Dr. Barbara Anthony teaches a variety of computer science courses, including Explorations in Computing for non-majors, introductory courses in Java and Data Structures, core courses in Discrete Math, Algorithms, and Programming Languages, electives in Databases, Operations Research, and Theory of Computation, and the capstone in Software Engineering. She has taught First Year Seminar, Colloquium in Computer Science, and a course designed to prepare students to compete in the International Collegiate Programming Contest. She sometimes teaches Introduction to Statistics on the Mathematics side of the department. She also has conducted numerous independent studies, from explorations of topics with first-year students through seniors doing research as part of an Honors project. She is the faculty supervisor for CSC academic internships. She is currently the faculty advisor for UPE, the CS Honor Society, and is happy to help organize a number of CS-themed events throughout the year.

She received her PhD in Algorithms, Combinatorics, and Optimization from Carnegie Mellon University in 2008 and her BA from Rice University in 2001 with majors in Computational and Applied Math, French, and Mathematics. She is an ACM Senior Member, a member of UPE, CCSC and SIAM, and a certified American Sign Language interpreter. 

“Computer Science is relevant for everyone these days, and has great interdisciplinary potential, especially at a liberal arts institution. As such, I believe lower-level courses should be accessible to any SU student, and students should be exposed to both the breadth and depth of the field.”
- Barbara Anthony

Details about some of my research can be found in my publication list, but one of the reasons that I came to Southwestern is so that I can work with students on research projects, not all of which lead to publications, though some do. Descriptions of some of the projects with students follow.

If you are an SU student interested in doing research with me, please email or drop by my office! 

If you want me to write a recommendation letter for you, please complete this form. 

Fall 2023: Camille James and Zoe VanDerVlugt worked with me to explore problems in computational social choice. The distortion of a social choice rule is the worst-case ratio of the total social cost achieved by the rule to that of the optimal solution. Our investigations include the distortion of voting rules like Copeland’s method and Single Transferable Vote where the metric space under consideration is a line. 

Summer/Fall 2023 and Spring 2024: Mark Mueller is combining skills from Operations Research and programming knowledge from various CS courses to implement software that automates aspects of course scheduling. It takes input from csv files created in Google Sheets, uses Python to create a linear program that incorporates the relevant constraints, feeds that to glpsol to determine an optimal solution, and reports the results. Using this backend, he and a team of students worked together in Spring 2024 in the CS Capstone to create a usable web interface for it. Mark presented the backend at the 2024 Texas Academy of Sciences Annual Meeting, earning 2nd place in the Mathematics and Computer Science presentations. We also wrote a paper that was accepted to the 21st International Conference on Cooperative Design, Visualization, and Engineering in Valencia Spain in September 2024. 

Spring/Summer 2023: Alejandro Medina implemented an Earliest Deadline First algorithm in Python for a variant of the Dial-a-Ride Problem. We focus on a specific DARP variant where the goal is to maximize the number of rides provided by their deadline using a single vehicle when all locations are equidistant. Alejandro presented the work in a poster on Evaluating an Earliest Deadline First Algorithm for a Dial-a-Ride Problem at the 2023 Tapia Celebration of Diversity in Computing. 

Spring 2023: Arden Neff continued a project begun in Operations Research about better predicting harvest dates for vineyards using historical information, current Brix data, and information about weather anomalies. She presented a poster on “Optimizing Grape Harvest Timing and Yield using Linear Programming” at the Texas Academy of Science 126th Annual Meeting, earning first place in the Mathematics and Computer Science poster section.

Fall 2022: Kate Nguyen worked with me on a project related to introducing less well-known computer scientists to students in the non-majors Explorations in Computing course. We presented a poster about our work at SIGCSE 2023.

