Colloquia Announcements


Wednesday, 26th April 2017, 5:00pm, UB230

Christopher Chung
School of Architecture
University of Miami

will present

RAD: Responsive Architecture + Design

University of Miami RAD-UM provides resources and expertise for project-based research on the spatial ramifications of embedded technology and ubiquitous computing.The research is premised on the notion that every building or landscape component can be equipped with computational power. Projects at RAD-UM develop models for such digitally enhanced environments to better handle persistent and emerging challenges in the areas of healthcare, building technology and sustainability.

This is another in the Department of Computer Science Pizza Seminar Series. Refreshments will be served at 4:30 p.m. in the reception area of the 3rd floor of Ungar.


Friday, 24th April 2017, 5:00pm, Ungar 506

Dr. Nitesh Saxena
University of Alabama at Birmingham

will present

All Your Voices Are Belong to Us: Stealing Voices to Fool Humans and Machines

A person's voice is one of the most fundamental attributes that enables communication with others. However, equipped with the current advancement in automated speech synthesis, an attacker can build a very close model of a victim's voice after learning only a very limited number of samples in the victim's voice (e.g., mined through the Internet, or recorded via physical proximity). Specifically, the attacker can use voice morphing techniques to transform its voice-- speaking any arbitrary message-- into the victim's voice. In this talk, we will examine the aftermaths of such a voice impersonation capability, based on an off-the-shelf voice morphing tool, against three important applications and contexts: (1) impersonating the victim in a voice-based user authentication system, (2) mimicking the victim in arbitrary speech contexts (e.g., posting fake samples on the Internet or leaving fake voice messages), and (3) mimicking the victim in a Crypto Phone (e.g., Zfone, Silent Circle or Redphone) VoIP secure channel establishment process and thereby compromising the security and privacy of VoIP communications. When considering voice impersonation attacks against human users, we will also share our experiences running a neuro-imaging study (a part of a larger, "neuro-security" project), which provide root-level insights from neurological and cognitive perspective. This talk is based on a line of joint work with Maliheh Shirvanian, which appeared at the ACM CCS, ESORICS and ACSAC conferences in 2015, as well as a recent neuro-security study with Ajaya Neupane and other colleagues, currently being reviewed for publication.

This is another in the Department of Computer Science Distinguished Scientist Seminar series.


Wednesday, 19th April 2017, 5:00pm, UB230

Dr. Jie Xu
Department of Electrical and Computer Engineering
University of Miami

will present

Collaborative Mobile Edge Computing

Pervasive mobile computing and the Internet of Things are driving the development of many new applications that are both compute-demanding and latency-sensitive. Although Cloud Computing enables convenient access to a centralized pool of configurable and powerful computing resources, it often cannot meet the stringent requirements of latency-sensitive applications due to the often unpredictable network latency and expensive bandwidth. The growing amount of distributed data further makes it impractical or resource-prohibitive to transport all the data over today's already-congested backbone networks to the remote cloud. As a remedy to these limitations, Edge Computing emerges as a new computing paradigm to push the frontier of computing applications, data, and services away from centralized cloud computing infrastructures to the logical extremes of a network thereby enabling analytics and knowledge generation to occur at the source of the data. However, many new challenges emerge and have to be addressed in this new computing paradigm to fully reap its benefit. In this talk, I will introduce some of our recent works on collaborative mobile edge computing. Game theoretic solutions are developed to enable efficient and secure collaboration among distributed edge devices in mobile networks, thereby significantly enhancing edge computing performance.

This is another in the Department of Computer Science Pizza Seminar Series. Refreshments will be served at 4:30 p.m. in the reception area of the 3rd floor of Ungar.


Friday, 14th April 2017, 2:30pm, Ungar 230

Dr. Jon Shlens
Google

will present

A Learned Representation for Artistic Style

The diversity of painting styles represents a rich visual vocabulary for the construction of an image. The degree to which one may learn and parsimoniously capture this visual vocabulary measures our understanding of the higher level features of paintings, if not images in general. In this work we investigate the construction of a single, scalable deep network that can parsimoniously capture the artistic style of a diversity of paintings. We demonstrate that such a network generalizes across a diversity of artistic styles by reducing a painting to a point in an embedding space. Importantly, this model permits a user to explore new painting styles by arbitrarily combining the styles learned from individual paintings. We hope that this work provides a useful step towards building rich models of paintings and offers a window on to the structure of the learned representation of artistic style.

