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Yoram Bresler University of Illinois, UrbanaChampaign 
Guaranteed Multichannel Sparse Blind Deconvolution Under Minimal Modeling Assumptions 
Robert Calderbank Duke University 
Recent Progress in Quantum Computing 
Joao Hespanha University of California Santa Barbara 
Estimation in CyberPhysical Systems Under Attack Computerbased sensors are heavily used in process of monitoring and controlling complex and largescale physical system, such as the power grid, transportation systems, chemical processes, and manufacturing plants. While these sensors can yield great benefits in terms of improved efficiency, lower costs, and increased safety, they are often prone to attacks and can introduce significant security risks. In this talk we explore how the formulation of classical estimation problems needs to be revisited to address scenarios where sensors are prone to attacks. By considering the joint design of estimators and attack policies, we obtain “resilient” estimators that use redundancy in an optimal fashion. While the design and construction of these optimal resilient estimators may be computationally expensive, we shall see that is often possible to find quasioptimal solutions that are computationally attractive. For concreteness, we illustrate these ideas in a case study involving the estimation of power system oscillations using Phase Measurements Units. 
P. R. Kumar Texas A&M University 
Security of Cyberphysical Systems
The coming decades may see the large scale deployment of networked cyber–physical systems to address global needs in areas such as energy, water, health care, and transportation. However, as recent events have shown, such systems are vulnerable to cyber attacks. We begin by revisiting classical linear systems theory, developed in more innocent times, from a securityconscious, even paranoid, viewpoint. Then we present a general technique, called "dynamic watermarking," for detecting any sort of malicious activity in networked systems of sensors and actuators. We present experimental demonstration of this technique on laboratory versions of a prototypical intelligent transportation system and a process control system, and a simulation study of defense against an attack on Automatic Gain Control (AGC) in a synthetic four area power system. [Joint work with Bharadwaj Satchidanandan, Jaewon Kim, Woo Hyun Ko, Le Xie and Tong Huang]. 
Jane Macfarlane University of California at Berkeley and Lawrence Berkeley National Laboratory 
High Performance Computing Solutions for RealWorld Transportation Systems Understanding 
Upamanyu Madhow University of California Santa Barbara 
Can signal models inform deep learning? Two case studies
Deep neural networks (DNNs) have become the state of the art for learning and inference in a diversity of fields, including computer vision, speech recognition, and natural language processing. A particular strength of DNNs is that, once the input is translated to a tensor, explicit feature engineering based on domain knowledge is not required. We argue in this talk, however, that applicationspecific signal models can be crucial as we apply DNNs to safety and securitycritical domains. We present preliminary results from two case studies. In the first, we discuss the problem of combating adversarial perturbations by invoking a rather general signal model, corresponding to the assumption that the input data lies in a lowdimensional manifold embedded in a highdimensional space. In the second, we consider the problem of using DNNs to extract radio frequency (RF) signatures that enable identification of transmitters in a manner robust to simple spoofing techniques (e.g., message, ID, carrier offset). We show how protocol and channelaware signal modeling is important in preventing the unreasonably good accuracy associated with vulnerability to such spoofing. 
Muriel Medard 
Guessing Random Additive Noise Decoding (GRAND) We introduce a new algorithm for Maximum Likelihood (ML) decoding based on guessing noise. The algorithm is based on the principle that the receiver rank orders noise sequences from most likely to least likely. Subtracting noise from the received signal in that order, the first instance that results in an element of the codebook is the ML decoding. For common additive noise channels, we establish that the algorithm is capacity achieving for uniformly selected codebooks, providing an intuitive alternate approach to the channel coding theorem. When the codebook rate is less than capacity, we identify exact asymptotic error exponents as the blocklength becomes large. We illustrate the practical usefulness of our approach in terms of speeding up decoding for existing codes. Joint work with Ken Duffy, Kishori Konwar, Jiange Li, Prakash Narayana Moorthy, Amit Solomon. 