Spring 2022: Alejandro Medina and Mark Mueller worked with me on the next steps of the Doodle Poll research project, extending the analysis to the data collected throughout the United States. We showed that many of the trends observed in the SU sample carried over to the largest sample, but not all. In general, people responding to polls may aim to be sincere, but are influenced by various factors, including the nature of the meeting. There are also indications of some differences based on demographic factors such as age and region of the country that can be explored further. This work may allow us to develop tools that will result in selections of meeting times that are `better’ in some sense, perhaps for the collective group. The work was presented at SU’s Research and Creative Works Symposium in Spring 2022 and the resulting paper was accepted to the 19th International Conference on Cooperative Design, Visualization, and Engineering in September 2022.

Fall 2020: Miryam Galvez and Chris Ojonta worked with me on the Doodle poll research project. We revised the study, originally conceived in Spring 2020, to work with social distancing during the pandemic. More than 200 college students participated in an IRB-approved research study where they were asked to complete Doodle-style polls on their availability based on a hypothetical schedule they were provided. Sam Taylor funding made it possible to provide participants with a gift card in appreciation of their time. We analyzed a subset of the responses fron Southwestern University participants in various ways, including using Python scripts we developed to characterize certain performance. Miryam presented some results from our work at the 2021 Texas Academy of Sciences (Virtual) Annual Meeting in February 2021, and we had a paper accepted to the 18th International Conference on Cooperative Design, Visualization, and Engineering in October 2021. 

Spring 2020: Daniel B. Merritt worked with me to begin user studies on my Doodle poll research, which had previously been largely theoretical. Since this is ongoing work that will likely involve user studies in Fall 2020, only limited details about the goals of the work can be provided publicly. Web-based Doodle polls (www.doodle.com) are a form of approval voting where participants indicate, for example, their reported availability to meet at a particular time. Since Doodle polls capture only the yes/no votes, and not other details, we will give human participants simulated Doodle polls where they report various aspects. Daniel’s contributions focused on creating the materials for the user study (though gaining IRB approval and beginning data collection was postponed due to COVID-19) and developing Python scripts to facilitate the processing of the data that will be collected. 

Spring 2019: Cameron Henkel extended upon a course project from the Operations Research elective I teach, where he and his groupmates analyzed the Georgetown bus system (GoGEO) and associated partnerships, including those with rideshare companies. In this project, he leveraged the Google Maps API in conjunction with public census tract data encoded as GeoJSON files to creating a data visualization tool for understanding rideshare data in a local context. While it can be fully locally hosted, allowing entities to maintain control over who has access to the data, online hosting is likewise supported by the web-based backend. 

Spring 2019: Continuing a project from the Operations Research course, Katie Dyo, Devon Fulcher, and Greg O’Brien worked to deploy the tool they had developed for the Office of Admission. Each spring, alumni volunteers write to the families of admitted students, with assignments made based on shared academic interests, co-curricular interests, and geographic proximity. Using operations research techniques, we developed a software solution to this problem, that generates a list of student-alumni matches based on a computed compatibility metric and student attributes. 

Spring 2019: Sara Boyd, Devon Fulcher, and Daniel Maldonado competed in the 35th annual Mathematical Contest in Modeling, sponsored by the Consortium for Mathematics and Its Applications (COMAP). For their research and modeling of a complex scenario, they earned an honorable mention. 

Fall 2018 and Spring 2019: Cameron Henkel and Colin Scruggs developed https://surad.io, a mobile app and website for streaming the live shows produced by Southwestern University Radio’s DJs and talk show hosts. This app allows students to listen in wherever they are, in a format that is familiar to them. This work was done as part of a King Creativity Project. 

Summer 2018: Sara Boyd, Bobby Garza, Alexander Hoffman, Stan Kannegieter, and Daniel Merritt competed in the Binance Dexathon Blockchain Coding Competition under Dr. Anthony’s supervision. The goal of the decentralized exchange coding competition was to improve on Binance’s current blockchain implementation. Along with learning more about the blockchain and practicing their software skills, the students also gained valuable experience in project management and working with teammates in remote locations. For their submission, Binance awarded the team a 10,000 BNB grant.

