Using funds from the National Science Foundation grant (under NSF’s TRIPODS and Convergence Programs), IFDS is funding five Research Assistants this semester to collaborate across departments on IFDS research. Each person is advised by one primary and one secondary adviser, all of them members of IFDS and all affiliated with …
MADLab/AFRl meeting co-sponsored by IFDS.
The new cluster will expand on existing strengths of the university in this critical area, contribute to the growth of new programs and institutes on campus, and promote synergy between the contributing disciplines of statistics, mathematics and computer science.
Sony Faculty Innovation Award provides up to $100K in funds to conduct pioneering research.
Using funds from the National Science Foundation grant (under NSF’s TRIPODS and Convergence Programs), IFDS is funding five Research Assistants this semester to collaborate across departments on IFDS research. Each person is advised by one primary and one secondary adviser, all of them members of IFDS and all affiliated with …
The mathematical foundations of Reinforcement Learning (RL), beginning with Multi-Armed Bandits (MAB)
Each are advised by two IFDS faculty and collaborate across departments.
Respondent-driven sampling is a popular network-based approach to
sample hard-to-reach populations, where participants refer contacts
into the sample through a coupon system. It has been particularly
useful in HIV research where individuals most at risk (e.g., people
who inject drugs) are unlikely to participate in conventional sampling
schemes. Many major health organizations, including the Centers for
Disease Control and the World Health Organization, employ this
approach to quantify the prevalence of HIV in these at-risk groups.
Unfortunately this type of network sampling suffers from a significant
drawback: because referred contacts often share similar
characteristics, samples are highly correlated which can lead to
exceedingly variable estimates.
In work that just appeared in the Proceedings of the National Academy
of Sciences, IFDS members Sebastien Roch and Karl Rohe introduced a
new estimation technique for respondent-driven sampling with a
substantially reduced variability…
A new approach to climate data analysis hopes to improve regional forecasts.
IFDS member Sebastien Roch and former mathematics Ph.D. student Kun-Chieh (Jason) Wang won Best Paper Award at the prestigious 22nd Annual International Conference on Research in Computational Molecular Biology (RECOMB) 2018.