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University Calendar
Events for April
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PhD Defense - Shahrzad Gholami
Wed, Apr 03, 2019 @ 10:30 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
Ph.D. Defense - Shahrzad Gholami
Wed, April 3, 2019
10:30 AM - 12:00 Noon
Location: EEB 132
Title:
Predicting and Planning against Real-world Adversaries: An End-to-end Pipeline to Combat Illegal Wildlife Poachers on a Global Scale
PhD Candidate: Shahrzad Gholami
Date, Time, and Location: Wednesday, April 3, 2019 at 10:30 am in EEB 132
Committee: Prof. Milind Tambe (chair), Prof. Aram Galstyan, and Prof. Emilio Ferrara, Prof. Richard John, Prof. Sze-Chuan Suen
Abstract:
Security is a global concern and a unifying theme in various security projects is strategic reasoning where the mathematical framework of machine learning and game theory can be integrated and applied. For example, in the environmental sustainability domain, the problem of protecting endangered wildlife from attacks (i.e., poachers' strikes) can be abstracted as a game between defender(s) and attacker(s). Applying previous research on security games to sustainability domains (denoted as Green Security Games) introduce several novel challenges that I address in my thesis to create computationally feasible and accurate algorithms in order to model complex adversarial behavior based on the real-world data and to generate optimal defender strategy. My thesis provides four main contributions to the emerging body of research in using machine learning and game theory framework for the fundamental challenges existing in the environmental sustainability domain, namely (i) novel spatio-temporal and uncertainty-aware machine learning models for complex adversarial behavior based on the imperfect real-world data, (ii) the first large-scale field test evaluation of the machine learning models in the adversarial settings concerning the environmental sustainability, (iii) a novel multi-expert online learning model for constrained patrol planning, and (iv) the first game theoretical model to generate optimal defender strategy against collusive adversaries. In regard to the first contribution, I developed bounded rationality models for adversaries based on the real-world data that account for the naturally occurring uncertainty in past attack evidence collected by defenders. To that end, I proposed two novel predictive behavioral models, which I improved progressively. The second major contribution of my thesis is a large-scale field test evaluation of the proposed adversarial behavior model beyond the laboratory. Particularly, my thesis is motivated by the challenges in wildlife poaching, where I directed the defenders (i.e., rangers) to the hotspots of adversaries that they would have missed. During these experiments across multiple vast national parks, several snares and snared animals were detected, and poachers were arrested, potentially more wildlife saved. The algorithm I proposed, that combines machine learning and game-theoretic patrol planning is planned to be deployed at 600 national parks around the world in the near future to combat poaching. The third contribution in my thesis introduces a novel multi-expert online learning model for constrained and randomized patrol planning, which benefits from several expert planners where insufficient or imperfect historical records of past attacks are available to learn adversarial behavior. The final contribution of my thesis is developing an optimal solution against collusive adversaries in security games assuming both rational and boundedly rational adversaries. I conducted human subject experiments on Amazon Mechanical Turk involving 700 human subjects using a web-based game that simulates collusive security games.
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Defense - Jens Windau
Thu, Apr 18, 2019 @ 03:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
University Calendar
Ph.D. Defense - Jens Windau
Thu, April 18, 2019
3:00 PM - 4:30 PM
Location: MCB 102
Title:
Smart Monitoring and Autonomous Situation Classification of Humans and Machines
PhD Candidate: Jens Windau
Date, Time, and Location: Thursday, April 18, 2019 at 3:00 pm in MCB 102
Committee: Prof. Laurent Itti (chair), Prof. Bartlett Mel, and Prof. Hao Li
Abstract:
Emerging wearable and cloud-connected sensor technologies offer new sensor placement options on the human body and machines. This opens new opportunities to explore cyber robotics algorithms (sensors and human motor plant) and smart manufacturing algorithms (sensors and manufacturing equipment). These algorithms process motion sensor data and provide situation awareness for a wide range of applications. Smart management and training systems assist humans in day-to-day living routines, healthcare and sports. Machines benefit from smart monitoring in manufacturing, retail machinery, transportation, and construction safety. During my PhD Research, I have developed several approaches for motion analysis and classification. (1) A situation awareness system (SAS) for head-mounted smartphones to respond to user activities (e.g., disable incoming phone calls in elevators, activate video recording while car driving), (2) a filter for head-mounted sensors (HOS) to allow full-body motion capturing by removing interfering head-motions, (3) an Inertial Machine Monitoring System (IMMS) to detect equipment failure or degraded states of a 3D-Printer, and (4) a "Smart Teaching System" (STS) for targeted motion feedback to refine physical tasks. To capture real-world sensor data, we designed hardware prototypes or used state-of-the-art wearable technology. We developed novel sensor fusion algorithms, implemented feature extraction methods based on gist, statistics, physics, frequency diagrams and validated classifiers: SAS achieved high accuracy (81.5%) when distinguishing between 20 real-world activities. HOS reduced the positional error of a traveled distance below 2.5 % with head-mounted sensors for pedestrian dead reckoning applications. IMMS yielded 11-way classification accuracy over 99% when distinguishing between normal operation vs. 10 types of real-world abnormal equipment behavior. STS demonstrated that combining motion sensors and provide targeted feedback yield significantly improved golf swing training (3.7x increased performance score).
