DISSERTATION DEFENSE Department of computer science and Engineering University of South Carolina Author : Lingxi Zhou Advisor: Jijun Tang Date: Thursday, June 23rd Time: 3:00pm Place : Swearingen 3A75 Abstract Gene order can be changed by events such as rearrangements, duplications, and losses, which can change both the order and content of the genome. These genetic changes account for all of genome evolution. Recently, the accumulation of genomic sequences provides researchers with the chance to handle long-standing problems about the phylogenies, or evolutionary histories, of sets of species, and ancestral genomic content and orders. Over the past few years such problems have had a large number of algorithms proposed in the attempt to resolve them, with each algorithm following a different standard. The work presented in this dissertation focuses on algorithms and models for whole-genome evolution and their applications in phylogeny and ancestral inferencing from gene order. We developed a pipeline involving maximum likelihood, weighted maximum matching, and variable length binary encoding for estimation of ancestral gene content to reconstruct ancestral genomes under the various evolutionary models, including genome rearrangements, additions, losses, and duplications, with high accuracy and low time consumption. Phylogenetic analyses of whole-genome data have been limited to small collections of genomes and low-resolution data, or data without massive duplications. We designed a probabilistic approach to phylogeny analysis (VLWD) based on variable length binary encoding, using the probabilistic model, to reconstruct phylogenies from whole genome data, scaling up in accuracy and make it capable of reconstructing phylogeny from whole genome data, like triploids and tetraploids. Maximum likelihood based approaches have been applied to ancestral reconstruction but remain primitive for whole-genome data. We developed a hierarchical framework for ancestral reconstruction, using variable length binary encoding in content estimation, then adjacencies fixing and missing adjacencies predicting in adjacencies collection and finally, weighted maximum matching in gene order assembly. Therefore it extensively improves the performance of ancestral gene order reconstruction. We designed a series of experiments to validate these methods and compared the results with the most recent and comparable methods. According to the results, they are proven to be fast and accurate. Thanks, Sri.

DISSERTATION DEFENSE Author: Jordan Bradshaw Advisor: Jason D. Bakos Date: Wednesday, June 1st Time: 10:00am Place: Swearingen 3A75 Abstract Genomic databases are exhibiting a growth rate that is outpacing Moore's Law, which has made database search algorithms a popular application for use on emerging processor technologies. NCBI BLAST is the standard tool for performing searches against these databases, which operates by transforming each database query into a filter that is subsequently applied to the database. This requires a database scan for every query, fundamentally limiting its performance by I/O bandwidth. In this dissertation we present a functionally-equivalent variation on the NCBI BLAST algorithm that maps more suitably to an FPGA implementation. This variation of the algorithm attempts to reduce the I/O requirement by leveraging FPGA-specific capabilities, such as high pattern matching throughput and explicit on-chip memory structure and allocation. Our algorithm transforms the database—not the query—into a filter that is stored as a hierarchical arrangement of three tables, the first two of which are stored on-chip and the third off-chip. Our results show that it is possible to achieve speedups of up to 8x based on the relative reduction in I/O of our approach versus that of NCBI BLAST, with a minimal impact on sensitivity. More importantly, the performance relative to NCBI BLAST improves with larger databases and query workload sizes.

All are invited to come see our Senior students demo the apps they have built over the last year for our Capstone Class. 20 teams will be demonstrating their apps, which include web applications, mobile applications, and desktop applications. Presentation schedule:

Time	Group	Description	Client
8:30	pSwizzy	A 3D Game for Tablets, using Unity.	Power BNC Energy
8:40	diet	An Android for tracking diet.	The Cancer Prevention and Control Program, USC
8:50	hygieia	A webapp for display and analysis of hydration data.	Hygieia Hydration
9:00	omaha	Hacking a voting machine.	Dr. Duncan Buell, CSE Department
9:10	imentor	A webapp for student-teacher video conferencing.	VC3 and the Richland 2 School District
9:20	FitnessEvolution	An Android app for fitness tracking.	Self startup
9:30	ZVerse	A webapp for 3D model editing.	ZVerse Inc.
9:40	RobotCA	An Android app for controlling robots.	Dr. Ioannis Rekleitis, CSE Department
9:50	DJJ	A webapp for providing information.	Institute for Families in Society University of South Carolina
10:00	pmba	A webapp for tracking incoming student application process.	Professional MBA Program, USC
10:10	wandrlust	A webapp for travellers.	Self startup
10:20	routegen	A webapp for building router configuration files.	Cisco Systems
10:30	ortho	A webapp for patient tracking.	Midlands Orthopedics
10:40	HelpMonger	An Android Ionic app for finding help.	Helpmonger.com
10:50	contextual	An educational platform (webapp) based on the mode neutral learning pedagogy.	Self startup
11:00	sscrm	A webapp for tracking students.	USC Student Success Center
11:10	5chords	An Android app for learning music.	South Carolina Philharmonic
11:20	FitLivin	An Android app for fitness tracking.	Self startup
11:30	dcon	A desktop app for data analysis.	Chemical Engineering Dept., USC

