-work plans for injured or ill patients.

Public policy experts have begun to connect patients’ ability to perform their productive endeavors, such as their job, to their state of general health and well-being. The findings from this project, by helping define when someone objectively has returned to health, could inform decision-making in virtually every healthcare episode.

The principal investigators in the project, Dr. Brian Denton and Dr. Jenna Wiens, are both renowned experts in medical machine learning. Dr. Denton, a professor of Industrial and Operations Engineering and Urology, and Dr. Wiens, an assistant professor of Computer Science and Engineering, are both affiliated with the Michigan Institute of Data Science (MIDAS) at U-M.

Peers Health recently announced an expanded partnership with ODG, an MCG company and part of the Hearst Health Network, to aggressively acquire new data to enhance ODG functionality and to fuel this research. Jon Seymour, MD, CEO of Peers, said, “This is a new phase in medical publishing where raw data collection is the editorial function and cutting-edge machine learning is the technology factor. We turned to the University of Michigan due to its impressive data science programs spanning multiple departments, as well as the specific experience of Dr. Denton and Dr. Wiens in medical applications. We’re confident this initiative will attract many new data contributors along the way.”

“The collaboration with Peers Health is exciting because it provides data that can help build a model that will reduce the time — from both a safety and productivity perspective — for people to return to work following sickness or injury,” Denton said. “Streaming data in from existing patients will allow our model to adapt and improve over time.”

Wiens added: “These data contain a particularly interesting training label: days away from work. We hypothesize that this will be a strong signal for the type, timing, and effectiveness of the treatments and therapies.”

The U-M partnership with Peers was established by MIDAS and the university’s Business Engagement Center (BEC).

“This partnership illustrates the power of combining data from the healthcare industry with the data science expertise of U-M faculty,” said Dr. Alfred Hero, professor of Engineering and co-director of MIDAS.

“It is energizing for the BEC to be part of these innovative collaborative relationships that create real impact in the world,” added BEC Director Amy Klinke.

 

U-M fosters thriving artificial intelligence and machine learning research

By | General Interest, HPC, News, Research

Research using machine learning and artificial intelligence — tools that allow computers to learn about and predict outcomes from massive datasets — has been booming at the University of Michigan. The potential societal benefits being explored on campus are numerous, from on-demand transportation systems to self-driving vehicles to individualized medical treatments to improved battery capabilities.

The ability of computers and machines generally to learn from their environments is having transformative effects on a host of industries — including finance, healthcare, manufacturing, and transportation — and could have an economic impact of $15 trillion globally according to one estimate.

But as these methods become more accurate and refined, and as the datasets needed become bigger and bigger, keeping up with the latest developments and identifying and securing the necessary resources — whether that means computing power, data storage services, or software development — can be complicated and time-consuming. And that’s not to mention complying with privacy regulations when medical data is involved.

“Machine learning tools have gotten a lot better in the last 10 years,” said Matthew Johnson-Roberson, Assistant Professor of Engineering in the Department of Naval Architecture & Marine Engineering and the Department of Electrical Engineering and Computer Science. “The field is changing now at such a rapid pace compared to what it used to be. It takes a lot of time and energy to stay current.”

Diagram representing the knowledge graph of an artificial intelligence system, courtesy of Jason Mars, assistant professor, Electrical Engineering and Computer Science, U-M

Johnson-Roberson’s research is focused on getting computers and robots to better recognize and adapt to the world, whether in driverless cars or deep-sea mapping robots.

“The goal in general is to enable robots to operate in more challenging environments with high levels of reliability,” he said.

Johnson-Roberson said that U-M has many of the crucial ingredients for success in this area — a deep pool of talented researchers across many disciplines ready to collaborate, flexible and personalized support, and the availability of computing resources that can handle storing the large datasets and heavy computing load necessary for machine learning.

“The people is one of the reasons I came here,” he said. “There’s a broad and diverse set of talented researchers across the university, and I can interface with lots of other domains, whether it’s the environment, health care, transportation or energy.”

