How to achieve open-endedness in artificial evolutionary systems has recently become a hot topic in the fields of Artificial Life and Artificial Intelligence. In this talk, two different approaches will be presented, both using the Artificial Chemistry framework. The first approach uses heterogeneous swarms of self-propelled particles (Swarm Chemistry) that evolve spontaneously without any explicit fitness evaluation involved. The second approach uses an even simpler model of random-walking particles whose higher-order properties are determined by a hash function (Hash Chemistry). Results obtained and common challenges arising from these models will be discussed.

Urbanization and the decline of access to nature have coincided with a rise of mental health problems. Here, we use Twitter to investigate how mental affect (mood) varies before, during, and after visits to a large urban park system. We find that affect is substantially higher during park visits and remains elevated for several hours following the visit. Visits to Regional Parks, which are greener and have greater vegetative cover, result in a greater increase in affect compared to Civic Plazas and Squares. These results point to the most beneficial types of nature contact for mental health benefits and can be used by urban planners and public health officials to improve the well-being of growing urban populations.

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Modeling the behavior of human agents in complex systems is a difficult task which can often be improved upon by introducing elements of machine learning. As a part of my research, I focus on using DDQN reinforcement learning to model the behavior of human and governmental agents in the Lake Champlain Basin to determine potential behavioral and policy changes in response to projected climatological and environmental scenarios. This talk will focus on some of the methods used for integrating reinforcement learning into agent-based models and preliminary results from my own work.

Typically, in AI and robotics, researchers try to realize intelligent behavior in machines by tuning parameters of a predefined structure (body plan and/or neural network architecture) using evolutionary or learning algorithms. However, these systems tend to be extremely brittle to slight aberrations, as highlighted by the growing deep learning literature on adversarial examples. In this talk, I will show robustness can be achieved by evolving the geometry of soft robots, their control systems, and how their material properties develop in response to certain kinds of interoceptive stimulus during their lifetimes.

Ruptured Abdominal Aortic Aneurysm's (AAA) represent a serious surgical risk, and are responsible for around 4500 deaths a year in the United States alone. The most common treatment method for AAA is by means of endovascular aneurysm repair a non-invasive surgery where a stent graft is placed within the aorta, and while generally successful still carries the risk of complications, typically in the form an an Endoleak. This work seeks to automate the detection of Endoleaks within the larger 3D CTA volume through the application of cascaded convalutional neural networks. Additional goals include segmentation of the Aneurysm region, a useful diagnostic tool in tracking Aneurysm growth over time.

Modularity is becoming increasingly important in design because of the ability to modify one structure without greatly affecting other structures. In the first half of the talk, we will explore the definition of modularity and structures, how to quantify or search for them, and their applications. In the second half, I will go over my work regarding developing modular graphs via evolutionary algorithms.

Despite the infrequent occurrence of cascading power failures, their large sizes and enormous social costs mean that they contribute substantially to the overall risk to society from power failures in the grid. Therefore it is important to accurately capture the risk associated with such events. A cascading event may be triggered by a small subset of k components failing simultaneously or in rapid succession. While most prior work, including our own work into an efficient “random chemistry” method for risk analysis, has assumed that components fail independently, this paper proposes a method for deriving correlated outage probabilities such that pairs of branches that are proximate in space are more likely to fail together than distant ones. Combining random chemistry risk analysis with this approach to correlating outages shows that overall blackout risk can increase substantially with even small correlations between component outages. Results of correlated vs. uncorrelated risk estimates are compared for the 2896 branch Polish Grid test case under various loads.

One of the key challenges in machine learning is feature engineering, the creation of the features or predictors that go into the learning problem. Feature engineering often requires diverse knowledge and experience, and so crowdsourcing the ideation of features is a prime candidate for discovering diverse and useful predictors. We study the problem of supervised learning when workers simultaneously provide both data and predictors (new survey questions) online. This process leads to a distinct pattern of missing values in the data matrix, which needs new, specialized methods in order to analyze the resulting data.