Jonathon Shlens received his Ph.D in computational neuroscience from UC San Diego in 2007 where his research focused on applying machine learning towards understanding visual processing in real biological systems. He was previously a research fellow at the Howard Hughes Medical Institute, a research engineer at Pixar Animation Studios and a Miller Fellow at UC Berkeley. He has been at Google Research since 2010 and is currently a research scientist focused on building scalable vision systems. During his time at Google, he has been a core contributor to deep learning systems including the recently open-sourced TensorFlow. His research interests have spanned the development of state-of-the-art image recognition systems and training algorithms for deep networks.

This is another in the Department of Computer Science Distinguished Scientist Seminar series.


Wednesday, 12th April 2017, 5:00pm, UB230

Dr. Mei-Ling Shyu
Department of Electrical and Computer Engineering
University of Miami

will present

Correlation-assisted Mining and Deep Learning for Semantic Concept
Retrieval from Imbalanced Multimedia Big Data

With the extensive use of smart devices and blooming popularity of social media websites such as Flickr, YouTube, Twitter, and Facebook, we have witnessed an explosion of multimedia data. As a result, multimedia semantic concept mining and retrieval whose objective is to mine useful information from the large amount of multimedia data including texts, images, and videos has become more important. Furthermore, many real-world datasets do not have uniform class distributions, and the minority class usually represents the concept of interests such as frauds in transactions, intrusions in network security, and unusual events in surveillance. The classifiers developed on datasets with such skewed distributions tend to favor the majority classes and are biased against the minority class. Despite extensive research efforts, the huge amount of multimedia data, the semantic gap issue, and the imbalanced concept retrieval remain challenging problems in multimedia research. To address these challenges, the joint efforts from big data, data mining, and deep learning for multimedia imbalanced data have been sought. In this talk, a novel correlation-assisted mining and deep learning system for semantic concept retrieval from imbalanced multimedia big data is introduced. To bridge the semantic gap, a concept correlation analysis model using the correlations between the retrieval scores and classes is proposed. To overcome the expensive computation issue, a convolutional neural network (CNN) based deep learning solution integrated with a bootstrapping technique is proposed. To handle big datasets, a Spark infrastructure has been built that shows promising performance of our proposed model with respect to feasibility and scalability. Finally, I will briefly introduce some of our other research work.

This is another in the Department of Computer Science Pizza Seminar Series. Refreshments will be served at 4:30 p.m. in the reception area of the 3rd floor of Ungar.


Wednesday, 5th April 2017, 5:00pm, UB230

Dr. Mark A. Finlayson
School of Computing and Information Sciences
Florida International University

will present

Automatically Extracting Narrative Structure: Results & Directions

Narratives are a ubiquitous language phenomenon found in every society and culture.  Perhaps the single most important feature that distinguishes narratives from other discourse is their structure: the structure of a narrative communicates its meaning and purpose and gives rise to numerous cognitive benefits that improve comprehension, retention, understanding, and use of information given in narrative form.  I present results from the COGNAC Lab at FIU on automatically extracting narrative structure using approaches drawn from machine learning and computational linguistics. First, I present experiments that show that a classic theory of narrative structure (Vladimir Propp's morphology of the folktale) can be reliably reproduced by people.  Second, I demonstrate a specially-designed learning algorithm can learn Propp's theory from raw data.  Finally, I outline the most recent results from student work in the lab, including algorithms for story detection, character classification, and motif extraction, which point the way forward to systems for fully automatic narrative structure extraction and a major advance in machine intelligence.

Dr. Mark Finlayson is Assistant Professor of Computer Science in the School of Computing and Information Sciences at Florida International University.  He received his Ph.D. from MIT in 2011, and from 2012-2014 was a Research Scientist in MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL).