Spring 2018: Sara Boyd worked with me on research on a variant of the Dial-a-Ride problem, particularly the version with uniform revenues and a uniform metric. By showing that even this fundamental version is NP-hard, we give insights into techniques and analysis for other variants of the problem. In addition, we explored algorithms for the uniform revenue uniform metric variant. The culmination of the work resulted in a co-authored paper on “Maximizing the number of rides served for Dial-a-Ride”, whose details are in the publication list. In Spring 2020, in part due to this work, Sara was named a finalist for the Computing Research Association Outstanding Undergraduate Researchers Award. 

Fall 2017 and Spring 2018: Cameron Henkel worked on a Smart Medication Dispenser (a King Creativity project). Motivated by a desire to help his grandmother and others like her, he used a Raspberry Pi equipped with a Z-Waves Developer Kit, gained experience with 3D printing, and developed software solutions which allow features like dispensing medication, restricting it to those with access, and notifications to caregivers about the dispensing. Cameron was the grand prize winner of Z-Wave Smart Home Maker Challenge, and presented his work at the CES Conference in Vegas. 

Spring 2017: Ryan Beeman continued a course project from my Operations Research course to develop a scheduling tool for Faith in Action Georgetown, a central Texas-based nonprofit organization whose primary goal is to preserve the independence of local senior citizens by providing them transportation to errands, social events, and medical appointments. As a non-profit organization that relies heavily on the support and activity of volunteers, efficient use of their limited funding and staff time is crucial in maintaining positive relationships with volunteers and providing the most benefit to the community. Prior to this project, Faith in Action spent a substantial amount of time scheduling rides by pairing clients (senior citizens who requested a ride) with volunteer drivers. Motivated by operations research techniques, we developed a tool to improve the scheduling process. Using data from Faith in Action’s database we created a user-friendly Excel spreadsheet to calculate a volunteer driver’s “willingness” to accept any particular ride. By contacting volunteers with a higher willingness score first, schedulers can more quickly match clients with drivers, thus reducing the total time necessary to complete scheduling each week, while maintaining a personal interaction with volunteers. Ryan plans to gain some work experience and then enroll in a master’s program in Data Science.

Spring 2017: As a first-year computer science major, Austin Moninger worked with me to develop Java code used in ongoing research about Doodle polls. Online Doodle polls allow for the selection of a ‘good’ meeting time, with participants indicating their availability for a selection of times provided by the poll creator, essentially by voting yes or no to each time slot. Yet, a single participant can greatly impact the quality of the selected time, as measured by the total social welfare, either positively or negatively. We seek Nash equilibria: informally, there is a certain appeal when all participants look at the overall responses and cannot change their reported availability to better their personal outcome based on what other individuals have indicated. We implement Java code that aids in checking for Nash equilibria while maintaining that responses must be sincere, ensuring that no one says no to a time slot that is more desirable than one to which they said yes. This research helped Austin obtain an REU at Texas State in summer 2017.

Fall 2014 - Fall 2015: Christine Harbour and Jordan King worked with me on empirically evaluating approximation algorithms for various problems. The earlier work resulted in a paper for them in the Consortium for Computing Sciences in Colleges: South Central Region Student Paper E-Journal titled “An Empirical Evaluation of a k-Center 2-Approximation Algorithm”, which they were able to present at the April 2015 conference. Later work then focused on approximation algorithms for bottleneck matching, and Christine presented a poster at the 2015 Grace Hopper Celebration of Women in Computing entitled “An Empirical Evaluation of Approximation Algorithms for Two Graph Problems”. Her 2016 poster at the Consortium for Computing Sciences in Colleges: South Central Conference, “An Empirical Evaluation of Approximation Algorithms for Online Bottleneck Matching”, earned first place in the student poster competition. The culmination of the work resulted in a co-authored paper on “Greedy Is Good: An Empirical Evaluation of Three Algorithms for Online Bottleneck Matching”, whose details are in the publication list. Christine is a Software Developer at eOne Solutions, and Jordan is a Software Developer at Spiceworks.

