Information Systems Eminent Scholar Talk

Big Microbiome Data

Xiaohua Hu, Drexel University

10:00am Tuesday, 2 May 2017, ITE 459, UMBC

We know little about the microbial world. Microbiome sequencing (i.e., metagenome, 16s rRNA) extracts DNA directly from a microbial environment without culturing any species. Recently, huge amount of data are generated from many micorbiome projects such as Human Microbiome Project (HMP), Metagenomics of the Human Intestinal Tract (MetaHIT), et al. Analyzing these data will help us to better understand the function and structure of microbial community of human body, earth and other environmental eco-systems. However, the huge data volume, the complexity of microbial community and the intricate data properties have created a lot of opportunities and challenges for data analysis and mining. For example, it is estimate that in the microbial eco- system of human gut, there are about 1000 kinds of bacteria with ten billion bacteria and more than four million genes in more than 6000 orthologous gene family. The challenges are due to the complex properties of microbiome: large-scale, complicated, diversity, correlation, composition, hierarchy, incompleteness etc.

Current microbiomes data analysis methods seldom consider these data properties and often make some assumptions such as linear, Euclidean space, metric-space, continue data type, which conflict with the true data properties. For example, some similarities are non-metric because the prevalent existence of some species; and the interactions among species and environment are complex in high order. Thus it is urgent to develop novel computational methods to overcome these assumptions and consider the microbiome data properties in the analysis procedure.  In this talk, we will discuss some computational methods to analyze and visualize microbiome big data. Our studies are focusing on 1) novel machine learning and computational technologies for dimension reduction and visualization of microbiome data based on non-Euclidean spaces (manifold learning) to discover nonlinear intrinsic features and patterns in these data to overcome the linear assumptions, 2) novel statistical methods for variable selection in microbiome data by integrating group information among variables.

Xiaohua Tony Hu is a full professor and the founding director of the data mining and bioinformatics lab at the College of Computing and Informatics. He is also serving as the founding Co-Director of the NSF Center on Visual and Decision Informatics, IEEE Computer Society Bioinformatics and Biomedicine Steering Committee Chair, and IEEE Computer Society Big Data Steering Committee Chair. He joined Drexel University in 2002. He founded the International Journal of Data Mining and Bioinformatics, the IEEE International Conference on Big Data and the IEEE International Conference on Bioinformatics and Biomedicine. In 2001, he founded the DMW Software in Silicon Valley, California. He received many awards, including NSF CAREER Award and IEEE Data Mining Outstanding Service Award.  Tony’s current research interests are in data/text/web mining, big data, bioinformatics, information retrieval and information extraction, social network analysis, healthcare informatics, rough set theory and application. He has published more than 270 peer-reviewed research papers in various journals, conferences and books He has obtained more than US$8.5 million research grants in the past ten years as PI or Co-PI. He has graduated 19 Ph.D. students from 2006 to 2017 and is currently supervising nine Ph.D. students.

The UMBC Cyber Defense Lab presents

A Practical Introduction to Penetration Testing

Dr. Arno Wacker
University of Kassel, Germany
and UMBC 2017

12:00noon Friday, 28 April 2017, ITE 227, UMBC

While many students learn the theoretical concepts of cybersecurity and cryptology at universities, their exposure to real life systems and the application of learned theoretical foundations in the real world is usually limited. Additionally, most students and sometimes even students of cybersecurity often deal with cybersecurity threats on a very abstract level, thereby being unaware that these threats are not abstract but real for everyone, including for themselves.

Therefore, this talk intends to raise the awareness about real cybersecurity threats for everyone by demonstrating live the process of penetration testing a system. I will show live how an attacker can gain control over a victim’s PC in a matter of seconds, and how this attack can be prevented. To do so, several techniques and tools will be used, including breaking a WPA-protected wireless network, defeating SSL/TLS encryption, and obtaining a reverse shell with system rights on the victim’s computer.

By experiencing these attacks in a simulated penetration test, we can gain a deeper understanding of the theoretical foundations and their implications for real-life scenarios. With this knowledge, the attack vectors can be mitigated to a bare minimum. In many cases, the cybersecurity-aware usage of IT systems is already countering many real threats.

