FPO

Andrew Jones will present his FPO "Probabilistic models for structured biomedical data" on Friday, December 16, 2022 at 9:30 AM in COS 402 and Zoom.

Location: Zoom link: https://princeton.zoom.us/j/95479201507

The members of Andrew’s committee are as follows:
Examiners: Barbara Engelhardt (Adviser), Ben Raphael, Adji Bousso Dieng
Readers: Jonathan Pillow, Olga Russakovsky

A copy of his thesis will be available, upon request, two weeks before the FPO.  Please email gradinfo@cs.princeton.edu if you would like a copy of the thesis.

Everyone is invited to attend his talk.

Abstract follows below:

Modern biomedical datasets—from molecular measurements of gene expression to pathology images—hold promise for discovering new therapeutics and probing basic questions about the behavior of cells. Thoughtful statistical modeling of these complex, high-dimensional data is crucial to elucidate robust scientific findings. A common assumption in data analysis that the data samples are independent and identically distributed. However, this assumption is nearly always violated in practice. This is especially true in the setting of biomedical data, which often exhibit some amount of structure, such as subgroups of patients, cells, or tissue types or other correlation structure among the samples.

In this body of work, I propose data analysis and experimental design frameworks to account for several types of highly-structured biomedical data. These approaches, which take the form of Bayesian models and associated inference algorithms, are specifically tailored for datasets with group structure, multiple data modalities, and spatial organization of samples.

In the first line of work, I propose a model for contrastive dimension reduction that decomposes the sources of variation in samples that belong to case and control conditions. Second, I propose a computational framework for aligning spatially-resolved genomics data into a common coordinate system that accounts for spatial correlation among the samples and models multiple data modalities. Finally, I propose a family of methods for optimally designing spatially-resolved genomics experiments that is tailored to the highly-structured data collection process of these studies. Together, this body of work advances the field of biomedical data analysis by developing models that directly exploit common types of structure within these data and demonstrating the advantage of these modeling approaches across an array of data types.

Zhuqi Li will present his FPO "Cross-layer Optimization for Video Delivery on Wireless Networks" on Thursday, January 26, 2023 at 11am in CS 302

The members of his committee are as follows: 

Examiners: Kyle Jamieson (adviser), Ravi Netravali, and Jennifer Rexford

Readers: Amit Levy and Victor Bahl (Microsoft)

Zoom link: https://princeton.zoom.us/j/6871275896 

A copy of his thesis will be available before the FPO upon request.  Please email gradinfo@cs.princeton.edu if you would like a copy of the thesis.

Everyone is invited to attend his talk.

Abstract follows below:

Mobile video applications have gained increasing popularity and become part of everyone’s daily experience. The quality of video has a significant impact on both the quality of users’ experience for video streaming and the accuracy of video analytic systems, which further impact the application revenue.

The challenge to building a consistently high-quality video delivery system lies in two aspects. On the application side, the emerging new video applications are evolving to become more user-interactive, where existing prefetch and buffering algorithms cannot work properly. On the network side, the wireless network itself is fundamentally dynamic and unreliable due to the multipath effect and interference on the wireless channel.

In this thesis, we present cross-layer optimizations from the application layer, network layer, and physical layer to improve the quality of video streaming over wireless network with the design and implementation of the following systems: Dashlet, a short video streaming system tailored for a high quality of experience by adapting to dynamic user actions. Dashlet proposes a novel out-of-order video chunk pre-buffering mechanism that leverages a simple, non machine learning-based model of users’ swipe statistics to determine the pre-buffering order and bitrate. Spider, a multi-hop, millimeter-wave (mmWave) wireless relay network design to maximize the video analytic accuracy for the delivered video. Spider integrates a low-latency Wi-Fi control plane with a mmWave relay data plane, allowing agile re-routing around blockages. Spider proposes a novel video bit-rate allocation algorithm coupled with a scalable routing algorithm that maximizes application-layer video analytics accuracy. LAIA, a system to programmable control the wireless channel so that the wireless network can achieve consistently high throughput for robust video delivery. With the programmable interface to control the wireless channel, LAIA can improve wireless channels on the fly for single- and multi-antenna links, as well as nearby networks operating on adjacent frequency bands.

Putting it together, this thesis demonstrates a set of optimizations in different layers in through network stack for building a high quality and robustness wireless video delivery system. The extensive evaluation demonstrates a significant improvement on both quality of experience for video streaming and accuracy for video analytics.