Spring 2015: Natalia Rodriguez’s project involved “Visualizing Big Data Through Juxtaposition: Mapping Body Image with Instagram Data”. She presented this work at the 2015 ACM Richard Tapia Celebration of Diversity in Computing. Natalia was one of three winners of the inaugural NCWIT Collegiate Award in 2015 for this research. She has been a Software Developer at Indicative, and a Helen Fellow in Data Visualization at the American Museum of Natural History.

Fall 2014: Kathryn Reagan had been a Community Engaged Learning Teaching Assistant in my Operations Research course in Spring 2014, and in Fall 2014 we began an IRB-approved study of the impact of the community-engaged learning component of that course, which resulted in a joint publication on “Community-Engaged Projects in Operations Research”, with details in the publication list. After graduation, Kathryn became a consultant at ThoughtWorks, a position she had sought since first encountering that company at a Grace Hopper Celebration of Women in Computing conference she had attended early in her Southwestern years.

Fall 2014: Rebecca Wilson worked with me on several projects related to supporting underrepresented groups in computer science. One of them resulted in a publication for her entitled “CS Club: A Student Built Culture of Computing” in the Consortium for Computing Sciences in Colleges: South Central Region Student Paper E-Journal, which she also got to present at the conference in April 2015. Rebecca is an Application Developer at Union Pacific Railroad.

Fall 2013: Paris Nelson worked with me on a project about genetic algorithms, with his poster presentation entitled “There and Back Again: A Genetic Algorithm Approach to the TSP” winning a prize at the Consortium for Computing Sciences in Colleges: South Central Conference. An article about another of Paris’s projects can be found here. After Southwestern, he began his career at Accenture.

Fall 2012: Erick Bauman worked with me to create a website and underlying database for a local nonprofit foster care organization, allowing tracking of information about employees and children in their system, with the necessary security and privacy constraints. Erick went on to the PhD program in Computer Science in the Systems and Software Security Lab at The University of Texas at Dallas. Erick was also involved in several other projects at Southwestern, including ones described in this link.

Fall 2010-Spring 2011: As part of an Honors Project, Matthew Flatau and I developed software to implement Ruppert’s algorithm for generic curves in two-dimensions, used in mesh generation. This resulted in the Research Note at the International Meshing Roundtable (see Publications list), where Matthew presented about our work. Matthew went on to graduate study in Computer Science at the University of Wisconsin-Madison, and then became a software developer at Epic.

Spring 2009: Dak Erwin (as of 2017, a Software Engineer at b.well Connected Health) and I explored Latency on Wireless Sensor Networks when he was a first-year student at Southwestern.

Selected Funding Awards

NCWIT Extension Services Learning Circles $10,000 to support the recruitment and retention of women in computing, 2019.

NSF S-STEM Award, Broadening the Net: Promoting Success in the Sciences for All Students, Southwestern University, Co-PI, 5 years, $614,325, July 2015.

Google CS Engagement Award ($5000), towards the development and integration of instructional materials for increasing student engagement and retention in introductory computer science classes, Spring 2015. One of 53 CS faculty and instructors from colleges and universities in 24 U.S. states to receive this award.

Student co-authors underlined.

“Course scheduling made easier: A user-friendly web-based timetabling tool using PyGLPK” with Mark Mueller. 21st International Conference on Cooperative Design, Visualization, and Engineering (CDVE 2024). To appear, September 2024.

Course scheduling is a necessary but often challenging task, requiring consideration of university requirements, practical limitations, departmental needs, and faculty preferences. Furthermore, it can be difficult to determine if a proposed schedule satisfies all of the requirements, especially as changes may be made within the process. Clearly articulated schedule requirements can be translated into an integer linear program by operations researchers, and those with domain-specific knowledge can then use linear programming solvers like PyGLPK. However, few faculty members in a university are likely to have that experience, so we developed a web-based tool, including the underlying Python code, that allows for entry of information about courses and the faculty teaching them and creates a schedule consistent with the requirements. More than simply a scheduler, a user can propose changes such as swapping two courses or placing a course at a specific time; the website will either do so, or report that such a change is infeasible. In addition, a user can share the website and file with colleagues, allowing them to better appreciate what is and is not possible for the overall schedule. Recognizing that the precise set of scheduling requirements is often unique to a given university, the source code is made available in a public GitHub repository, providing opportunities for other universities to create a version customized to their needs and structure.

