Seminar - Combinatorial Nano-Biomaterials with Intelligent Polymer Automation
Thursday, October 1, 2020;
12:05 p.m. to 1:20 p.m.
Via Zoom
Speaker: Dr. Adam Gormley from Rutgers University
Dr. Adam Gormley
Assistant Professor of Biomedical Engineering
Rutgers University
Seminar via Zoom, please email acv5167@psu.edu for link and password.
Abstract/Bio
Hosted by: Jamie Oberdick, jco11@psu.edu
EE Colloquium: The Fourth Generation Optics: The Concept, Materials, Technology, and Applications
Friday, October 2, 2020;
1:25 p.m.
Zoom
Speaker: Nelson Tabiryan from BEAM Corporation
Abstract: The Seminar provides an intuitive introduction into the underlying principles of the new generation of optical components and systems, requiring only basic knowledge (or just an interest) in optics. The course will make it evident for attendees that:
- a micrometer-thin film may perform as a high power lens, prism, spiral phase plate, beam shaper, etc., and in a broadband of wavelengths comparable to that of glass and other transparent optical materials;
- optical functions can be switched on and off with low voltage controls;
- thin film optical components can be combined to provide versatile beam control functions such as all-electronic beam steering, switching between multiple focal points, spectral tuning, variable transmission, etc.
- an ultralight and ultrathin film may be used as primary optics for a very large telescope, both for deep space communication as well as imaging.
The first generation of optics relied on shaping an optically transparent material such as glass. Modulating refractive index instead of shape – the second generation of optics – allows thinner components but compromises bandwidth. Anisotropic materials make available two more parameters for controlling light beams. LCD industry is exploring one of them, modulation of effective birefringence - the third generation of optics. A recent breakthrough, the fourth generation of optics, relates to patterning optical axis orientation in the plane of anisotropic films. Obtained as thin film coatings on any desired substrate, including plastic, flat or curved, all different varieties of optical functions can now be obtained using the same materials and processes. 4G lenses, prisms, vortex waveplates, etc., are thin films of continuous structure allowing low-cost manufacturing just by inducing desired orientation patterns by a touch of polarization modulated light.
Unlike metamaterials, 4G optics technology is at a high level of maturity and is being studied and adapted for modern applications including LiDARs for auto-navigation, augmented reality displays, adaptive ophthalmic lenses, space telescopes, and even solar sails.
[1] N. Tabiryan, D. Roberts, D. Steeves, and B. Kimball, “4G Optics: New Technology Extends Limits to the Extremes,” Photonics Spectra, March, 2017, pp. 46-50.
Biography: Dr. Nelson Tabiryan pioneered development of the Fourth Generation Optics technology - Diffractive Waveplates - marking a major breakthrough in planar optics. He is the CEO of Beam Engineering for Advanced Measurements Corporation that demonstrated the first thinnest and largest planar lenses, prisms, and other spectrally and angularly broadband optics and photonics devices. He received a Ph.D. degree in Physics and Mathematics from the Institute of Physical Investigations of the Armenian Academy of Sciences, Yerevan (1982), and D. Sc. Degree from the Highest Certifying Commission of the USSR (1986). Dr. Tabiryan is OSA Fellow, NIAC (NASA) Fellow, Alexander Von Humboldt Research Scholar, recipient of the Frederiks Medal, and the American Chemical Society’s Cooperative Research Award (2013). He is the chairman of the International Meeting on Photoalignment and Soft Matter (PhoSM-2020) and other international meeting. Dr. Tabiryan is the program committee member, keynote, plenary and invited speaker in numerous conferences, and principal investigator of many advanced research programs being conducted for the Governmental and commercial organizations. He has over 250 refereed publications and 50 issued patents.
