Gregory Dudak, McGill University Abstract: This talk will address the deployment of robotic systems for data collection. This includes task specification, gait learning and data analysis. As a concrete example I will discuss the automated analysis of video data, and specifically video data collected underwater with an amphibious vehicle (the Aqua 2 hexapod). Automated systems can collect data at prodigious rates and the timely analysis of this data is a growing challenge, especially when there are bandwidth constraints between the data source and the people who must examine the data. We are specifically interested in the real-time summarization and detection of the most interesting events in a video sequence, for use by humans who will analyze the data either in real time, or offline. To do this, we are developing methods that adapt to video data streams in real time to collect salient events and using them in the context of a group of vehicles that fly, swim and float. Bio: Gregory Dudek is the Director of the School of Computer Science, a James McGill Professor, member of the McGill Research Centre for Intelligent Machines (CIM) and an Associate member of the Dept. of Electrical Engineering at McGill University. He is the former Director of McGill's Research Center for Intelligent Machines, a 25 year old inter-faculty research facility. In 2010 he was awarded the Fessenden Professorship in Science Innovation and also received the prix J. Armand Bombardier for Technological Innovation Robotics from ACFAS, the Association francophone pour le savoir (the French learned society). He is also the recipient of the Canadian Image Processing and Pattern Recognition Award for Research Excellence and the award for Service to the Community at the Conference on Computer and Robot Vision. He directs the McGill Mobile Robotics Laboratory. He has been on the organizing and/or program committees of Robotics: Systems and Science, the IEEE International Conference on Robotics and Automation (ICRA), the IEEE/RSJ International Conference on Intelligent Robotics and Systems (IROS), the International Joint Conference on Artificial Intelligence (IJCAI), Computer and Robot Vision, IEEE International Conference on Mechatronics and International Conference on Hands-on Intelligent Mechatronics and Automation among other bodies. He is president of CIPPRS, the Canadian Information Processing and Pattern Recognition Society, an ICPR national affiliate. He was on leave in 2000-2001 as Visiting Associate Professor at the Department of Computer Science at Stanford University and at Xerox Palo Alto Research Center (PARC). During his sabbatical in 2007-2008 he visited the Massachusetts Institute of technology and co-founded the company Independent Robotics Inc. He obtained his PhD in computer science (computational vision) from the University of Toronto, his MSc in computer science (systems) at the University of Toronto and his BSc in computer science and physics at Queen's University. He has published over 200 research papers on subjects including visual object description and recognition, robotic navigation and map construction, distributed system design and biological perception. This includes a book entitled "Computational Principles of Mobile Robotics" co-authored with Michael Jenkin and published by Cambridge University Press. He has chaired and been otherwise involved in numerous national and international conferences and professional activities concerned with Robotics, Machine Sensing and Computer Vision. His research interests include perception for mobile robotics, navigation and position estimation, environment and shape modelling, computational vision and collaborative filtering. This seminar is open to anyone who is interested, not just students enrolled in the CSCE 791 class. Please consider attending.

TECH TUESDAY TALKS Presented by TecHealth-Technology Center to Promote Healthy Lifestyles A South Carolina SmartState Center Date: Tuesday, February 9 Time: Noon Place: Discovery 1, 915 Greene Street, Room 140 Speaker: Dr. Jenay M. Beer Dr. Beer is an engineering psychologist specializing in human-robot interaction (HRI) for the older adult population. Primary research interests include the application of technology to improve the quality of lives for older adults, as well as the application of assistive technology for older individuals with disabilities. Assistant Professor-joint position in USC Computer Science and Engineering Department & the College of Social Work · Director of the Assistive Robotics and Technology Lab · Associate Director of Usability for SmartHOME Research Initiative Tech Tuesday Talks, is a new, monthly seminar series bringing together researchers from across the USC campus who share interest in technology-assisted health promotion and disease prevention interventions and research. The series presents a forum to learn about one another’s work, spark collaborations, as well as to introduce students to the ongoing research conducted on the USC campus which incorporates technology in health promotion. All interested faculty, staff, students and the general public are invited to attend. Tech Tuesday Talks are presented on the second Tuesday of the month in the Discovery I Building, Room 140 at noon. To learn more, visit http://techealth.sc.edu/tech-tuesday-talks

Nicholas Stiffler, University of South Carolina Abstract: The speed at which robots begin to enter various application domains is now largely dependent on the availability of robust and efficient algorithms that are capable of solving the complex planning problems inherent to the given domain. This talk will present a line of research that makes progress towards solving some of the complex planning problems found in target tracking applications where a robot or team of robots seeks to locate and follow a group of moving targets. First, I will describe an algorithm for computing the optimal searcher motion strategy when there exists just a single searcher. Second, I will discuss the multiple searcher scenario and present a deterministic and sampling-based algorithm that coordinates the motion of the searchers. The overall theme is that the design and implementation of robust and efficient planners is imperative for robots to manage the complex tasks we envision for them. Bio: Nicholas Stiffler is a Ph.D. candidate in the department of Computer Science and Engineering at the University of South Carolina. His research focuses on on the design and implementation of planners for a variety of complex robotic planning problems. He received his M.S. (2012) and B.S. (2009) degrees from the University of South Carolina. This seminar is open to anyone who is interested, not just students enrolled in the CSCE 791 class. Please consider attending.