Prof. Dr. Arno Wacker is an assistant professor with the University of Kassel in Germany and the head of the research group Applied Information Security (AIS). Currently, he is a visiting assistant professor at UMBC teaching the network security class. He is also the lead of the open source project CrypTool 2  and a member of the steering group of MysteryTwister C3 . His main research interests are modern security protocols for decentralized distributed systems, computerized cryptanalysis of classical ciphers, and cybersecurity awareness. At the University of Kassel, he teaches classes about cryptology and cybersecurity. Additionally, he regularly offers cryptology workshops for students at local schools and gives talks about penetration testing for companies. Email: <>

Host: Alan T. Sherman,

The UMBC Center for Hybrid Multicore Productivity Research (CHMPR)
is pleased to present as part of our distinguished lecture series

Human-Like Strategies for Language-Endowed Intelligent Agents

Dr. Sergei Nirenburg
Professor of Cognitive Science
Rensselaer Polytechnic Institute

11:00am Friday, 28 April 2017, ITE 325b

Artificial intelligent agents functioning in human-agent teams must correctly interpret perceptual input and make appropriate decisions about their actions. These are arguably the two central problems in computational cognitive modeling. The RPI LEIA Lab builds language-endowed intelligent agents that extract meaning of text and dialog and use the results together with input from other perception modes, a long-term belief repository, rich models of the world and of other agents, and a model of the interaction situation to make decisions about actions. Specific phenomena we currently concentrate on include incrementality, treatment of unexpected input and non-literal language (e.g., metaphor), analysis of agent biases and “mindreading,” and deliberate concept learning. All these studies are characterized by our belief in the ultimate utility of building causal models of agent capabilities that are inspired by human strategies in language processing and decision-making that go beyond analogical reasoning. In this talk I will give an overview of our recent work in the above areas.

Sergei Nirenburg is Professor of Cognitive Science and Computer Science at the Rensselaer Polytechnic Institute. He also serves as Head of the Department of Cognitive Science. He has worked in the areas of cognitive science, artificial intelligence and natural language processing for over 35 years, leading R&D teams of up to 80. Dr. Nirenburg’s professional interests include developing computational models of human cognitive capabilities and implementing them in computer models of societies of human and computer agents, continuing development of the theory of ontological semantics, and the acquisition and management of knowledge about the world and about language. Academic R&D teams under Dr. Nirenburg’s leadership have implemented a variety of proof-of-concept and prototype application systems for cognitive modeling, intelligent tutoring and a variety of NLP tasks (machine translation, question answering, text summarization, information extraction, computational field linguistics, knowledge elicitation and learning). Dr. Nirenburg has written two and edited five books and published over 200 scholarly articles in journals and peer-reviewed conference proceedings.

UMBC CSEE Seminar Series

Resynchronization of circadian neurons and the east-west asymmetry of jet-lag recovery

Zhixin Lu
University of Maryland, College Park

1-2pm Friday, 21 April 2017, ITE 231

Cells in the brain’s Suprachiasmatic Nucleus (SCN) are known to regulate circadian rhythms in mammals. We model synchronization of SCN cells using the forced Kuramoto model, which consists of a large population of coupled phase oscillators (modeling individual SCN cells) with heterogeneous intrinsic frequencies and external periodic forcing. Here, the periodic forcing models diurnally varying external inputs such as sunrise, sunset, and alarm clocks. We reduce the dimensionality of the system using the ansatz of Ott and Antonsen and then study the effect of a sudden change of clock phase to simulate cross-time-zone travel. We estimate model parameters from previous biological experiments. By examining the phase space dynamics of the model, we study the mechanism leading to the difference typically experienced in the severity of jet-lag resulting from eastward and westward travel.

Zhixin Lu, PhD Candidate, joined the Nonlinear Dynamics and Chaos Group in the University of Maryland, College Park in 2011, as a Graduate Research Assistant in Dr. Edward Ott’s group. He acquired expertise in nonlinear dynamics and complex systems. Together with the colleagues from UMD, he used methods from nonlinear dynamics theory to investigate the synchronization of circadian neurons, the statistical properties of critical avalanching firing in integrate-and-fire neuron models, as well as dynamical behavior of artificial recurrent neuronal networks. His main research interests are the applications of nonlinear dynamics and the theory of complex networks to biological and artificial neural networks.

Host: Fow-Sen Choa; Organizer: Tulay Adali

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.

UMBC CSEE Seminar Series

Dynamic Causal Modelling of Neuroimaging (hemodynamic) Time Series and Current Applications in Psychiatric Research

Eugenia Radulescu
The Lieber Institute for Brain Development

1-2pm Friday, 14 April 2017, ITE 231

Abnormal neural processing due to interregional dysconnectivity at brain systems level is widely accepted as a generic mechanism in the etiopathogeny of major psychiatric disorders (i.e., schizophrenia and autism). Dynamic Causal Modeling (DCM) is a modern framework for creating, estimating and comparing generative models of hemodynamic time series, useful for investigating the effective connectivity of neuronal populations. This presentation will discuss the role of DCM in mapping the “ineffective” connectivity between brain regions in mental disorders and it will be structured in three parts: a. An introduction to the theoretical background of DCM applied to task-dependent hemodynamic response during functional MRI; b. Examples of clinical applications of DCM in schizophrenia and autism; c. Challenges and limitations of DCM in psychiatric research, followed by a general question: how can collaborate psychiatrists, computer scientists and engineers in improving and better adapting mathematical models for understanding the biological disturbances in psychiatric conditions.

