Hello!

I'm a PhD candidate in Applied Mathematics (AMSC) at the University of Maryland, and my research advisor is John P Dickerson.

We work on problems in public resource allocation, matching, and market design. My primary tools include optimization, computational modeling, and machine learning.

Research

My research has touched on several areas, including Kidney Exchange, Blood Donation, Preference Elicitation for Policy Design, and how to design and apply these tools in a responsible way.

Kidney Exchange

kidney exchange

Patients with kidney failure have only two options: a lifetime on dialysis, or kidney transplantation. Dialysis is far more expensive and burdensome than transplantation, however donor kidneys are scarce — on average, 20 people die each day in the US while waiting for a transplant. Furthermore, many patients in need of a kidney have willing living donors, but cannot undergo transplantation due to medical incompatibilities.

To address this supply-demand mismatch, kidney exchange allows patients with willing living donors to swap their donors in order to find a compatible (or better) patient-donor match. Formulated as an optimization problem, kidney exchange is NP-hard and APX-hard, though modern exchanges are solvable in a reasonable amount of time (due to efficient formulations such as PICEF and PC-TSP).

My Work

In addition to being computationally hard, kidney exchange raises several logistical and ethical challenges. My research has touched on several of these challenges, including: fairness for marginalized patients, robustness to uncertainty, and moral considerations of designing a kidney exchange mechanism.

Fairness in Kidney Exchange

How can we prioritize marginalized patients, without severely impacting the overall exchange? We study several different methods for enforcing this notion of fairness, and demonstrate their effects on data collected from real exchanges.

Relevant Work:
  • McElfresh, Duncan C, and John P Dickerson, "Balancing lexicographic fairness and a utilitarian objective with application to kidney exchange." Conference on Artificial Intelligence (AAAI), 2018. (pdf)

Robustness to Uncertainty

There are many sources of uncertainty in real kidney exchanges — due to medical, moral, and policy factors. These sources of uncertainty are difficult to characterize, and can severely impact the outcome of an exchange. We investigate techniques from robust optimization to address this problem.

Relevant Work:
  • McElfresh, Duncan C, Hoda Bidkhori, and John P Dickerson, Scalable Robust Kidney Exchange.” Conference on Artificial Intelligence (AAAI), 2019. (pdf)
  • Bidkhori, Hoda, John P Dickerson, Ke Ren, and Duncan C McElfresh. “Kidney exchange with Inhomogeneous Edge Existence Uncertainty.” Conference on Uncertainty in Artificial Intelligence (UAI). 2020.

Ethics and Kidney Exchange

Designing a kidney exchange program requires input from medical professionals, policymakers, computer scientists, and ethicists. A "good" program should be both technically- and morally-sound — however technical experts (e.g., computer scientists) and stakeholders (e.g., medical professionals) often work independently. We propose a formal division of labor between technical experts and stakeholders, and outline a framework through which these experts can collaborate. Through this framework we analyze existing kidney exchange programs and survey the technical literature on kidney exchange algorithms. We identify areas for future collaboration between technical experts and stakeholders.

Relevant Work:
  • McElfresh, Duncan C, Vincent Conitzer, and John P Dickerson. “Ethics and Mechanism Design in Kidney Exchange.” (Working paper).
  • Presentation: McElfresh, Duncan C, Patricia Mayer, Gabriel Schnickel, and John P Dickerson. "Ok Google: Who Gets the Kidney?": Artificial Intelligence and Transplant Algorithms. Panel presentation and discussion at the annual meeting of the American Society of Bioethics and Humanities (ASBH), Anaheim, CA. (slides)

Blood Donation

blood matching

Blood is a scarce resource that can save the lives of those in need, and managing the blood supply chain has been a topic of research for decades. We consider an aspect of the blood supply chain that is seldom addressed by the literature: coordinating a network of donors to meet demand from a network of recipients. The advent of massive online networks presents an unprecedented opportunity to increase the number and impact of blood donations.

