William B. Haskell

819 total citations
46 papers, 425 citations indexed

About

William B. Haskell is a scholar working on Management Science and Operations Research, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, William B. Haskell has authored 46 papers receiving a total of 425 indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Management Science and Operations Research, 20 papers in Artificial Intelligence and 7 papers in Computational Mechanics. Recurrent topics in William B. Haskell's work include Risk and Portfolio Optimization (25 papers), Reinforcement Learning in Robotics (10 papers) and Sparse and Compressive Sensing Techniques (7 papers). William B. Haskell is often cited by papers focused on Risk and Portfolio Optimization (25 papers), Reinforcement Learning in Robotics (10 papers) and Sparse and Compressive Sensing Techniques (7 papers). William B. Haskell collaborates with scholars based in United States, Singapore and China. William B. Haskell's co-authors include Rahul Jain, Guodong Yu, Alejandro Toriello, P. L. Yu, M. Poremba, Wenjie Huang, Dileep Kalathil, Vincent Y. F. Tan, Milind Tambe and Sixiang Zhao and has published in prestigious journals such as IEEE Transactions on Automatic Control, Management Science and European Journal of Operational Research.

In The Last Decade

William B. Haskell

45 papers receiving 407 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
William B. Haskell United States	12	181	112	64	60	45	46	425
Pavlo Krokhmal United States	13	246 1.4×	37 0.3×	60 0.9×	55 0.9×	47 1.0×	39	505
Mingyang Li China	15	209 1.2×	69 0.6×	64 1.0×	42 0.7×	25 0.6×	40	690
Laxminarayan Sahoo India	13	181 1.0×	70 0.6×	120 1.9×	48 0.8×	100 2.2×	36	557
Robert G. Batson United States	10	104 0.6×	40 0.4×	49 0.8×	81 1.4×	27 0.6×	44	482
Banu Soylu Türkiye	14	102 0.6×	89 0.8×	84 1.3×	175 2.9×	13 0.3×	24	429
Eduardo Conde Spain	15	261 1.4×	58 0.5×	130 2.0×	183 3.0×	51 1.1×	38	608
Jianping Fan China	14	358 2.0×	59 0.5×	115 1.8×	24 0.4×	65 1.4×	61	504
K Darby-Dowman United Kingdom	12	195 1.1×	47 0.4×	74 1.2×	128 2.1×	26 0.6×	31	478
Shio Gai Quek Malaysia	12	227 1.3×	103 0.9×	89 1.4×	17 0.3×	61 1.4×	26	440

Countries citing papers authored by William B. Haskell

Since Specialization

Citations

This map shows the geographic impact of William B. Haskell's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by William B. Haskell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William B. Haskell more than expected).

Fields of papers citing papers by William B. Haskell

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by William B. Haskell. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by William B. Haskell. The network helps show where William B. Haskell may publish in the future.

Co-authorship network of co-authors of William B. Haskell

This figure shows the co-authorship network connecting the top 25 collaborators of William B. Haskell. A scholar is included among the top collaborators of William B. Haskell based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with William B. Haskell. William B. Haskell is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Haskell, William B., et al.. (2023). A dynamic analytic method for risk-aware controlled martingale problems. The Annals of Applied Probability. 33(3). 1 indexed citations

Haskell, William B., et al.. (2023). An Inexact Primal-Dual Smoothing Framework for Large-Scale Non-Bilinear Saddle Point Problems. Journal of Optimization Theory and Applications. 200(1). 34–67. 3 indexed citations

Berenguer, Gemma, et al.. (2023). Managing Volunteers and Paid Workers in a Nonprofit Operation. Management Science. 70(8). 5298–5316. 10 indexed citations

Zhao, Sixiang, William B. Haskell, & Michel‐Alexandre Cardin. (2022). A flexible system design approach for multi-facility capacity expansion problems with risk aversion. IISE Transactions. 55(2). 187–200. 1 indexed citations

Ng, Szu Hui, et al.. (2021). A Multilevel Simulation Optimization Approach for Quantile Functions. INFORMS journal on computing. 34(1). 569–585. 10 indexed citations

Huang, Wenjie & William B. Haskell. (2020). Stochastic Approximation for Risk-Aware Markov Decision Processes. IEEE Transactions on Automatic Control. 66(3). 1314–1320. 5 indexed citations

Jain, Rahul, et al.. (2019). Empirical Algorithms for General Stochastic Systems with Continuous States and Actions. 9. 6344–6349. 1 indexed citations

Zhao, Sixiang, William B. Haskell, & Michel‐Alexandre Cardin. (2018). Decision rule-based method for flexible multi-facility capacity expansion problem. IISE Transactions. 50(7). 553–569. 21 indexed citations

Haskell, William B., et al.. (2017). A primal-dual smoothing gap reduction framework for strongly convex-generally concave saddle point problems. arXiv (Cornell University). 4 indexed citations

10.

Haskell, William B., et al.. (2017). Stochastic L-BFGS Revisited: Improved Convergence Rates and Practical Acceleration Strategies. arXiv (Cornell University). 2 indexed citations

11.

Yu, P. L., William B. Haskell, & Huan Xu. (2017). Dynamic programming for risk-aware sequential optimization. 4934–4939. 2 indexed citations

12.

Haskell, William B., J. George Shanthikumar, & Zuo‐Jun Max Shen. (2017). Primal-Dual Algorithms for Optimization with Stochastic Dominance. SIAM Journal on Optimization. 27(1). 34–66. 8 indexed citations

13.

Haskell, William B., et al.. (2016). Ambiguity in risk preferences in robust stochastic optimization. European Journal of Operational Research. 254(1). 214–225. 25 indexed citations

14.

Haskell, William B., et al.. (2015). Robust Strategy against Unknown Risk-averse Attackers in Security Games. 1341–1349. 6 indexed citations

15.

Haskell, William B. & Rahul Jain. (2015). A Convex Analytic Approach to Risk-Aware Markov Decision Processes. SIAM Journal on Control and Optimization. 53(3). 1569–1598. 35 indexed citations

16.

Kwak, Jun-young, Debarun Kar, William B. Haskell, Pradeep Varakantham, & Milind Tambe. (2014). Building THINC: user incentivization and meeting rescheduling for energy savings. Adaptive Agents and Multi-Agents Systems. 925–932. 1 indexed citations

17.

Haskell, William B., et al.. (2014). Online planning for optimal protector strategies in resource conservation games. Adaptive Agents and Multi-Agents Systems. 733–740. 11 indexed citations

18.

Haskell, William B. & Rahul Jain. (2013). Stochastic Dominance-Constrained Markov Decision Processes. SIAM Journal on Control and Optimization. 51(1). 273–303. 16 indexed citations

19.

Haskell, William B., J. George Shanthikumar, & Zuo‐Jun Max Shen. (2013). Optimization with a class of multivariate integral stochastic order constraints. Annals of Operations Research. 206(1). 147–162. 7 indexed citations

20.

Swenson, Harry N., et al.. (2011). Optimal time advance in terminal area arrivals: Throughput vs. fuel savings. 2011 IEEE/AIAA 30th Digital Avionics Systems Conference. 2D2–1. 7 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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