Dan Stoecklein (“Stek-Line”) received B.S. degrees in physics and mechanical engineering and a Ph.D. in mechanical engineering from Iowa State University and was a postdoctoral researcher at the University of California, Los Angeles, in the department of bioengineering. His research work focuses on integrating computational mechanics with machine learning and optimization for inverse design problems, focusing on fluid-structure interaction, microscale manufacturing, and material design. Learn more about Dr. Stoecklein at his website: https://stoeck.github.io
Academic Degrees
- BS in Mechanical Engineering, Iowa State University, 2011
- BS in Physics, Iowa State University, 2011
- PhD in Mechanical Engineering, Iowa State University, 2017
Publications & Presentations
- Antognoli, D. Stoecklein, C. Gallitti, E. Brunazzi, D. Di Carlo, “Optimized design of obstacle sequences for microfluidic mixing in an inertial regime”, Lab on a Chip (2021), 21 (20), 3910-3923
- Kommajosula, D. Stoecklein, D. Di Carlo, B. Ganapathysubramanian, “Shape design for stabilizing microparticles in inertial microfluidic flows”, Journal of Fluid Mechanics (2020), 886, A14
- Stoecklein, M. Davies, J. M. de Rutte, C.-Y. Wu, D. Di Carlo, B. Ganapathysubramanian, “FlowSculpt: software for efficient design of inertial flow sculpting devices”, Lab on a Chip (2019), 19, 3277-3291
- Y. Lee, A. Balu, D. Stoecklein, B. Ganapathysubramanian, S. Sarkar, “A case study of deep reinforcement learning for engineering design: application to microfluidic devices for flow sculpting”, Journal of Mechanical Design (2019), 1-24
- Stoecklein, D. Di Carlo, “Nonlinear microfluidics”, Analytical Chemistry (2019), 91 (1), 296-314
Research Experiences
- Microfluidics
- Fluid-Structure Interaction
- Computational Design
- Machine Learning
- Optimization
Teaching Interests
- Graphical Communications
- Fluid Mechanics
- Fluid-Structure Interaction
- Measurement Systems
- Numerical Methods
- Optimization