Dr. Jinny Rhee, Dean, CSULB College of Engineering
Dr. Jinny Rhee joined the CSULB College of Engineering as Dean in July 2021, bringing to The Beach years of experience developing innovative student success and faculty-development programs and forging strong relationships with industry leaders, alumni, and community partners.
Dr. Rhee comes to CSULB from San Jose State University, where, as Associate Dean of the Charles W. Davidson College of Engineering, she led programs that raised graduation rates, increased faculty and student diversity, improved academic advising, and pioneered staff and faculty workshops on anti-racism and microaggressions.
As Associate Dean, Dr. Rhee administered the $1.8 million Silicon Valley Engineering Scholarship program, and was involved in over $9.2 million in philanthropy to the SJSU College of Engineering and its affiliated programs, engaging corporate, alumni, and community partners.
She developed Engineering Programs in Community Service (EPICS @SJSU) to attract underrepresented minorities and women to engineering, and helped initiate the Industrial Technology Network Security Apprenticeship Program with Cisco Systems.
Dr. Rhee received her B.S. (’89), M.S. (’90), and Ph.D. (’95) degrees, all in mechanical engineering, from Stanford University. Her research interests include renewable energy, thermal management and student success strategies, particularly for engineering and STEM majors. She is the author of numerous peer-reviewed articles on engineering education, student success, and mechanical engineering.
Dr. Rhee is a member of the EPICS Consortium Institution, California Engineering Liaison Council (CAELC), American Society of Engineering Education (ASEE), American Society of Mechanical Engineering (ASME), and Institute of Electrical and Electronics Engineers (IEEE). She was the recipient of the SJSU College of Engineering’s 2013 Applied Materials Teaching Excellence Award, and from 1995-2002 was sole proprietor of Rhee Thermosciences. You can follow her on Twitter @DeanRheeCSULB.
Application of Data Analytics to Assist Human Decision-Making
Paul C. Hershey, Ph.D.
Both commercial and military systems are rapidly evolving into complex Systems of Systems (SoS) that incorporate advanced technologies, such as Machine Learning (ML) and Artificial Intelligence (AI). Associated with these systems are emerging behaviors that require decision support beyond the capacity of human reasoning alone. The field of Data Analytics provides methodologies to assist human decision-making with respect to complex SoS.
Data analytics is the science of analyzing raw data in order to make conclusions about the information derived from it. In fact, many data analytics methods have been designed to autonomously transform raw data into information for human consumption. This information can then be used to optimize processes and increase the overall efficiency of a business or system. The data analytics process includes the following steps: 1. Determine data requirements; 2. Define the process for collecting data; 3. Organize that data so it can be analyzed; 4. Clean the data before analysis; 5. Perform analysis and generate results to assist with human decision-making. Data analytics is broken down into four basic types: Descriptive analytics describes what has happened over a given time period; Diagnostic analytics focuses on why something happened; Predictive analytics describes what is likely going to happen in the near term; Prescriptive analytics suggests a course of action. Data analytics assist human decision making for quality control systems in the financial world. The retail industry also uses data analytics to meet the ever-changing demands of shoppers. The travel and hospitality industries have also adopted data analytics to assist with human decision making where quick response times are critical. Likewise, the healthcare industry applies data analytics to assist doctors and nurses with time-critical decisions on which life and death depend1.
This presentation provides an in depth review of the concept of “Data Analytics” methods, such as data analysis, data fusion, data storage, data sources, infrastructure and technology, screening and filtering algorithms, machine learning, and complexity; and it discusses how each of these assists human decision making. The presentation then describes specific commercial and military use cases where these methods can assist human decision-making. These use case include:
- Information Collection Architecture (ICA) -. This data analytics use case focuses on providing accurate and timely information collection as the primary enabler for effective high-speed data network management and services. Information assessment for these emerging networks is computationally intensive to the point of stressing both technology and network architecture. Commercial companies such as IBM and British Telecom have used the ICA for high-speed electrical and optical network analysis.
- Multifactor Information Distributed Analytics Technology Aide (MiData). This use case applies data analytics to reduce the flood of sensor data to only actionable information that is directly applicable to military missions-at-hand. MiData focuses on target discovery and analysis, communication capacity management, and automation techniques that enable Intelligence, Surveillance, Reconnaissance (ISR) system operators and analysts to derive the knowledge they need to meet end-user mission requirements. By doing so, MiData greatly improves productivity of operators and analysts to enable them to meet end-user time-critical needs while using fewer resources.
- Mission Information Autonomous Intelligent Decision Engine (MiAide). This use case integrates data analytics capabilities to create an automated system of systems (SoS) that provides end-user capacity improvement in support of end-to-end mission activities. MiAide has been demonstrated for aspects of both manned and Unmanned Air Systems (UASs) and has proven to reduce staffing while improving mission capacity (e.g., multiply number of missions and mission functions) across all stages of the mission life cycle.
- MiData Application to Local / Regional / Global Joined Object Recognition (MAJOR). For this use case, MAJOR applies sensors and data analytics technology in a new way to create a novel capability to rapidly screen massive collections of sensor images (still and video) that will transform raw data into actionable information from which analysts can locate lost objects in arbitrary geographic locations in a timely manner. This system has been applied to time-critical events, such as attempting to locate the missing Malaysian Boeing jet that disappeared in flight traveling from Kuala Lumpur to Beijing China on May 8, 2014.
- Object Recognition and Detection Enhancement via Reinforcement Learning Yield (ORDERLY). This use case assumes a commercial environment in which ORDERLY autonomously screens massive collections of sensor data from multiple and diverse data sources in order to transform raw data into actionable information. Similar to MAJOR, ORDERLY assist human analysts with locating objects in arbitrary geographic locations in a timely manner. ORDERLY improves MAJOR by applying Reinforcement Learning (RL), as an independent, self-teaching system. An ORDERLY prototype was implemented for which preliminary results achieved the goal of reducing overall processing time on set of test images, along with improving analyst time from image ingestion to actionable intelligence by 33%.
- Self-Healing Course of Action Revision (SCOAR). This use case applies Markov Decision Processes (MDP) and a Stochastic Mathematical Model (SMM) to assist humans with the generation of step-by-step military mission plans, sometimes called a Courses of Action (COAs). SCOAR assists with human decision making for both deliberate (non-real-time) COA generation and with crisis (dynamic, real-time) COA generation during execution of complex missions. A SCOAR prototype was implemented that generated Probability of Success (Psuccess), and other selected metrics, analytic results for both deliberate and crisis complex mission plans.
In summary, the audience will emerge from this presentation with a focused understanding of data analytics principles and, through the examples from 6 use cases, how these principles can be applied to assist humans in making decisions for complex SoS.
Paul Hershey works for Raytheon Technologies Company, where he is a Principal Engineering Fellow focusing on data analytics, autonomous systems, modeling and simulation, and cyber security. He has been a member of IEEE since 1980 and was elevated to IEEE Fellow in 2021. He received his Ph.D. and M.S. degrees in electrical engineering from the University of Maryland, College Park, MD, USA, and the A.B. degree in mathematics from the College of William and Mary, Williamsburg, VA, USA. Dr. Hershey has published 39 patents (granted) and over 60 peer-reviewed technical articles. Previously, he was an adjunct professor at George Washington University where he also served on the Curriculum Advisory Board. He presently serves on technical program committees for the IEEE International Systems Conference and the IEEE International System of Systems Engineering Conference. Dr. Hershey is a Distinguished Lecturer on data analytics for the IEEE Systems Council.