This is another in the Department of Computer Science Pizza Seminar Series. Refreshments will be served at 4:30 p.m. in the reception area of the 3rd floor of Ungar.


Tuesday, 4th April 2017, 12:15pm, Ungar 330G

Mr. Faisal Sikder
Department of Computer Science
University of Miami

will present

Human Fall and Activity Detection, and Muscle Spasm Identification

Efficient image data compression algorithms are required to minimize the cost of data transmission and storage as the quality and file sizes of images keep increasing. With the advancements in image sensors and processing units of mobile devices, the use of complex but more effective compression algorithms is becoming more prevalent on a wide variety of devices.  There are two types of compression algorithms for images: lossy (e.g.  JPEG) and lossless (e.g. PNG). Lossy image compression results in information loss at the expense of compression gain. Lossless compression, in contrast, preserves image integrity fully, which is an important consideration in certain critical applications such as medical image processing.  In this talk, we will first give an overview of the techniques and the applications of image compression. Then, we will present our research efforts on enhancing the performance of some of the existing algorithms and propose new ones to better gains of lossless image compression algorithms. More specifically, we will introduce pre-processing techniques (dynamic pseudo-distance matrix and scanning paths) and describe how we compress the pre-processed image data using the block-sorting transformations, and inversion coding technique along with an entropy coder. We will demonstrate, using various standard image data sets, that our proposed image compression techniques perform better than GIF, PNG, and JPEG 2000. Finally, we will discuss the implementation issues of our algorithms on microcontrollers (Arduino Uno, TI MSP432) as well as its parallelization on multi-processors.

This is a Department of Computer Science PhD Defence.


Friday, 31st March 2017, 2:30pm, Ungar 330G

Mr. Basar Koc
Department of Computer Science
University of Miami

will present

Lossless Compression of Images

Efficient image data compression algorithms are required to minimize the cost of data transmission and storage as the quality and file sizes of images keep increasing. With the advancements in image sensors and processing units of mobile devices, the use of complex but more effective compression algorithms is becoming more prevalent on a wide variety of devices.  There are two types of compression algorithms for images: lossy (e.g.  JPEG) and lossless (e.g. PNG). Lossy image compression results in information loss at the expense of compression gain. Lossless compression, in contrast, preserves image integrity fully, which is an important consideration in certain critical applications such as medical image processing.  In this talk, we will first give an overview of the techniques and the applications of image compression. Then, we will present our research efforts on enhancing the performance of some of the existing algorithms and propose new ones to better gains of lossless image compression algorithms. More specifically, we will introduce pre-processing techniques (dynamic pseudo-distance matrix and scanning paths) and describe how we compress the pre-processed image data using the block-sorting transformations, and inversion coding technique along with an entropy coder. We will demonstrate, using various standard image data sets, that our proposed image compression techniques perform better than GIF, PNG, and JPEG 2000. Finally, we will discuss the implementation issues of our algorithms on microcontrollers (Arduino Uno, TI MSP432) as well as its parallelization on multi-processors.

This is a Department of Computer Science PhD Defence.


Wednesday, 29th March 2017, 5:00pm, UB230

Dr. Dilip Sarkar
Department of Computer Science
University of Miami

will present

Threats and Vulnerabilities in Cyberspace

Individual users, small businesses, large enterprises, governments, national and international infrastructures depend on cyberspace for regular operations. However, cyberspace and their users have vulnerabilities, which malicious actors are exploiting. In particular, malicious actors use /mal/icous soft/wares/ (malwares), including viruses, worms, Trojan horses, backdoors, rootkits, adware, key loggers, spywares, ransomewares, and botnets. After discussing common methods used to distribute malwares, we will discuss bots and botnets, because they are the biggest threat in the cyberspace. In the past they have inflicted most harm to cyberspace infrastructures as well as cyberspace users; in the future they are expected to do so again and again. Also, we will discuss phishing and social engineering techniques.

This is another in the Department of Computer Science Pizza Seminar Series. Refreshments will be served at 4:30 p.m. in the reception area of the 3rd floor of Ungar.


