Proud to be part of a team to receive a NSF RAPID on graph predictive models for COVID-19 modeling. Led by Gautam Dasarathy (EE), the team additionally includes Doug Cochran (Math), Huan Liu (CS), Patricia Solis (Geography, KER).
Honored to be invited for several talks this spring and summer. SIAM Mathematics of Data Science, DiffCVML workshop in conjunction with CVPR 2020, Math Department Seminar at the University of Notre Dame, and the TGDA@Ohio-State NSF Tripods Institute. Thank you 🙏
Humbled to announce that a team comprising me (AME + ECE), and Matt Buman (CHS), Anuj Srivastava (FSU, applied math), were just awarded a NIH R01. The title of the grant is “Dense life-log health analytics from wearable sensors using functional analysis and Riemannian geometry”. Very excited !!
Moving away from standard parametric priors, our approach using non-parametric priors helps GANs counter distribution-mismatch problems. Just been accepted at the Intl. Conference on Machine Learning (ICML) 2019. Congrats to Rajhans Singh and team. Opens potentially new ways to enforce constraints on GAN outputs by designing priors more deliberately. Paper and code coming soon.
Happy to report that GML grad student Anirudh Som and visiting grad student Ankita Shukla (IIIT-Delhi), have both been selected to participate in the NSF-CBMS workshop on topological methods. Both have also received travel support to attend. Thank you NSF and CBMS. https://blogs.cofc.edu/cbms-tda2019/