About

Vahideh Manshadi is the Michael H. Jordan Professor of Operations at Yale School of Management. Her research focuses on the operation of online and matching platforms, especially those with profound societal impact, including volunteer crowdsourcing, organ allocation, and information platforms. She has collaborated with nationwide platform-based nonprofits such as Food Rescue US and VolunteerMatch, and has worked on operational problems in e-commerce platforms related to online shopping and advertising. Her academic background includes a PhD and MS from Stanford University and a BS from Sharif University of Technology.

Research topics

• Computer Science
• Machine Learning
• Economics
• Business
• Operations management
• Mathematics
• Microeconomics
• Operations research
• Mathematical optimization
• Econometrics

Selected publications

• How Much Should a Conversational Recommender System Converse?

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Redesigning VolunteerMatch’s Search Algorithm: Toward More Equitable Access to Volunteers

  Management Science · 2025-11-26

  article1st authorCorresponding

  In collaboration with VolunteerMatch (VM)—the world’s largest online platform for connecting volunteers with volunteering opportunities—we designed and implemented a new display ranking algorithm. VM’s original ranking algorithm was intended to maximize efficiency (i.e., the total number of connections), but as a consequence, it repeatedly displayed the same few opportunities at the top of its ranking, effectively limiting access to volunteers for the other opportunities. To incorporate VM’s desire for equity (defined as the weekly number of opportunities with at least one connection) along with efficiency, we propose a modeling framework for online display ranking in settings where it is important to manage the trade-off between the total number of connections and the equitable allocation of these connections. We take an adversarial approach in evaluating the performance of online algorithms and show that a class of algorithms that applies a penalty to opportunities after each connection provides a strong (and, in certain regimes, optimal) performance guarantee. Inspired by our theoretical results yet mindful of practical considerations on VM’s platform, we propose SmartSort, a simple score-based ranking algorithm that enjoys comparable guarantees in many regimes. We implemented SmartSort in two experiments, covering Dallas–Fort Worth and all of Southern California. Using a difference-in-differences analysis, we find that the implementation of SmartSort led to an estimated 8% increase in the weekly average number of opportunities with at least one connection (consistent across both experiments) without any significant decrease in the total number of connections. If SmartSort has a similar distributional effect on a national scale, an additional 30,000 connections every year will go to opportunities that would have otherwise lacked access to volunteers. This paper was accepted by Omar Besbes, revenue management and market analytics. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.03838 .

  PublisherDOI

• Why the Rooney Rule Fumbles: Limitations of Interview-stage Diversity Interventions in Labor Markets

  2025-07-02

  articleOpen access

  Many industries, including the NFL with the Rooney Rule and law firms with the Mansfield Rule, have adopted interview-stage diversity interventions requiring a minimum representation of disadvantaged groups in the interview set. However, the effectiveness of such policies remains inconclusive. In light of this, we develop a framework of a two-stage hiring process, where rational firms, with limited interview and hiring capacities, aim to maximize the match value of their hires. The labor market consists of two equally sized social groups, m and w, with identical ex-post match value distributions. Match values are revealed only post-interview, while interview decisions rely on partially informative pre-interview scores. Pre-interview scores are more informative for group m, while interviews reveal more for group w; as a result, if firms could interview all candidates, both groups would be equally hired. However, due to limited interview capacity and information asymmetry, we show that requiring equal representation in the interview stage does not translate into equal representation in the hiring outcome, even though interviews are more informative for group w. In certain regimes, with or without intervention, a firm may interview more group w candidates but still hire fewer. At an individual level, we show that strong candidates from both groups benefit from the intervention as the candidate-level competition weakens. For borderline candidates, only group w candidates gain at the expense of group w. To understand the impact of non-universal interview-stage interventions on the market, we study a model with two vertically differentiated firms, where only the top firm adopts the intervention. We characterize the unique equilibrium and demonstrate potentially negative effects: we show that in certain regimes the lower firm hires fewer group w candidates due to increased firm-level competition for them, and further find examples where overall fewer group w candidates are hired across the market. At an individual level, while superstar candidates in both groups benefit, surprisingly the impact on borderline candidates may reverse: the lower firm may replace borderline group w candidates with borderline group m candidates in its interview set, effectively reducing the chance of those borderline group w candidates being hired. Overall, our findings highlight challenges in diversifying the labor market at early hiring stages due to information asymmetry, filtering, and competition. Beyond our context, our natural framework of a market with two-stage hiring may be of independent interest.

