TexasWISE, 2017
Southern Methodist University

­­8:30 am


9:00 am to 9:45 am

Alan Gatherer, Huawei

9:45 am to 10:30 am

Gayatri Mehta, UNT

10:30 am to 11:00 am

Coffee Break

11:00 am to 11:45 am

Michael Polley, Samsung

11:45 am to 12:45 pm


12:45 pm to 1:30 pm

Eric Larson, SMU

1:30 pm to 2:15 pm

J.-C. Chiao, University of Texas at Arlington

2:15 pm to 3:30 pm

Poster Session

3:30 pm to 4:30 pm


Keynote Speakers

Alan Gatherer
On behalf of Ywh-Pyng Harn

The rise of Statistical Modeling and Data Science in IP and system level design

As the era of 5G is emerging, we are facing more and more complex and challenging wireless system design. For this aspect, this talk is aimed at providing academic researchers ideas about the kind of problems the industry is facing, as well as how we approach the problem for a solution. In the first part, we will show that statistical and mathematical modeling is an excellent technique to provide guidelines and design direction in the early stages of system development, before a cycle-accurate model exists. In the second part, we will discuss how data science can be applied and now become indispensable for very large-scale and complicated system design.

Biography (Ywh-Pyng Harn)
Ywh-Pyng Harn got his BS and MS degrees from National Taiwan University, and Ph.D. degree from UC Berkeley, all from the Department of Electrical Engineering. He worked for companies like Integrated Systems, Avanti, Broadcom and Cadence in the area of optimization-based control system design and IC physical layout design automation. He joined Huawei in July, 2013, and is currently working on next-generation system architecture exploration and optimization.

Biography (Alan Gatherer)
Alan Gatherer is the CTO for Baseband System on Chip in Huawei Technologies, USA and Fellow of the IEEE. He is responsible for R&D efforts in the US to develop next generation baseband chips and software for 3G and 4G basestation modems. His group is presently developing new technologies for baseband SoC in the areas of message passing hardware and middleware, isolation for multimode, interconnect fabric, CPU/DSP clusters and virtualization. Recently they have focused on open systems, SLA enforcement and now 5G requirements. Alan joined Huawei in January 2010. Prior to that he was a TI Fellow and CTO at Texas Instruments where he led the development of high performance, multicore DSP at TI and worked on various telecommunication standards. Alan has authored multiple journal and conference papers and is regularly asked to give keynote and plenary talks at communication equipment conferences. In addition, he holds over 70 awarded patents and is author of the book “The Application of Programmable DSPs in Mobile Communications.” Alan holds a bachelor of engineering in microprocessor engineering from Strathclyde University in Scotland. He also attended Stanford University in California where he received a master’s in electrical engineering in 1989 and his doctorate in electrical engineering in 1993

Eric Larson

: Flipping the Clinic with Mobile Machine Learning

Mobile health technology has long been touted as a solution to global health access, allowing remote diagnosis, low cost disease management, and rapid training of health workers. The mHealth “revolution” has promised to deliver in-home health care that parallels the care we might receive in a physician’s office. However, the panacea of digital health has proven to be more problematic and messy than its vision, especially for collecting and interpreting medical quantities from the home. In this talk, I cover a number of existing research projects that lower the cost of sensing by offloading the computation to a mobile phone. I examine the hurdles in performing this research, including the importance of evaluating solutions with realistic context.

Eric C. Larson is an Assistant Professor in the department of Computer Science and Engineering in the Bobby B. Lyle School of Engineering, Southern Methodist University. Dr. Larson has developed a number of mobile health technologies; including medical applications that use mobile phone’s to track baselines for patients with pulmonary ailments, depression, and neonatal jaundice. Dr. Larson’s main research interests are in machine learning, sensing, and signal & image processing for ubiquitous computing applications, in particular, for healthcare and education. His work in both areas has been commercialized and he holds a variety of patents for embedded sensing and machine learning in mobile phone-based health sensing. Dr. Larson has more than 30 research publications, 2 books, and 7 patents. He received his Ph.D. from the University of Washington in 2013.

Michael Polley

Mobile Platform Evolution

Considering industry trends and increased market demands for better, faster and smarter mobile devices, this presentation will contrast computational capabilities of embedded devices with the evolving requirements of new and existing mobile equipment and applications. In addition to considering how to match next-generation devices with the needs, we will also consider what part of the system should be on-device versus in the cloud.