If you can't make it, you can always watch the demos online.

It will be a celebratory event recognizing the accomplishments of our department's undergraduate and graduate students, staff, and faculty. Be part of the celebration and enjoy some frozen treats. Hope to see you all there.

Chin-Tser Huang, University of South Carolina Abstract: Mobile Health (mHealth), which refers to the use of mobile technologies to improve the quality of health care, has attracted increasing attention thanks to the continuous growth of mobile devices and smartphones. It is desirable to integrate mHealth with Electronic Health Record (EHR), the preferred new method to store patients’ health records. However, several security properties need to be satisfied to make the integration practical, such as data privacy, fine-grained access control and scalable access between different clouds. In this talk, we first introduce latest trends of mHealth technologies and applications, and present a secure and scalable framework for EHR data sharing, which combines Identity-based Encryption and Attribute-based Encryption together to enforce a fine-grained access control scheme on EHR and to enable scalable access between multiple clouds. Bio: Dr. Chin-Tser Huang is an Associate Professor in the Department of Computer Science and Engineering at University of South Carolina at Columbia. He received the B.S. degree in Computer Science and Information Engineering from National Taiwan University, Taipei, Taiwan, in 1993, and the M.S. and Ph.D. degrees in Computer Sciences from the University of Texas at Austin in 1998 and 2003, respectively. His research interests include network security, network protocol design and verification, and distributed systems. He is the director of the Secure Protocol Implementation and Development (SPID) Laboratory at the University of South Carolina. He is the author (along with Mohamed Gouda) of the book ‘‘Hop Integrity in the Internet,’’ published by Springer in 2005. His research has been funded by DARPA, AFOSR, AFRL, and NSF. He received the US Air Force Summer Faculty Fellowship Award from 2008 to 2010, and also worked as a Visiting Faculty Researcher with Air Force Research Lab in the summers of 2011 to 2015. He served as the President of The Chinese-American Academic and Professional Association in Southeastern United States (CAPASUS) in 2014-2015. This seminar is open to anyone who is interested, not just students enrolled in the CSCE 791 class. Please consider attending.

The culmination of The Entrepreneurship Club, The College of Engineering and Computing, & The Darla Moore School of Business at The University of South Carolina CSCE 590 app creation project taught by Dr. Jianjun Hu and MGMT 473 Emerging Ventures taught by Juliana Iarossi. The event will include demos of all projects as well as a competition judged by local startup leaders. Written about in a Carolina Money article, this project commenced with a pitch night that included more than 25 pitches. The Computer Science students selected their favorite ideas and began working collaboratively with the entrepreneur to create a minimum viable product. This event will be the official showcase of what our brilliant students have worked on throughout the semester. All are welcome free of charge. Sponsorship options are available and highly encouraged for startups and entrepreneurial. More info.

COLLOQUIUM Glenn T. Starkman COO and Co-founder Soteria, LLC Cyber Security Data and Analytics How do you go from technology to entrepreneurial success? This talk will explore the nuts and bolts, including communication and networking, that go into a successful tech startup. An example of the entrepreneurial process that will be presented is the founding of Soteria. Common themes that recur among entrepreneurs will be examined. Glenn Starkman is the COO and Co-founder of Soteria, a cyber security company that consults businesses with both pre and post breach security incidents. Soteria founders are former NSA elite operatives now based in Charleston, SC. He is a graduate of Boston University with a background in Economics and Math. He is also a Board Member, Co-Founder and Angel Investor in BevBucks, Vixen Enterprises and The Code Lady.. He was previously the Managing Director and Global Head of Sales at UBS, Goldman Sachs and was previously a Partner at Sanford Bernstein, an Asset management company, as well as the Founder and CEO of Starkman Capital LLC. Glenn is also Entrepreneur in Residence at the College of Charleston and is responsible for the Tommy Baker Lecture Series in Entrepreneurial Leadership, is on The College of Charleston Board or Entrepreneurship and leads a weekly lecture series ENTR 445. From this class Glenn will share many of the themes described by speakers dating back to 2014. Glenn will be available for Q and A post his comments.