Eugenia Radulescu, MD, PhD joined the Lieber Institute in 2013, as a Research Fellow in Dr. Weinberger’s group. She earned her MD and PhD at the University of Medicine and Pharmacy “C. Davila” Bucharest, Romania and practiced as a psychiatrist for thirteen years before switching to a career in psychiatric research. In 2003-2009 she was a post-doctoral fellow in Dr. Weinberger’s lab, Clinical Brain Disorders Branch, NIMH/ NIH. There Eugenia acquired expertise in neuroimaging methods- structural and functional MRI. Together with the colleagues from the Neuroimaging Core, she used these methods to investigate the effects of schizophrenia risk genetic variants on well characterized imaging intermediate phenotypes. In 2010-2014 Eugenia was a visiting fellow at Brighton and Sussex Medical School and Sackler Center for Consciousness Science in United Kingdom, where she continued to use MRI for studying brain structural and functional abnormalities associated with neurodevelopmental disorders (i.e. schizophrenia and autism). In the Division of Clinical Sciences at Lieber Institute, she pursues her long standing interest in applying complex fMRI analytical methods to test genetic epistasis effects on brain networks relevant as novel drug targets in neuro-developmental disorders.

Organizer & Host: Tulay Adali

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.

CHMPR Distinguished Lecturer Series

Stepping Away From the Edge of Illness

Dr. Ancha Baranova, George Mason University

4:30-5:30pm Thursday, 6 April 2017, UC 310, UMBC
3:30-4:30pm Reception, UC 310

The human body may be afflicted by a multitude of chronic diseases. In principle, any chronic ailment develops along with one or more of the four fundamental pathophysiological processes, namely Insulin Resistance, Systemic Inflammation, Metabolic Deficiency and Tissue/Organ Involution and Degeneration. All of these four fundamental processes are known harbingers of the aging process. Borders between health and disease are blurry, and typical diagnostic cut-offs are arbitrary and in the recent past were subjects for revision. Therefore, neither physicians nor patients should wait until clear signs of crossing the border between health and sickness manifest themselves. To the contrary, they must constantly at all times consciously apply their efforts to ensure the maintenance of proper body homeostasis. By doing so, they can best resist the metabolic derangement which defines an “aged” state. Optimally, for humans to remain healthy throughout inevitable process of aging, biochemical parameters must be monitored longitudinally and balanced with available means. For relatively healthy individuals, these means should be centered on non-pharmacological, predominantly nutritional and nutraceutical approaches. Accordingly, it is reasonably foreseeable that a novel “Health Integrator” profession is anticipated to emerge in order to support the growing need for life-long health maintenance.

Dr. Ancha Baranova is an Associate Professor in School of Systems Biology, George Mason University, Fairfax VA. Dr. Baranova runs both experimental and computational research programs in highly interdisciplinary and collaborative field of Personalized Medicine. She is an expert in systems biology driven analysis of human metabolism, with an emphasis on diseases associated with the process of ageing. She is an author of more than 150 research papers, reviews and opinion pieces in the field of human systems biology.

UMBC CSEE Seminar Series

Quantum plane and plucking polynomial of rooted trees

Józef H. Przytycki
George Washington University

1:00-2:00pm, Friday, 7 April 2017, ITE 231

We describe here a new invariant of rooted trees and following up state sum invariant of pointed graphs. We argue that the invariant is interesting on it own, and that it has connections to knot theory and homological algebra. Another reason that we propose this invariant is that we deal here with an elementary, interesting new mathematics and after the talk everybody can take part in developing the topic, inventing new results and connections to other disciplines of mathematics (and likely statistical mechanics and combinatorial biology). The staring point of the talk is the well known formula for $(x+y)^n$ in the quantum plane ($yx=qxy$).

Józef Henryk Przytycki is a mathematician specializing in the fields of knot theory and topology.  A native of Poland, Przytycki received a Master of Science degree in mathematics from Warsaw University in 1977 and, after emigrating to the United States, a Ph.D. in mathematics from Columbia University, where his advisor was Joan Birman. He is currently a professor of mathematics at George Washington University in Washington, DC. He has supervised nine Ph.D. students and has authored and co-authored many mathematical publications, including more than 100 research papers, 10 conference proceedings and 2 books.

