I would hope, in the proposal, something was said about unintended feature detection, e.g. https://www.newstatesman.com/science-tech/2020/04/how-biased-algorithms-perpetuate-inequality
On 5/4/20 1:19 PM, George Duncan wrote: > Most appropriate topic > > > ---------- Forwarded message --------- > From: *Michelle E Wirtz* <[email protected] > <mailto:[email protected]>> > Date: Mon, May 4, 2020 at 6:29 AM > Subject: Thesis Proposal - Dylan Fitzpatrick - Today - Monday, May 4 at 9am - > via Zoom > > > /Friendly reminder – ____/ > > __ __ > > Hi all,____ > > Please join us today, Monday, May 4, 2020 via Zoom at 9am when Dylan > Fitzpatrick will be presenting his thesis proposal.____ > > *Title:* Predicting Health and Safety: Essays in Machine Learning for > Decision Support in the Public Sector*____* > > *Thesis committee: *Daniel Neill, Rayid Ghani, Wilpen Gorr, Roni Rosenfeld____ > > *__ __* > > *Zoom Link:*____ > > https://cmu.zoom.us/j/95758239810?pwd=RmhFL1hDY3pYUzJTWC9GMzBCdndnUT09____ > > *Meeting ID:* 957 5823 9810 > *Password:* 032643____ > > *Abstract: *Public service agencies are increasingly turning to machine > learning techniques for support in settings where accurate predictions or > characterization of patterns in spatiotemporal data can improve social > conditions. This thesis presents three case studies in which we propose novel > methods to inform operational decisions in the domains of public health and > safety. > > First, we present a subset scan approach for detecting localized and > irregularly shaped anomalous patterns in spatial data. The proposed method > iterates between a penalized fast subset scan and a kernel support vector > machine classifier to accurately detect spatial clusters without imposing > hard constraints on the shape or size of the anomalous pattern. We > demonstrate the performance of this approach in simulated experiments and on > the real-world applications of disease outbreak detection, crime hot-spot > detection, and pothole cluster detection. > > Second, we leverage prescription drug monitoring data to assess risk of > opioid misuse based on individual-level opioid timelines. We introduce a > shape-based clustering framework to evaluate risk of misuse in new > individuals when patient outcomes are unknown. We also develop a new method > for semi-supervised learning with recurrent generative adversarial networks, > designed to assess risk of opioid misuse in new patients when labeled > instances of unsafe drug use are available but sparse. > > Last, we discuss the design, implementation, and evaluation of a > hot-spot-based predictive policing program in Pittsburgh, PA, highlighting > results from a randomized field trial. We find statistically and practically > significant reductions in violent crime counts within treated hot spots, and > find minimal evidence of crime displacement to other areas resulting from > increased patrols to treated areas. ____ > > __ __ > > *Link to paper: > *https://www.dropbox.com/s/h6l151fs7k8uzf5/Fitzpatrick_proposal.pdf?dl=0____ -- ☣ uǝlƃ .-. .- -. -.. --- -- -..-. -.. --- - ... -..-. .- -. -.. -..-. -.. .- ... .... . ... FRIAM Applied Complexity Group listserv Zoom Fridays 9:30a-12p Mtn GMT-6 bit.ly/virtualfriam unsubscribe http://redfish.com/mailman/listinfo/friam_redfish.com archives: http://friam.471366.n2.nabble.com/ FRIAM-COMIC http://friam-comic.blogspot.com/