Relevant Work:
  • McElfresh, Duncan C, Christian Kroer, Sergey Pupyrev, Eric Sodomka, Karthik Abinav Sankararaman, Zack Chauvin, Neil Dexter, John P Dickerson. “Matching Algorithms for Blood Donation” The 21st ACM Conference on Economics and Computation (EC). 2020
  • Poster and Presentation: McElfresh, Duncan C, Christian Kroer, Sergey Pupyrev, Eric Sodomka, John P Dickerson. “Matching Algorithms for Blood Donation.” Workshop on Mechanism Design for Social Good MD4SG, 2019.

Preference Elicitation

for Policy Design

preference elicitation

Preference elicitation is concerned with figuring out what people want, by asking carefully selected questions. This field has a rich literature with roots in marketing, auctions, and finance — among other applications. We use preference elicitation for a different purpose: to design algorithms and policies that respect stakeholder interests. We are currently developing elicitation schemes to identify policy priorities in kidney exchange and public housing allocation.

Relevant Work:
  • Phebe Vayanos, Duncan C McElfresh, Yingxiao Ye, John P Dickerson, and Eric Rice. “Active Preference Elicitation via Adjustable Robust Optimization.” (pdf) (Under review at Management Science.)
  • Presentation: McElfresh, Duncan C, Phebe Vayanos, Eric Rice, and John P Dickerson. “Optimizing Public Policy for Homelessness Assistance.” INFORMS Annual Meeting, 2019.
  • Presentation: McElfresh, Duncan C, Phebe Vayanos, John P Dickerson. “Robust Active Preference Elicitation, for Learning Policy Priorities.” Presentation at 2019 INFORMS Revenue Management & Pricing Workshop.

Responsible AI Design

responsible AI design

AI applications in resource allocation and market design can have a measurable positive impact on society; indeed, in the case of kidney exchange and blood donations, AI-powered tools can literally help save lives. These AI applications can also have unintended consequences. Some of my work has focused on understanding these consequences. For example: How does an AI-generated suggestion impact a decision-maker's behavior? Does the general public agree with, or understand, the notions of fairness or bias defined by computer scientists?

Relevant Work:
  • Saha, Debjani, Candice Schumann, Duncan C McElfresh, John P Dickerson, Michelle L Mazurek and Michael Carl Tschantz. “Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics.” Proceedings of the Thirty-seventh International Conference on Machine Learning (ICML). 2020
  • Chan, Lok, Kenzie Doyle, Duncan C McElfresh, Vincent Conitzer, John P Dickerson, Jana Schaich Borg and Walter Sinnott-Armstrong. “Artificial Artificial Intelligence: Measuring Influence of AI "Assessments" on Moral Decision-Making.” AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2020.
  • Saha, Debjani, Candice Schumann, Duncan C McElfresh,, John P Dickerson, Michelle L Mazurek and Michael Carl Tschantz. “Human Comprehension of Fairness in Machine Learning." AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2020.
  • McElfresh, Duncan C, Vincent Conitzer, and John P Dickerson. “Ethics and Mechanism Design in Kidney Exchange.” (Working paper.)
  • McElfresh, Duncan C, Samuel Dooley, Charles Cui, Kendra Griesman, Weiqin Wang, Tyler Will, Neil Sehgal and John Dickerson. “Can an Algorithm be My Healthcare Proxy?” 2020 International Workshop on Health Intelligence (at AAAI). 2020. (Workshop Paper.)
  • McElfresh, Duncan C. “A Framework for Technically- and Morally-Sound AI.” AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2019. (Student program and poster.)