“Maximizing the number of rides served for time-limited Dial-a-Ride” with Christine Chung, Ananya Das and David Yuen. Optimization Methods in Software, 2024.

We consider a variant of the offline Dial-a-Ride problem on weighted graphs with a single server where each request has a source and destination. The server’s goal is to serve requests so as to maximize the total number of requests served within a given time limit. We first prove that no polynomial-time algorithm will always serve the optimal number of requests, even when the algorithm’s time limit is augmented by any factor c≥1, unless P = NP. We also show that the approximation ratio is unbounded for a reasonable class of algorithms for this problem. We then present k-Sequence, an algorithm that repeatedly serves the fastest set of k remaining requests. We show that k-Sequence has approximation ratio at most 2+⌈λ⌉/k and at least 1+λ/k, where λ denotes the aspect ratio of the graph, and that the ratio 1+λ/k is tight when 1+λ/k≥k. We also show that even as k grows beyond the size of λ, the ratio never improves below 9/7. 

“Earliest Deadline First Is a 2-Approximation for DARP with Time Windows”, with Christine Chung, Ananya Das, and David Yuen, In: Wu, W., Guo, J. (eds) Combinatorial Optimization and Applications. COCOA 2023. Lecture Notes in Computer Science, vol 14462. Springer, Cham, December 2023. 

Dial-a-Ride problems (DARP) require determining a schedule to efficiently serve transportation requests in various scenarios. We consider a variant of offline DARP in a uniform metric space where requests have release times and deadlines, and are all of equal duration and value. The goal is for a single unit-speed, unit-capacity server to serve as many requests as possible by an overall time limit, and this problem is NP-hard. We show that a natural greedy algorithm, Earliest Deadline First, is a 2-approximation, and this is tight.

A typical US college student who is asked to name people who have made significant contributions to computing will likely produce a short, predictable, fairly homogeneous list. To help increase awareness of the diversity of people involved in computing, we introduced a biography project into an introductory non-majors course at a small liberal arts college. With each student exploring and present on the life and contributions of a less well-known contributor to computing, the class is exposed to a number of people that students likely haven’t heard of previously. Moreover, with fairly open-ended requirements for the assignment, we can learn what students find interesting and noteworthy about these individuals, perhaps finding ways to more broadly increase awareness.

“Prioritizing Self, Team, or Job: Trends in Sincerity in Cooperative Polls” with Alejandro Medina and Mark Mueller. 19th International Conference on Cooperative Design, Visualization, and Engineering (CDVE 2022). LNCS, Vol. 13492, Springer, pp. 34-44, September 2022. 

As automated tools become commonplace for coordinating meeting times and other forms of decentralized cooperative decision-making, it is important to understand the behavior of people using those tools. Even when a tool or online platform is simply a form of approval voting, the specifics of the voting scenario need to be considered. Approval voting often assumes that voters are sincere, never voting yes to an option that is less desirable than one for which they have voted no. A small study suggested that the assumption of sincerity among users in cooperative polls should not be taken for granted. This work expands the study to a larger sample of college students at multiple institutions, showing that people responding to polls may aim to be sincere, but are influenced by various factors, including the nature of the meeting.

“Unplugged Parallelism for First-Year CS Majors” [Poster] with D. Cenk Erdil, Olga Glebova, and Robert Montante. 53rd ACM Technical Symposium on Computer Science Education (SIGCSE ’22), pp. 1079, March 2022.

