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It’s that time of year. Students enrolled in the cross-College Critical Interactives class (CSCE 571, FAMS 511, MART 591, FAMS 710, CSCE 790) have been developing the 2017 version of Ward One App, which they will present and demonstrate on Tuesday, April 18, 2017, at 4:30 - 5:30pm in Booker T. Washington Auditorium. We invite you join us for the event. The presentation and demonstration will be followed by Q&A.

The Entrepreneurship Club at USC hosts an event called Backers & Hackers, a program that brings computer science and business students, along with Columbia’s entrepreneurial community together to transform app ideas into working mobile applications. Last year at our demo day, we showcased 13 mobile apps, had 100 attendees, 15 sponsors, and 4 investors. Backers & Hackers Demo Day will take place at the Sonoco Pavilion at the Darla Moore School of Business on April 13th at 6:00 PM. Food will be provided. https://tockify.com/incubator/detail/113/1492120800000

DISSERTATION DEFENSE Department of Computer Science and Engineering University of South Carolina Author : Jun Zhou Advisor: Jijun Tang Date : April 6th Time: 2:40 – 4:00 pm Place : 3A75, Swearingen Abstract Over millions of year of evolutionary history, the order and content of the genomes got changed by rearrangements, duplications and losses. There is always a consistent passion to find out what happened and what can happen in the evolutionary process. Due to the great development of various technology, the information about genomes is exponentially increasing, which make it possible figure the problem out. The problem has been shown so interesting that a great number of algorithms have been developed rigorously over the past decades in attempts to tackle these problems following different kind of principles. However, difficulties and limits in performance and capacity, and also low consistency largely prevent us from confidently statement that the problem is solved. To know the detailed evolutionary history, we need to infer the phylogeny of the evolutionary history (Big Phylogeny Problem) and also infer the internal nodes information (Small Phylogeny Problem). The work presented in this thesis focuses on assessing methods designed for attacking Small Phylogeny Problem and algorithms and models design for genome evolution history inference from FISH data for cancer data. During the recent decades, a number of evolutionary models and related algorithms have been designed to infer ancestral genome sequences or gene orders. Due to the difficulty of knowing the true scenario of the ancestral genomes, there must be some tools used to test the robustness of the adjacencies found by various methods. When it comes to methods for Big Phylogeny Problem, to test the confidence rate of the inferred branches, previous work has tested bootstrapping, jackknifing, and isolating and found them good resampling tools to corresponding phylogenetic inference methods. However, till now there is still no system work done to try and tackle this problem for small phylogeny. We tested the earlier resampling schemes and a new method inversion on different ancestral genome reconstruction methods and showed different resampling methods are appropriate for their corresponding methods. Cancer is famous for its heterogeneity, which is developed by an evolutionary process driven by mutations in tumor cells. Rapid, simultaneous linear and branching evolution has been observed and analyzed by earlier research. Such process can be modeled by a phylogenetic tree using different methods. Previous phylogenetic research used various kinds of dataset, such as FISH data, genome sequence, and gene order. FISH data is quite clean for the reason that it comes from single cells and shown to be enough to infer evolutionary process for cancer development. RSMT was shown to be a good model for phylogenetic analysis by using FISH cell count pattern data, but it need efficient heuristics because it is a NP-hard problem. To attack this problem, we proposed an iterative approach to approximate solutions to the steiner tree in the small phylogeny tree. It is shown to give better results comparing to earlier method on both real and simulation data. In this thesis, we continued the investigation on designing new method to better approximate evolutionary process of tumor and applying our method to other kinds of data such as information using high-throughput technology. Our thesis work can be divided into two parts. First, we designed new algorithms which can give the same parsimony tree as exact method in most situation and modified it to be a general phylogeny building tool. Second, we applied our methods to different kinds data such as copy number variation information inferred form next generation sequencing technology and predict key changes during evolution.

