CHMPR Seminar

Medical Epistemology: A Gerontologist’s Perspective

Dr. John D. Sorkin, M.D., Ph.D.
University of Maryland School of Medicine

3:00pm Wednesday, 27 April 2016, ITE 325b

The randomized clinical trial is the gold standard method by which we test a hypothesis positing an association between an exposure and outcome. Unfortunately many hypotheses are not grist for a clinical trial. It would, for example not be ethically permissible to randomize people to smoking vs. non-smoking if we wanted to study the hypothesis that smoking is associated with increased incidence of lung cancer. Similarly it would not be ethical to randomize pregnant women to being infected or not infected with Zika virus to determine if maternal Zika infection is associated with microcephaly. Clinical trials are also not helpful in determining the relation between a putative exposure and a rare disease such as Pick’s disease (a rare type of frontotemporal dementia) as the number of subjects who would need to be studied is prohibitively large.

The movement over the last decade away from paper-based charts to the electronic medical record (EMR) and advances in the speed of computers allow us to process large volumes of data in near real-time, and herald the advent of clinical studies based on “big data”. The availability of big data requires us to rethink how we can establish an association between cause and effect because the big data we obtain from the EMR are not collected from randomized clinical trials, and as noted above a clinical trial cannot be used to study many diseases. Further making inferences based on the EMR can be difficult because data gleamed from the electronic medical record can be confounded by changes brought about by the aging process which include primary aging (i.e., the aging process itself), secondary aging (i.e., changes brought about by changes in lifestyle as we get older) and tertiary aging (i.e., disease). Fortunately epidemiologists have designed and used study designs other than the clinical trials for years to gain insight into the relation between exposure and disease. The aim of my talk is to review five study designs, cross-sectional, time-series and longitudinal, case-control and cohort study designs, that can be used to identify change, quantify the rate at which changes occurs with aging, and to separate biological aging from the effects of life style and disease. In addition to presenting the five study designs, I will review the strengths and weaknesses of the five study designs. It is my hope that thinking about five study designs will help you design analyses that make use of big data to examine questions relevant to public health and treatment of disease.

Dr. John Sorkin is a professor at the University of Maryland School of Medicine. His research examines the changes that occur with aging in carbohydrate and lipid metabolism, obesity, and body fat distribution. He is interested in measuring the changes and determining the relation of the changes to the development of diabetes, cardiovascular disease, death, morbidity, and mortality. These interests have lead him to try to identify the phenotypes associated with longevity and the genetics of longevity in collaboration with Drs. Shuldiner and Mitchell. Dr. Sorkin is Chief of Biostatistics and Informatics for the Division of Gerontology and is PI of the Statistics Core for the University of Maryland Claude D. Pepper Older Americans Independence Center and Baltimore VA Geriatrics Research, Education and Clinical Center.

UMBC Cyber Defense Lab

Securing the Cloud: The Need for Quantum Network Security
Brian Kelley, Senior Member IEEE
Associate Professor of ECE
The University of Texas at San Antonio

11:15am-12:30pm Friday, 22 April 2016, UMBC, ITE 227

A significant trend in cloud data centers virtualization has been the migration away from virtual machines (VMs) with multiple guest operating systems (OS) to containers with a single Host OS. Whereas VMs incorporate a hypervisor manager layer enabling the Host OS to spawn multiple guest OSs, containers support all the code, run-time tools, and system libraries to run workload applications from a single Host OS.

While all cloud-based platforms posses security vulnerabilities, the additional security challenges with container systems stem from the sharing of the Host OS among independent container applications.

In this presentation we pose the question, “Can we use quantum information concepts to protect the cloud?” We introduce Quantum Key Distribution (QKD) protocols. We present schemes for cloud container security based upon concepts drawn from QKD and related concepts in quantum teleportation. We also propose a new framework for Quantum Container Security drawing upon concepts of quantum entanglement. We will also present information the Cloud Academic Research Center at the University of Texas at San Antonio.