Adviser: Matt Weinberg

Readers: Sam Taggart, Mark Braverman

Examiners: Olga Russakovsky, Huacheng Yu 

Zoom link: https://princeton.zoom.us/j/93585578182

Meeting ID: 935 8557 8182

Title:  "User-Centered Algorithmic Mechanism Design"

Abstract:

In algorithmic mechanism design, classical desiderata define standards used to evaluate the performance of an algorithm or mechanism. Examples of such desiderata are efficiency, revenue maximization, and strategyproofness. These desiderata are oftentimes seller-centered and tend to create negative externalities that harm the users targeted by the market. Examples of such externalities are not treating the users equally, not protecting against adversarial sellers, and not maximizing user utility. In this thesis, we explore the frontier between user-centric and seller-centric performance at different levels of leverage offered by central regulation. We ask: how can we design new solutions that prioritize user-centered desiderata while maintaining the pre-existing seller-centered desiderata or without too great a cost to them? We present theoretical frameworks at three levels of intervention“protected”goods, and significant regulation for essential and public goods. Our first framework is credible auctions. We consider a revenue-maximizing seller with a single item for sale to multiple users with independent-and-identicallydistributed valuations. In this work, assuming the existence of cryptographically-secure commitment-schemes, we identify a new single-item auction that is credible, strategyproof, revenue-optimal, and terminates in constant rounds in expectation for all distributions with finite monopoly price. Our second framework is fair and symmetric ad auctions. We consider a revenuemaximizing seller with multiple items for sale to a single population of additive buyers with independent item values. We motivate this via fairness in ad auctions where items correspond to (views from) users, and equally-qualified users from different demographics should be shown the same desired ad at equal rates. We show that bundling all items together achieves a constant-factor approximation to the revenue-optimal itemsymmetric mechanism; as any item-symmetric auction is also fair. Observe that in this domain, bundling all items together corresponds to concealing all demographic data. i Our last framework is markets with mandatory purchase. We study a problem inspired by regulated health-insurance markets, and investigate whether limiting entry of providers increases or decreases the utility(welfare minus revenue) of users who purchase from the providers, specifically in settings where the outside option of “purchasing nothing” is prohibitively undesirable.

Adviser:  Arvind Narayanan

Readers: Adji Dieng, Barbara Engelhardt

Examiners: Andrés Monroy-Hernández, Matt Salganik

Title: "Human-machine Collaboration in Real-World Machine-Learning Applications”

Abstract:

Automation tools like machine learning are a necessity in our big data world. Thanks to the Internet and advancements in all facets of computer and storage technology, almost everyone has a voice in the Internet connected world. However, there are still very real physical limits in our physical world. This dichotomy—the seemingly limitless nature of technology enabled data colliding with the physical limits of the real world—has made automation tools a necessity, and predictive models powered by machine learning algorithms are one such tool. The promise of machine learning to accurately predict future human behavior and human preferences has lead practitioners and researchers alike to apply machine learning automation tools to tasks such as product recommendations and speculatory activities such as long term job applicant success. However, due to the mercurial nature of humans, developing mathematical intermediaries to attempt to model and predict human behavior is challenging and not a straight-forward task. One way of harnessing the power of machine-learning backed automation to help reduce the scale of many real-world applications in more challenging domain settings is by having humans and machines collaborating in non-trivial ways. In this dissertation, we delineate the various ways in which humans and machines collaborate in challenging real-world applications. Moreover, we highlight three specific ways in which we can use human-machine collaboration to keep or increase utility and reduce real-world harm when using these systems in the wild: (i) humans enabling computers with domain specific knowledge, (ii) computers providing humans with algorithmic explanations, (iii) humans and computers working together in decision making.

Committee: Margaret Martonosi (Advisor) Frederic Chong (UChicago, reader) Kyle Jamieson (reader) Amit Levy (examiner) Steve Lyon (examiner) Abstract: (forthcoming)

 "A Serverless Architecture for Application-level Orchestration" 

This thesis examines the problem of building large-scale applications using the serverless computing model and proposes decentralized, application-level orchestration for serverless workloads. Comparing with standalone orchestrators, the state-of-the-art solution to building large-scale serverless applications, we demonstrate that application-level orchestration is possible and practical using just the basic APIs of existing serverless infrastructures, and that it benefits both cloud users and cloud providers. It empowers cloud users with the flexibility of application-specific optimizations. It frees cloud providers from hosting and maintaining yet another performance-critical service. Furthermore, the performance and efficiency of application-level orchestration improve as the underlying systems develop. Thus, cloud providers can direct freed-up resources to core services in their serverless infrastructure and automatically reaps the benefits of a better orchestrator. This thesis describes mechanisms and implementations that help realize the goal of applicationlevel orchestration. In particular, we explain the necessity and challenges of decentralizing orchestration, and present a system for decentralized orchestration named Unum. Unum introduces an intermediate representation (IR) language to express execution graphs using only node-local information to decentralize the orchestration logic of applications. Unum implements orchestration as a library that runs in-situ with user-defined FaaS functions, rather than as a standalone service. The library relies on a minimal set of existing serverless APIs—function invocation and a few basic datastore operations—that are common across cloud platforms. Unum ensures workflow correctness despite multiple executions of non-deterministic functions by using checkpoints to commit to exactly one output for a function invocation. Our results show that a representative set of applications scale better, run faster, and cost significantly less with Unum than a state-of-the-art centralized orchestrator. We also show that Unum’s IR allows hand-tuned applications to run faster by using application-specific optimizations and supporting a richer set of application patterns. We hope the results in this thesis inspire cloud practitioners to reconsider the approach of supporting new functionalities by simply adding more services to the cloud infrastructure. And we hope to encourage the building of other application-level orchestration systems from the serverless community.

Adviser: Amit Levy

Readers: Wyatt Lloyd, Ravi Netravali

Examiners: Mike Freedman, Jennifer Rexford