Novel categories names:['High_Jump', 'Front_Crawl', 'Pole_V ault', 'Hammer_Throw', All experiments are conducted under the 16-shot setting. An incremental bayesian approach tested on 101 object categories. Conditional prompt learning for vision-language models.

In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. Ananya Joshi recently completed her PhD, where she developed a system that experts have used for the past two years to identify respiratory outbreaks (like COVID-19) in large-scale healthcare streams across the United States using her novel algorithms for ranking real-time events from large-scale time series data. In this interview, she tells us more about this project, how healthcare applications inspire basic AI research, and her future plans. When I started my PhD during the COVID-19 pandemic, there was an explosion in continuously-updated human health data. Still, it was difficult for people to figure out which data was important so that they could make decisions like increasing the number of hospital beds at the start of an outbreak or patching a serious data problem that would impact disease forecasting.

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 daily-updated 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.

Agencies, such as the U.S. Census Bureau, release data sets and statistics about groups of individuals that are used as input to a number of critical decision processes. To conform with privacy and confidentiality requirements, these agencies are often required to release privacy-preserving versions of the data. This paper studies the release of differentially private data sets and analyzes their impact on some critical resource allocation tasks under a fairness perspective. The paper shows that, when the decisions take as input differentially private data, the noise added to achieve privacy disproportionately impacts some groups over others. The paper analyzes the reasons for these disproportionate impacts and proposes guidelines to mitigate these effects. The proposed approaches are evaluated on critical decision problems that use differentially private census data.