Dr. Brian Kelley is Associate Professor of ECE at the University of Texas at San Antonio. He is a leading researcher on communication systems, 4G and 5G cellular, cloud communications, and smart grid communications. He is also a member of the Cloud Academic Center at the University of Texas. Dr. Kelley is currently on sabbatical leave as a consultant with the DoD in Washington D.C. His current research focus is on the intersection of software-defined networks, 5G communications, and cloud systems. He is Senior Member of the IEEE, was an Oak Ridge National Laboratory Summer Faculty Fellow in Quantum Information Science during the summer of 2015, was Globecom 2014 Chair for the High-Level Technical Program Committee, Associate Editor and Editorial Board of IEEE System Journal, 2011-2012, and Associate Editor of Computers & Electrical Engineering, Elsevier, 2008-2011; he founded the San Antonio IEEE Communications and Signal Processing Chapter, in 2008. From 2000-2006, he was Distinguished Member of the Technical Staff at Motorola and a senior lecturer at the University of Texas at Austin. Since 2007, he has been Associate Professor of ECE and Director of the Wireless Next Generation Systems (WiNGS) Lab at the University of Texas at San Antonio. Dr. Kelley received his BSEE from Cornell University and his MS/PhD in EE from the Georgia Institute of Technology in 1992, where he was an ONR Fellow. He is a member of Tau Beta Pi and Eta Kappa Nu. Contact: Dr. Brian Kelley, (210) 706-0854

Host: Alan T. Sherman,

The UMBC Cyber Defense Lab meets biweekly Fridays (May 6: Enis Golaszewski, Hash bit sequences)

Distinguished Lecture Series, UMBC Department of Information Systems

Statistical Methods for Integration and Analysis of Opinionated Text Data

Dr. ChengXiang Zhai
Professor and Willett Faculty Scholar
University of Illinois at Urbana-Champaign

10:00am Thursday 21 April 2016, ITE 459, UMBC

Opinionated text data such as blogs, forum posts, product reviews and online comments are increasingly available on the Web. They are very useful sources for public opinions about virtually any topics. However, because the opinions are scattered and abundant, it is a significant challenge for users to collect all the opinions about a topic and digest them efficiently. In this talk, I will present a suite of general statistical text mining methods that can help users integrate, summarize and analyze scattered online opinions to obtain actionable knowledge for decision making. Specifically, I will first present approaches to integration of scattered opinions by aligning them to a well- structured article or relevant ontology. Second, I will discuss several techniques for generating a concise opinion summary that can reveal the major sentiments and opinion points buried in large amounts of opinionated text data. Finally, I will present probabilistic generative models for analyzing review data in depth to discover latent aspect ratings and relative weights placed by reviewers on different aspects. These methods are general and can thus potentially help users integrate and analyze large amounts of online opinionated text data on any topic in any natural language

ChengXiang Zhai is a Professor of Computer Science at the University of Illinois at Urbana-Champaign, where he also holds a joint appointment at the Institute for Genomic Biology, Statistics, and the Graduate School of Library and Information Science. His research interests include information retrieval, text mining, natural language processing, machine learning, and bioinformatics, and has published over 200 papers in these areas with an H-index of 58 in Google Scholar. He is an Associate Editor of ACM Transactions on Information Systems, and Information Processing and Management, and the Americas Editor of Springer’s Information Retrieval Book Series. He is a conference program co-chair of ACM CIKM 2004, NAACL HLT 2007, ACM SIGIR 2009, ECIR 2014, ICTIR 2015, and WWW 2015, and conference general co-chair for ACM CIKM 2016. He is an ACM Distinguished Scientist and a recipient of multiple best paper awards, Rose Award for Teaching Excellence at UIUC, Alfred P. Sloan Research Fellowship, IBM Faculty Award, HP Innovation Research Program Award, and the Presidential Early Career Award for Scientists and Engineers (PECASE).

UMBC CSEE Seminar

IoT Device Security Research at Morgan State University

Dr. Kevin T. Kornegay

Professor and IoT Security Endowed Chair,
School of Electrical and Computer Engineering, Morgan State University

