Reminder: Maria's defense at 11am this Monday

[Artur Dubrawski]

On Wed, Apr 29, 2020 at 12:39 PM Artur Dubrawski <awd at cs.cmu.edu> wrote:

> And the details of Maria's defense on Monday:
>
> Please join us on Monday, May 4 via Zoom at 11am when Maria De-Arteaga (ML
> & Public Policy Joint PhD) will be defending her thesis.
>
> *Title:* Machine Learning in High-Stakes Settings: Risks and Opportunities
>
> *Thesis committee:* Artur Dubrawski (Co-Chair), Alexandra Chouldechova
> (Co-Chair), Roni Rosenfeld, Adam Tauman Kalai (Microsoft Research)
>
> *Zoom Link:*
> https://cmu.zoom.us/j/94967473449?pwd=b09lL29qblg1ZU5BWHZhVDB2NjFjQT09
>
> *Meeting ID:* 949 6747 3449
> *Password:* 000312
>
> *Abstract: * Machine learning (ML) is increasingly being used to support
> decision-making in critical settings, where predictions have potentially
> grave implications over human lives. Examples include healthcare, hiring,
> child welfare, and the criminal justice system. In this thesis, I study the
> risks and opportunities of machine learning in high-stakes settings. In the
> first chapter I focus on opportunities of ML to support experts' decisions
> when dealing with high-resolution multivariate data, a type of data that is
> particularly hard for humans to interpret. I propose methodology to
> discover latent complex multivariate correlation structures and illustrate
> its use in two different domains: (1) identification of radioactive threats
> in nuclear physics, and (2) prediction of neurological recovery of comatose
> patients in healthcare. In the second chapter, focused on algorithmic
> fairness, I demonstrate how societal biases encoded in historical data may
> be reproduced and amplified by ML models, and introduce a new algorithm to
> mitigate biases without assuming access to protected attributes. Finally,
> in the third chapter I characterize challenges that arise from the
> limitations of available labels in decision support contexts--such as the
> selective labels problem and omitted payoff bias--and propose methodology
> to estimate and leverage human consistency to improve algorithmic
> recommendations and human-machine complementarity.
>
>
> *Paper Link:*
> https://www.dropbox.com/s/h449z85r6nls8oc/Dissertation_DeArteaga.pdf?dl=0
>
>
> On Tue, Apr 28, 2020 at 11:28 AM Artur Dubrawski <awd at cs.cmu.edu> wrote:
>
>> Dear Autonians,
>>
>> Please join me in attending 2 (yes, two) excellent virtual
>> presentations by our own Maria De-Arteaga and Chao Liu, both of which are
>> scheduled for the next week.
>>
>> (btw, I do not remember when was the last time we had more than one
>> doctoral thesis defense scheduled in one week at the Auton Lab...)
>>
>> Maria's defense will be on Monday May 4th at 11am,
>> The official announcement will be shared soon.
>>
>> Chao's defense is scheduled for Thursday May 7th at noon.
>> The official announcement with the zoom link is included below.
>>
>> Please help seeing these outstanding colleagues move to the next levels
>> of their professional lives by attending these presentations and cheering
>> for them :)
>>
>> Cheers,
>> Artur
>>
>> -----
>>
>> Date:  07 May 2020
>>
>> Time:  12:00 p.m.
>>
>> Place: *Virtual Presentation* https://cmu.zoom.us/j/2623852919
>>
>> Type:  Ph.D. Thesis Defense
>>
>> Who:  Chao Liu
>>
>> Title:  Vision with Small Baselines
>>
>>
>> Abstract:
>> 3D sensing with portable imaging systems is becoming more and more
>> popular in computer vision applications such as autonomous driving, virtual
>> reality, robotics manipulation and surveillance, due to the decreasing
>> expense and size of RGB cameras. Despite the compactness and portability of
>> the small baseline vision systems, it is well-known that the uncertainty in
>> range finding using multiple views and the sensor baselines are inversely
>> related. On the other hand, besides compactness, the small baseline vision
>> system has its unique advantages such as easier correspondence and large
>> overlapping regions across views.
>>
>> The goal of this thesis is to develop computational methods and small
>> baseline imaging systems for 3D sensing of complex scenes in real world
>> conditions. Our design principle is to physically model the scene
>> complexities and specifically infer the uncertainties for the images
>> captured with small baseline setups.
>>
>> With this design principle, we make four contributions. In the first
>> contribution, we propose a two-stage near-light photometric stereo method
>> using a small (6 cm diameter) LED ring. The imaging system is compact
>> compared to traditional photometric stereo systems. In the second
>> contribution, we develop an algorithm to simultaneously estimate the
>> occlusion pattern and depth for thin structures from a focal image stack,
>> which is obtained either by varying the focus/aperture of the lens or
>> computed from a one-shot light field image. As the third contribution, we
>> propose a  learning-based method to estimate per-pixel depth and its
>> uncertainty continuously from a monocular video stream, with small camera
>> baselines across adjacent frames. These depth probability volumes are
>> accumulated over time as more incoming frames are processed sequentially,
>> which effectively reduces depth uncertainty and improves accuracy,
>> robustness, and temporal stability. Finally, using a pair of high
>> resolution camera and laser projector, we develop a high spatial resolution
>> Diffuse Optical Tomography (DOT) system that can detect accurate boundaries
>> and relative depth of heterogeneous structures up to a depth of 8mm below a
>> highly scattering medium such as whole milk.
>>
>> We showcase the application of a small baseline vision system for in-vivo
>> micro-scale 3D reconstruction of capillary veins and develop a system for
>> real-time analysis of microvascular blood flow for critical care. We
>> believe that the computational methods developed in this thesis would find
>> more applications of compact 3D sensing under challenging conditions.
>>
>>
>>
>> Thesis Committee Members:
>>
>> Srinivasa G. Narasimhan, Co-chair
>> Artur W. Dubrawski, Co-chair
>> Aswin C. Sankaranarayanan
>> Manmohan Chandraker, University of California, San Diego
>>
>>
>> A copy of the thesis document is available at:
>>
>> https://www.dropbox.com/s/cz75koh96ragy4x/thesis-small-baseline.pdf?dl=0
>>
>>
>>
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