Bijaya Adhikari
University of Iowa, IA·Computer Science·Early-career
Patients in hospitals can pick up infections that have nothing to do with why they were admitted. These infections often come from dangerous or antimicrobial-resistant microbes.
- Fields
Computer Science
Public Health & Epidemiology
Mathematics & Statistics
- Topics
healthcare-associated infections
hospital-acquired infections
nosocomial infections
- Grants
- 1 known grant, through Apr 2030
What they work on
- Formulate a new class of online optimization problems for healthcare-associated infection (HAI) interventions and design online algorithms, which decide as data arrive, with worst-case approximation and hardness guarantees.
- Develop learning-augmented online algorithms that incorporate machine-learning forecasts of future infections, so that resource recommendations improve as forecasting accuracy improves.
- Infer unobserved infection events, such as asymptomatic cases and surface contamination, from electronic medical records and incorporate them into real-time intervention algorithms.
- Set standardized benchmarks for evaluating data-driven algorithms for HAI intervention, inference of hidden infections and forecasting.
More about the research
These healthcare-associated infections (HAIs) occur in about 4.5 of every 100 admissions and cost $28–45 billion each year. Timely interventions, such as deciding where to send limited cleaning, testing or isolation resources, can reduce that toll. The hard part is making good decisions in real time, as an outbreak unfolds and before the full picture is known. This project develops computational methods to choose effective interventions using electronic medical records.
Why it matters
Healthcare-associated infections harm patients and cost billions each year. Real-time algorithms that use hospital records, infection forecasts and estimates of hidden cases could help hospitals deploy limited resources before and during outbreaks. This could reduce infection risk, secondary outbreaks and the spread of antimicrobial-resistant organisms.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Rajmohan Rajaraman
Northeastern University, MA·Computer Science
Design communication-aware algorithms for scheduling precedence-constrained jobs, which are tasks that depend on earlier tasks. The jobs run on networks of machines with differing speeds, capabilities and delays, and the algorithms use new linear-programming techniques.
Paul Valiant
Purdue University, IN·Computer Science
Design error-correcting codes that withstand random or adversarial insertion and deletion errors while supporting local decoding, which recovers part of a message by reading only a small piece of the stored data.
Rajan Udwani
University of California-Berkeley, CA·Engineering·Early-career
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.
The school
University of Iowa: 32 labs on PI Pages, $703M a year on research, 418 research doctorates a year.
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