Eric Slyman (they/them)

referred works

1. ICLR

   Conditional Emulation of Global Precipitation With Generative Adversarial Networks

   Alexis Ayala, Chris Drazic, Seth Bassetti,  Eric Slyman, Brenna Nieva, Piper Wolters, Kyle Bittner, Claudia Tebaldi, Ben Kravitz, and Brian Hutchinson

   ICLR Workshop on AI for Earth and Space Science 2022

   Abstract PDF

   Climate models encode our knowledge of the Earth system, enabling research on the earth’s future climate under alternative assumptions of how human-driven climate forcings, especially greenhouse gas emissions, will evolve. One important use of climate models is to estimate the impacts of climate change on natural and societal systems under these different possible futures. Unfortunately, running many simulations on existing models is extremely computationally expensive. These computational demands are particularly problematic for characterizing extreme events, which are rare and thus demand numerous simulations in order to precisely estimate the relevant climate statistics. In this paper we propose an approach to generating realistic global precipitation requiring orders of magnitude less computation, using a conditional generative adversarial network (GAN) as an emulator of an Earth System Model (ESM). Specifically, we present a GAN that emulates daily precipitation output from a fully coupled ESM, conditioned on monthly mean values. The GAN is trained to produce spatio-temporal samples: 28 days of precipitation in a 92x144 regular grid discretizing the globe. We evaluate the generator by comparing generated and real distributions of precipitation metrics including average precipitation, average fraction of dry days, average dry spell length, and average precipitation above the 90th percentile, finding the generated samples to closely match those of real data, even when conditioned on climate scenarios never seen during training.

2. AAAI

   Fine-Grained Classroom Activity Detection from Audio with Neural Networks

   Eric Slyman, Chris Daw, Morgan Skrabut, Ana Usenko, and Brian Hutchinson

   AAAI Workshop on AI for Education 2022

   Abstract PDF Code Video

   Instructors are increasingly incorporating student-centered learning techniques in their classrooms to improve learning outcomes. In addition to lecture, these class sessions involve forms of individual and group work, and greater rates of student-instructor interaction. Quantifying classroom activity is a key element of accelerating the evaluation and refinement of innovative teaching practices, but manual annotation does not scale. In this manuscript, we present advances to the young application area of automatic classroom activity detection from audio. Using a university classroom corpus with nine activity labels (e.g., "lecture," "group work," "student question"), we propose and evaluate deep fully connected, convolutional, and recurrent neural network architectures, comparing the performance of mel-filterbank, OpenSmile, and self-supervised acoustic features. We compare 9-way classification performance with 5-way and 4-way simplifications of the task and assess two types of generalization: (1) new class sessions from previously seen instructors, and (2) previously unseen instructors. We obtain strong results on the new fine-grained task and state-of-the-art on the 4-way task: our best model obtains frame-level error rates of 6.2%, 7.7% and 28.0% when generalizing to unseen instructors for the 4-way, 5-way, and 9-way classification tasks, respectively (relative reductions of 35.4%, 48.3% and 21.6% over a strong baseline). When estimating the aggregate time spent on classroom activities, our average root mean squared error is 1.64 minutes per class session, a 54.9% relative reduction over the baseline.

3. NOAA

   Conditioned Emulation of Global Climate Models With Generative Adversarial Networks

   Alex Ayala, Chris Drazic,  Eric Slyman, Piper Wolters, Brenna Nieva, Brian Hutchinson, Claudia Tebaldi, and Ben Kravitz

   NOAA Workshop on Leveraging AI in Envrionmental Sciences 2021

   Abstract Video

   Climate models encapsulate our best understanding of the Earth system, allowing research to be conducted on its future under alternative assumptions of how human-driven climate forces are going to evolve. An important application of climate models is to provide metrics of mean and extreme climate changes, particularly under these alternative future scenarios, as these quantities drive the impacts of climate on society and natural systems. Because of the need to explore a wide range of alternative scenarios and other sources of uncertainties in a computationally efficient manner, climate models can only take us so far, as they require significant computational resources, especially when attempting to characterize extreme events, which are rare and thus demand long and numerous simulations in order to accurately represent their changing statistics. Here we demonstrate the capabilities of a Generative Adversarial Network (GAN) emulating a climate model. Our GAN is trained to produce spatio-temporal samples of precipitation and temperature conditioned on spatial monthly averages. We condition our GAN on several climate scenarios and task the generator with producing new realizations. These realizations are then evaluated under several climate metrics (SDII, dryness measures, etc.). Our trained GANs can generate realizations at a vastly reduced computational expense, compared to large ensembles of climate models, which greatly aids in estimating the statistics of extreme events.

4. W2D2S2

   Fine-Grained Classroom Activity Detection

   Eric Slyman, Chris Daw, Morgan Skrabut, Ana Usenko, and Brian Hutchinson

   Western Washington Data-driven Discovery Seminar Series 2021

   Abstract

   Instructors are increasingly incorporating student-centered learning techniques in their classrooms to improve learning outcomes. In addition to lecture, these class sessions involve forms of individual and group work, and greater rates of student-instructor interaction. Quantifying classroom activity is a key element of accelerating the evaluation and refinement of innovative teaching practices, but manual annotation does not scale. In this work, we present advances to the young application area of automatic classroom activity detection from audio. Using a university classroom corpus with nine activity labels (e.g., "lecture,” "group work,” "student question”), we propose and evaluate deep fully connected, convolutional, and recurrent neural network architectures, comparing the performance of mel-filterbank, prosodic, and self-supervised acoustic features. We compare 9-way classification performance with 5-way and 4-way simplifications of the task and assess two types of generalization: (1) new class sessions from previously seen instructors and (2) previously unseen instructors. We obtain strong results on the new fine-grained task and state-of-the-art on the 4-way task: our best model obtains frame-level error rates of 6.2%, 7.7% and 28.0% when generalizing to unseen instructors for the 4-way, 5-way and 9-way classification tasks, respectively (relative reductions of 35.4%, 48.3% and 21.6% over a strong baseline) and examine the effects of ensembleing and decoding its outputs. When estimating the aggregate time spent on classroom activities, our average root mean squared error is 1.64 minutes, a 54.9% relative reduction over the baseline.