Publications

"Top Tier" Conference Publications
  • Saha, Debjani, Candice Schumann, Duncan C McElfresh, John P Dickerson, Michelle L Mazurek and Michael Carl Tschantz. “Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics.” Proceedings of the Thirty-seventh International Conference on Machine Learning (ICML). 2020
  • McElfresh, Duncan C, Christian Kroer, Sergey Pupyrev, Eric Sodomka, Karthik Abinav Sankararaman, Zack Chauvin, Neil Dexter, John P Dickerson. “Matching Algorithms for Blood Donation” The 21st ACM Conference on Economics and Computation (EC). 2020
  • Bidkhori, Hoda, John P Dickerson, Ke Ren, and Duncan C McElfresh. “Kidney exchange with Inhomogeneous Edge Existence Uncertainty.” Conference on Uncertainty in Artificial Intelligence (UAI). 2020.
  • Chan, Lok, Kenzie Doyle, Duncan C McElfresh, Vincent Conitzer, John P Dickerson, Jana Schaich Borg and Walter Sinnott-Armstrong. “Artificial Artificial Intelligence: Measuring Influence of AI "Assessments" on Moral Decision-Making.” AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2020.
  • Saha, Debjani, Candice Schumann, Duncan C McElfresh, John P Dickerson, Michelle L Mazurek and Michael Carl Tschantz. “Human Comprehension of Fairness in Machine Learning." AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2020.
  • McElfresh, Duncan C, Hoda Bidkhori, and John P Dickerson, Scalable Robust Kidney Exchange.” Conference on Artificial Intelligence (AAAI), 2019. (pdf)
  • McElfresh, Duncan C, and John P Dickerson, "Balancing lexicographic fairness and a utilitarian objective with application to kidney exchange." Conference on Artificial Intelligence (AAAI), 2018. (pdf)
  • Bach, Jörg-Hendrik, Arne-Freerk Meyer, Duncan McElfresh, Jörn Anemüller, "Automatic classification of audio data using nonlinear neural response models." IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012. (pdf)
Working Papers
  • Phebe Vayanos, Duncan C McElfresh, Yingxiao Ye, John P Dickerson, and Eric Rice. “Active Preference Elicitation via Adjustable Robust Optimization.” (pdf) (Under review at Management Science.)
  • McElfresh, Duncan C, Vincent Conitzer, and John P Dickerson. “Ethics and Mechanism Design in Kidney Exchange.”
Other Publications
  • McElfresh, Duncan C, Samuel Dooley, Charles Cui, Kendra Griesman, Weiqin Wang, Tyler Will, Neil Sehgal and John Dickerson. “Can an Algorithm be My Healthcare Proxy?” 2020 International Workshop on Health Intelligence (at AAAI). 2020. (Workshop Paper.)
  • McElfresh, Duncan C, Christian Kroer, Sergey Pupyrev, Eric Sodomka, John P Dickerson. “Matching Algorithms for Blood Donation.” Workshop on Mechanism Design for Social Good MD4SG, 2019. (Poster and Presentation.)
  • McElfresh, Duncan C. “A Framework for Technically- and Morally-Sound AI.” AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2019. (Student program and poster.)
  • McElfresh, Duncan C, "Triplet exciton transport in the benzophenone-fluorene-naphthalene molecule." Master's Thesis, Colorado School of Mines, 2013. (pdf)

Service

Program Committee (conferences):
  • Conference on Neural Information Processing Systems (NeurIPS): 2020
  • Conference on Artificial Intelligence (AAAI): 2020
  • Conference on Autonomous Agents and Multiagent Systems (AAMAS): 2020
Program Committee (workshops):
  • AAMAS Workshop on Optimization and Learning in Multiagent Systems (OptLearnMAS 2020)
  • IJCAI workshop on AI for Social Good: 2019
  • NeurIPS workshop on ML and the Physical Sciences: 2020
  • NeurIPS workshop on AI for Social Good: 2019
IBM Watson AI XPrize (link):
  • Red Judge. Helped teams prepare for the semifinal review of the IBM Watson AI XPrize competition. (2018-2019)
  • Independent Observer. Attended site visits to validate claims made by semifinalist teams. (2019-2020)
Department Service and Outreach:
  • Student Representative. AMSC Student Council (2018-present).
  • Site Coordinator & Mentor. Site Coordinator, Girls Excelling in Math and Science (GEMS) of Prince George’s County, MD. (link)

Connect

Feel free to get in touch.
Email is best: dmcelfre@umd.edu.