Wednesday, 22nd March 2017, 5:00pm, UB230

Joel P. Zysman
Center for Computational Science
University of Miami

will present

The Internet of Things at University of Miami:
Projects and resources available for researchers of all levels

While we've all heard about the Internet of Things and how large it is going to become in the next few years; did you know that UM researchers are conducting IoT research in everything from Smart Cities to Climate Change?  Please join Advanced Computing Director Joel Zysman for a round table discussion of both current research and the resources that are available for future work.  See how UM researchers are using tools provided by the Center for Computational Science today and learn how to leverage these new technologies in your own work.

This is another in the Department of Computer Science Pizza Seminar Series. Refreshments will be served at 4:30 p.m. in the reception area of the 3rd floor of Ungar.


Wednesday, 8th March 2017, 5:00pm, UB230

Albert Harum-Alvarez
SmallCo Lead Designer

will present

CODE SMELL: Why Tech Goes Stale Fast, and what that can mean for your career

You're worried, and that's understandable. Some of the tech you're learning seems to be aging so fast that it will be obsolete before you can fully master it. While you're still young you're confident you can stay ahead of the curve, learning faster than the rate of obsolescence.  But you read about middle-aged tech workers left behind, and you wonder if that could be you someday.  The good news is that there are certain clear patterns in the way tech matures, and these patterns can be navigated and even managed. They are at the heart of app design at SmallCo [smallco.co]. SmallCo designs apps for clients such as Apple, Fidelity Investments, the investment bank BNP Paribas, the New York Public Library, Harvard University, The Cleveland Clinic and others. SmallCo's lead designer Albert Harum-Alvarez will give this talk, adapting material from his Design Masterclass, taught at sites around the world.  SmallCo is looking for computer science students like you who want to study these concepts in depth, both to lengthen your tech career and because you really love to do good design. SmallCo is looking for talented interns willing to work and study at our offices in Miami, New York, Belgrade, Mexico City, Rotterdam and Goteborg Sweden.

This is another in the Department of Computer Science Pizza Seminar Series. Refreshments will be served at 4:30 p.m. in the reception area of the 3rd floor of Ungar.


Wednesday, 1st March 2017, 5:00pm, UB230

Mr. Basar Koc and Dr. Huseyin Kocak
Department of Computer Science
University of Miami

will present

Making of a website: www.perlforbiologists.org

As an enhancement to the PRISM course CSC210 - Computing for Scientists, we have just unveiled the e-resource www.perlforbiologists.org consisting of a series of episodes featuring examples from genomics to help biologists learn the basics of the Perl programming language. After completing the final episode, students will be able to download a genome file from NCBI, and search and tally intricate motifs.  The website design, simple yet elegant, is platform-independent and works well on mobile devices. The creation of the website is a purely educational undertaking and is devoid of any commercial material; free access is granted to all, not just to our students.  In this talk, we will first discuss the choice of the content, the educational mission, and the design of the website. Then, we will introduce the following technical applications and standards that we used in the construction of the website: HTML5, Bootstrap, JavaScript, Google Analytics, Video Codecs (H264, H265, VP9), Podcast Audio Standards (ITU-R BS.1770-2), and Adobe Creative Cloud Apps.

This is another in the Department of Computer Science Pizza Seminar Series. Refreshments will be served at 4:30 p.m. in the reception area of the 3rd floor of Ungar.


Wednesday, 22nd February 2017, 5:00pm, MEA202

Faisal Sikder
Department of Computer Science
University of Miami

will present

Semi-Automatic Extraction of Training Examples from Sensor Readings
for Fall and Activity Identification

While inexpensive wearable motion-sensing devices have shown great promise for fall detection and human activity monitoring, two major challenges still exist and have to be solved: 1) a framework for the development of firmware, and 2) software to make intelligent decisions.  We address both the problems. In this talk, we show our proposed generic framework for developing firmware. We also demonstrate that the k-means clustering algorithm can semi-automatically extract training examples from motion data. Moreover, we discuss about several one- and two level classification networks combinations of neural networks and softmax regression to monitor non-fall activities and to detect fall events. We also illustrate how the datasets for training and testing have been collected using the devices we assembled with four off-the-shelf components. This work advances the state-of the-art for development and training of wearable devices for monitoring non-fall activities and detecting fall events.