We use “unplugged” activities to introduce parallel concepts in a first-year seminar for Computer Science majors. Student teams explore parallel approaches to computational tasks. Pre- and post-activity surveys, and a reflection paper, measure the impact of these activities on students’ views about parallel programming. Our goal is to encourage parallel thinking about programming tasks before sequential approaches become ingrained. Computer Science curricula have traditionally focused on sequential approaches to programming, which were well matched to earlier computer systems. However, current systems almost all use multiprocessor CPUs, and are frequently used in clusters or networks of multiple computers. Recent curricular guidelines from organizations such as ACM and ABET recommend exposure to parallel computing concepts.

“Questions of Sincerity in Cooperative Doodle Polls” with Miryam Galvez and Chris Ojonta. In: Luo Y. (eds) 18th International Conference on Cooperative Design, Visualization, and Engineering (CDVE 2021). Lecture Notes in Computer Science, vol 12983, Springer, October 2021.

Online tools like Doodle polls are frequently used for meeting coordination and other decentralized cooperative decision making. Since Doodle polls are a form of approval voting, theoretical results from voting theory often underpin work in this area. Sincerity, where a voter never says yes to a less-preferred option without saying yes to all more preferable choices, is a common assumption in approval voting. However, that does not take into account cooperative behavior sometimes exhibited by users when others’ responses are known. We conduct a user study investigating the extent to which college-student participants in Doodle-style polls were sincere, reporting on responses from one institution.

“Using the UCSC Genome Browser in a Database Course” Journal of Computing Sciences in Colleges, Volume 37, Number 2, pp. 23-29, October 2021. 

Students can benefit greatly from working with real databases in their first Database course. A database for a university is a common textbook example, in part due to its familiarity, but privacy and other considerations typically preclude course access to this and many other large, meaningful databases. This paper reports on two semesters’ experience using the University of California Santa Cruz Genome Browser [6] in a Database course, allowing mid-level computer science undergraduates to gain hands-on experience with a large real-world database. Anonymous survey feedback from students in both semesters was positive for both engagement and increased knowledge. The activity described within can easily be adopted by others, requires no software installation, and can be adapted to the desired length and difficulty level.

“Serving rides of equal importance for budgeted Dial-a-Ride” with Ananya Christman, Christine Chung and David Yuen. In: Pardalos P., Khachay M., Kazakov A. (eds) Mathematical Optimization Theory and Operations Research (MOTOR 2021). Lecture Notes in Computer Science, vol 12755, pp. 35-50, conference held July 2021.

We consider a variant of the offline Dial-a-Ride problem with a single server where each request has a source, destination, and a prize earned for serving it. The goal for the server is to serve requests within a given time limit so as to maximize the total prize money. We consider the variant where prize amounts are uniform which is equivalent to maximizing the number of requests served. This setting is applicable when all rides may have equal importance such as paratransit services. We first prove that no polynomial-time algorithm can be guaranteed to serve the optimal number of requests, even when the time limit for the algorithm is augmented by any constant factor c >=1. We also show that if lambda = t_max/t_min, where t_max and t_min denote the largest and smallest edge weights in the graph, the approximation ratio for a reasonable class of algorithms for this problem is unbounded, unless lambda is bounded. We then show that the segmented best path (SBP) algorithm from [8] is a 4-approximation. We then present our main result, an algorithm, k- Sequence, that repeatedly serves the fastest set of k remaining requests, and provide upper and lower bounds on its performance. We show k-Sequence has approximation ratio at most 2+ ceil(lambda)/k and at least 1 + lambda/k and that is tight when 1+lambda/k \geq k. Thus, for the case of k = 1, i.e., when the algorithm repeatedly serves the quickest request, it has approximation ratio 1+lambda, which is tight for all lambda. We also show that even as k grows beyond the size of lambda, the ratio never improves below 9/7.

“Directed Zagreb Indices” with Alison M. Marr, 18th Cologne-Twente Workshop on Graphs and Combinatorial Optimization, Graphics and Combinatorial Optimization: from Theory to Applications, AIRO Springer Series, Volume 5, pp. 181-193, 2021. (Conference in Ischia, Italy in September 2020 (postponed from June 2020 due to COVID-19). 

