We are happy to announce that two CSE ACM student teams placed first and second at the College of Charleston location for the International Collegiate Programming Contest (ICPC) southeast regional competition.
In the photo above, from left to right: Viraj Patel, Thomas Panetti, Jeremy Day, Tori McQuinn, Eduardo Romero, Noel Raley and Dylan Madisetti.Csilla Farkas, associate professor in computer science and engineering, has long understood the need for cybersecurity to protect against such attacks. Hired in 2000, she has led the development of cybersecurity education at the University of South Carolina and serves as director of the university’s Center for Information Assurance Engineering. The center is a National Center of Academic Excellence in Information Assurance and Cyber Defense Education (an achievement first earned in 2010 and bestowed jointly by the National Security Agency and the Department of Homeland Security); it earned the same designation for research in 2014.
We are proud to announce that Nathaniel Stone and Theodore Stone, both undergraduates, won the first prize in the undergraduate category of the Student Research Competition at ACM MobiCom 2016. Their research poster is titled "Assessing Header Impacts in Soccer with Smartball". It represents one of the many possible Internet of Things applications that Computer Science researchers are investigating. You can click on their poster on the right to see a larger view.
Our Computational Biology Research lab, headed by Dr. Valafar, has been awarded a grant from the National Institute of General Medical Sciences (NIGMS)/NIH for their project entitled "South Carolina IDeA Network of Biomedical Research Excellence (SC INBRE) Bioinformatics."
Maribeth Bottorff is one of our three class-of-2016 undergraduate majors who went to work for Google. We are extremelly proud of her. At our request, she has written the short article below about her undergraduate experiences and her advice for getting a job at Google or other major tech companies.
My name is Maribeth Bottorff and I graduated in May 2016 with a Bachelor of Science in Computer Science from UofSC. In July, I started my full-time job as a software engineer with Google through their Engineering Residency, a rotational program for new graduates. When I tell people I work at Google, I get the same reactions: “Wow, you must be so smart!” or “Wow, how did you get that job?” or “Wow, that’s awesome!” Yes, it is awesome and, yes, I’m really excited about it. But, no, I am not a prodigy, though I have worked hard to get here. And now I’m going to tell you how I got the job.
But I want to stress that this is not a formula, it’s just a glorified list of things I did and somehow at the end of it all they wanted to hire me. You could do all of these things and not get interviewed. You could do none of them and get hired and make way more money than me. Just remember that these are not instructions for you to follow, just some ideas that you could perhaps take inspiration from.
We congratulate Dr. Ioannis Rekleitis for receiveing an NSF research award for his project titled "Enhancing Mapping Capabilities of Underwater Caves using Robotic Assistive Technology"
This project develops robotic assistive technologies to improve mapping capabilities of underwater caves. The project enables the practical construction of accurate volumetric models for water-filled caves. The technology of this robotic system can also be deployed on underwater vehicles enabling the autonomous exploration of caves and other underwater structures. Furthermore, the data and software, released under an open-source license, enable researchers to test algorithms on computer vision, state estimation, and sensor fusion, in challenging environments. The project integrates research and education through training graduate and undergraduate students and enhancing several graduate and undergraduate courses at the University of South Carolina.Full Abstract:
This project develops robotic assistive technologies to improve mapping capabilities of underwater caves. The project enables the practical construction of accurate volumetric models for water-filled caves. The technology of this robotic system can also be deployed on underwater vehicles enabling the autonomous exploration of caves and other underwater structures. The developed techniques can also be used in some applications of aerial and ground vehicles. Collected data from field deployments of the developed sensor are made available to the wider robotic, geological, and speleological research community through public-domain releases in order to further innovation. Furthermore, the data and software, released under an open-source license, enable researchers to test algorithms on computer vision, state estimation, and sensor fusion, in challenging environments. The project integrates research and education through training graduate and undergraduate students and enhancing several graduate and undergraduate courses at the University of South Carolina. The project also engages undergraduate students from Benedict College, a Historically Black College or University (HBCU). The collected data are used in outreach activities to recruit high-school students of the greater Columbia area in STEM education, engaging students and educators, particularly in underserved communities.
This research develops 3D reconstruction algorithms utilizing the environmental characteristic of a cave system. The research team studies robotic technologies for sensor fusion of multiple data streams in a single unit and validates experimentally the developed system via extensive testing in underwater cave explorations in collaboration with expert cave divers. The project introduces robotic technology to the underwater cave explorer community by capitalizing on existing practices in three steps: (a) deploying stereo cameras to be used in conjunction with structured light carried by the divers, (b) developing a bearing-only Cooperative Localization system for accurately recording the skeleton of explored caves; (c) developing a sensor suite that seamlessly integrates inertial measurement unit, sonar, depth, and visual data with state estimation algorithms for the volumetric mapping of the cave. The project enhances underwater cave mapping abilities by increasing: 1) the scale of the area mapped, 2) the safety of the divers by reducing their cognitive load during exploration and 3) the quality of the produced maps.
Dr Jianjun Hu has received an equipment award from NVIDA corporation for his project on Breast Cancer Diagnosis with Deep learning based image and microarray analysis.
The main purpose of this project is to develop and apply methods in deep learning to solve problems in the emerging field of computational pathology of breast cancer using both large scale of histopathology images and microarray datasets. This project may lead to the development of novel data-driven diagnostic tools for cancer detection, risk prediction, and diagnosis.
We would like to congratulate our Discovery Day winners. Omar Ansari won a first place award for his poster "The use of remote telepresence in collegiate classrooms to facilitate eLearning". Steven Dao and Austin Pahl won a first place award for "Enhancing Interactor Experience in the Ward One App". Nicholas Weidner won second place for "Underwater Cave Mapping using Stereo Vision". Blakeley Hoffman won second place for "Cooperative Set Function Without Communication". Theodore Stone and Nathaniel Stone won an honarable mention for "Correlating a Smart Soccer Ball’s Impact Acceleration to Impact Force". Adel Alamri won an honarable mention for "Speech Signs - Signing with Children."
Full List of winners.
Dr. Gabriel Terejanu, along with Dr. Sourav Banerjee (Mechanical Engineering), Dr. Anindya Chanda and Dr. Robin Kloot (Environmental Health Sciences), has received an award from the Natural Resources Conservation Service (NRCS)/USDA for a data collection project with the title "Using Cover Crops and Soil Health to Reduce Crop Stress and Aflatoxin Contamination". As part of this project the researchers will deploy a sensor network to collect environmental data from a cornfield in collaboration with a local farmer. The data will be used to develop and demonstrate a predictive framework for calculating aflatoxin occurrence in South Carolina cornfields prior to harvest. Aflatoxin is a carcinogenic toxin naturally produced by Aspergillus family of fungi (flavus, parasiticus) occurring in soil and decaying vegetation, which can contaminate corn along with other relevant South Carolina crops like peanuts and cotton before harvest and/or during storage. It is well know that aflatoxin exposure causes one of the deadliest cancers worldwide, namely liver cancer in humans and a variety of animal species. Prediction and control of aflatoxin contaminations before harvest are a fundamental challenge for US grain industry, poultry producers, and makers of dairy products. The predictive model will be used to continuously monitor the aflatoxin incidence in cornfields and provide the farmers with actionable information regarding the best time to harvest and efficient isolation of contaminated areas, as well as testing the role of cover crops and irrigation in plant stress regulation and aflatoxin contamination.
529 Seminar Room, AI Institute