Initiatives - LaCross AI Institute | UVA Darden School of Business

LaCross AI Institute

Initiatives

The LaCross Institute is home to activities of many kinds that support our mission to make the world a better place through the responsible use of AI in business.

2024 Fellowship Application

LaCross Institute initiatives focus on faculty and students at Darden and across UVA, managers and leaders in business, and on the community and society at large. Initiatives are the major activities that are formally supported by the Institute. In addition to key these stakeholders, Initiatives also align with our focus areas and serve to ensure that the insight and expertise we develop on these key topics is shared with key audiences to enhance their knowledge of AI and their ability to develop and employ it ethically.

For more about our major initiatives, explore the information below.

Congratulations to this year's Fellowship in AI Research (FAIR) recipients!

MAX BIGGS
Assistant Professor of Business Administration at the UVA Darden School of Business

Max received his Ph.D. from the Operations Research Center at the Massachusetts Institute of Technology. Prior to joining Darden, Max was a Post-Doctoral Researcher at IBM Thomas J Watson Research Center. His research focuses on data-driven optimization and prescriptive analytics. Specifically, he is interested in using data, machine learning and optimization techniques to help solve operational decisions. Recent application areas include pricing and revenue management, healthcare, and logistics. Striving for research which is relevant to practice, he has collaborated with a range of companies including StubHub, Delta, IBM, Amazon and incasa - an on demand healthcare start-up.

FERDINANDO FIORETTO
Assistant Professor of Computer Science at UVA

Ferdinando (Nando) Fioretto is an assistant professor of Computer Science at the University of Virginia. Before joining the University of Virginia, he was an assistant professor at Syracuse University and prior to that a postdoctoral research associate at the Georgia Institute of Technology and a research fellow at the University of Michigan. He obtained a dual PhD degree in Computer Science from the University of Udine and New Mexico State University.

His research focuses on addressing foundational challenges to advance artificial intelligence, privacy, fairness, and the intersection between machine learning and optimization. His work has been recognized with the 2022 Caspar Bowden PET award, the IJCAI-22 Early Career spotlight, the 2017 AI*AI Best AI dissertation award, and several best paper awards.
Fioretto is a recipient of the NSF CAREER award, the Google Research Scholar Award, the Amazon Research Award, the ISSNAF Mario Gerla Young Investigator Award, and the ACP Early Career Researcher Award in Constraint Programming.
He serves as (senior) area chair for several premier ML and AI conferences, including NeurIPS, ICML, AAAI, IJCAI, and FAccT, is a member of the editorial board for Artificial Intelligence, the premier AI journal, and has been a member of the organizing committee of several workshops, tutorials, and other events with focus on privacy, fairness, and constrained reasoning at premier AI and ML venues.

CHIRAG AGARWAL
Assistant Professor of Data Science at the UVA School of Data Science

Chirag Agarwal is an assistant professor of data science and leads the Aikyam lab, which focuses on developing trustworthy machine learning frameworks that go beyond training models for specific downstream tasks and satisfy trustworthy properties, such as explainability, fairness, and robustness.

Before joining UVA, he was a postdoctoral research fellow at Harvard University and completed his Ph.D. at the University of Illinois at Chicago in electrical and computer engineering and bachelor's degree in electronics and communication. His Ph.D. thesis was on the "Robustness and Explainability of Deep Neural Networks," and his research encompasses different trustworthy topics, such as explainability, fairness, robustness, privacy, transferability estimation, and their intersection in the age of large-scale models. He has developed the first-of-its-kind, large-scale, in-depth study to support systematic, reproducible, and efficient evaluations of post hoc explanation methods for (un)structured data to understand algorithmic decision-making on diverse tasks ranging from bail decisions to loan credit recommendations.

Agarwal has published in top-tier machine learning and computer vision conferences (NeurIPS, ICML, ICLR, UAI, AISTATS, CVPR, SIGIR, ACCV) as well as in top journals in datasets (Nature Scientific Data) and health care (Journal of Clinical Sleep Medicine and Cardiovascular Digital Health Journal). His research has received Spotlight and Oral presentations at NeurIPS, ICML, CVPR, and ICIP conferences, and industrial grants from Adobe, Microsoft, and Google to support his work on trustworthy machine learning.

SAMUEL LEVY
Assistant Professor of Business Administration at the UVA Darden School of Business

Samuel Levy is an Assistant Professor of Business Administration at the Darden School of Business, where he teaches the marketing core course for the full-time MBA program. He holds a B.S. in Economics from École Normale Supérieure Paris-Saclay in his native France, an M.S. in Marketing from Tilburg University, and a Ph.D. in Marketing from Carnegie Mellon University.