12:00-1:00pm Friday, 15 April 2016, ITE 239, UMBC

The Internet of Things (IoT) and its myriad of components are proliferating as they increasingly permeate all areas of life and work, with unprecedented economic effect. The IoT is the network of dedicated physical objects (things) whose embedded system technology senses or interacts with their internal state or external environment. Embedded systems use a combination of computer hardware and software to perform dedicated functions within a larger mechanical or electrical system. Examples of embedded systems include cell phones, personal digital assistants, gaming consoles, global positioning systems, etc. Over 98 percent of all microprocessors being manufactured are used in embedded system applications. In private industry and the public sector, IoT growth and possible uses are evolving rapidly. Critical infrastructures in transportation, smart grid, manufacturing and health care are highly dependent on embedded systems for distributed control, tracking, and electronic data collection. While it is paramount to protect these systems from hacking, intrusion or physical tampering, our current solutions are often based on a patchwork of legacy systems, and this is unsustainable as a long-term solution. Transformative solutions are required to protect these systems by engineering secure embedded systems. Secure embedded systems use cryptography and countermeasures to protect electronic data and commands to systematically achieve resiliency, stability, safety, integrity, and privacy. Engineering secure embedded implementations that are resistant to attacks is vital. Essential to achieving this goal is obtaining fundamental knowledge and understanding of the various types of vulnerabilities embedded systems present. Hence, in this talk, we will present our embedded systems security research activities including the IoT testbed, side-channel and fault injection analysis, and associated research projects.

Kevin T. Kornegay received the B.S. degree in electrical engineering from Pratt Institute, Brooklyn, NY, in 1985 and the M.S. and Ph.D. degrees in electrical engineering from the University of California at Berkeley in 1990 and 1992, respectively. He is presently Professor and IoT Security Endowed Chair in the School of Electrical and Computer Engineering at Morgan State University in Baltimore, MD. His research interests include hardware assurance, reverse engineering, secure embedded system design, side-channel analysis, differential fault analysis, radio frequency and millimeter wave integrated circuit design, high-speed circuits, and broadband wired and wireless system design. Dr. Kornegay serves or has served on the technical program committees of several international conferences including the IEEE Symposium on Hardware Oriented Security and Trust (HOST), EEE International Solid State Circuits Conference, the IEEE Custom Integrated Circuits Conference, and the Radio Frequency Integrated Circuits Symposium. He has also served a two-year term on the IEEE Solid-State Circuits AdCom committee, as well as, on the editorial board of the IEEE Transactions on Circuits and Systems II and as Editor of IEEE Electron Device Letters and Guest Editor of the IEEE Journal of Solid-State Circuits Special Issue on the 2004 Compound Semiconductor IC Symposium. He is the recipient of numerous awards, including the National Society of Black Engineers’ Dr. Janice A. Lumpkin Educator of the Year in 2005, the 2002 Black Engineer of the Year Award in Higher Education from U.S. Black Engineer and Information Technology magazine, the NSF CAREER Award, an IBM Faculty Partnership Award, the National Semiconductor Faculty Development Award, and the General Motors Faculty Fellowship Award. He was also selected as a participant in the National Academy of Engineering Frontiers of Engineering Symposium, and the German–American Frontiers of Engineering, where he later served on the organizing committee. He is a Distinguished Lecturer of the IEEE Electron Devices Society and a senior member of the IEEE, as well as a member of Eta Kappa Nu and Tau Beta Pi.

Hosts: Professors Fow-Sen Choa () and Alan T. Sherman ()

About the CSEE Seminar Series: The UMBC Department of Computer Science and Electrical Engineering presents technical talks on current significant research projects of broad interest to the Department and the research community. Each talk is free and open to the public. We welcome your feedback and suggestions for future talks.

The UMBC Cyber Defense Lab presents

Firmware Instruction Identification
using Side-Channel Power Analysis

Deepak Krishnankutty
Computer Science and Electrical Engineering
University of Maryland, Baltimore County

11:15am-12:30pm, Friday, April 8, 2016, ITE 237

Power supply transients of encryption devices have been analyzed from the perspective of performing attacks to extract secret key or confidential information. Such attacks are based on exploiting the correlation between the power consumption of the device under attack and its underlying logic operations. However, side channel leakage through the power supply of instruction level events occurring on soft/hard core processors has not been extensively studied. Power traces of firmware running on general purpose processing units observed at low frequencies tend to reveal not just the variations in current consumption during individual clock cycles, but also information related to the sequence of instruction executions. In this talk, we present results from Side-Channel Analysis performed over multiple power supply pins and demonstrate the relationship between the power transients and machine-level instructions on an instance of the openMSP430 processor on an FPGA. This process is also applicable to standalone ASIC instances. Our approach is based on templates constructed from principal components representing instructions identified from the power profiles of different instruction sequences. The templates are then utilized for determining the order of clock cycles per instruction. This technique can be used to predict the sequence of clock cycles per instruction from the observed power profiles and identify anomalies caused by modification of code on a tightly constrained embedded system.

