4th IEEE Conference on Control Technology and Applications

Abstract

Organizations that want to perform at the highest levels and be competitive in their industry must effectively leverage their leadership talent. Yet, in nearly all companies, a leadership gender gap persists. In addition, research has identified an individual's emotional intelligence (EQ) as a key aspect and driver of leadership effectiveness. EQ assessment have found men and women to be strong in different areas. These differences often advantage men and disadvantage women at work and can lead to very different outcomes. Finally, men and women are raised in different cultures. It begins at birth and carries into the workplace as adults. As a result, we approach virtually every aspect of business differently. Different approaches result in different perceptions, which have a significant impact on promotion. These topics are based on Dr. Andrews’ research and best-selling book titled, “The Power of Perception: Leadership, Emotional Intelligence, and the Gender Divide” (2018 Morgan James Publishing). Practical strategies for career advancement will be provided, as well as approaches to building a more diverse & inclusive workforce. During this interactive presentation, you will learn how to: • Examine barriers that contribute to the leadership gender gap • Identify emotional intelligence attributes and their impact on leadership • Leverage gender culture differences (hard-wired and socialized) which show up every day at work and home • Apply knowledge and tactics to improve career advancement Biography: Dr. Shawn Andrews is a keynote speaker, organizational consultant, business school professor, and author of the best-selling book, The Power of Perception: Leadership, Emotional Intelligence, and the Gender Divide (2018 Morgan James Publishing). She is a Forbes contributor, quoted in the Chicago Tribune, interviewed on dozens of podcast and radio shows, including NPR, and is a Women’s Media Center SheSource expert. Shawn speaks and consults to a diverse range of clients, including SABMiller Brewing Company, Broadcom, Vizio, Johnson and Johnson, Biogen, BristolMyers Squibb, Rust-Oleum, Experian, National Diversity Council, Association for Talent Development, and Society for Human Resource Management. With over two decades of corporate experience in the biopharmaceutical industry, she has helped thousands of leaders improve and develop using presentations, workshops, coaching, and psychological instruments. She is an accredited practitioner for EQ-i 2.0 and EQ 360 model and Insights Discovery Colors. She serves as professor at both UC Irvine Paul Merage School of Business and Pepperdine Graziadio Business School, teaching courses on Women and Leadership, Organizational Behavior, Diversity in Organizations, and Leadership and Ethics. Her specific areas of focus include Organizational Leadership, Learning & Development, Talent Management, Diversity & Inclusion, and Unconscious Bias. Shawn earned her Ed.D. degree in Organizational Leadership from Pepperdine University, an M.B.A. degree from Pepperdine University, and a B.A. degree in Psychology from University of California, Irvine. She serves as Board President, Healthcare Businesswomen’s Association, Orange County, is a member of 2020 Women on Boards Leadership Committee, and is founder and CEO of Andrews Research International. Contact Information Email: shawn@drshawnandrews.com Phone: 714-367-6063 Website: www.drshawnandrews.com Twitter: @drshawnandrews Conference Highlights 10 IEEE CSS Young Professional Event Title: Interactive Machine Learning in Control with Azure ML and Kaggle platform Application Speakers: Dr. S.L. Brunton (University of Washington, USA), Dr.R.Lakshmana Kumar (Hindusthan College of Engineering and Technology, Coimbatore, India) Abstract: Machine learning (ML) is the study of algorithms and mathematical models that computer systems use to enhance their performance on a particular task consistently. Usually, ML consist of three areas Scientific computing, Mathematics and Statistics. Control theory deals with the control of continuously operating dynamical systems in engineered processes and machines. The association between control theory and machine learning is a vital one. Today, ML algorithms are used to approximate functions that cannot be hardcoded. Furthermore, there is sufficient intersection within these two areas where machine learning, intelligent control and control theory can solve optimal control problems with the help of ML techniques. In this session, a brief overview of the fundamentals of ML and the various ways ML intersects with control theory will bed discussed. Then, in a hands-on session, Microsoft Azure Machine Learning (Azure ML) will be used along with the Kaggle platform, so particpants can learn how ML can be applied to any engineering related problem, including dynamics and control. This session will enable the audience to leverage their existing data processing and model