This is another in the Department of Computer Science Pizza Seminar Series. Refreshments will be served at 4:30 p.m. in the reception area of the 3rd floor of Ungar.


Friday, 17th February 2017, 2:30pm, Cox 42

Dr. Houssam Nassif
Amazon

will present

Diversifying Amazon Recommendation

Dr. Nassif will be presenting two recently published papers on diversifying Amaz on recommendations: "Diversifying Music Recommendations" (ICML'16 Workshop) uses submodular diversity to significantly improve Prime Music App recommendations quality and user engagement.  "Adaptive, Personalized Diversity for Visual Discovery" (RecSys'16 Best Short Paper Award) describes Amazon Stream's seasonal, personalized and diversified recommendation framework. Amazon Stream (http://www.amazon.com/stream/), a new website for fashion discovery, uses Bayesian regression to score products, balances exploration and exploitation, applies submodularity to diversify recommendations, and learns seasonal and personalized weights to produce the final recommended personalized stream.

This is another in the Department of Computer Science Seminar series, presented jointly with the Department of Neuroscience and the Center for Computational Science.


Wednesday, 15th February 2017, 5:00pm, UB230

Dr. Athena Hadjixenofontos
Center for Computational Science
University of Miami

will present

The Many Forces of Nature: Lessons on the Genetic Architecture of Multiple Sclerosis
from the Isolated Population of Sardinia

Complex diseases are the most prevalent causes of death and disability in developed countries. Susceptibility to complex diseases is determined by the cumulative results of hundreds to thousands of genetic variants, as well as environmental exposures. In the last twenty years, numerous large scale projects have been undertaken to identify the genetic variants that underlie susceptibility to a number of complex diseases, including multiple sclerosis. Success has been limited: for multiple sclerosis, 110 genetic variants have been associated with the disease in outbred Caucasian populations, and it is estimated that hundreds to thousands more remain in the dark. The parameters that define the genetic architecture of a complex disease extend beyond the number of variants that underlie susceptibility, and include their effect sizes, population frequencies and whether or not they act additively. The limited success in forming a complete picture has led to the exploration of alternative hypotheses. Some of the alternative hypotheses target areas of the genetic architecture that traditional experiments are not designed to address. In this talk I will recount our contributions to understanding the genetic architecture of multiple sclerosis through the study of the isolated population of Sardinia. I will then lay out a set of future directions that are designed to challenge our assumptions about the hidden areas of genetic susceptibility, through forward-in-time genetic simulations. The completion of the proposed experiments will explain the reasons for our limited success in mapping disease variants and position us to choose the methods that can be effective in uncovering the remaining effects.

This is another in the Department of Computer Science Pizza Seminar Series. Refreshments will be served at 4:30 p.m. in the reception area of the 3rd floor of Ungar.


Wednesday, 8th February 2017, 5:00pm, UB230

Dr. Fabian Soto
Department of Psychology
Florida International University

will present

Extending Multidimensional Signal Detection Theory to Study the Independence of Brain Representation

A common goal in visual neuroscience is to determine whether some stimulus dimensions (e.g., shape and spatial information, different shape properties, face expression and identity) are processed and represented independently from others. Such representation can be extremely useful; for example, if most objects can be recognized on the basis of a few shape dimensions, then representing those shape dimensions independently from any other visual information would allow fast object learning, by focusing attention only on the relevant dimensions and ignoring the irrelevant information. Furthermore, such learning would generalize broadly to any new object image from which the relevant shape dimensions can be extracted, regardless of how different this new image is from the training images. Such fast, generalizable learning is easily observed in people, but poorly understood.  Unfortunately, analytical tools and models tailored specifically to study the independence of brain representations have not been developed.  Without such tools, cognitive neuroscientists have resorted to proposing a multiplicity of operational tests of independence, each a small modification of traditional analyses adapted to measure a vaguely defined construct. Unsurprisingly, this research strategy has yielded contradictory results in most areas. This talk summarizes recent work aimed at solving this problem by extending General Recognition Theory, a multidimensional version of signal detection theory, to the study of independence of brain representations, including neuroimaging and neurophysiology.