Deepak Krishnankutty is a PhD student in computer engineering at UMBC,

Host: Alan T. Sherman,

The UMBC Cyber Defense Lab meets biweekly Fridays. (April 22, Brian Kelley, Securing the cloud. May 8, Enis Enis Golaszewski, Hash bit sequences).

Visualizing (Scientific) Simulations with
Geometric and Topological Features

Prof. Joshua A. Levine, Clemson University
12:00pm Monday, 4 April 2016, ITE 325b, UMBC

Today’s HPC resources are an essential component for enabling new scientific discoveries. Specifically, scientists in all fields leverage HPC to do computational simulations that complement laboratory experimentation. These simulations generate truly massive data; visualization offers a mechanism to help understand the simulated phenomena this data describes.

This talk will present two recent research projects, both of which highlight new techniques for visualization based on characterizing and computing features of interest. The first project describes an algorithm for surface extraction from particle data. This data is commonly used in simulations for phenomena at small (molecular dynamics), medium (fluid flow, fracture), and large (astrophysics) length scales. Surface geometry allows standard computer graphics approaches to be used to visualize complex behaviors. The second project introduces a new data structure for representing vector field data commonly found in computational fluid dynamics and climate modeling. This data structure enables robust extraction of topological features that provide summary visualizations of vector fields. Both projects exemplify my vision for how collaborative efforts between experts in scientific and computational fields are necessary to make the best use of our HPC systems.

Joshua A. Levine is an assistant professor in the Visual Computing division of the School of Computing at Clemson University. He received his PhD from The Ohio State University after completing his BS and MS in Computer Science from Case Western Reserve University. His research interests include visualization, geometric modeling, topological analysis, mesh generation, vector fields, volume and medical imaging, computer graphics, and computational topology.

Host: Prof. Adam Bargteil ()

Reverse Engineering of Dynamic Regulatory Networks
from Morphological Experimental Data

Prof. Daniel Lobo, Biological Sciences, UMBC
3:00pm 11:00am April 7 6th,  ITE Building, Room 325b

Many crucial experiments in developmental, regenerative, and cancer biology are based on manipulations and perturbations resulting in morphological outcomes. For example, planarian worms can regenerate a complete organism from almost any amputated piece, but knocking down certain genes can result in the regeneration of double-head phenotypes. However, the inherent complexity and non-linearity of biological regulatory networks prevent us from manually discerning testable comprehensive models from patterning and morphological results, and existent bioinformatics tools are generally limited to genomic or time-series concentration data. As a consequence, despite a huge experimental dataset in the literature, we still lack mechanistic explanations that can account for more than one or two morphological results in many model organisms. To bridge this gulf separating morphological data from an understanding of pattern and form regulation, we developed a computational methodology to automate the discovery of dynamic genetic networks directly from formalized phenotypic experimental data. In this seminar, I will present novel formal ontologies and databases of surgical, genetic, and pharmacological experiments with their resultant morphological phenotypes, together with artificial intelligence tools based on evolutionary computation and in silico simulators that can directly mine these data to reverse-engineer mechanistic dynamic genetic models. We demonstrated this approach by automatically discovering the first comprehensive model of planarian regeneration, which not only explains at once all the key experiments available in the literature (including surgical amputations, knock-down of specific genes, and pharmacological treatments), but also predicts testable novel pathways and genes. This approach is readily paving the way for understanding the regulation (and dis-regulation) of complex patterns and shapes in developmental, regenerative, and cancer biology.

Daniel Lobo is an Assistant Professor at the University of Maryland, Baltimore County. His research aims to understand, control, and design the dynamic regulatory mechanisms governing complex biological processes. To this end, his group develops new computational methods, ontologies, and high-performance in silico experiments to automate the reverse-engineering of quantitative models from biological data and the design of regulatory networks for specific functions. They seek to discover the mechanisms of development and regeneration, find therapies for cancer and other diseases, and streamline the application of synthetic biology. His work has received widespread media coverage including Wired, TechRadar, and Popular Mechanics.