George J. Pappas University of Pennsylvania

Robustness Analysis of Neural Networks via Semidefinite Programming Deep neural networks have dramatically impacted machine learning problems in numerous fields. Despite these major advances, neural networks are not robust and hence not suitable for safetycritical applications. In this lecture, we will present a novel framework for analyzing the robustness of deep neural networks against normbounded nonlinearities. In particular, we develop a semidefinite programming (SDP) framework for safety verification and robustness analysis of neural networks with general activation functions. Our main idea is to abstract various properties of activation functions (e.g., monotonicity, bounded slope, bounded values, and repetition across layers) with the formalism of quadratic constraints. We then analyze the safety properties of the abstracted network via the Sprocedure and semidefinite programming. Compared to other approaches proposed in the literature, our method is less conservative, especially for deep networks, with an order of magnitude reduction in computational complexity. Furthermore, our approach is applicable to any activation functions. Such bounds are very important in analyzing the safety of control systems regulated by neural networks. 
Yannis Paschalidis Boston University 
Distributionally Robust Learning with Applications to Health Analytics
We will present a distributionally robust optimization approach to learning predictive models, using general loss functions that can be used either in the context of classification or regression. Motivated by medical applications, we assume that training data are contaminated with (unknown) outliers. The learning problem is formulated as the problem of minimizing the worst case expected loss over a family of distributions within a certain Wasserstein ball centered at the empirical distribution obtained from the training data. We will explore the generality of this approach, its robustness properties, its ability to explain a host of "adhoc" regularized learning methods, and we will establish rigorous outofsample performance guarantees. Beyond predictions, we will discuss methods that can leverage the robust predictive models to make decisions and offer specific personalized prescriptions and recommendations to improve future outcomes. We will provide some examples of medical applications of our methods, including predicting hospitalizations for chronic disease patients, predicting hospital lengthofstay for surgical patients, and making treatment recommendations for diabetes and hypertension. (joint work with Ruidi Chen) 
H. Vincent Poor Princeton University 
Learning at the Edge This talk will present an overview of some results on distributed learning in wireless networks, in which machine learning algorithms interact with the physical limitations of the wireless medium. After an overview of some previous work in this area, new results on federated learning will be discussed, including a general formulation for performance analysis of scheduling policies, and results providing a fundamental connection between the prediction accuracy of federated learning algorithms and the performance of the underlying wireless network. 
Sanjay Shakkottai University of Texas at Austin 
MultiFidelity TreeSearch for Hyperparameter Tuning
We study the application of online learning techniques in the context of hyperparameter tuning, which is of growing importance in general machine learning. Modern neural networks have several tunable parameters, where training for even one such parameter configuration can take several hours to days. We first cast hyperparameter tuning as optimizing a multifidelity blackbox function (which is noiseless) and propose a multifidelity tree search algorithm for the same. We then present extensions of our model and algorithm, so that they can function even in the presence of noise. We show that our treesearch based algorithms can outperform state of the art hyperparameter tuning algorithms on several benchmark datasets.

Venu Veeravalli University of Illinois at UrbanaChampaign 
Quickest Detection of Dynamic Events in Networks
We study the problem of efficiently detecting a dynamically evolving event using a sensor network. At some unknown time, an event occurs, and a subset of nodes in the network are affected, which undergo a change in the statistics of their observations. It is assumed that the event propagates dynamically along the edges in the network, in that the affected nodes form a connected subgraph. The event propagation dynamics are assumed to be unknown. The goal is to design a sequential algorithm that can detect a ``significant" event, i.e., when the event has affected a large enough number of nodes, as quickly as possible, while controlling the false alarm rate. We construct computationally efficient algorithms for this problem, for both structured and unstructured networks, and establish their firstorder asymptotically optimality as the false alarm rate goes to zero. We end with a discussion of distributed approaches to implementing the algorithms. 
Qing Zhao Cornell University 
The problem of detecting a few target processes with abnormally high mean values among a large number of processes is considered. Each process is i.i.d. with an unknown distribution. Processes follow a treestructured hierarchy, which encodes the following relationship: the abnormal mean of a target propagates through all ancestors of the target on the tree. The objective is an active learning strategy that determines, sequentially, which node on the tree to sample and when to terminate the search in order to detect all targets with a minimum sample complexity under a reliability constraint. A strategy that induces a biased random walk on the tree based on confidence bounds of sample statistics is proposed and shown to be order optimal in terms of both the size of the tree and the reliability requirement. The results find applications in hierarchical heavy hitter detection, noisy group testing, stochastic online convex optimization, and adaptive sampling for classification and stochastic root finding.
