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

He Wang, University of Illinois at Urbana Champaign Friday, January 15, 2:50-4:05 PM, 2A26 Swearingen Engineering Center Abstract: Indoor localization has been a tantalizing problem in mobile computing, and despite significant research, there is no solution yet in the mainstream. In this talk, I will discuss the landscape of indoor localization. I will also talk about my own research, UnLoc, which breaks away from pure RF based localization (e.g., cellular, Wi-Fi) and shows the benefits of leveraging smartphone sensors (accelerometers, gyroscopes, magnetometers, etc.) into the solution framework. I will describe additional solutions, VideoLoc, where feeds from surveillance cameras can be leveraged for highly precise localization and customer interaction, without compromising privacy of individual users. I will end with how some of our core techniques are not specific to localization and can be extended to other applications such as augmented reality. Bio: He Wang is a PhD candidate in the department of Electrical and Computer Engineering at University of Illinois at Urbana Champaign. His research focuses on designing mobile sensing systems, with an emphasis on indoor localization. His work has been featured in the media such as Scientific American, MIT Technology Review, LA Times, Yahoo News and Daily Mail. He received his master’s degree from Duke University in 2013 and bachelor’s degree from Tsinghua University in 2011. This seminar is open to anyone who is interested, not just students enrolled in the CSCE 791 class. Please consider attending.

ACM is hosting a Career Fair Prep presentation by the Technical Youth program at Brooksource. They are going to give tips on preparing for a career fair and interviews as well as share IT market trends and LinkedIn tips and tricks. Pizza will be provided so please RSVP on the Facebook event on the ACM group if you are able. Below is the link to the event and attached is the flyer. https://www.facebook.com/events/457789534405726/