Rajan Udwani
University of California-Berkeley, CA·Engineering·Early-career
Many modern services must hand out limited resources the moment requests arrive, without knowing what comes next. Examples include matching riders to shared vehicles, assigning charged batteries to electric vehicles at swap stations, and choosing which ads to show.
- Fields
Engineering
Computer Science
Mathematics & Statistics
- Topics
online algorithms
online resource allocation
algorithm design
- Grants
- 1 known grant, through Jun 2029
What they work on
- Formulate new models of online resource allocation that capture the complex features, objectives and constraints of applications such as shared mobility, electric-vehicle battery swapping and online advertising.
- Identify general structural properties that yield scalable online algorithms that apply broadly and are robust to changing environments, with worst-case performance guarantees.
- Develop methods to analyze adaptive online algorithms that react to random outcomes as they unfold, and test them against leading approaches in numerical experiments.
- Identify and exploit connections between online resource allocation problems and queueing theory, the mathematics of waiting lines and service systems.
More about the research
These are online resource allocation problems. Each decision is made in real time under uncertainty. This project aims to build the next generation of models and algorithms for such problems. The algorithms should handle the complex features, objectives and constraints of real platforms, scale to very large problems, and come with the strongest possible guarantees on performance.
Why it matters
Ride-sharing services, electric-vehicle battery-swapping stations and online advertising platforms must allocate limited resources instantly. Algorithms that are scalable, robust and backed by performance guarantees could help these platforms run more reliably and efficiently as demand changes. This could benefit both operators and the customers they serve.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Chiwei Yan
University of California-Berkeley, CA·Engineering
Develop state-aware demand control models that use real-time vehicle and rider movement to shape demand toward ride-pooling in autonomous fleets such as robotaxis, robovans and urban air mobility.
Seyed Iravani
Northwestern University, IL·Engineering
Develop behavioral and analytical models of how customers react to delay announcements, combining stochastic dynamic programming and queueing theory with prospect theory and econometrics to decide what wait information to give and when.
Vijay Vazirani
University of California-Irvine, CA·Computer Science
Design algorithms for online hypergraph matching, where each match can involve more than two parties, and its generalizations, building on the simple proof of the RANKING algorithm, with applications to ride-sharing and network revenue management.
The school
University of California-Berkeley: 46 labs on PI Pages, $1.1B a year on research, 825 research doctorates a year.
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