This is another in the Department of Computer Science Pizza Seminar Series. Refreshments will be served at 4:30 p.m. in the reception area of the 3rd floor of Ungar.


Wednesday, 1st February 2017, 5:00pm, UB506

Dr. Geoff Sutcliffe
Department of Computer Science
University of Miami

will present

Automated Reasoning for the Dialetheic Logic RM3

A dialiethic logic allows formulae to be true, or false, or (differently from classical logic) both true and false, and the connectives are interpreted in terms of these three truth values. Consequently some inferences rules of classical logic are invalid in RM3, and some theorems of classical logic are not theorems of RM3. An automated theorem prover for RM3 has been developed, based on translations of RM3 formulae to classical first-order order logic, and use of an existing first-order theorem prover to reason over the translated formulae. Examples and results are provided to highlight the differences between reasoning in classical logic and in RM3.

This is another in the Department of Computer Science Pizza Seminar Series. Refreshments will be served at 4:30 p.m. in the reception area of the 3rd floor of Ungar.


Friday, 27th January 2017,2:30pm, Ungar 230

Dr. Hosna Jabbari
Ingenuity Lab
University of Alberta

will present

In Silico Tool to Improve Efficacy of Gene Therapy

With the amount of genomic data produced every day, advances in medical sciences and development of new gene modification tools, a revolution in medicine is expected.Recent approval of the first gene therapies by FDA is a leap towards this revolution. Computational methods provide unique opportunities to realize this revolution by providing both an inexpensive framework (in terms of cost, time and safety) to explore the complex biological systems of diseases, and a reduced search space for treatment options. In this talk, I will describe an example of such framework for treatment of Duchenne muscular dystrophy through gene therapy, and highlight some of the challenges and future opportunities.

This is another in the Department of Computer Science Seminar series.


Wednesday, 25th January 2017, 5:00pm, Ungar 230

Dr. Tatiana Engel
Department of Bioengineering
Stanford University

will present

Discovering Dynamic Computations in the Brain from Large-Scale Neural Recordings

Neuronal responses and behavior are influenced by internal brain states, such as arousal, vigilance, or task context. Ongoing variations of these internal states affect global patterns of neural activity, giving rise to apparent variability of neuronal responses to sensory stimuli, from trial to trial and across time within single trials. Demultiplexing these endogenously generated and externally driven signals proved difficult with traditional techniques based on trial-averaged responses of single neurons, which dismiss neural variability as noise. In this talk, I will describe my recent work leveraging multi-electrode neural activity recordings and computational models to uncover how internal brain states interact with goal-directed behavior. I will show that ensemble neural activity within single columns of the primate visual cortex spontaneously fluctuates between phases of vigorous (On) and faint (Off) spiking. These endogenous On-Off dynamics, reflecting global changes in arousal, are also modulated at a local scale during spatial attention and predict behavioral performance. I will also demonstrate that these On-Off dynamics provide a single unifying mechanism that explains general features of correlated variability classically observed in cortical responses (e.g., changes in neural correlations during attention). I will conclude by sketching out a roadmap for developing a general theory that will allow us to discover dynamic computations from large-scale neural recordings and to link these computations to behavior.

This is another in the Department of Computer Science Seminar series.


Monday, 23rd January 2017, 2:15pm, Ungar 230

Dr. Catie Chang
National Institute of Health

will present

Uncovering New Dimensions of Human Brain Function from fMRI Data

Functional magnetic resonance imaging (fMRI) is a powerful technique for human neuroscience. The richness and complexity of fMRI data present exciting challenges at the interface between computation and neuroscience, and require innovative data analysis methods together with deeper understanding of the neural and physiological basis of fMRI signals. I will describe my studies revealing features of brain function embedded in the dynamics of intrinsic brain networks. I will also discuss how, by integrating fMRI with electrophysiological, behavioral, and heart rate data, we uncovered components of fMRI dynamics related to vigilance and autonomic activity and developed a data-driven approach for detecting vigilance fluctuations in fMRI scans. These studies highlight ways in which previously unexplored dimensions of systems-level brain activity may be extracted from fMRI signals, and open new directions for neuroimaging biomarkers in health and disease.