Host: Samuel Lomonaco

Seminar Organizer: Tulay Adali

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.

Making in the Classroom: the Rationale, the Challenge and the Imperative

Professor Francis Quek
Department of Visualization, Texas A&M University

3:00-4:00pm Thursday, 6 April 2017, ITE 217, UMBC

Computing is increasingly focused in interaction with the physical world rather than just in the abstract virtual world of screens and pixels. Physical computing combines the design of physical electronics with computation to bring about possibilities that simply interacting with pixels behind glass cannot. One manifestation of physical computing in our culture is seen in the Maker movement. Technologies such as 3D printing and open source electronics and accessible computing have combined to give rise to a Maker movement that promises to broaden participation in technology-based innovation and production. The potential of Making to enhance learning, especially in areas of Science, Technology, Engineering and Mathematics (STEM) has led to calls to bring Making into education. However, the characteristics of innovation, discovery, and student-directed learning for which Making is prized is not easily incorporated into public school learning. Making-based learning are thus often provided in clubs, community Makerspaces, and workshops. This poses a severe issue of equity as youth participants are implicitly self-selected through parents who have the knowledge and means to enroll their children at such venues. Taking a human-centered perspective, we present a project where Making is integrated with the formal curriculum of a public elementary school that serves predominantly underrepresented populations. We will examine the rationale for employing Making-based classroom learning and review our strategy for curriculum alignment. We will see how our ‘double scaffolding’ approach supports both learning of STEM curricula and knowledge and skills associated with computing and Making. Beside learning STEM material, our approach seeks to support the development of STEM self-efficacy and self-identities in children who may not otherwise see these possibilities in themselves. We present results of our year-long study that show the promise of our approach.

Professor Francis Quek is a Professor of the Department of Visualization (and by courtesy, Professor of Computer Science and Engineering and Professor of Psychology) at Texas A&M University. He joined Texas A&M University as an interdisciplinary President’s Signature Hire to bridge disparities in STEM. Formerly he has been the Director of the Center for Human-Computer Interaction at Virginia Tech. Francis received both his B.S.E. summa cum laude (1984) and M.S.E. (1984) in electrical engineering from the University of Michigan. He completed his Ph.D. in Computer Science at the same university in 1990. Francis is a member of the IEEE and ACM. He performs research in Making for STEM learning, embodied interaction, embodied learning and sensemaking, multimodal verbal/non-verbal interaction, multimodal meeting analysis, interfaces to support learning, vision-based interaction, multimedia databases, medical imaging, assistive technology for the blind, human computer interaction, computer vision, and computer graphics. He leads several multiple-disciplinary research efforts to understand the communicative realities of multimodal interaction.

UMBC CSEE Seminar Series

Phase synchrony in heart-brain interactions predicts personality and emotions

Ehsan Shokri Kojori
NIH, National Institute on Alcoloh Abuse and Alcoholism

1:00-2:00pm Friday, 17 March 2017, ITE 231

Despite the historical interest in the link between brain and heart, it is unknown whether brain and heart interactions provide meaningful information about emotions and personality. Here we studied the phase and amplitude of coherence between cardiac pulse and resting state fMRI signals in 203 subjects. We show low-frequency (LF, < 0.1 Hz) components of the resting-state networks (RSN) share significant content with corresponding components in physiological recordings. We found LF cardiovascular components precede those in RSNs, and LF respiratory components follow those in RSNs. Phase dispersion (in LF) between cardiac (but not respiratory) and RSN signals predicted a main positivity-negativity dimension of personality (r = 0.31, p < 0.0001) and emotions (r = 0.24, p = 0.001). Specifically, higher phase dispersion between cardiac and brain RSNs predicted higher tendency toward negative inclinations. In summary, these results provide evidence that brain-wide sensitivity to cardiovascular signaling predicts a main dimension of personality and emotions. Finally, our analysis of phase dispersion may have diagnostic value in specific neuropsychiatric disorders.

Dr. Ehsan Shokri Kojori joined the Laboratory of Neuroimaging at the NIH National Institute on Alcohol Abuse and Alcoholism
 as a postdoctoral IRTA fellow in August 2014 and became a Research Fellow in May 2016. He earned a PhD degree in cognitive neuroscience from the University of Texas at Dallas in Spring 2014. Ehsan also has a background in electrical engineering and signal processing. His interests include combining brain imaging modalities (e.g., fMRI, DTI, and PET) and behavioral measurements to understand the neurocognitive underpinnings of goal directed behavior. His current work involves studying how addiction and alcohol abuse affect efficiency and energetic cost of the brain networks. He is also working on developing novel methodologies to better characterize anatomical and functional brain connectivity indices.