Selected Publications

Sze-chuan Suen, Diana Negoescu, Joel Goh
Designing Incentives to Promote Treatment Adherence Premature cessation of antibiotic therapy is common and can severely compromise health outcomes, potentially leading to worsening health, disease transmission, and antibiotic resistance. In “Design of Incentive Programs for Optimal Medication Adherence in the Presence of Observable Consumption,” Sze-chuan Suen, Diana Negoescu, and…
Operations research
Sze-chuan Suen, Diana Negoescu, Joel Goh
Designing Incentives to Promote Treatment Adherence Premature cessation of antibiotic therapy is common and can severely compromise health outcomes, potentially leading to worsening health, disease transmission, and antibiotic resistance. In “Design of Incentive Programs for Optimal Medication Adherence in the Presence of Observable Consumption,” Sze-chuan Suen, Diana Negoescu, and…
Operations research
Eryn Juan He , Joel Goh
Modern digital technology has enabled the emergence of the hybrid workforce in service organizations, where a firm uses on-demand freelancers to augment its traditional labor supply of employees. Freelancers are typically supplied by an electronic platform. How should demand be allocated between employees and freelancers? Under what conditions is…
Management Science
Eryn Juan He , Joel Goh
Modern digital technology has enabled the emergence of the hybrid workforce in service organizations, where a firm uses on-demand freelancers to augment its traditional labor supply of employees. Freelancers are typically supplied by an electronic platform. How should demand be allocated between employees and freelancers? Under what conditions is…
Management Science
Kris Ferreira, Joel Goh
Assortment rotation—the retailing practice of changing the assortment of products offered to customers—has recently been used as a competitive advantage for both brick-and-mortar and online retailers. We focus on product categories where consumers may purchase multiple products during a season and investigate a new reason why frequent assortment rotations…
Management Science
Kris Ferreira, Joel Goh
Assortment rotation—the retailing practice of changing the assortment of products offered to customers—has recently been used as a competitive advantage for both brick-and-mortar and online retailers. We focus on product categories where consumers may purchase multiple products during a season and investigate a new reason why frequent assortment rotations…
Management Science
Lianjun Li, Haiqing Zhao, Noah Lim, Joel Goh , Bernard Ng
Importance: Electronic appointment reminder systems are increasingly used across health systems. However, their association with patients’ waiting times for their appointments, a measure of timely access to care, has yet to be assessed. Objective: To assess the associations between the introduction of an electronic appointment reminder system and the…
JAMA Network Open
Lianjun Li, Haiqing Zhao, Noah Lim, Joel Goh , Bernard Ng
Importance: Electronic appointment reminder systems are increasingly used across health systems. However, their association with patients’ waiting times for their appointments, a measure of timely access to care, has yet to be assessed. Objective: To assess the associations between the introduction of an electronic appointment reminder system and the…
JAMA Network Open
PingCao , ShuangchiHe , JunfeiHuang , YunanLiu
There are two basic queue structures commonly adopted in service systems: the pooled structure, where waiting customers are organized into a single queue served by a group of servers, and the dedicated structure, where each server has her own queue. Although the pooled structure, known to minimize the servers’…
Operations Research, vol. 69, no. 6, pp. 1866-1885, Nov.-Dec. 2021
PingCao , ShuangchiHe , JunfeiHuang , YunanLiu
There are two basic queue structures commonly adopted in service systems: the pooled structure, where waiting customers are organized into a single queue served by a group of servers, and the dedicated structure, where each server has her own queue. Although the pooled structure, known to minimize the servers’…
Operations Research, vol. 69, no. 6, pp. 1866-1885, Nov.-Dec. 2021
Lei Yu, Vincent Y. F. Tan
We leverage proof techniques Fourier analysis and an existing result in coding theory to derive new bounds for the problem of non-interactive simulation of binary random variables. Previous bounds in the literature were derived by applying data processing inequalities concerning maximal correlation or hypercontractivity. We show that our bounds…
IEEE Transactions on Information Theory, Vol. 67, No. 4, Pages 2528 – 2538, Apr 2021
Lei Yu, Vincent Y. F. Tan
We leverage proof techniques Fourier analysis and an existing result in coding theory to derive new bounds for the problem of non-interactive simulation of binary random variables. Previous bounds in the literature were derived by applying data processing inequalities concerning maximal correlation or hypercontractivity. We show that our bounds…
IEEE Transactions on Information Theory, Vol. 67, No. 4, Pages 2528 – 2538, Apr 2021
Dana Lahat, Yanbin Lang, Vincent Y. F. Tan, Cédric Févotte