Sam’s research focuses on solving marketing problems using empirical methods, particularly in the areas of customer analytics, customer relationship management (CRM), data fusion, and privacy in marketing. He develops innovative methodologies such as digital marketing twins, leveraging probabilistic machine learning techniques to provide detailed, individual-level counterfactual insights regarding brand affinity and service performance from multiple data sources. Additionally, his work on privacy-preserving data fusion combines multiple datasets while ensuring user privacy, addressing the challenges of merging customer survey data with CRM databases in the US telecommunications industry. This approach enables marketers and researchers to understand customer satisfaction and run effective customer retention campaigns without compromising privacy.

2024 Fellowships in AI Research (FAIR) Program

The UVA Darden LaCross AI Institute invites applications from UVA faculty for the 2024 Fellowships in AI Research (FAIR) Program. 2024 Fellowships are expected to be awarded in January 2025.

The LaCross AI Institute was established in 2024 with the mission to make the world a better place through the responsible use of AI in business by developing leaders who can manage AI businesses and solutions, guided by ethics, values, and the advancement of human well-being. As part of this mission, a key emphasis is on collaboration across disciplines, including to advance academic research and knowledge creation on timely and important topics in ethical AI in business where such collaboration may accelerate progress or lead to novel approaches and solutions.

The Fellowships in AI Research program, which originally launched under the Darden-Data Science Collaboratory (DCADS) in the fall of 2023, is now organized by the LaCross AI Institute. It is the Institute’s primary vehicle to pursue and support collaboration in research. It is designed to support scholars, practitioners and UVA students who are, or intend to be, engaged in research that has beneficial practical outcomes and provides the foundation for substantive future work. The FAIR Program is structured to provide initial funding for work that is conducted within the University of Virginia, as a collaboration among UVA faculty, staff and students, representing multiple disciplines. The program is also open to others in Virginia provided the work is led by UVA affiliated faculty.

We invite all applications covering the broad spectrum of topics related to ethical AI in business. However, for 2024, we will give preference to proposals that align with one of the following topics of interest:

Bias and Misinformation: exploring algorithms and data-intensive business practices that increase equity and promote truthfulness in business and in society using AI.
Analytical Leadership: managing and leading analytical individuals, high-performing teams, and distinctive organizations in the face of an explosion of data and the near ubiquity of technologies that enable leaders to use or misuse AI.
Healthy Choices: understanding and influencing consumer and healthcare professional behavior through AI-enabled interventions, experiments, and analysis using data and technology, with the objective of improving health and better managing care.
Human ↔ AI Performance: Understanding and enhancing human performance using AI and machine learning, and evaluating and advancing AI performance using insights and understanding from the study of performance in humans.
Privacy & Ethical AI: Understanding the role of data privacy and data ethics, and developing tools and management approaches to ensure they are prominent considerations in the development and deployment of ethical AI.

FAIR Information

Fellowship Details

The fellowship will support a project in ethical artificial intelligence of significance to scholars and practitioners in business and data science. The ideal project is aligned with one of the above topics and involves collaboration among multiple disciplines to address a problem or opportunity in a novel way. The intent is to highlight disciplines, develop or apply methods, and pursue approaches to ethical AI that may be unfamiliar or inaccessible in the individual disciplines of data science and management. The goal of the FAIR Program is to produce output that demonstrates the efficacy of such collaboration in identifying novel solutions and unique pathways for continued future research.

Fellow Responsibilities

Fellowships may include UVA and non-UVA faculty as Faculty Fellows, current and new UVA students as Student Fellows, and non-academic practitioners and other experts as Practitioner Fellows. Proposed projects must be led by a member of the UVA faculty serving as Principal Investigator.

FAIR Fellows will join and have recognized standing as part of the UVA Darden and UVA Data Science communities, and potentially others across UVA, during the Fellowship.

The primary activities of the Fellows will be those research-related activities that are necessary for completion of the proposed project, including as originally defined and as subsequently modified by agreement during the Fellowship.

Secondary activities may include participation in seminars, classes, meetings, etc. with faculty, students and staff at UVA Darden, UVA Data Science, and in the UVA community at large, including at other UVA schools and units that are usefully related to the proposed project.

Faculty Fellows will be responsible for overseeing the work of others involved in the proposed project, including Post-doctoral Fellows, Student Fellows (doctoral or graduate students, NOT including undergraduate), Practitioner Fellows (individuals from business or other experts, etc.) and associated research staff (e.g., research assistants or support) who may be hired using Fellowship funds.