DISSERTATION DEFENSE Department of Computer Science and Engineering, University of South Carolina Candidate: Emrah Atilgan Advisor: Dr. Jianjun Hu Abstract Developing new materials have historically been time-consuming. Computational material discovery can search large design space to identify promising candidates for experimental verification. Recently, Density Functional Theory (DFT) based first principle calculation has been able to calculate many electrical and physical properties of materials, making them suitable for computational doping based material discovery. In material doping, given a base material, one can change its properties by substituting some elements with new ones or adding additional elements. In computational doping, we have a grid of atoms in a supercell, some of which can be substituted with dopant atoms. There are many possible doping positions for the doped elements in the supercell, among which the most stable supercell with the lowest free electronic energy is the one that most likely appears in experiments. So finding the most stable doped supercell configuration is the first step for computational doping, which is usually done exhaustively nowadays. For each such substitution, the Vienna Ab-Initio Simulation Package is usually used to calculate its energy and higher level physicochemical properties. Free energy calculations take about 15-30 hours for a supercell of 75 atoms for substituting two positions out of 15 with a single dopant element, and it may take days to weeks for multiple dopant elements. This is a typical optimization problem with expensive evaluation functions. Here we first developed a genetic algorithm for finding the most stable structure of the doped material with the lowest free electronic energy for a single dopant element. It can reduce the running time for computational doping by up to 75%. We used SrTiO3 perovskite as the base material and Nb as the substitution element. We also developed another genetic algorithm for multiple dopant elements. Since the search space becomes larger, the genetic algorithm works better and saves up to 85% of calculations for finding the most stable structures. Finally, we developed a genetic programming (GP) algorithm for computational doping which can simultaneously determine multiple dopant elements with different doping ratios. The simultaneous search of dopant elements and their ratios can speed up the search process for large doping spaces.

Gregory Gay, University of South Carolina Abstract: There are two key artifacts necessary to test software, the test data - inputs given to the system under test (SUT) - and the oracle - which judges the correctness of the resulting execution. Substantial research efforts have been devoted towards the creation of effective test inputs, but relatively little attention has been paid to the creation of oracles. Specifying test oracles remains challenging for many domains, such as real-time embedded systems, where small changes in timing or sensory input may cause large behavioral differences. Models of such systems, often built for analysis and simulation before the development of the final system, are appealing for reuse as oracles. These models, however, typically represent an idealized system, abstracting away certain considerations such as non-deterministic timing behavior and sensor noise. Thus, even with the same test data, the model’s behavior may fail to match an acceptable behavior of the SUT, leading to many false positives reported by the oracle. This talk will present an automated framework that can adjust, or steer, the behavior of the model to better match the behavior of the SUT in order to reduce the rate of false positives. This model steering is limited by a set of constraints (defining acceptable differences in behavior) and is based on a search process attempting to minimize a numeric dissimilarity metric. This framework allows non-deterministic, but bounded, behavior differences, while preventing future mismatches, by guiding the oracle—within limits—to match the execution of the SUT. Results show that steering significantly increases SUT-oracle conformance with minimal masking of real faults and, thus, has significant potential for reducing false positives and, consequently, development costs. Bio: Gregory Gay is an Assistant Professor of Computer Science & Engineering at the University of South Carolina. His research interests include automated testing and analysis—with an emphasis on test oracle construction—and search-based software engineering. Greg received his Ph.D. from the University of Minnesota, working with the Critical Systems research group, and an M.S. from West Virginia University. He has previously worked with NASA Ames Research Center and the Chinese Academy of Sciences. This seminar is open to anyone who is interested, not just students enrolled in the CSCE 791 class. Please consider attending.