About

Rad Niazadeh is an Associate Professor of Operations Management at the University of Chicago Booth School of Business. His research involves operations management, with a focus on topics related to decision analytics, optimization, and applied AI. He is actively involved in mentoring PhD students and pre-doctoral researchers, contributing to the academic community through his research and teaching activities.

Research topics

• Computer Science
• Mathematical optimization
• Artificial Intelligence
• Geography
• Algorithm
• Operations research
• Statistics
• Mathematics

Selected publications

• Non-Exclusive Notifications for Ride-Hailing at Lyft I: Single-Cycle Approximation Algorithms

  ArXiv.org · 2026-03-23

  articleOpen access

  Ride-hailing platforms increasingly rely on non-exclusive notifications-broadcasting a single request to multiple drivers simultaneously-to mitigate inefficiencies caused by uncertain driver acceptance. In this paper, the first in a two-part collaboration with Lyft, we formally model the 'Notification Set Selection Problem' for a single decision cycle, where the platform determines the optimal subset of drivers to notify for each incoming ride request. We analyze this combinatorial optimization problem under two contention-resolution protocols: 'First Acceptance (FA)', which prioritizes speed by assigning the ride to the first responder, and 'Best Acceptance (BA)', which prioritizes match quality by selecting the highest-valued accepting driver. We show that welfare maximization under both mechanisms is strongly NP-hard, ruling out a Fully Polynomial Time Approximation Scheme (FPTAS). Despite this, we derive several positive algorithmic results. For FA, we present a Polynomial Time Approximation Scheme (PTAS) for the single-rider case and a constant-factor approximation (factor 4) for the general matching setting. We highlight that the FA valuation function can be viewed as a novel discrete choice model with theoretical properties of independent interest. For BA, we prove that the objective is monotone and submodular, admitting a standard $(1 - 1/e)$-approximation. Moreover, using a polynomial-time demand oracle that we design for this problem, we show it is possible to surpass the $(1 - 1/e)$ barrier. Finally, in the special case of homogeneous acceptance probabilities, we show that the BA problem can be solved exactly in polynomial time via a linear programming formulation. We validate the empirical performance our algorithms through numerical experiments on synthetic data and on instances calibrated using real ride-sharing data from Lyft.

  PublisherOA PDF

• Non-Exclusive Notifications for Ride-Hailing at Lyft II: Simulations and Marketplace Analysis

  ArXiv.org · 2026-03-23

  articleOpen access

  Ride-hailing platforms increasingly face uncertain driver acceptance, which makes traditional one-to-one 'exclusive dispatch (ED)' less efficient: rejections and timeouts force sequential retries and lengthen rider wait times, which in turn creates friction in the marketplace. 'Non-exclusive dispatch (NED)' mitigates this friction by broadcasting a request to multiple drivers in parallel. While NED can reduce latency, it introduces new design challenges -- most notably, how to choose notification sets and how to resolve driver contention (when multiple drivers accept the same ride). In this paper -- the second in a two-part collaboration with Lyft -- we develop a theoretically grounded framework to evaluate the long-run performance and marketplace effects of transitioning from ED to NED. We bridge theory and practice by combining (i) an optimization model that formulates NED as a constrained welfare maximization problem with (ii) large-scale discrete-event simulations on proprietary Lyft traces and (iii) a stylized macroscopic equilibrium model. Across simulation and equilibrium analysis, we find that NED improves key fulfillment metrics relative to ED: it reduces match time (and hence rider reneging) while increasing both the number and the average quality of completed matches. We also quantify the speed--quality trade-off between two common contention resolution rules, 'First-Accept' and 'Best-Accept': First-Accept maximizes speed and throughput, whereas Best-Accept is required to maximize per-match quality. Finally, we show that slightly conservative notification heuristics can improve long-run efficiency by avoiding excessive locking of high-value drivers and preserving future availability.