Hosted by: I.C. Khoo, ick1@psu.edu
Gap plasmon resonance and coupling with epsilon-near-zero mode
Wednesday, September 30, 2020;
3:35 PM - 4:25 PM
ZOOM
Speaker: Junpeng Guo from University of Alabama in Huntsville
Abstract: When a plasmonic nanostructure is placed near a metal surface with a deep subwavelength insulating gap, a gap plasmon resonance mode can be excited. A gap plasmon mode is non-radiative dark mode which traps optical energy in a small volume inside the gap. In this talk, I will give a review on gap plasmon resonance and discuss the coupling of gap plasmon resonance with epsilon-near-zero mode for applications such as wideband perfect light absorption, nonlinear optics, and quantum nanophotonics.
Bio: Junpeng Guo earned his Ph.D. degree in Electrical Engineering from the University of Illinois at Urbana-Champaign. After receiving his Ph.D. degree, he started his career as a research scientist in the former Rockwell Science Center in Thousand Oaks, California and then moved to Sandia National Laboratories in Albuquerque, New Mexico as a member of technical staff. He joined the faculty of University of Alabama in Huntsville in 2005 and now is a Professor of Electrical Engineering and Optics. He received the Alan Berman Research Publication Award from in 2013 and was the only recipient of the University Distinguished Faculty Research Award in 2016. He is a Fellow of the SPIE-International Society for Optics and Photonics and currently serves as an Associate Editor of the Journal of Photonics Research and also as an Associate Editor of Journal of Nanophotonics. His research interest is nanophotonics, plasmonics, and quantum nanophotonics.
https://psu.zoom.us/j/91823970096
For password please contact Lisa Spicer@lms8@psu.edu
Hosted by: Lisa Spicer, lms8@psu.edu
Artificial Intelligence in Mechanical Engineering
Tuesday, September 29, 2020;
3:35 p.m. (EST)
via Zoom (Contact sps5072@psu.edu for details)
Speaker: Dr. Amir Barati Faramani from Carnegie Mellon University
ABSTRACT: With the rise of Artificial Intelligence (AI) and Machine Learning (ML) in recent years, many complex problems in vision and computer science have been solved that were intractable for decades. In mechanical engineering (ME), complex problems still exist that conventional techniques could not offer viable solutions. Recent advances in AI has provided us with opportunities to merge and apply them to challenges in mechanical engineering, however; to accurately model and predict an engineering system, the representation and formulation of that problem into AI frameworks remain a challenge. The domain knowledge of ME is needed to represent and beneficially use AI algorithms to achieve viable solutions. To this end, I will talk about how effectively different areas of engineering can take advantage of AI to find solutions by integrating the physics and engineering domain knowledge. I will focus on examples in transport phenomena, material discovery, and manufacturing. I will then show how modern deep learning models, such as generative adversarial networks, can be used to learn the physics of transport without the knowledge of underlying constitutive equations and how novel feature extraction techniques can lead to the discovery of complex Partial Differential Equations. In addition, I will demonstrate how integration of chemistry and physics into graph convolutional neural networks can enhance the accuracy of material property prediction.
BIOGRAPHY: Professor Farimani joined the Department of Mechanical Engineering at Carnegie Mellon University in the fall of 2018. He was previously a postdoctoral fellow at Stanford University. He received his PhD in Mechanical Engineering in 2015 from University of Illinois at Urbana-Champaign. His lab at CMU focuses on the problems at the interface of Mechanical Engineering, data science and machine learning. His lab uses the state-of-the-art deep learning and machine learning algorithms and tools to learn, infer and predict the physical phenomena pertinent to mechanical engineering. Currently, he is teaching AI and ML to a large class of graduate students at CMU. He received the Stanley I. Weiss best thesis award from the University of Illinois in 2016 and was recognized as an Outstanding Graduate Student in 2015. During his post-doctoral fellowship at Stanford, Dr. Barati Farimani has developed data-driven, deep learning techniques for inferring, modeling, and simulating the physics of transport phenomena and for materials discovery for energy harvesting applications.
Hosted by: Serena Sidwell, sps5072@psu.edu