“Access to high powered computing is critical for the computing-intensive tasks, and being able to leverage that is important,” he continued. “One of the challenges is the data. A major driver in machine learning is data, and as the datasets get more and more voluminous, so does the storage needs.”

Yuekai Sun, an assistant professor in the Statistics Department, develops algorithms and other computational tools to help researchers analyze large datasets, for example, in natural language processing. He agreed that being able to work with scientists from many different disciplines is crucial to his research.

“I certainly find the size of Michigan and the inherent diversity that comes with it very stimulating,” he said. “Having people around who are actually working in these application areas helps guide the direction and the questions that you ask.”

Sun is also working on analyzing the potential discriminatory effects of algorithms used in decisions like whether to give someone a loan or to grant prisoners parole.

“If you use machine learning, how do you hold an algorithm or the people who apply it accountable? What does it mean for an algorithm to be fair?” he said. “Can you check whether a particular notion of non-discrimination is satisfied?”

Jason Mars, an assistant professor in the Electrical Engineering and Computer Science department and co-founder of a successful spinoff called Clinc, is applying artificial intelligence to driverless car technology and a mobile banking app that has been adopted by several large financial institutions. The app, called Finie, provides a much more conversational interface between users and their financial information than other apps in the field.

“There is going to be an expansion of the number of problems solved and number of contributions that are AI-based,” Mars said. He predicted that more researchers at U-M will begin exploring AI and ML as they understand the potential.

“It’s going to require having the right partner, the right experts, the right infrastructure, and the best practices of how to use them,” he said.

He added that U-M does a “phenomenal job” in supporting researchers conducting AI and ML research.

“The level of support and service is awesome here,” he said. “Not to mention that the infrastructure is state of the art. We stay relevant to the best techniques and practices out there.”

Advanced Research Computing at U-M, in part through resources from the university-wide Data Science Initiative, provides computing infrastructure, consulting expertise, and support for interdisciplinary research projects to help scientists conducting artificial intelligence and machine learning research.

For example, Consulting for Statistics, Computing and Analytics Research, an ARC unit, has several consultants on staff with expertise in machine learning and predictive analysis with large, complex, and heterogeneous data. CSCAR recently expanded capacity to support very large-scale machine learning using tools such as Google’s TensorFlow.

CSCAR consultants are available by appointment or on a drop-in basis, free of charge. See cscar.research.umich.edu or email cscar@umich.edu for more information.

CSCAR also provides workshops on topics in machine learning and other areas of data science, including sessions on Machine Learning in Python, and an upcoming workshop in March titled “Machine Learning, Concepts and Applications.”

The computing resources available to machine learning and artificial intelligence researchers are significant and diverse. Along with the campus-wide high performance computing cluster known as Flux, the recently announced Big Data cluster Cavium ThunderX will give researchers a powerful new platform for hosting artificial intelligence and machine learning work. Both clusters are provided by Advanced Research Computing – Technology Services (ARC-TS).

All allocations on ARC-TS clusters include access to software packages that support AI/ML research, including TensorFlow, Torch, and Spark ML, among others.

ARC-TS also operates the Yottabyte Research Cloud (YBRC), a customizable computing platform that recently gained the capacity to host and analyze data governed by the HIPAA federal privacy law.

Also, the Michigan Institute for Data Science (MIDAS) (also a unit of ARC) has supported several AI/ML projects through its Challenge Initiative program, which has awarded more than $10 million in research support since 2015.

For example, the Analytics for Learners as People project is using sensor-based machine learning tools to translate data on academic performance, social media, and survey data into attributes that will form student profiles. Those profiles will help link academic performance and mental health with the personal attributes of students, including values, beliefs, interests, behaviors, background, and emotional state.

Another example is the Reinventing Public Urban Transportation and Mobility project, which is using predictive models based on machine learning to develop on-demand, multi-modal transportation systems for urban areas.

In addition, MIDAS supports student groups involved in this type of research such as the Michigan Student Artificial Intelligence Lab (MSAIL) and the Michigan Data Science Team (MDST).

(A version of this piece appeared in the University Record.)