Hamsa Bastani

• Hamsa Bastani, Osbert Bastani, Tsai-Hsuan (Angel) Chung, “Optimizing Health Supply Chains in LMICs with Machine Learning: A Case Study in Sierra Leone”. In Responsible and Sustainable Operations: The New Frontier, edited by Tang, Christopher S. (Switzerland: Springer Nature, 2024), pp. 187-202 Abstract

  This chapter overviews the challenges in pharmaceutical supply chains (PSCs) in Low- and Middle-Income Countries (LMICs), with a focus on Sierra Leone. Furthermore, it describes how traditional supply chain optimization strategies can be used to improve performance of PSCs in Sierra Leone. Finally, it describes the significant potential for using machine learning in this framework for effective demand forecasting. We highlight challenges such as limited data availability, the need to ensure equitable distribution, as well as the potential for transfer learning to address some of these challenges.

• Xinmeng Huang, Kan Xu, Donghwan Lee, Seyed Hamed Hassani, Hamsa Bastani, Edgar Dobriban Optimal Heterogeneous Collaborative Linear Regression and Contextual Bandits.
• Arielle Anderer, Hamsa Bastani, John Silberholz (2022), Adaptive Clinical Trial Designs with Surrogates: When Should We Bother?, Management Science (2022). Abstract

  The success of a new drug is assessed within a clinical trial using a primary endpoint, which is typically the true outcome of interest, e.g., overall survival. However, regulators sometimes approve drugs using a surrogate outcome — an intermediate indicator that is faster or easier to measure than the true outcome of interest, e.g., progression-free survival — as the primary endpoint when there is demonstrable medical need. While using a surrogate outcome (instead of the true outcome) as the primary endpoint can substantially speed up clinical trials and lower costs, it can also result in poor drug approval decisions since the surrogate is not a perfect predictor of the true outcome. In this paper, we propose combining data from both surrogate and true outcomes to improve decision-making within a late-phase clinical trial. In contrast to broadly used clinical trial designs that rely on a single primary endpoint, we propose a Bayesian adaptive clinical trial design that simultaneously leverages both observed outcomes to inform trial decisions. We perform comparative statics on the relative benefit of our approach, illustrating the types of diseases and surrogates for which our proposed design is particularly advantageous. Finally, we illustrate our proposed design on metastatic breast cancer. We use a large-scale clinical trial database to construct a Bayesian prior, and simulate our design on a subset of clinical trials. We estimate that our design would yield a 16% decrease in trial costs relative to existing clinical trial designs, while maintaining the same Type I/II error rates.

• Kan Xu and Hamsa Bastani (Under Review), Learning Across Bandits in High Dimension via Robust Statistics. Abstract

  Decision-makers often face the “many bandits” problem, where one must simultaneously learn across related but heterogeneous contextual bandit instances. For instance, a large retailer may wish to dynamically learn product demand across many stores to solve pricing or inventory problems, making it desirable to learn jointly for stores serving similar customers; alternatively, a hospital network may wish to dynamically learn patient risk across many providers to allocate personalized interventions, making it desirable to learn jointly for hospitals serving similar patient populations. We study the setting where the unknown parameter in each bandit instance can be decomposed into a global parameter plus a sparse instance-specific term. Then, we propose a novel two-stage estimator that exploits this structure in a sample-efficient way by using a combination of robust statistics (to learn across similar instances) and LASSO regression (to debias the results). We embed this estimator within a bandit algorithm, and prove that it improves asymptotic regret bounds in the context dimension d; this improvement is exponential for data-poor instances. We further demonstrate how our results depend on the underlying network structure of bandit instances.

• Hamsa Bastani, Pavithra Harsha, Georgia Perakis, Divya Singhvi (2021), Learning Personalized Product Recommendations with Customer Disengagement, MSOM. Abstract

  Problem definition: We study personalized product recommendations on platforms when customers have unknown preferences. Importantly, customers may disengage when offered poor recommendations. Academic/practical relevance: Online platforms often personalize product recommendations using bandit algorithms, which balance an exploration-exploitation trade-off. However, customer disengagement—a salient feature of platforms in practice—introduces a novel challenge because exploration may cause customers to abandon the platform. We propose a novel algorithm that constrains exploration to improve performance. Methodology: We present evidence of customer disengagement using data from a major airline’s ad campaign; this motivates our model of disengagement, where a customer may abandon the platform when offered irrelevant recommendations. We formulate the customer preference learning problem as a generalized linear bandit, with the notable difference that the customer’s horizon length is a function of past recommendations. Results: We prove that no algorithm can keep all customers engaged. Unfortunately, classical bandit algorithms provably overexplore, causing every customer to eventually disengage. Motivated by the structural properties of the optimal policy in a scalar instance of our problem, we propose modifying bandit learning strategies by constraining the action space up front using an integer program. We prove that this simple modification allows our algorithm to perform well by keeping a significant fraction of customers engaged. Managerial implications: Platforms should be careful to avoid overexploration when learning customer preferences if customers have a high propensity for disengagement. Numerical experiments on movie recommendations data demonstrate that our algorithm can significantly improve customer engagement.

