Mentor Areas
My work studies different aspects of operating systems, security, privacy, and networking.
Description:
Operating Systems (OSes) facilitate the simultaneous operation of multiple applications by managing a device's hardware, but they rely today on outdated manually crafted heuristic policies that cannot adapt to modern computing advancements. These policies struggle to efficiently support new hardware technologies and the evolving and dynamic demands of applications across platforms with significant inherent complexity like the cloud, personal robots, and mobile access edges. The continuous and manual updating of OSes to match technological progress is costly, time-consuming, and increasingly untenable in the face of rapid innovation.
This project aims to build an intelligent, self-adaptive Operating System (OS) that can optimally support modern applications’ performance and resource needs in diverse scenarios that are highly dynamic and exhibit significant complexity.
In our clean-slate Learning Directed OS, or LDOS, advanced machine learning, not human-crafted heuristics, makes rich data-driven resource management decisions that meet application needs at maximal efficiency, avoiding overprovisioning. Further, a single base LDOS implementation can auto-adapt to broad and different settings, avoiding manual heuristic design and customization.
Preferred Qualifications
Ideal candidate would have the following skills:
- Familiarity with Operating Systems (CIS 3800)
- Familiarity with Machine Learning (ML) concepts (any of the ML classes suffices)
- Interest in diving deeply into the Linux code base
Project Website
Learn more about the researcher and/or the project here. Learning Directed Operating System
Details:
Preferred Student Year
Second-Year, Junior, Senior, First-year
Academic Term
Fall, Spring, Summer
I prefer to have students start during the above term(s).Volunteer
Yes
Yes indicates that faculty are open to volunteers.Paid
Yes
Yes indicates that faculty are open to paying students they engage in their research, regardless of their work-study eligibility.Work Study
Yes
Yes indicates that faculty are open to hiring work-study-eligible students.