Friday, January 29, 2:50 - 4:05 PM, 2A27 Swearingen Engineering Center Jijun Tang, University of South Carolina Abstract: Evolution of cancer cells are characterized by large scale and rapid changes in the chromosomal landscape. The fluorescence in situ hybridization (FISH) technique provides a way to measure the copy numbers of preselected genes in a group of cells and has been found to be a reliable source of data to model the evolution of tumor cells. Chowdhury recently developed a theoretically sound and scalable model for tumor progression driven by gains and losses in cell count patterns obtained by FISH probes. Their model aims to find the Rectilinear Steiner Minimum Tree (RSMT) that describes progression of FISH cell count patterns over its branches in a parsimonious manner. This model is found to effectively model tumor evolution and is also useful in tumor classification. However the RSMT problem is NP--complete and efficient heuristics are necessary to obtain solutions, especially for large datasets. In this talk we will present a new algorithm for the RSMT problem, based on Maximum Parsimony phylogeny inference. Experimental results from both simulated and real tumor data show that our approach outperforms previous heuristics for the RSMT problem, thus obtaining better models for tumor evolution. Bio: Jijun Tang is a professor in the department of Computer Science and Engineering, University of South Carolina, USA. He obtained his Master degree from Tianjin University China and PhD degree from the University of New Mexico, USA. His research interests include computational biology, algorithm design and computer game development, with focus on phylogenetic reconstruction and ancestral genome inference, using higher level genomic data such as genome rearrangements and copy number variations. He has coauthored more than 80 research papers in international conferences and journals. He was program co-chair of 2016 APBC and 2012 WABI conferences and was on the program committees of more than 50 international conferences.

Hello everyone, TEKsystems will be giving a presentation on Monday, Jan. 25th, at 6pm in 2A21. They are a staffing agency that will share the IT skills their clients are currently looking for and will review resumes. Pizza will be provided so please RSVP on the Facebook event listed below if you are able. https://www.facebook.com/events/1662903917317082/ Thank you, Lacie Cochran ACM, Vice Chair

Friday, January 22, 2:50 - 4:05 PM, Asif Jamil Chowdhury, University of South Carolina Abstract: Markov Chain Monte Carlo (MCMC) has become the main computational workhorse in scientific computing for solving statistical inverse problems. It is difficult however to use MCMC algorithms when the likelihood function is computational expensive to evaluate.Here, a novel Metropolis-Hastings algorithm is proposed to sample from posterior distributions corresponding to computationally expensive simulations. The main innovation is emulating the likelihood function using Gaussian processes. The proposed emulator is constructed on the fly as the MCMC simulation evolves and adapted based on the uncertainty in the acceptance rate. The algorithm is tested on a number of benchmark problems where it is shown that it significantly reduces the number of forward simulations. Bio: Asif Jamil Chowdhury is a graduate student in the department of Computer Science and Engineering at University of South Carolina. His supervisor is Dr. Gabriel Terejanu. His primary research interests lie in the field of uncertainty quantification and model validation. At present he is working on the use of Gaussian Processes in Bayesian optimization and Markov Chain Monte Carlo methods. Before starting his graduate studies he worked as software developer for seven years. This seminar is open to anyone who is interested, not just students enrolled in the CSCE 791 class. Please consider attending.

Candidate: Xiaochuan Fan Advisor: Dr. Song Wang Date: January 20, 2016 Time: 1:00 P.M. Place: Swearingen 3A75 Abstract In this research, we mainly focus on the problem of estimating 2D and 3D human poses from monocular images. Different from many previous works, neither our 2D nor 3D pose estimation approaches uses hand-crafted graphical model. Instead, our approaches learn the knowledge on human body using machine learning techniques. Reconstructing 3D human poses from a single set of 2D locations is an ill-posed problem without considering the human body model. In this research, we propose a new approach, namely pose locality constrained representation (PLCR), to model the 3D human body and use it to improve 3D human pose estimation. In this approach, PLCR utilizes a block-structural pose dictionary to explicitly encourage pose locality in human-body modeling. Finally, PLCR is combined into the matching-pursuit based algorithm for 3D human-pose estimation. The 2D locations used by our 3D pose estimation approach may come from manual annotation or estimated 2D poses. This research proposes a new learning-based 2D human pose estimation approach based on a Dual-Source Deep Convolutional Neural Networks (DS-CNN). The proposed DS-CNN model takes a set of category-independent object proposals detected from the image as the input and then learns the appearance of each local part by considering their holistic views in the full body. We also develop an algorithm to combine these results from all the object proposals for estimating the 2D human pose. The experimental results shows that our PLCR-based 3D pose estimation approach outperforms the state-of-the-art algorithm based on the standard sparse representation and physiological regularity in reconstructing a variety of 3D human poses from both synthetic data and real images. Furthermore, the proposed DS-CNN model produces superior or comparable performance against the state-of-the-art 2D human-pose estimation methods based on pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective. Surprised by CNN's power shown in our 2D human pose estimation approach and many other computer vision tasks, we are interested on such a question, if we can discover new knowledge from a CNN model? In this research, we evaluate the impact of all image regions and then show that different regions have different impact and the regions with large impact can provide important cue or signature for a given computer vision task. Note that this cue is not included in the ground truth of training samples. So we consider the signature regions as an interesting representation of new knowledge.

Women in Computing will be hosting their first event this semester on Tuesday Jan 19th at 6pm, in Swearingen 3A75. Come join us as we prepare for the Spring 2016 SET fair! We will have a short presentation on the making of a great resume followed by a "speed dating" style resume review. Be sure to bring your resume! As always, pizza will be provided and all, both men and women, are welcome! Please sign up via the following link: https://www.facebook.com/events/731357456966498/ Hope to see you there, ~Jenay