Thesis Defense Author : Ahmed Al-Qari Advisor : Dr. John Rose Abstract Proteomics has made major progress in recent years after the sequencing of the genomes of a substantial number of organisms. A typical method for identifying peptides uses a database of peptides identified using tandem mass spectrometry (MS/MS). The profile of accurate mass and elution time (AMT) for peptides that need to be identified will be compared with this database. Restricting the search to those peptides detectable by MS will reduce processing time and more importantly increase accuracy. In addition, there are significant impacts for clinical studies. Proteotypic peptides are those peptides in a protein sequence that are most likely to be confidently observed by current MS-based proteomics methods. There has been rapid improvement in the prediction of proteotypic peptides for AMT studies based on amino acid properties such as amino acid content, polarity, charge and hydrophobicity using a support vector machine (SVM) classification approach. Our goal is to improve proteotypic peptide prediction. We describe the development of a classifier that considers amino acid usage that has achieved a classification sensitivity of 90% and specificity 81% on the Yersinia pestis proteome (using 3-AAU). Using Ordered Amino Acid Usage (AAU) feature, we were able to identify a different set of peptides that was not identified by the 35 peptides features that STEP (Webb-Robertson, 2010)[2] have used. This means that Ordered Amino Acid Usage (AAU) feature could complement other features used by STEP to improve identification accuracy. Building on this success, we used STEP (Webb-Robertson, 2010)[2] 35 amino acids features to complement Ordered Amino Acid Usage (AAU) feature in order to enhance the overall accuracy.

THESIS DEFENSE Author : Shannon Hood Advisor : Dr. Ioannis Rekleitis Abstract This paper proposes a solution to the problem of cooperative exploration using an Unmanned Ground Vehicle (UGV) and an Unmanned Aerial Vehicle (UAV). More specifically, the UGV navigates through the free space, and the UAV provides enhanced situational awareness via its higher vantage point. The motivating application is search and rescue in a damaged building. A camera atop the UGV is used to track a fiducial tag on the underside of the UAV, allowing the UAV to maintain a fixed pose relative to the UGV. Furthermore, the UAV uses its front facing camera to provide a birds-eye-view to the remote operator, allowing for observation beyond obstacles that obscure the UGV's sensors. The proposed approach has been tested using a TurtleBot 2 equipped with a Hokuyo laser ranger finder and a Parrot Bebop 2. Experimental results demonstrate the feasibility of this approach. This work is based on several open source packages and the generated code will be available online.

Thesis Defense Author : Nicholas Weidner Advisor : Dr. Ioannis Rekleitis Abstract The proposed work presents a systematic approach for 3-D mapping and reconstruction of underwater caves. Exploration of underwater caves is very important for furthering our understanding of hydrogeology, managing efficiently water resources, and advancing our knowledge in marine archaeology. Underwater cave exploration by human divers however, is a tedious, labor intensive, extremely dangerous operation, and requires highly skilled people. As such, it is an excellent fit for robotic technology, which has never before been addressed. The proposed solution employs a stereo camera and a video-light. The approach utilizes the intersection of the cone of video-light with the cave boundaries resulting in the construction of a wire frame outline of the cave. Successive frames produce a scalable accurate point cloud which, through the use of adapted 3-D geometry reconstruction techniques, creates a fully replicated model of the cave system.

People know when they need to hire a dentist or an accountant, but they rarely know when they need to hire a linguist. This talk focuses on the professional opportunities available to people with traditional linguistics and computational linguistics training, from undergraduate to graduate degrees. Laura Walsh Dickey shares her experience transitioning from academia to industry. As part of the talk, she discusses specific problems she’s worked on and the kinds of interesting challenges that linguists might find themselves working on in industry. She talks about how to spot jobs that might be appropriate for linguists and gives practical tips about finding them, applying for them, and deciding what’s right for you. Laura Walsh Dickey is a Program Manager at Google, focusing on machine learning and language technology. She joined Google in 2013 with a PhD in Linguistics from the University of Massachusetts, Amherst and 25 years of experience in academia, consulting, and industry. Her research at the Max Planck Institute for Psycholinguistics and Northwestern University focused on the phonology of liquid consonants, speech perception, and speech production. Her forays into the consulting world opened up a new area of linguistic problems which needed to be solved, from drug name confusability to teaching foreign language pronunciation to understanding what people mean when they type in that Google search box.