Location: 102
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD defense - Yaguang Li
Tue, Apr 23, 2019 @ 01:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
Ph.D. Defense - Yaguang Li
Tue, April 23rd, 2019
1:00 pm - 3:00 pm
Location: PHE 325
Title:
Spatiotemporal Prediction with Deep Learning on Graphs
PhD Candidate: Yaguang Li
Date, Time, and Location: Tuesday, April 23rd, 2019 at 1pm in PHE 325
Committee: Prof. Cyrus Shahabi, Prof. Yan Liu, and Prof. Antonio Ortega
Abstract:
Spatiotemporal data is ubiquitous in our daily life, ranging from climate science, via transportation, social media, to various dynamical systems. The data is usually collected from a set of correlated objects over time, where objects can be sensors, locations, regions, particles, users, etc. For instance, in the transportation network, road sensors constantly record the traffic data at various correlated locations; in social networks, we observe activity data of correlated users, as indicated by friendships, evolving over time, and in dynamical systems, e.g., physics, climate, we observe the movement of particles interacting with each other. Spatiotemporal prediction aims to model the evolution of a set of correlated objects. It has various applications, ranging from classical subjects such as intelligent transportation system, climate science, social media, physics simulation to emerging fields of sustainability, Internet of Things (IoT) and health-care.
Spatiotemporal prediction is challenging mainly due to the complicated spatial dependencies and temporal dynamics. In this thesis, we study the following important questions in spatiotemporal prediction: (1) How to model complex spatial dependency among objects that are usually non-Euclidean and multimodal? (2) How to model the non-linear and non-stationary temporal dynamics for accurate long-term prediction? (3) How to infer the correlations or interactions among objects when they are not provided nor can be constructed a prior?
To model the complex spatial dependency, we represent the non-Euclidean pair-wise correlations among objects using directed graphs and then propose the novel diffusion graph convolution which captures the spatial dependency with bidirectional random walks on the graph. To model the multimodal correlations among objects, we further propose the multi-graph convolution network. To model the non-linear and non-stationary temporal dynamics, we integrate the novel diffusion graph convolution into the recurrent neural network to jointly model the spatial and temporal dependencies. To capture the long-term temporal dependency, we propose to use the sequence to sequence architecture with scheduled sampling. To utilize the global contextual information in the temporal correlation modeling, we further propose the contextual gated recurrent neural network which augments the recurrent neural network with a contextual-aware gating mechanism to re-weights different historical observations. To infer correlation among objects, we propose a structure-informed variational graph autoencoder based model, which infers the explicit interactions considering both observed movements and structural prior knowledge, e.g., node degree distribution, edge type distribution, and sparsity. The model represents the structural prior knowledge as differentiable constraints on the interaction graph and optimizes it using gradient-based methods.
We conduct extensive experiments on multiple real-world large-scale datasets for various spatiotemporal prediction tasks, including traffic forecasting, spatiotemporal demand forecasting, travel time estimation, relational inference and simulation. The results show the proposed models consistently achieve clear improvements over state-of-the-art methods. The proposed models and their variants have been deployed in real-world large-scale systems for applications including road traffic speed prediction, Internet traffic forecasting, air quality forecasting, travel time estimation, and spatiotemporal demand forecasting.
Location: Charles Lee Powell Hall (PHE) - 325
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