development skills, and help them scale their workloads to the cloud. Machine learning (ML) is the study of algorithms and mathematical models that computer systems use to enhance their performance on a particular task consistently. Usually, ML consist of three areas Scientific computing, Mathematics and Statistics. Control theory deals with the control of continuously operating dynamical systems in engineered processes and machines. The association between control theory and machine learning is a vital one. Today, ML algorithms are used to approximate functions that cannot be hardcoded. Furthermore, there is sufficient intersection within these two areas where machine learning, intelligent control and control theory can solve optimal control problems with the help of ML techniques. In this session, a brief overview of the fundamentals of ML and the various ways ML intersects with control theory will bed discussed. Then, in a hands-on session, Microsoft Azure Machine Learning (Azure ML) will be used along with the Kaggle platform, so particpants can learn how ML can be applied to any engineering related problem, including dynamics and control. This session will enable the audience to leverage their existing data processing and model development skills, and help them scale their workloads to the cloud. Biographies: Dr. Steven L. Brunton is Associate Professor of Mechanical Engineering at the University of Washington. He is also Adjunct Associate Professor of Applied Mathematics and a Data-Science Fellow at the eScience Institute. His research applies data science and machine learning for dynamical systems and control to fluid dynamics, biolocomotion, optics, energy systems, and manufacturing. He has co-authored three textbooks, received the Army and Air Force Young Investigator Program (YIP) awards and the Army Early Career in Science and Engineering (ECASE), and was awarded the University of Washington College of Engineering teaching award. Dr R. Lakshmana Kumar is currently associated with Hindusthan College of Engineering and Technology, Coimbatore. Tamil Nadu. He is a Chief Research Scientist in a Canadian based company (ASIQC) in Vancouver region of British Columbia, Canada. He holds the certification in Data Science from John Hopkins University, United States. He also holds the Amazon Cloud Architect certification from Amazon Web Services. He is the Founding Member of IEEE SIG of Big Data for Cyber Security and Privacy, IEEE. He serves as a core member in the Editorial Advisor Board of Artificial Intelligence group in Cambridge Scholars Publishing, UK. Pre-Conference Workshops 11 CCTA 2020 is pleased to be offering workshops on topics in control technology and applications. These tutorials are proposed, organized and delivered by international experts from academia, national laboratories, and industry. All Workshops will take place prior to the conference on Sunday, August 23, and will be held online. Participants may register for workshops via the conference webpage. WS 1: Half Day Workshop 9:00 to 13:30 Machine Learning for Scalable, Reliable and Online Design and Decision Making Organizers: Mahdi Imani, George Washington University Seyede Fatemeh Ghoreishi, University of Maryland Demand for learning and decision making is higher than ever before. Autonomous vehicles need to learn how to ride safely by recognizing pedestrians, traffic signs, and other cars, or in cyber-physical systems, one needs to process a large amount of data for proper learning and decision making, while continuously adapting the learning process or strategies according to possible gradual (e.g., natural or aging process) or sudden (e.g., faults or malicious attacks) changes in systems. Despite several advances made in learning and decision making in recent years, unrealistic assumptions, inefficiency and lack of interpretability combined with unavoidable ethical, economic, and physical constraints avoid the applicability of the existing techniques in many practical problems. This workshop will focus on three main topics: 1) The first topic will be around Bayesian optimization techniques for enhancing the reliability, speed, and efficiency of design and decision-making processes. These include developing online/nonstationary, dimensionality reduction and multi-fidelity Bayesian optimization techniques to go beyond the existing techniques; 2) The second topic will be around reinforcement learning and how Bayesian statistical frameworks can allow going beyond the existing learning techniques and enabling online/real-time self-learning frameworks that are highly scalable, capable of considering various sources of uncertainty, acting safe and making informative decisions in non-stationary environments; 3) The last topic will be around our developed non-stationary risk-based time-dependent classification techniques to overcome the deterministic decision making and unrealistic assumption regarding stationarity of the process, a critical factor in dealing with most practical