Mike is Senior Vice President and Head of the Mobile Processor Innovation Lab at Samsung where he leads a team of world-class algorithm and system designers and chipset architects focused on creating new technologies for next-generation smartphones and wearable devices. Prior to Samsung, Mike worked at Texas Instruments for 18 years defining chipset architectures and leading embedded signal processing R&D. He was recognized for his technical accomplishments by election to TI Fellow in 2009. Mike received his B.S., M.S., and Ph.D. degrees in electrical engineering from MIT. He holds 32 U.S. patents on a broad range of products across communications and multimedia systems.

Gayatri Mehta

: Interactive Game-Like Design Environments for Electronic Design Automation

Custom reconfigurable computing platforms offer low power and high performance for a suite of applications. They can be tailored according to the needs of an application domain. The ability to successfully create these highly customized domain-specific reconfigurable architectures offers tremendous advantages, including orders of magnitude power savings, longer battery life, smaller, faster, more robust devices, and shorter time to market. However, making extreme customization an integral part of the design process requires design to be significantly simpler and easier to create novel, out of the box architectures that directly address the requirements of a specific application domain. This challenge can be categorized into three areas: (1) discovering faster and efficient algorithms that allow exploring design space rapidly; (2) broadening participation by promoting computational thinking among non-scientists and non-engineers; and (3) educating the next generation of custom chip designers through innovative highly visual interactive frameworks.
In this talk, I will present the interactive design frameworks that we have developed to harness human intelligence to discover efficient algorithms for mapping and architecture design. People excel at navigating complex and dynamically changing situations, recognizing recurring patterns, and identifying potential opportunities. I will show that these reasoning and problem solving skills can be brought to bear in solving real problems in Electronic Design Automation. Our mapping game, UNTANGLED has received People’s Choice Award in the Games & Apps category of the 2012 International Science and Engineering Visualization Challenge conducted by the National Science Foundation and Science.

Gayatri Mehta is currently an Associate Professor in the department of Electrical Engineering at the University of North Texas, Denton, TX. She received her Ph.D. in Electrical and Computer Engineering from the University of Pittsburgh in 2009. Her research interests are broadly in the areas of Electronic Design Automation, Reconfigurable Computing, Low-Power VLSI Design, System on a Chip Design, Embedded Computing, and Portable/Wearable Computing.

J.-C. Chiao
University of Texas at Arlington

: Flipping the Clinic with Mobile Machine Learning

Mobile technologies have changed our life style significantly. Personalized tools such as wearable and implantable devices through wireless communication and Internet of Things have been utilized in healthcare to provide unique functions and reduce costs. Individuals can be empowered with tailored solutions without limitation in mobility or daily activities. Quantitative documentation of physiological parameters presents more accurate assessment. Direct stimulation on tissues or organs by electrical signals can restore or improve body functions. Continuous monitoring and adaptive administration of therapy to treat symptoms via wireless body networking can adaptively optimize the closed-loop health management. This presentation discusses the development of wireless micro devices and integrated systems for clinical applications. The systems are based on batteryless, wireless implants with enhancement in miniaturization and functionalization. Miniaturization owing to flexible substrates and the elimination of bulky batteries allows endoscopic or minimally invasive procedures to deploy the implants without painful surgeries. Several diagnosis and therapeutic treatment examples for management of gastric and neural disorders, particularly as closed-loop systems, will be introduced. These examples aim to inspire new system application ideas to address the implementation and cost challenges in healthcare, and enable integration of electronics and medicines to improve human welfare and assist better living.

J.-C. Chiao is Greene professor and Garrett professor of Electrical Engineering at University of Texas – Arlington. He received his PhD at Caltech and was with Bellcore, University of Hawaii-Manoa and Chorum Technologies before he joined UT-Arlington in 2002. Dr. Chiao has published more than 260 peer-reviewed papers and received 12 patents. He received the 2011 O'Donnell Award in Engineering presented by The Academy of Medicine, Engineering and Science of Texas. He received the Tech Titan Technology Innovator Award; Lockheed Martin Aeronautics Excellence in Engineering Teaching Award; Research in Medicine milestone award by Heroes of Healthcare; IEEE MTT Distinguished Microwave Lecturer; IEEE Region 5 Outstanding Engineering Educator and individual Achievement awards. Currently, he is an IEEE Sensors Council Distinguished Lecturer and serving as the Editor-in-Chief for Journal of Electromagnetics, RF and Microwaves in Medicine and Biology. More information about him can be found at his website .


David Pan (Panel Moderator)

UT Austin

Daniel Engels

Southern Methodist University

Rama Venkatasubramanian


Alan Gatherer


Jeyavijayan(JV) Rajendran

University of Texas at Dallas

Jingtong Hu

Oklahoma State University

Xiaolin Lu