• Hamsa Bastani, Kimon Drakopoulos, Vishal Gupta (Forthcoming), Interpretable OR for High-Stakes Decisions: Designing the Greek COVID-19 Testing System. Abstract

  In the summer of 2020, in collaboration with the Greek government, we designed and deployed Eva – the first national scale, reinforcement learning system for targeted COVID-19 testing. In this paper, we detail the rationale for three major design/algorithmic elements: Eva’s testing supply chain, estimating COVID-19 prevalence, and test allocation. Specifically, we describe the design of Eva’s supply chain to collect and process thousands of biological samples per day with special emphasis on capacity procurement. Then, we propose a novel, empirical Bayes estimation strategy to estimate COVID-19 prevalence among different passenger types with limited data and showcase how these estimates were instrumental for a variety of downstream decision-making. Finally, we propose a novel, multi-armed bandit algorithm that dynamically allocates tests to arriving passengers in a non-stationary environment with delayed feedback and batched decisions. All of our design and algorithmic choices emphasize the need for transparent reasoning to enable human-in-the- loop analytics. Such transparency was crucial to building trust and buy-in among policymakers and public health experts in a period of global crisis.

• Kan Xu, Xuanyi Zhao, Hamsa Bastani, Osbert Bastani (2021), Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings, ICML. Abstract

  Sparse regression has recently been applied to enable transfer learning from very limited data. We study an extension of this approach to unsupervised learning—in particular, learning word embeddings from unstructured text corpora using low-rank matrix factorization. Intuitively, when transferring word embeddings to a new domain, we expect that the embeddings change for only a small number of words—e.g., the ones with novel meanings in that domain. We propose a novel group-sparse penalty that exploits this sparsity to perform transfer learning when there is very little text data available in the target domain—e.g., a single article of text. We prove generalization bounds for our algorithm. Furthermore, we empirically evaluate its effectiveness, both in terms of prediction accuracy in downstream tasks as well as the interpretability of the results.

• Pia Ramchandani, Hamsa Bastani, Emily Wyatt (Under Review), Unmasking Human Trafficking Risk in Commercial Sex Supply Chains with Machine Learning. Abstract

  The covert nature of sex trafficking provides a significant barrier to generating large-scale, data-driven insights to inform law enforcement, policy and social work. We leverage massive deep web data (collected globally from leading commercial sex websites) in tandem with a novel machine learning framework to unmask suspicious recruitment-to-sales pathways, thereby providing the first global network view of trafficking risk in commercial sex supply chains. This allows us to infer likely recruitment-to-sales trafficking routes of criminal entities, deceptive approaches used to recruit victims, and regional variations in recruitment vs. sales pressure. These insights can help law enforcement agencies along trafficking routes better coordinate efforts, as well as target local counter-trafficking policies and interventions towards exploitative behavior frequently exhibited in that region.

• Wanqiao Xu, Kan Xu, Hamsa Bastani, Osbert Bastani (Under Review), Mind the Gap: Safely Bridging Offline and Online Reinforcement Learning. Abstract

  A key challenge to deploying reinforcement learning in practice is exploring safely. We propose a natural safety property—uniformly outperforming a conservative policy (adaptively estimated from all data observed thus far), up to a per-episode exploration budget. This property formalizes the idea that we should spread out exploration to avoid taking actions significantly worse than the ones that are currently known to be good. We then design an algorithm that uses a UCB reinforcement learning policy for exploration, but overrides it as needed to ensure safety with high probability. To ensure exploration across the entire state space, it adaptively determines when to explore (at different points of time across different episodes) in a way that allows “stitching” sub-episodes together to obtain a meta-episode that is equivalent to using UCB for the entire episode. Then, we establish reasonable assumptions about the underlying MDP under which our algorithm is guaranteed to achieve sublinear regret while ensuring safety; under these assumptions, the cost of imposing safety is only a constant factor.

