We are interested in developing mathematical models for understanding and improving complex systems. Some broad areas of interest are detailed below.

Learning, attribution and incentives

  • G. Iyengar, Y. Ma, T. J. Rivera and J. Sethuraman. The Distributional Effects of ‘Fulfilled By Amazon’ (FBA). 2023. [SSRN].

  • W. Kim, G. Iyengar, and A. Zeevi. Improved algorithms for multi-period multi-class packing problems with bandit feedback. 2023. [arXiv].

  • G. Iyengar and M. Sridharan. Scalable computation of causal bounds. 2022. [ICML].

  • V. Goyal, G. Iyengar, and R. Udwani. Online Allocation of Reusable Resources via Algorithms Guided by Fluid Approximations. 2020. [arXiv].

  • M-H. Oh, G. Iyengar, and A. Zeevi. Sparsity-Agnostic Lasso Bandit. 2020. [ICML] [arXiv].

  • G. Iyengar, F. Saleh, J. Sethuraman, and W. Wang. Economics of permissioned blockchain adoption. 2020. [Management Science] [SSRN].

  • R. Singal, O. Besbes, A. Desir, V. Goyal and G. Iyengar. Shapley Meets Uniform: An axiomatic framework for attribution in online advertising. [Management Science] [SSRN] [WWW 2019].

  • C. Kocyigit, G. Iyengar, D. Kuhn, and W. Wiesemann. Distributionally robust mechanism design. [Management Science] [SSRN].

  • O. Toubia, G. Iyengar, R. Bunnell, and A. Lemaire. Extracting features of entertainment products: A guided Latent Dirichlet Allocation approach informed by the psychology of media consumption. [JMR] [PDF].

  • M-H. Oh and G. Iyengar. Thompson sampling for multinomial logit contextual bandits. 2019. [NeurIPS] [arXiv].

  • M-H. Oh and G. Iyengar. Sequential anomaly detection using inverse reinforcement learning. [KDD 2019] [arXiv].

  • H. Alsabah, B. Bernard, A. Capponi, G. Iyengar, and J. Sethuraman. Multiregional oligopoly with capacity constraints. [Management Science] [SSRN].

Optimization and Applications

  • G. Iyengar, H. Lam, and T. Wang. Hedging against complexity: Distributionally robust optimization with parametric approximation. 2023. [arXiv].

  • A. Federgruen, D. Guetta, G. Iyengar, and X. Liu. An Asymptotically Optimal Heuristic for Multi-Item Inventory Models with Joint Inventory Constraints. 2022. [SSRN].

  • A. Federgruen, D. Guetta, G. Iyengar, and X. Liu. Scalable Approximately Optimal Policies for Multi-Item Stochastic Inventory Problems. 2022. [SSRN].

  • G. Costa and G. Iyengar. Distributionally Robust End-to-End Portfolio Construction. 2022. [arXiv].

  • R. Mazumder, A. Choudhury, G. Iyengar, and B. Sen. A computational framework for multivariate convex regression and its variants. [JASA] [arXiv].

  • A. Federgruen, C. D. Guetta, and G. Iyengar. Two-echelon distribution systems with random demands and storage constraints. [NRL] [PDF].

  • Y. Chen, R. Iyengar, and G. Iyengar. Modeling multimodal continuous heterogeneity in conjoint analysis: a sparse learning approach. Marketing Science, 36(1):140–156, 2017. Wharton Mkting Papers.

  • M. Haugh, G. Iyengar, and I. Song. A generalized risk budgeting approach to portfolio construction. Computational Finance, 21(2):29–60, 01 2017. SSRN.

  • M. Haugh, G. Iyengar, and C. Wang. Tax-aware dynamic asset allocation. Operations Research, 64(4):849–866, 2016. PDF.

Information in Biological Systems

  • A. Yadav, Q. Vagne, P. Sens, G. Iyengar, and M. Rao. Glycan processing in the Golgi – optimal information coding and constraints on cisternal number and enzyme specificity. 2020. [eLife] [bioRxiv].

  • S. G. Das, M. Rao, and G. Iyengar. Cascade of transitions in molecular information theory. 2018. [J. Stat. Mech.] [arXiv].

  • S. G. Das, M. Rao, and G. Iyengar. Universal lower bound on the free-energy cost of molecular measurements. Physical Review E, 95(6):062410, 2017. arXiv.

  • G. Iyengar and M. Rao. A cellular solution to an information-processing problem. PNAS, 111(34):12402–12407, 2014. PNAS.