This is another in the Department of Computer Science Seminar series.


Thursday, 19th January 2017, 8:30am, Ungar 230

Dr. Xuan Guo
Oak Ridge National Laboratory

will present

Multi-omics Data Analyses via High-performance Computing for
Complex Biological Systems

Systems biology aims to model complex biological interactions at the system level by integrating information from interdisciplinary fields using a holistic perspective approach. Emerging high-throughput omics technologies promote current biology research into the age of systems biology and also raise huge challenges in multi-omics data integration, modeling, and systems-level analyses. High-performance computing techniques are promising to overcome the limits posed by conventional methods to the mining and exploration of large amounts of multi-omics data. In this talk, I will explore how to analyze high-throughput multi-omics data to better understand complex diseases and microbial communities. I will present several parallel algorithms and high-performance computing framework that are broadly applicable for the analyses of large data and complex biological systems.

This is another in the Department of Computer Science Seminar series.


Thursday, 12th January 2017, 8:30am, Ungar 230

Dr. Zheng Wang
University of Southern Mississippi

will present

The Complex Systems of Protein Domain Co-Occurrences and
the Three-Dimensional Structure of the Genome

This presentation will start from a biological network named protein domain co-occurrence network that has not been fully studied but important, in which each node represents a protein domain, and if two domains co-exist in a protein, an edge is created to connect them. After repeating this procedure for all of the proteins of a species, a network of the species is created that contains species-specific biological signatures. Dr. Wang's research verifies that this type of network is scale-free network. Other topological properties including the shortest path distribution and the clustering coefficient distribution will also be shown in the presentation. A robustness test shows that this network is vulnerable to attack (remove nodes starting from the ones with the highest degree value) and robust to failure (remove nodes randomly).  Dr. Wang has successfully used this type of network to predict protein and domain functions using neighbor-counting, biological function enrichment, and a machine learning algorithm based on the network topology. Another experiment of Dr. Wang has achieved an accuracy of 93.43% when applying this network to infer the phylogenetic relationships of 398 single-chromosome prokaryotic species using a graph alignment algorithm.

After showing these research outcomes, the presentation will continue to a newly-emerged and potentially ground-breaking research topic, that is, the three-dimensional (3D) structure of the genome. The 3D conformations of healthy human chromosomes will be shown, followed by the comparisons of the intra-chromosomal spatial proximities of healthy, leukemia, and lymphoma human B-cells or cell-lines. The inter-chromosomal (between different chromosomes) spatial proximities and the chromosome translocation between chromosome 11 and 14 (a segment of chr.11 is exchanged with a segment of chr.14) in leukemia will also be illustrated in the presentation. Furthermore, a novel type of biological complex network will be introduced, which can indicate the 3D spatial proximities among biological components including protein coding genes, transcription factors, and lncRNAs. Using this type of novel complex network, Dr. Wang's lab has recently reconstructed the 3D structure of the chromosome X of mouse embryonic stem cells mapped with the localization intensities of Xist transcripts (an lncRNA that can inactivate the entire chromosome X by altering its 3D structure). Dr.  Wang will present their algorithms and the corresponding reconstructed 3D structures in the resolutions of 500K base pair and, more excitingly, 40K base pair that has reached the gene level.

After that, Dr. Wang's latest research in topological domains in mammalian genomes, which are recently believed to be the structural and functional units of the genome, will be briefly discussed. Particularly, Dr. Wang will show a novel structural measurement for topological domains and its correlations with genetic and epigenetic features.  Together with Dr. Wang's internationally-recognized research in protein function prediction and protein model quality assessment using deep networks (stacked denoising autoencoders, SdAs), a newly-designed bioinformatics course proposed in a pending NSF CAREER proposal will also be briefly mentioned. Grant proposal ideas with computer science, physics, biological sciences, biostatistics, and medical school will be proposed throughout the presentation.

This is another in the Department of Computer Science Seminar series.


Previous Colloquia Announcements