Positive semidefinite matrix factorization (PSDMF) expresses each entry of a nonnegative matrix as the inner product of two positive semidefinite (psd) matrices. When all these psd matrices are constrained to be diagonal, this model is equivalent to nonnegative matrix factorization. Applications include combinatorial optimization, quantum-based statistical models, and recommender…
IEEE Transactions on Signal Processing, Vol. 69, Pages 3059 – 3074, Apr 2021
Dana Lahat, Yanbin Lang, Vincent Y. F. Tan, Cédric Févotte
Positive semidefinite matrix factorization (PSDMF) expresses each entry of a nonnegative matrix as the inner product of two positive semidefinite (psd) matrices. When all these psd matrices are constrained to be diagonal, this model is equivalent to nonnegative matrix factorization. Applications include combinatorial optimization, quantum-based statistical models, and recommender…
IEEE Transactions on Signal Processing, Vol. 69, Pages 3059 – 3074, Apr 2021
Sandra S. Y. Tan, Antonios Varvitsiotis, Vincent Y. F. Tan
We introduce a new framework for unifying and systematizing the performance analysis of first-order black-box optimization algorithms for unconstrained convex minimization. The low-cost iteration complexity enjoyed by first-order algorithms renders them particularly relevant for applications in machine learning and large-scale data analysis. Relying on sum-of-squares (SOS) optimization, we introduce…
Journal of Optimization Theory and Applications, Vol. 190, Pages 56 – 81, Jul 2021
Sandra S. Y. Tan, Antonios Varvitsiotis, Vincent Y. F. Tan
We introduce a new framework for unifying and systematizing the performance analysis of first-order black-box optimization algorithms for unconstrained convex minimization. The low-cost iteration complexity enjoyed by first-order algorithms renders them particularly relevant for applications in machine learning and large-scale data analysis. Relying on sum-of-squares (SOS) optimization, we introduce…
Journal of Optimization Theory and Applications, Vol. 190, Pages 56 – 81, Jul 2021
Yonglong Li, Vincent Y. F. Tan, Marco Tomamichel
We consider sequential hypothesis testing between two quantum states using adaptive and non-adaptive strategies. In this setting, samples of an unknown state are requested sequentially and a decision to either continue or to accept one of the two hypotheses is made after each test. Under the constraint that the…
Communications in Mathematical Physics, Vol. 392, 993 – 1027, Apr 2022
Yonglong Li, Vincent Y. F. Tan, Marco Tomamichel
We consider sequential hypothesis testing between two quantum states using adaptive and non-adaptive strategies. In this setting, samples of an unknown state are requested sequentially and a decision to either continue or to accept one of the two hypotheses is made after each test. Under the constraint that the…
Communications in Mathematical Physics, Vol. 392, 993 – 1027, Apr 2022
Zixin Zhong, Wang Chi Cheung, Vincent Y. F. Tan
Motivated by the pressing need for efficient optimization in online recommender systems, we revisit the cascading bandit model proposed by Kveton et al. (2015). While Thompson sampling (TS) algorithms have been shown to be empirically superior to Upper Confidence Bound (UCB) algorithms for cascading bandits, theoretical guarantees are only…
Z Journal of Machine Learning Research, Vol. 22, No. 218, Pages 1 – 66, 2021
Zixin Zhong, Wang Chi Cheung, Vincent Y. F. Tan
Motivated by the pressing need for efficient optimization in online recommender systems, we revisit the cascading bandit model proposed by Kveton et al. (2015). While Thompson sampling (TS) algorithms have been shown to be empirically superior to Upper Confidence Bound (UCB) algorithms for cascading bandits, theoretical guarantees are only…
Z Journal of Machine Learning Research, Vol. 22, No. 218, Pages 1 – 66, 2021
John R. Birge , Yifan Feng , N. Bora Keskin , Adam Schultz
We study the profit maximization problem of a market maker in a spread betting market. In this market, the market maker quotes cutoff lines for the outcome of a certain future event as “prices,” and bettors bet on whether the event outcome exceeds the cutoff lines. Anonymous bettors with…
Operations Research
John R. Birge , Yifan Feng , N. Bora Keskin , Adam Schultz
We study the profit maximization problem of a market maker in a spread betting market. In this market, the market maker quotes cutoff lines for the outcome of a certain future event as “prices,” and bettors bet on whether the event outcome exceeds the cutoff lines. Anonymous bettors with…
Operations Research
Yifan Feng , René Caldentey , Christopher Thomas Ryan
This paper studies a class of ranking and selection problems faced by a company that wants to identify the most preferred product out of a finite set of alternatives when consumer preferences are a priori unknown. The only information available is that consumer preferences satisfy two key properties: (i)…
Operations Research
Yifan Feng , René Caldentey , Christopher Thomas Ryan
This paper studies a class of ranking and selection problems faced by a company that wants to identify the most preferred product out of a finite set of alternatives when consumer preferences are a priori unknown. The only information available is that consumer preferences satisfy two key properties: (i)…
Operations Research