NISS Virtual Short Course: Data-Driven Problem Solving and Neural Computing

Full details available on the NISS website here: NISS Virtual Short Course: Data-Driven Problem Solving and Neural Computing: From Prediction to Prescription, No-Code to High-Code | National Institute of Statistical Sciences

Welcome to our exciting online event! Join us for a deep dive into data-driven problem solving and neural computing. This course is perfect for anyone looking to enhance their skills in these cutting-edge fields. Our expert instructors will guide you through practical applications and hands-on exercises to help you master these topics. Don't miss out on this opportunity to expand your knowledge and stay ahead in the ever-evolving world of data science. Sign up now!

Join us for an interactive short course that takes you on a journey from the foundations of data literacy to the frontiers of neural computing! Our instructor Dr. Padmanabhan Seshaiyer (Padhu), Professor and Director at George Mason University (GMU), will begin with computational thinking and exploratory data analysis, using no-code tools that make data exploration and prediction accessible to everyone. From there, you’ll discover how to transition into high-code platforms, where you can design scalable, customized machine learning models.

The course will also open the door to neural computing—showing how neural networks and the laws of physical systems can be combined to create models that not only predict but also prescribe solutions. By embedding scientific knowledge into machine learning, you’ll gain new perspectives on how to build robust, interpretable AI systems with real-world impact.

Whether you are a researcher, practitioner, educator, or student, this course is designed to meet you where you are—offering approachable tools for beginners and deeper insights for those with advanced experience in data science and machine learning.

Abstract:

Title: Data-Driven Problem Solving and Neural Computing: From Prediction to Prescription, No-Code to High-Code

Instructor: Dr. Padmanabhan Seshaiyer (Padhu), Professor and Director at George Mason University (GMU)

Abstract: This course begins with an introduction to computational thinking along with exploratory data analysis to develop foundational data literacy. Participants are guided through a no-code environment for data exploration and prediction to high-code platforms for building custom, scalable machine learning models. Building on these foundations, participants will explore neural computing, leveraging both the architecture of neural networks and the physical laws that govern complex systems, embedding scientific knowledge directly into machine learning models.

About the Instructor: Padmanabhan Seshaiyer

Dr. Padmanabhan Seshaiyer (Padhu) is a Professor and Director at George Mason University (GMU) where he has served in multiple leadership positions including the Associate Dean for Academic Affairs, Director of the STEM Accelerator Program and Director of the Center for Outreach in Mathematics Professional Learning and Educational Technology. During the last decade, he initiated and directed a variety of research, educational and outreach programs including faculty development, post-graduate, graduate and undergraduate research, K-12 outreach, teacher professional development, and enrichment programs, to foster the interest of students and teachers in Mathematics education at all levels. He has also served on national positions including the Chair of the US National Academies Commission for Mathematics Instruction, served as the country representative for the United States at the General Assembly held at the International Congress on Mathematics Education (2024) and an elected councilor for the Mathematics and Computer Science division for the Council on Undergraduate Research. He currently serves as the Chair of the US National Academies Board on International Scientific Organizations. He also serves on multiple state-wide positions including serving as an appointed member of two different boards to the Office of the Governor including the VA STEM Advisory Board and the VA Workforce Development Board, an appointed member of the SCHEV AI-taskforce and a member of the Virginia Mathematics and Science Coalition. He helped to lead the VDoE higher education taskforce for Data Science Standards and Implementation for Virginia and was an author on the 2024 NCTM Position Statement on Teaching Data Science in High School. In April 2019, he was selected as one of the “Figures that Matter” for his contributions to Academia and Society and was awarded an honorary doctorate by Vrije Universiteit Brussels.

Current Appointments:

• Professor and Director, George Mason University
• Appointee, Virginia STEM Advisory Board, Office of the Governor
• Appointee, Virginia Workforce Development Board, Office of the Governor
• Appointee, Governor’s EO30 AI Task Force, State Council for Higher Education for Virginia
• President, Board of Directors, Discover Engineering
• Chair, Board on International Scientific Organizations, US National Academies
• Councilor, Council on Undergraduate Research, Mathematical, Computing and Statistics
• Director, Center for Outreach in Mathematics Professional Learning and Educational Technologies