• Kan Xu, Hamsa Bastani, Osbert Bastani (Under Review), Robust Generalization of Quadratic Neural Networks via Function Identification. Abstract

  A key challenge facing deep learning is that neural networks are often not robust to shifts in the underlying data distribution. We study this problem from the perspective of the statistical concept of parameter identification. Generalization bounds from learning theory often assume that the test distribution is close to the training distribution. In contrast, if we can identify the “true” parameters, then the model generalizes to arbitrary distribution shifts. However, neural networks are typically overparameterized, making parameter identification impossible. We show that for quadratic neural networks, we can identify the function represented by the model even though we cannot identify its parameters. Thus, we can obtain robust generalization bounds even in the overparameterized setting. We leverage this result to obtain new bounds for contextual bandits and transfer learning with quadratic neural networks. Overall, our results suggest that we can improve robustness of neural networks by designing models that can represent the true data generating process. In practice, the true data generating process is often very complex; thus, we study how our framework might connect to neural module networks, which are designed to break down complex tasks into compositions of simpler ones. We prove robust generalization bounds when individual neural modules are identifiable.

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Current Courses

• OIDD9950 - Dissertation Preparation

  OIDD9950030 ( Syllabus )

Past Courses

• OIDD2990 - Judg & Dec Making Res Im

  This class provides a high-level introduction to the field of judgment and decision making (JDM) and in-depth exposure to the process of doing research in this area. Throughout the semester you will gain hands-on experience with several different JDM research projects. You will be paired with a PhD student or faculty mentor who is working on a variety of different research studies. Each week you will be given assignments that are central to one or more of these studies, and you will be given detailed descriptions of the research projects you are contributing to and how your assignments relate to the successful completion of these projects. To complement your hands-on research experience, throughout the semester you will be assigned readings from the book Nudge by Thaler and Sunstein, which summarizes key recent ideas in the JDM literature. You will also meet as a group for an hour once every three weeks with the class's faculty supervisor and all of his or her PhD students to discuss the projects you are working on, to discuss the class readings, and to discuss your own research ideas stimulated by getting involved in various projects. Date and time to be mutually agreed upon by supervising faculty and students. the 1CU version of this course will involve approx. 10 hours of research immersion per week and a 10-page paper. The 0.5 CU version of this course will involve approx 5 hours of research immersion per week and a 5-page final paper. Please contact Professor Joseph Simmons if you are interested in enrolling in the course: jsimmo@wharton.upenn.edu

• OIDD3990 - Supervised Study

  This course number is currently used for several course types including independent studies, experimental courses and Management & Technology Freshman Seminar. Instructor permission required to enroll in any independent study. Wharton Undergraduate students must also receive approval from the Undergraduate Division to register for independent studies. Section 002 is the Management and Technology Freshman Seminar; instruction permission is not required for this section and is only open to M&T students. For Fall 2020, Section 004 is a new course titled AI, Business, and Society. The course provides a overview of AI and its role in business transformation. The purpose of this course is to improve understanding of AI, discuss the many ways in which AI is being used in the industry, and provide a strategic framework for how to bring AI to the center of digital transformation efforts. In terms of AI overview, we will go over a brief technical overview for students who are not actively immersed in AI (topic covered include Big Data, data warehousing, data-mining, different forms of machine learning, etc). In terms of business applications, we will consider applications of AI in media, Finance, retail, and other industries. Finally, we will consider how AI can be used as a source of competitive advantage. We will conclude with a discussion of ethical challenges and a governance framework for AI. No prior technical background is assumed but some interest in (and exposure to) technology is helpful. Every effort is made to build most of the lectures from the basics.

• OIDD9410 - Dist System Sem

  Seminar on distribution systems models and theory. Reviews current research in the development and solution of models of distribution systems. Emphasizes multi-echelon inventory control, logistics management, network design, and competitive models.

• 1st Place, Wagner Prize for Excellence in Operations Research Practice, 2021
• 1st Place, Pierskalla Award for Best Paper in Healthcare, 2021
• 2nd Place, Public Sector in Operations Best Paper Award, 2021
• 1st Place, Behavioral OM Best Working Paper Award, 2021
• 2nd Place, TIMES Working Paper Award, 2021
• Finalist, Public Sector in Operations Best Paper Award, 2020
• People’s Choice Award, Early-Career Sustainable OM Workshop, 2020
• 2nd Place, Service Science Best Paper Award, 2019
• 1st Place, Pierskalla Award for Best Paper in Healthcare, 2019
• People's Choice Award, Early-Career Sustainable OM Workshop, 2019
• Finalist, Pierskalla Award for Best Paper in Healthcare, 2018
• 1st Place, IBM Service Science Best Student Paper Award, 2016
• 1st Place, MSOM Student Paper Competition, 2016
• 1st Place, George Nicholson Student Paper Competition, 2016
• 1st Place, Pierskalla Award for Best Paper in Healthcare, 2016
• 1st Place, Health Applications Society Best Student Paper Award, 2015

• Smarter health: How Greece used AI to reopen its borders and curb COVID, WBUR - 06/17/2022

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