Wednesday, April 5, 2023
Driving the Future: Exploring the Intersection of AI and Autonomous Vehicles
Tosiron Adegbija - University of Arizona (Chair)
Fareena Saqib - University of North Carolina, Charllotte (Co-Chair)
Pallab Chatterjee - Roadway Media
Summary: Two rapidly emerging technologies, Artificial Intelligence (AI) and autonomous vehicles (AVs), are poised to revolutionize everyday life. Industries worldwide are investing billions of dollars to advance these transformative technologies, which have seen remarkable advancements over the past few decades. However, we have only scratched the surface of their potential impact on society. This panel will feature experts in the fields of AI and AVs who will discuss the current trends in AVs, with a focus on leveraging advancements in AI to accelerate the development of AVs. The panel will explore topics such as the reality of AI-driven electronic design, short-term and long-term predictions of how AI and AVs will interact and their potential impact, security considerations in AI-driven AV design, practical research challenges and gaps, and potential directions for addressing these gaps.
Dr. Yankin Tanurhan, Senior Vice President Engineering, Solutions Group at Synopsys is responsible for DesignWare Processor Cores, Security IP, Wireless Interface IP, Smart Subsystems and Non-Volatile Memory business units. The products in his portfolio include low power and high-performance ARC embedded CPUs, NPUs, DSPs targeting markets from Mobile, IoT, Embedded Vision, AI/ML, Digital Home, Automotive/Industrial, Security to Storage, ASIP tools with products like ASIP Designer and Programmer, IP Subsystems products like Sensor Fusion, Audio, Vision and Security Subsystems and CMOS based Non Volatile OTP and MTP memory IP blocks. His teams additionally develop a wide variety of Security IP from TRNGs to full blown HSMs and IDEs. Lately he extended the R&D activities into Bluetooth, Zigbee products. Dr. Tanurhan has authored 100+ papers in refereed publications. He holds a B.S. and M.S. in Electrical and Computer Engineering from Rheinisch Westfaellische Technische Hochschule (RWTH) in Aachen, Germany and a Dr. Ing. degree summa cum laude in Electrical Engineering from the University of Karlsruhe (TH) in Karlsruhe, Germany.
Pratik Prabhanjan Brahma is an experienced Engineering Manager at Cruise, currently serving as the lead of Data-centric AI development for Prediction and Planning machine learning models that help understand the surrounding scene, forecast the near future and make the best possible decisions for autonomous vehicles (AVs) to drive safely. With a team of ML Engineers and Applied Scientists, Pratik has spearheaded the development of cutting-edge technologies along several dimensions like scalable mining for long-tail scenarios, continual and fast learning algorithms, proactive ways for identifying and addressing potential safety issues, techniques for improving data qualitatively & quantitatively to enhance the performance of AVs across multiple cities and markets, etc. With close to 7 years of experience in the AV industry, Pratik previously held the position of Principal ML Engineer at Volkswagen Group of America where he led research and series development projects across all levels of autonomy. He has 9 patents to his name and has published numerous papers in top-tier workshops and conferences such as ICCV, CVPR, NeurIPS, WACV, as well as in top journals like IEEE Transactions on Neural Networks and Learning Systems, among others. He has also been an invited speaker at events such as ICPR and Re.WORK Summit on AVs. Pratik's academic background includes a PhD from the University of Florida, where his doctoral dissertation studied the theory behind why deep learning works effectively. He received his B.Tech and M.Tech degrees from the esteemed Indian Institute of Technology, Kharagpur.
Daryush is the Director of Product Management for NVIDIA's AutonomousVehicles and AI Infrastructure team. In this role, Daryush leads the tooling, platform and infrastructure efforts for Machine Learning, Model Validation, Training Infrastructure, Experimentation, Simulation Testing, Replay Testing, and developer productivity. Prior to NVIDIA, Daryush led Engineering and Product Management efforts for JP Morgan's firm-wide machine learning platform. Daryush has also led efforts in Google Cloud AI, launching the very first version of GCP's Contact Center AI system, and had previously worked in various roles at Azure, SQL Server, Bing, and Visual Studio. Daryush holds a Masters degree in Computer Science from University of Nebraska at Omaha, and an MBA from University of Chicago Booth School of Business.
Qi Zhu is an Associate Professor at the ECE Department in Northwestern University. He received a Ph.D. in EECS from University of California, Berkeley in 2008, and a B.E. in CS from Tsinghua University in 2003. His research interests include design automation for cyber-physical systems (CPS) and Internet of Things, safe and secure machine learning for CPS and IoT, cyber-physical security, and system-on-chip design, with applications in domains such as connected and autonomous vehicles, energy-efficient smart buildings, and robotic systems. He is a recipient of the NSF CAREER award, the IEEE TCCPS Early-Career Award, and the Humboldt Research Fellowship for Experienced Researchers. He received best paper awards at DAC 2006, DAC 2007, ICCPS 2013, ACM TODAES 2016, and DATE 2022. He is the Conference Chair of IEEE TCCPS, and VP of Young Professionals at IEEE CEDA. He is an Associate Editor for IEEE TCAD, ACM TCPS, and IET Cyber-Physical Systems: Theory & Applications, and has served as a Guest Editor for the Proceedings of the IEEE, ACM TCPS, IEEE T-ASE, Elsevier JSA, and Elsevier Integration, the VLSI journal.
Ravi is a Sr Director of Software at AMD Inc. He leads Open Source Software development at Xilinx including but not limited to Linux kernel, UBoot, OpenAMP, Xen, FreeRTOS, V4L, GStreamer, QEMU, Yocto, TVM/VTA, Autoware etc. He is currently leading solutions and runtime software stack, AI/ML and acceleration stack development for AMD’s next generation silicon programs catering to various market segments. During over two decades in the industry he has lead many projects in Embedded space spanning Automotive, Industrial, Data center, Storage, Aerospace and Defense, Wireless, Multimedia and Imaging solutions. He received his Master’s in Computer Science from Texas A&M University.