Down the rabbit hole: An Android system call study

Prajit Kumar Das

10:30 am, Monday, March 28, 2016 ITE 346

App permissions and application sandboxing are the fundamental security mechanisms that protects user data on mobile platforms. We have worked on permission analytics before and come to a conclusion that just studying an app’s requested access rights (permissions) isn’t enough to understand potential data breaches. Techniques like privilege escalation have been previously used to gain further access to user and her data on mobile platforms like Android. Static code analysis and dynamic code execution may be studied to gather further insight into an app’s behavior. However, there is a need to study such a behavior at the lowest level of code execution and that is system calls. The system call is the fundamental interface between an application and the Linux kernel. In our current project, we are studying system calls made by apps for gathering a better understanding of their behavior.

A Probabilistic Approach to Modeling Socio-Behavioral Interactions

Arti Ramesh, University of Maryland, College Park

Noon Thursday, 24 March 2016, ITE325b

The vast growth and reach of the Internet and social media have led to a tremendous increase in socio-behavioral interaction content on the web. The ever-increasing number of online interactions has led to a growing interest to understand and interpret online communications to enhance user experience. My work focuses on building scalable computational methods and models for representing and reasoning about rich, heterogeneous, interlinked socio-behavioral data. In this talk, I focus on one such emerging online interaction platform—online courses (MOOCs). I develop a family of probabilistic models to represent and reason about complex socio-behavioral interactions in the following real-world problems: 1) modeling student engagement, 2) predicting student completion and dropouts, 3) modeling student sentiment in discussion forums toward various course aspects (e.g., academic content vs. logistics) and its effect on their course completion, and 4) designing an automatic system to predict fine-grained topics and sentiment in online course discussion forums. I demonstrate the efficacy of these models via extensive experimentation on data from twelve Coursera courses. These methods have the potential to improve learning and teaching experience of online education participants and focus limited instructor resources to increase student retention.

Arti Ramesh is a PhD candidate at University of Maryland, College Park. Her primary research interests are in the field of machine learning and data science, particularly on probabilistic graphical models. Her advisor is Prof. Lise Getoor. Her research focuses on building scalable models for reasoning about interconnectedness, structure, and heterogeneity in socio-behavioral networks. She has published papers in peer-reviewed conferences such as AAAI and ACL. She has served on the TPC for ACL workshop on Building Educational Applications and has served as a reviewer for notable conferences and journals such as NIPS, Social Networks and Mining, and Computer Networks. She has won multiple awards during her graduate study including the outstanding graduate student Dean’s fellowship 2016, Dean’s graduate fellowship (2012-2014), and yahoo scholarship for grace hopper. She has worked at IBM research and LinkedIn during her graduate study. She received her Masters in Computer Science from University of Massachusetts, Amherst.

Adversarial Machine Learning in Relational Domains

Prof. Daniel Lowd, University of Oregon

12:00-1:00 Tuesday, 22 March 2016, ITE 325b, UMBC

Many real-world domains, such as web spam, auction fraud, and counter-terrorism, are both adversarial and relational. In adversarial domains, a model that performs well on training data may do poorly in practice as adversaries modify their behavior to avoid detection. Previous work in adversarial machine learning has assumed that instances are independent from each other, both when manipulated by an adversary and labeled by a classifier. Relational domains violate this assumption, since object labels depend on the labels of related objects as well as their own attributes.

In this talk, I will present two different methods for learning relational classifiers that are robust to adversarial noise. Our first approach assumes that related objects have correlated labels and that the adversary can modify a certain fraction of the attributes. In this case, we can incorporate the adversary’s worst-case manipulation directly into the learning problem and find optimal weights in polynomial time. Our second method generalizes to any relational learning problem where the perturbations in feature space are bounded by an ellipse or polyhedron. In this case, we show that adversarial robustness can be achieved by a simple regularization term or linear transformation of the feature space. These results form a promising foundation for building robust relational models for adversarial domains.

Daniel Lowd is an Assistant Professor in the Department of Computer and Information Science at the University of Oregon. His research interests include learning and inference with probabilistic graphical models, adversarial machine learning, and statistical relational machine learning. He received his Ph.D. in 2010 from the University of Washington. He has received a Google Faculty Award, an ARO Young Investigator Award, and the best paper award at DEXA 2015.

Host: Cynthia Matuszek,