Zagreb indices for undirected graphs were introduced nearly 50 years ago. Their original development was related to uses in chemistry, but over time mathematicians have also found them to be an interesting topic of study. We define and introduce Zagreb indices for directed graphs, give results that parallel many of the conjectures and theorems that exist for the original Zagreb indices, and produce results specific to the directed graph case.

“Equilibria in Doodle Polls under Three Tie-Breaking Rules” with Christine Chung, Theoretical Computer Science, Volume 822, pp. 61-71, June 2020.

Doodle polls allow people to schedule meetings or events based on time preferences of participants. Each participant indicates on a web-based poll form which time slots they find acceptable and a time slot with the most votes is chosen. This is a social choice mechanism known as approval voting, in which a standard assumption is that all voters vote sincerely—no one votes “no” on a time slot they prefer to a time slot they have voted “yes” on. We take a game-theoretic approach to understanding what happens in approval voting assuming participants vote sincerely. While our instances are framed in the context of the Doodle poll application, the results apply more broadly to approval voting. First we characterize Doodle poll instances where sincere pure Nash Equilibria (NE) exist, under lexicographic tie-breaking, random candidate, and random voter tie-breaking. We then study the quality of such NE voting profiles in Doodle polls, showing the price of anarchy and price of stability are both unbounded, even when a time slot that many participants vote yes for is selected. Finally, we find some reasonable conditions under which the quality of the NE (and strong NE) is good.

”Introducing Parallelism to First-Year CS Majors” with D.C. Erdil, O. Glebova, and R. Montante, [Poster], 51st ACM Technical Symposium on Computer Science Education (SIGCSE ’20), March 2020. Conference cancelled on day of presentation due to COVID-19, moved to online format.

”Inefficiency of Equilibria in Doodle Polls”, with Christine Chung, In: Kim D., Uma R., Zelikovsky A. (eds) Combinatorial Optimization and Applications. COCOA 2018. Lecture Notes in Computer Science, vol 11346. Springer, 2018. 

Doodle polls allow people to schedule meetings or events based on time preferences of participants. Each participant indicates on a web-based poll form which time slots they find acceptable and a time slot with the most votes is chosen. This is a social choice mechanism known as approval voting, in which a standard assumption is that all voters vote sincerely—no one votes “no” on a time slot they prefer to a time slot they have voted “yes” on. We take a game-theoretic approach to understanding what happens in Doodle polls assuming participants vote sincerely. First we characterize Doodle poll instances where sincere pure Nash Equilibria (NE) exist, both under lexicographic tie-breaking and randomized tie-breaking. We then study the quality of such NE voting profiles in Doodle polls, showing the price of anarchy and price of stability are both unbounded, even when a time slot that many participants vote yes for is selected. Finally, we find some reasonable conditions under which the quality of the NE (and strong NE) is good.

“Modernizing the Mythical Man-Month”, with Valentin Cantu, Jr., Journal of Computing Sciences in Colleges, Volume 34, Number 2, pp. 110-116, December 2018.

The Mythical Man-Month: Essays on Software Engineering by Dr. Fred Brooks is often required reading in many software engineering classes and recommended by practitioners throughout industry as a text with which computer scientists should be familiar. Yet, while many of the conceptual ideas that were first presented decades ago are still applicable today, aspects of the presentation of the material are lacking in inclusivity, posing additional challenges for faculty who are trying to increase diversity within the discipline. Thus, we propose a student-developed portrayal of some of the concepts in a modernized fashion, presented as a menu and incorporating ideas from the food-service industry, a model that more college students are directly familiar with than the surgical team.

“How Bad is Selfish Doodle Voting?” [Extended Abstract], with C. Chung, Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018), Stockholm, Sweden, pp. 1856-1858, July 10-15, 2018.

