More Like this

1. Public Health Disaster Research Award Program Evaluation Questionnaire

   https://doi.org/10.17603/ds2-8dtb-9k68  

   Adams, Rachel; Mordy, Meghan; Peek, Lori (January 2023, Designsafe-CI)

   This survey instrument was administered to evaluate the impact of the Public Health Disaster Research Award Program. It includes closed- and open-ended questions about the following topics: (1) the skills, knowledge, and connections that research team members acquired during the award period; (2) the publications, grant proposals, educational materials, and/or traineeships that team members developed post-award using research findings; (3) the collaborations with community partners, public health departments, and other hazards and disasters scholars or practitioners that team members built post-award; and (4) how team members applied their research findings to practice by developing new public health tools or promoting changes to public health policies or programs. 

   more » « less
2. An Interview with Dr. Thorsten Joachims, Winner of ACM SIGKDD 2020 Innovation Award

   https://doi.org/10.1145/3447556.3447560  

   Joachims, Dr. Thorsten (January 2021, ACM SIGKDD Explorations Newsletter)

   Thorsten Joachims, Ph.D. is the recipient of the 2020 ACM SIGKDD Innovation Award, the highest honor for technical excellence in the field of knowledge discovery and data mining. The Innovation Award is conferred annually on an individual or group of collaborators whose outstanding technical innovations have greatly influenced the direction of research and development in the field. 

   more » « less
3. Reports From The Frontier-Strategies to Design Stable Layered Oxide Cathodes for Na-Ion Batteries

   https://doi.org/10.1149/2.F10224IF  

   Rahman, Muhammad Mominur; Lin, Feng (December 2022, The Electrochemical Society Interface)

   Cushing, Scott (Ed.)

   This feature page is intended to let ECS award winning students and post-docs write a primary-author perspective on their field, their work, and where they believe things are going. This month we highlight the work of Muhammad Mominur Rahman, the Battery Division 2021 Student Research Award winner. 

   more » « less
4. Assortment Planning for Recommendations at Checkout Under Inventory Constraints

   https://doi.org/10.1287/moor.2023.1357  

   Chen, Xi; Ma, Will; Simchi-Levi, David; Xin, Linwei (February 2024, Mathematics of Operations Research)

   In this paper, we consider a personalized assortment planning problem under inventory constraints, where each arriving customer type is defined by a primary item of interest. As long as that item is in stock, the customer adds it to the shopping cart, at which point the retailer can recommend to the customer an assortment of add-ons to go along with the primary item. This problem is motivated by the new “recommendation at checkout” systems that have been deployed at many online retailers, and it also serves as a framework that unifies many existing problems in online algorithms (e.g., personalized assortment planning, single-leg booking, and online matching with stochastic rewards). In our problem, add-on recommendation opportunities are eluded when primary items go out of stock, which poses additional challenges for the development of an online policy. We overcome these challenges by introducing the notion of an inventory protection level in expectation and derive an algorithm with a 1/4-competitive ratio guarantee under adversarial arrivals. Funding: This work was supported by the Adobe Data Science Research Award and the Alibaba Innovation Research Award. L. Xin was partly supported by the National Science Foundation (NSF) [Award CMMI-1635160], X. Chen was supported by the NSF [CAREER Award IIS-1845444]. W. Ma and D. Simchi-Levi were supported by the Accenture and MIT Alliance in Business Analytics. 

   more » « less
5. Newton-CG Methods for Nonconvex Unconstrained Optimization with Hölder Continuous Hessian

   https://doi.org/10.1287/moor.2023.0356  

   He, Chuan; Huang, Heng; Lu, Zhaosong (May 2025, Mathematics of Operations Research)

   In this paper, we consider a nonconvex unconstrained optimization problem minimizing a twice differentiable objective function with Hölder continuous Hessian. Specifically, we first propose a Newton-conjugate gradient (Newton-CG) method for finding an approximate first- and second-order stationary point of this problem, assuming the associated Hölder parameters are explicitly known. Then, we develop a parameter-free Newton-CG method without requiring any prior knowledge of these parameters. To the best of our knowledge, this method is the first parameter-free second-order method achieving the best-known iteration and operation complexity for finding an approximate first- and second-order stationary point of this problem. Finally, we present preliminary numerical results to demonstrate the superior practical performance of our parameter-free Newton-CG method over a well-known regularized Newton method. Funding: C. He was partially financially supported by the Wallenberg AI, Autonomous Systems and Software Program funded by the Knut and Alice Wallenberg Foundation. H. Huang was partially financially supported by the National Science Foundation [Award IIS-2347592]. Z. Lu was partially financially supported by the National Science Foundation [Award IIS-2211491], the Office of Naval Research [Award N00014-24-1-2702], and the Air Force Office of Scientific Research [Award FA9550-24-1-0343]. 

   more » « less