Modern public health data contains information about changes in disease dynamics that can have significant downstream benefits if these phenomena can be identified. However, systemic data quality issues hamper automated analysis of these vast data volumes, and there is now far too much data (3-4 million data points/day) for public health data experts to inspect manually as they may have done in the past. This interdisciplinary thesis addresses practical questions about large-scale data monitoring that impact public health data users and are also reflected in the larger public health community. This work has been deployed for over a year and a half at the Delphi Research Group at Carnegie Mellon University, a national public health data curator, where data reviewers have been able to detect approximately 200 significant outbreaks, data issues, or changes in disease dynamics from 15 million new data points weekly.

Disease control experts inspect public health data streams daily for outliers worth investigating, like those corresponding to data quality issues or disease outbreaks. However, they can only examine a few of the thousands of maximally-tied outliers returned by univariate outlier detection methods applied to large-scale public health data streams. To help experts distinguish the most important outliers from these thousands of tied outliers, we propose a new task for algorithms to rank the outputs of any univariate method applied to each of many streams. Our novel algorithm for this task, which leverages hierarchical networks and extreme value analysis, performed the best across traditional outlier detection metrics in a human-expert evaluation using public health data streams. Most importantly, experts have used our open-source Python implementation since April 2023 and report identifying outliers worth investigating 9.1x faster than their prior baseline. Other organizations can readily adapt this implementation to create rankings from the outputs of their tailored univariate methods across large-scale streams.

Automated systems must adapt to evolving environments, yet many struggle with drift phenomena affecting healthcare, finance, and cybersecurity domains.The DELTA workshop addresses this by distinguishing between data and concept drift, aiming to create a practical, human-centric framework for managing drift.The workshop seeks innovative drift detection, prediction, and analysis solutions by uniting researchers and practitioners.DELTA fosters collaboration to advance the understanding and management of drift in dynamic data landscapes by featuring keynotes, paper presentations, interactive sessions, and discussions.

Irregularities in public health data streams (like COVID-19 Cases) hamper data-driven decision-making for public health stakeholders. A real-time, computer-generated list of the most important, outlying data points from thousands of public health data streams could assist an expert reviewer in identifying these irregularities. However, existing outlier detection frameworks perform poorly on this task because they do not account for the data volume or for the statistical properties of public health streams. Accordingly, we developed FlaSH (Flagging Streams in public Health), a practical outlier detection framework for public health data users that uses simple, scalable models to capture these statistical properties explicitly. In an experiment where human experts evaluate FlaSH and existing methods (including deep learning approaches), FlaSH scales to the data volume of this task, matches or exceeds these other methods in mean accuracy, and identifies the outlier points that users empirically rate as more helpful. Based on these results, FlaSH has been deployed on data streams used by public health stakeholders.

Computer science (CS) has historically struggled with issues related to diversity, equity, and inclusion (DEI). Based on how these issues were affecting PhD students in our department (the Carnegie Mellon University CS Department), we identified required DEI education for PhD students as a potentially high-impact approach to improving the PhD student experience in our program. Given that no existing curriculum met the desired criteria, we (PhD students)-alongside many members of the CMU community-developed and implemented CS-JEDI: Justice, Equity, Diversity, and Inclusion in Computer Science. CS-JEDI is a 6-week DEI curriculum that is now taken by all first-year PhD students in our department. This paper covers CS-JEDI's motivation and goals; describes how its evidence-based curriculum is tailored to these goals and to the CS PhD context; and gives a data-driven evaluation of the extent to which CS-JEDI's first offering, in Spring 2022, achieved these goals.

Caching is at the core of most modern communication systems, where caches are used to store content and traffic classification rules. While network components can leverage caching in a cooperative manner, one important aspect of such systems concerns possible dependencies among stored items. A major use case of such dependencies appears in rule placement across software-defined networks (SDNs). Despite the tremendous success of SDNs in datacenters, their wide adoption still poses a key challenge: the packet-forwarding rules in switches require fast and power-hungry memories. Rule tables, which serve as caches, are of limited size in cheap and energy-constrained devices, motivating novel solutions to achieve high hit rates. We leverage device connectivity in the fast data plane, where delays are in the order of few milliseconds, and propose multiple switches to work together to avoid accessing the control plane, where delays are orders of magnitude greater. As a low priority rule in a cache entails caching higher priority rules, we pose the problem of cooperative caching with dependencies. We provide models and algorithms accounting for dependencies among rules implied by existing switch memory types, andlay the foundations of cooperative caching with dependencies.