Data streams are continuous flows of data. Examples of data streams include network traffic, sensor data, call center records and so on. Data streams demonstrate several unique properties that together conform to the characteristics of big data (i.e., volume, velocity, variety and veracity) and add challenges to data mining. In this talk I will present an organized picture on how to handle various data mining/machine learning techniques in data streams. In addition, I will present a number of stream applications such as adaptive website fingerprinting, textual stream classification, new political actor identification over textual stream, and domain adaptation.

This research was funded in part by NSF, NASA, Air Force Office of Scientific Research (AFOSR), NSA, IBM Research, and Raytheon.

The revolution in sensing, with the emergence of many new imaging techniques, offers the possibility of gaining unprecedented access to the physical world, but this revolution can only bear fruit through the skilful interplay between the physical and computational worlds. This is the domain of computational imaging which advocates that, to develop effective imaging systems, it will be necessary to go beyond the traditional decoupled imaging pipeline where device physics, image processing and the end-user application are considered separately. Instead, we need to rethink imaging as an integrated sensing and inference model. In this talk we cover two research areas where computational imaging is likely to have an impact.

We first focus on the heritage sector which is experiencing a digital revolution driven in part by the increasing use of non-invasive, non-destructive imaging techniques. These new imaging methods provide a way to capture information about an entire painting and can give us information about features at or below the surface of the painting. We focus on Macro X-Ray Fluorescence (XRF) scanning which is a technique for the mapping of chemical elements in paintings. After describing in broad terms the working of this device, a method that can process XRF scanning data from paintings is introduced. The method is based on connecting the problem of extracting elemental maps in XRF data to Prony's method, a technique broadly used in engineering to estimate frequencies of a sum of sinusoids. The results presented show the ability of our method to detect and separate weak signals related to hidden chemical elements in the paintings. We then discuss results on the Leonardo’s The Virgin of the Rocks and show that our algorithm is able to reveal, more clearly than ever before, the hidden drawings of a previous composition that Leonardo then abandoned for the painting that we can now see.

In the second part of the talk, we focus on two-photon microscopy and neuroscience. To understand how networks of neurons process information, it is essential to monitor their activity in living tissue. Multi-photon microscopy is unparalleled in its ability to image cellular activity and neural circuits, deep in living tissue, at single-cell resolution. However, in order to achieve step changes in our understanding of brain function, large-scale imaging studies of neural populations are needed and this can be achieved only by developing computational tools that can enhance the quality of the data acquired and can scan 3-D volumes quickly. In this talk we introduce light-field microscopy and present a method to localize neurons in 3-D. The method is based on the use of proper sparsity priors, novel optimization strategies and machine learning.

Economic data and financial markets are intriguing to researchers working on big data and quantitative models. With rapid growth and increasing access to data in digital form, finance, economics, and marketing data are poised to become one of the most important and tangible big data applications, owing not only to the relative clean organization and structure of the data but also to clear application objectives and market demands. However, data related economic and social science studies often have different viewpoints from signal processing (SP) and artificial intelligence (AI).

This talk intends to introduce some foundational concepts in finance/economics/marketing research, from signal and data processing point of view. Some of these ideas led to Nobel Prize in Economics. We explain the different focuses between economic and social science data analysis and physical signal processing, such as co-integration and causality analysis. For example, in most physical systems using signal processing and machine learning, the causality (input/output) relationship is often known and taken for granted, but it is generally not obvious/unknown in social and economic sciences. It is critical to discriminate causalities from spurious correlations in data. We illustrate a marketing dynamic response model that uses signal processing tools to identify and catch fleeting business opportunities. We also introduce the concept of predictive analytics from probabilistic point of view. We hope to inspire signal processing researchers to broaden their knowledge beyond their current areas of expertise and grasp some basics concepts and evaluation criteria in economics and social science fields.

This talk will cover a wide variety of challenges and opportunities that occur at Oncor. The talk will be divided into 3 or 4 parts. Part 1 will cover the response to 2021 storm Uri. Part 2 will cover Oncor’s approach to analytics. Part 3 will be a short description of the type of technical based careers at Oncor. If time allows, Part 4 will cover some career tips that have been learned over time .

Brain machine interfaces or neural prosthetics have the potential to restore movement to people with paralysis or amputation, bridging gaps in the nervous system with an artificial device. Microelectrode arrays can record from up to hundreds of individual neurons in motor cortex, and machine learning can be used to generate useful control signals from this neural activity. Performance can already surpass the current state of the art in assistive technology in terms of controlling the endpoint of computer cursors or prosthetic hands. The natural next step in this progression is to control more complex movements at the level of individual fingers. Our lab has approached this problem in three different ways. For people with upper limb amputation, we acquire signals from individual peripheral nerve branches using small muscle grafts to amplify the signal. Human study participants have been able to control individual fingers on a prosthesis using indwelling EMG electrodes within these grafts. For spinal cord injury, where no peripheral signals are available, we implant Utah arrays into finger areas of motor cortex, and have demonstrated the ability to control flexion and extension in multiple fingers simultaneously. Decoding “spiking band” activity at much lower sampling rates, we also recently showed that power consumption of an implantable device could be reduced by an order of magnitude compared to existing broadband approaches, and fit within the specification of existing systems for upper limb functional electrical stimulation. Finally, finger control is ultimately limited by the number of independent electrodes that can be placed within cortex or the nerves, and this is in turn limited by the extent of glial scarring surrounding an electrode. Therefore, we developed an electrode array based on 8 um carbon fibers, no bigger than the neurons themselves to enable chronic recording of single units with minimal scarring. The long-term goal of this work is to make neural interfaces for the restoration of hand movement a clinical reality for everyone who has lost the use of their hands.

In this presentation, Professor Kamesh Namuduri will discuss a brand-new project that UNT is leading to design the very first air track for unmanned air transportation in the Nation. This project serves as one of the best examples of private-public-community-government partnership. Collaborating with the aviation and telecommunication companies across the country, UNT is working towards the design of the air-track concept that forms the foundation for the future of advanced air mobility. As part of this presentation, Professor Namuduri will discuss several technical details of this ambitious project including strategic deconfliction / conflict management between aerial vehicles, vehicle-to-vehicle communications, and related standards along with real-world examples. This presentation also highlights the importance of community engagement in this project.

Fifth generation mobile communication systems (5G) have to accommodate both Ultra-Reliable Low-Latency Communication (URLLC) and enhanced Mobile Broadband (eMBB) services. While eMBB applications support high data rates, URLLC services aim at guaranteeing low-latencies and high-reliabilities. eMBB and URLLC services are scheduled on the same frequency band, where the different latency requirements of the communications render the coexistence challenging.

In this talk, we review, from an information theoretic perspective, coding schemes that simultaneously accommodate URLLC and eMBB transmissions and show that they outperform traditional scheduling approaches. We consider various communication scenarios, including point-to-point channels, broadcast channels, interference networks, cellular models, and cloud radio access networks (C-RANs). The main focus is on the set of rate pairs that can simultaneously be achieved for URLLC and eMBB messages, which well captures the tension between the two types of communications. We also discuss finite-blocklength results where the measure of interest is the set of error probability pairs that can simultaneously be achieved on the two communication regimes.

Joint work with Michèle Wigger (Tèlècom Paris, France), Malcolm Egan (INRIA, France) , Shlomo Shamai (Shitz) (Technion, Israel), Jean-Marie Gorce (INRIA, France), and H.Vincent Poor (Princeton University).