  PublisherOA PDF

• Non-Exclusive Notifications for Ride-Hailing at Lyft I: Single-Cycle Approximation Algorithms

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Optimal Bayesian Online Allocation of Reusable Resources

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Non-Exclusive Notifications for Ride-Hailing at Lyft II: Simulations and Marketplace Analysis

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Stationary Online Contention Resolution Schemes

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Stationary Online Contention Resolution Schemes

  ArXiv.org · 2026-03-23

  articleOpen access

  Online contention resolution schemes (OCRSs) are a central tool in Bayesian online selection and resource allocation: they convert fractional ex-ante relaxations into feasible online policies while preserving each marginal probability up to a constant factor. Despite their importance, designing (near) optimal OCRSs is often technically challenging, and many existing constructions rely on indirect reductions to prophet inequalities and LP duality, resulting in algorithms that are difficult to interpret or implement. In this paper, we introduce "stationary online contention resolution schemes (S-OCRSs)," a permutation-invariant class of OCRSs in which the distribution of the selected feasible set is independent of arrival order. We show that S-OCRSs admit an exact distributional characterization together with a universal online implementation. We then develop a general `maximum-entropy' approach to construct and analyze S-OCRSs, reducing the design of online policies to constructing suitable distributions over feasible sets. This yields a new technical framework for designing simple and possibly improved OCRSs. We demonstrate the power of this framework across several canonical feasibility environments. In particular, we obtain an improved $(3-\sqrt{5})/2$-selectable OCRS for bipartite matchings, attaining the independence benchmark conjectured to be optimal and yielding the best known prophet inequality for this setting. We also obtain a $1-\sqrt{2/(πk)} + O(1/k)$-selectable OCRS for $k$-uniform matroids and a simple, explicit $1/2$-selectable OCRS for weakly Rayleigh matroids (including all $\mathbb{C}$-representable matroids such as graphic and laminar). While these guarantees match the best known bounds, our framework also yields concrete and systematic constructions, providing transparent algorithms in settings where previous OCRSs were implicit or technically involved.

  PublisherOA PDF

• 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

• 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

• Online Job Assignment

  ArXiv.org · 2025-06-07

  preprintOpen accessSenior author

  Motivated primarily by applications in cloud computing, we study a simple, yet powerful, online allocation problem in which jobs of varying durations arrive over continuous time and must be assigned immediately and irrevocably to one of the available offline servers. Each server has a fixed initial capacity, with assigned jobs occupying one unit for their duration and releasing it upon completion. The algorithm earns a reward for each assignment upon completion. We consider a general heterogeneous setting where both the reward and duration of a job depend on the job-server pair. The objective of the online algorithm is to maximize the total collected reward, and remain competitive against an omniscient benchmark that knows all job arrivals in advance. Our main contribution is the design of a new online algorithm, termed Forward-Looking BALANCE (FLB), and using primal-dual framework to establish that it is (asymptotically) optimal-competitive. This meta-algorithm has two main primitives: (i) keeping track of the capacity used for each server at each time and applying a penalty function to this quantity, and (ii) adjusting the reward of assigning a job to a server by subtracting the total penalty of a particularly chosen subset of future times, in contrast to just looking at the current time. The FLB algorithm then assigns the arriving job to the server with the maximum adjusted reward. If R and D are the ratios of maximum over minimum rewards and durations, we show that the FLB algorithm obtains an asymptotic competitive ratio of ln(RD)+3lnln(max(R,D))+O(1). We further show this bound has optimal dependencies on all the parameters. Our main analysis combines a novel dual-fitting technique, which leverages the configuration LP benchmark for this problem, and a novel inductive argument to establish the capacity feasibility of the algorithm, which might be of independent interest.

  PublisherOA PDFDOI

Frequent coauthors

• Yiding Feng

  University of Chicago

  31 shared

• Amin Saberi

  21 shared

• Jason D. Hartline

  Northwestern University

  14 shared

• Ali Shameli

  Stanford University

  12 shared

• Robert Kleinberg

  12 shared

• Vahideh Manshadi

  Yale University

  10 shared

• Shaddin Dughmi

  10 shared

• Christian Jutten

  9 shared

Labs

• Rad Niazadeh LabPI

Education

• Ph.D., Computer Science (minored in Applied Mathematics)

  Cornell University

Awards & honors

• Asness Junior Faculty Fellowship
• INFORMS Auctions and Market Design Rothkopf Junior Researche…
• INFORMS Auctions and Market Design Rothkopf Junior Researche…
• INFORMS Auctions and Market Design Rothkopf Junior Researche…
• INFORMS MSOM Best Student Paper Award 2024