Fwd: Thesis Proposal - Dylan Fitzpatrick - Today - Monday, May 4 at 9am - via Zoom

[George Duncan-2]

Most appropriate topic 

---------- Forwarded message ---------
From: Michelle E Wirtz <[hidden email]>
Date: Mon, May 4, 2020 at 6:29 AM
Subject: Thesis Proposal - Dylan Fitzpatrick - Today - Monday, May 4 at 9am - via Zoom
To: [hidden email] <[hidden email]>, [hidden email] <[hidden email]>, Daniel Neill <[hidden email]>, Roni Rosenfeld <[hidden email]>
Cc: Diane L Stidle <[hidden email]>

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

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[gepr]

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* <[hidden email] <mailto:[hidden email]>>
> 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____

--
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Zoom Fridays 9:30a-12p Mtn GMT-6  bit.ly/virtualfriam
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