Doodle polls allow people to schedule meetings or events based on the time preferences of participants. Each participant indicates on a web-based poll form which time slots they find acceptable and a time slot with the most votes is chosen. This is a social choice mechanism known as approval voting, in which a standard assumption is that all voters vote sincerely: no one votes no on a time slot they prefer to a time slot they have voted yes on. We take a game theoretical approach to understanding what happens in Doodle polls assuming participants vote sincerely.

Many computer science courses use final exams as one means of evaluating student performance within the course, but developing meaningful assessments can be both difficult and time-consuming. Students and faculty alike can experience both stress and frustration with various aspects of the exam process. By stepping back and evaluating the overall goals of computer science courses and the curriculum, some questions arise as viable options for final exams in a variety of computer science courses. These questions enable students to consider prior courses, projects or presentations within the course, future use of the material, or handle what may be perceived as incomplete coverage in the final exam. Some potential concerns about these questions are addressed. While these questions will not replace all of the traditional questions, they may provide different types of assessment information, and enable reflection for both students and faculty.

“Community-Engaged Projects in Operations Research,” with Kathryn Reagan, Science Education & Civic Engagement: An International Journal, Volume 9, Issue 2, pp. 5-12, Summer 2017.

Community-engaged learning is not very common in technical fields, but including relevant projects in courses can make it feasible and successful. We present an implementation of an operations research course at a liberal arts college. Working with one of four nonprofit community partners to optimize aspects of their organization, students gained insight into relevant, real-world applications of the field of operations research. By considering many aspects of their solution when presenting it to community partners, students were exposed to some issues facing local nonprofit organizations. We discuss the specific implementation of this course, including both positive learning outcomes and challenging factors.

While enrollments in computer science courses may be increasing overall, the recruitment and retention of women and minorities in computer science remains a pervasive problem. First Year Seminars at universities are relatively common, and though they take on a variety of formats, many are designed to help with the transition to college and to increase retention. For three years, a computer science faculty member taught a First Year Seminar, broadly about the Internet, though with limited technical details, surveying the students about their perceptions of computer science. The results, while not conclusive, may provide motivation for other computer science professors to teach a First Year Seminar related to technology.

“How Well Do Doodle Polls Do?,” with Danya Alrawi and Christine Chung, Proceedings of the 8th International Conference on Social Informatics (SocInfo16), LNCS 10046, Volume 1, pp. 3-23, November 2016.

Web-based Doodle polls, where respondents indicate their availability for a collection of times provided by the poll initiator, are an increasingly common way of selecting a time for an event or meeting. Yet group dynamics can markedly influence an individual’s response, and thus the overall solution quality. Via theoretical worst-case analysis, we analyze certain common behaviors of Doodle poll respondents, including when participants are either more generous with or more protective of their time, showing that deviating from one’s “true availability” can have a substantial impact on the overall quality of the selected time. We show perhaps counter-intuitively that being more generous with your time can lead to inferior time slots being selected, and being more protective of your time can lead to superior time slots being selected. We also bound the improvement and degradation of outcome quality under both types of behaviors.

“Serve or Skip: The Power of Rejection for Online Bottleneck Matching,” with Christine Chung, Journal of Combinatorial Optimization, Volume 32, Issue 4, pp. 1232-1253, November 2016.

We consider the online matching problem, where n server-vertices lie in a metric space and n request-vertices that arrive over time each must immediately be permanently assigned to a server-vertex. We focus on the egalitarian bottleneck objective, where the goal is to minimize the maximum distance between any request and its server. It has been shown that while there are effective algorithms for the utilitarian objective (minimizing total cost) in the resource augmentation setting where the offline adversary has half the resources, these are not effective for the egalitarian objective. Thus, we propose a new Serve-or-Skip (SoS) bicriteria analysis model, where the online algorithm may reject or skip up to a specified number of requests, and propose two greedy algorithms: GriNN(t) and GRIN_(t) . We show that the SoS model of resource augmentation analysis can essentially simulate the doubled-server-capacity model, and then examine the performance of GriNN(t) and GRIN_(t).