ICS Events

Data Science Community Talks

Date: Monday, March 30

Time: 10:00 a.m.–11:00 a.m.

Location: Online: https://psu.zoom.us/j/677349894

Join the Penn State data science community online (via Zoom) for two 13-minute talks from researchers leveraging advanced data science techniques in their work.

Pathways of combining process-based knowledge with deep learning for hydrologic modeling”

Chaopeng Shen, Associate Professor, Civil and Environmental Engineering

Abstract: Here I demonstrate selected pathways, out a many, to combine the power of both machine learning and process-based knowledge in improving our predictive capability of hydrologic variables. Compared to purely data-driven models, process-based models (PBM) can produce seamless solutions of observed or unobserved hydrologic variables at continental scales. However, a longstanding difficulty was to effectively and efficiently obtain parameters for PBMs. Here we show the vastly superior efficiency of a deep-learning-based parameter estimation framework that is based on a completely different paradigm of parameter estimation. We can gain five orders of magnitude of computational savings in calibration/training while achieving better calibrated parameters using the new framework. In addition, we comment on other forms of physics-informed neural networks.

My Journey to Dynamical Systems Modeling as a Behavioral Data Scientist”

Sy-Miin Chow, Professor, Human Development and Family Studies

Abstract: Dynamical systems models have historically been workhorses of the physical sciences and applied mathematics, but have begun to gain traction in statistics, and more recently, in the behavioral sciences. The recent influx of intensive longitudinal data from wearable devices, smartphones, Global Positioning System (GPS), and other sensors has introduced a pressing need, and also unique opportunities for developing novel data science approaches to examining the systems dynamics of individuals, family systems, social networks, and their interplay with environmental factors. In this talk, I will highlight some of my current work and ideas for future collaborations utilizing intensive longitudinal health data from individuals and family systems.

Join the Talks on Zoom