  PublisherOA PDFDOI

• Offsetting Carbon with Lemons: Adverse Selection and Certification in the Voluntary Carbon Market

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Robust Dynamic Staffing with Predictions

  2025-07-02 · 1 citations

  articleOpen access

  Motivated by the challenges in last-mile delivery operations, we consider a natural dynamic staffing problem in which a decision-maker sequentially hires staff over a finite time horizon to meet an unknown target demand at the end. The decision-maker also receives a sequence of predictions about the demand that become increasingly more accurate over time. Consequently, the decision-maker prefers to delay hiring decisions to avoid overstaffing. However, workers' availability decreases over time, resulting in a fundamental trade-off between securing staff early (thus risking overstaffing) versus hiring later based on more accurate predictions (but risking understaffing).

  PublisherOA PDFDOI

• Who to Offer, and When: Redesigning Feeding America's Real-Time Donation Tool

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Why the Rooney Rule Fumbles: Limitations of Interview-stage Diversity Interventions in Labor Markets

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Robust Dynamic Staffing with Predictions

  ArXiv.org · 2025-10-18

  preprintOpen access

  We consider a natural dynamic staffing problem in which a decision-maker sequentially hires workers over a finite horizon to meet an unknown demand revealed at the end. Predictions about demand arrive over time and become increasingly accurate, while worker availability decreases. This creates a fundamental trade-off between hiring early to avoid understaffing (when workers are more available but forecasts are less reliable) and hiring late to avoid overstaffing (when forecasts are more accurate but availability is lower). This problem is motivated by last-mile delivery operations, where companies such as Amazon rely on gig-economy workers whose availability declines closer to the operating day. To address practical limitations of Bayesian models (in particular, to remain agnostic to the underlying forecasting method), we study this problem under adversarial predictions. In this model, sequential predictions are adversarially chosen uncertainty intervals that (approximately) contain the true demand. The objective is to minimize worst-case staffing imbalance cost. Our main result is a simple and computationally efficient online algorithm that is minimax optimal. We first characterize the minimax cost against a restricted adversary via a polynomial-size linear program, then show how to emulate this solution in the general case. While our base model focuses on a single demand, we extend the framework to multiple demands (with egalitarian/utilitarian objectives), to settings with costly reversals of hiring decisions, and to inconsistent prediction intervals. We also introduce a practical "re-solving" variant of our algorithm, which we prove is also minimax optimal. Finally we conduct numerical experiments showing that our algorithms outperform Bayesian heuristics in both cost and speed, and are competitive with (approximate or exact) Bayesian-optimal policies when those can be computed.

  PublisherOA PDFDOI

• Markovian Search with Ex-Ante Constraints: Theory and Applications to Socially Aware Algorithmic Hiring

  ArXiv.org · 2025-01-23

  preprintOpen access

  We develop an algorithmic framework to incorporate "ex-ante" constraints on outcomes (that hold only on average) into stateful sequential search with costly inspection. Our framework encompasses the classical Weitzman's Pandora's box [Weitzman, 1979] as well as its extensions to joint Markovian scheduling [Dumitriu et al., 2003; Gittins, 1979], modeling richer processes such as multistage search with multiple layers of inspection. Ex-ante constraints in search are particularly motivated by social considerations in algorithmic hiring, where they adjust outcome distributions to promote equity and access. Building on the optimality of index-based policies in the unconstrained problems, we show that optimal policies under a single ex-ante constraint (e.g., demographic parity) retain an index-based structure but require (i) dual-based adjustments of the indices and (ii) randomization between two such adjustments via a "tie-breaking rule," both easy to compute and economically interpretable. We then extend our results to handle multiple affine constraints by reduction to a variant of the exact Carathéodory problem and providing a polynomial-time algorithm to construct an optimal randomized dual-adjusted index-based policy that satisfies all constraints simultaneously. For general affine and convex constraints, we develop a primal-dual algorithm that randomizes over a polynomial number of dual-based adjustments, yielding a near-feasible, near-optimal policy. All these results rely on the key observation that a suitable relaxation of the Lagrange dual function for these constrained problems admits index-based policies akin to those in the unconstrained setting. Finally, through a numerical study, we investigate the implications of various socially aware ex-ante constraints on the utilitarian loss (price of fairness), and examine whether they achieve their intended socially desirable outcomes.

  PublisherOA PDFDOI

• Why the Rooney Rule Fumbles: Limitations of Interview-stage Diversity Interventions in Labor Markets

  2025-10-29

  article
  PublisherDOI

Frequent coauthors

• Scott Rodilitz

  Anderson University - South Carolina

  28 shared

• Patrick Jaillet

  Massachusetts Institute of Technology

  17 shared

• Rad Niazadeh

  University of Chicago

  10 shared

• Itai Ashlagi

  Stanford University

  8 shared

• Amin Saberi

  7 shared

• Negin Golrezaei

  Moscow Institute of Thermal Technology

  7 shared

• Cynthia Barnhart

  7 shared

• Virgile Galle

  6 shared