Catastrophic interference occurs when an agent improves in one training instance but becomes worse in other instances. Many methods intended to combat interference have been re- ported in the literature that modulate properties of a neural controller, such as synaptic plasticity or modularity. Here, we demonstrate that adjustments to the body of the agent, or the way its performance is measured, can also reduce catas- trophic interference without requiring changes to the con- troller. Additionally, we introduce new metrics to quantify catastrophic interference. We do not show that our approach outperforms others on benchmark tests. Instead, by more pre- cisely measuring interactions between morphology, fitness, and interference, we demonstrate that embodiment is an im- portant aspect of this problem. Furthermore, considerations into morphology and fitness can combine with, rather than compete with, existing methods for combating catastrophic interference.

This research aims to develop a deep learning-based system to process a stream of road-images in order to detect and classify traffic signs, then localize them on a map by estimating their GPS coordinates. Furthermore, we introduce a new and large-scale dataset to serve as one of the very few benchmarks for US. traffic signs recognition (TSR).

Previous work has shown that robots whose bodies and brains reflect the modularity and regularity present in task environments can be evolved in a subset of the total environments and still retain adequate behavior in the remaining unseen environments. Future work will look at how to give evolution the tools necessary to evolve robots with the correct forms of modularity and regularity without human bias or direction.

Large microbial populations generate competing beneficial mutations. A common assumption has been that evolution acts like a greedy search algorithm, favoring locally optimal competing trajectories. However, prior studies have not fully considered how competition between successive alleles on the same trajectory impacts adaptation rates along, or likelihood of following, individual trajectories. We derive a metric that quantifies the competition between successive alleles along adaptive trajectories and show that this competition governs the rate of evolution in simulations on empirically determined fitness landscapes for proteins involved in drug resistance in two species of malaria parasite and three species of bacteria. We show that rates of adaptation vary by orders of magnitude along different trajectories. In some circumstances, "speedier" trajectories can out-compete "greedier" trajectories. Our findings are shown to have important implications for efforts to manage antimicrobial resistance in real-world settings, and are relevant to the broader intellectual pursuit of predictive evolution on complex adaptive fitness landscapes.

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Many river water quality constituents such as turbidity, suspended sediments, and nutrients are predominantly transported during storm events. The analysis of hydrological systems at event scales helps to characterize the dynamics and flux of such constituents. Hydrological events have commonly been analyzed through study of event concentration-discharge (C-Q) plots and identification of two-dimensional hysteresis loop patterns in the C-Q plots. While effective and informative to some extent, this approach has shortcomings in capturing the temporality of variables, as it ``collapses'' their values as projected on the C-Q plane. This study analyzes the categories of hydrological events using three-dimensional spatiotemporal trajectory plots. Specifically, computational clustering methods are used to categorize the trajectories of "moving points" that represent the measurements from two sensors -- in this study, river discharge and suspended sediment concentration. This in-progress research utilizes data from turbidity-based monitoring of suspended sediment from the Mad River watershed, located in the Lake Champlain Basin in the northeastern United States. The project aims toward building classes of spatiotemporal trajectories and comparison with the existing classes of hysteresis loops that are currently being used for categorizing storm events.

Over the past few decades, human-induced climate change has accelerated at an alarming pace. If the risks of climate change are not well-studied, the impacts to human and environmental systems could be devastating. it is imperative that we are able to conduct accurate assessments of climate change impacts at global, regional, and local scales. However, even the most advanced climate models produce biased output at resolutions too coarse for local impact studies. In this study we use a Bayesian hierarchical spatial model that concomitantly improves the accuracy and downscales climate model projections. First, we will fit the Bayesian model to climate projections and make predictions to actual weather station locations. To downscale the projections to a finer spatial resolution, we will adjust the predictions with elevational lapse rates calculated from historical station data. The bias of the resulting downscaled projections can be quantified by comparing them to actual station data. To correct the bias, we will apply the quantile-mapping technique. Our preliminary results show that, when fitted to simulated spatial data, our Bayesian spatial model outperformed a simple linear model in test-set predictive accuracy, especially when the data exhibited strong spatial autocorrelation.

“Interdisciplinary research” has been a buzzword among universities and funding agencies for quite some time. However, being interdisciplinary also brings a lot of practical challenges. In this talk, I will share some of my own experiences as an interdisciplinary being in academia, including how one could manage/avoid/exploit such challenges to fully entertain this otherwise fun-packed way of life.