Fellows are not expected to be in residence at UVA on a continuous basis, but will be expected to spend 2-3 periods in Charlottesville during the duration of the Fellowship. During the rest of the Fellowship, they must be accessible remotely as necessary to ensure the successful completion of the proposed project. Additional residency in the Charlottesville, Virginia area may be proposed, but Fellowship funding is not available to cover further residency-related expenses.

Fellows are expected to complete the activities and produce the outputs as agreed upon in the project proposal.

Fellows will be required to present their work and/or conduct public and private workshops and participate in related events to further disseminate their work. These may include virtual and/or in-person engagements.

Fellows are expected to be aware of and to adhere to UVA policies and guidelines that are relevant to their work as Fellows of the LaCross AI Institute.

Project Guidelines

The proposed project must be led by a UVA faculty member. Additional team members may be affiliated with other academic institutions and/or businesses with a presence in Virginia.

Proposed projects must be new work and not previously completed or published.

Project outputs should include, at a minimum, a paper, article, presentation or other document that is suitable for dissemination to a scholarly audience, such as through a peer-reviewed journal in artificial intelligence, data science, or business, or another venue as proposed and agreed.

Work is expected to be sufficient in quality and other respects so as to be useful in subsequent proposals for funding from public or private sources.

Duration

Compensation

The funds for the fellowships are provided by the LaCross AI Institute. Each fellowship will include a maximum of $100,000 in total funding. Of this amount, $75,000 must be used for student-related expenses, including funding Student Fellows such as existing or new student researchers (post-doc, phd, masters), covering student travel, conference fees, etc and providing necessary resources such as data, software, training, compute, storage, etc. In addition, applicable fringe will also be covered. The remaining $25,000 can be used for any project-related expense at the Principal Investigator's discretion. All funds must be utilized as described in the submitted and accepted fellowship proposal.

Qualifications

For Faculty: Any full-time faculty member from the University of Virginia with any school or department and of any academic rank. Faculty in the fields of artificial intelligence, data science or business will be given preference, as will collaborations between faculty representing multiple disciplines and/or UVA units.

For Non-faculty: Experienced practitioners and other non-academic professionals with more than 10 years of experience in artificial intelligence, data science or business. UVA Darden and UVA Data Science alumni will be given preference.

FAIR Fellowship recipients must meet UVA work eligibility requirements.

Information Session Dates

Nomination

Anyone may nominate candidate(s) for the 2024 FAIR Program, however nominations are optional. Direct applications from qualified individuals are encouraged.

If you would like to nominate someone else for the 2024 FAIR Program, please email the following to fair@darden.virginia.edu:

1. Your name and contact information,

2. The name and contact information of the person you are nominating,

3. A short statement (500 words max.) of why you are making this nomination and what you expect the nominee will contribute if he or she is selected for this Fellowship.

A LaCross AI Institute representative will review all nominations and invite qualified individuals to submit an application for the 2024 FAIR Program.

Nomination deadline: 30 November 2024

Application

To apply for the 2024 FAIR Program, individuals must submit the following information using the Application link on the LaCross AI Institute website. Note that a nomination is not required.

An abbreviated CV (3-pages maximum) for each Faculty member in the project proposal,
A project proposal that addresses the following at a minimum (5 pages maximum)
- An overview of your proposed project,
- A summary of the plan, timing and approach that you intend to follow,
- A preliminary list or table of the resources (e.g., students, research staff, data, computing, etc.) and associated budget that you require (note that requested budget may not exceed $100,000 but may be less),
- What type of UVA student resources (e.g., Post-doc, PhD, Masters) you intend to use,
- What role, work, and other activities you expect the student(s) to perform in the project,
- Outputs and end-products that you will produce,
- Potential avenues to disseminate your work,
- Potential opportunities to extend or expand this work using other sources of funding in the future.
A personal statement that addresses the following at a minimum (1 page maximum)
- Why are you a compelling candidate for the FAIR Program and a strong fit with the LaCross AI Institute?
- Why do this Fellowship and the LaCross AI Institute represent a uniquely compelling opportunity for you?
- How does this Fellowship support your current and potential future professional activities?

Application deadline: 10 January 2025

Successful candidates will be expected to provide a letter of support from their current school/unit/manager. They may be asked for professional references prior to the final awarding of the Fellowship.

Key Dates & Timelines

For Additional Information

To request a 1x1 information session, please contact FAIR@darden.virginia.edu.

To view information about this fellowship, please review our PDF.