SEMESTER/MASTER PROJECT: Optical Machine Learning with Nanoparticles
In our recent study, we demonstrated that light propagation inside lithium niobate waveguide can be utilized as nonlinear, high-dimensional data transform units, and thanks to its rich physical dynamics, machine learning tasks could be performed with a much smaller dependence on digital electrical hardware (such as GPUs). In our first approach, the waveguide does not include any memory elements, which is one of the essential ingredients of modern models like GPT-3. Having an optical memory is a long-standing challenge in the field. We are trying to solve it by benefitting the memory effect that we have observed in nanoparticles recently.
Upconversion nanoparticles (UCNPs) are capable of converting near-infrared (NIR) light to visible or ultra-violet (UV) light. These UCNPs also have great photostability and non-photoblinking nature. UCNPs have a relatively long rise and decay time compared to organic dyes, thus providing a memory in the optical domain. More….
SEMESTER/MASTER PROJECT: Multimode Fiber Based High Speed Optical Computing Modelling and Control with Neural Networks
We demonstrated that light propagation inside multimode optical fibers can be utilized as nonlinear, high-dimensional data transform units and thanks to their rich physical dynamics, machine learning tasks could be performed with a much smaller dependence on digital electrical hardware (such as GPUs). This phenomenon holds promise to alleviate the consequences of overparameterization, which allowed neural networks to perform extraordinarily complicated tasks, but has the downsides such as high energy consumption and exponential increase in the need of digital computation resources.
For achieving competitive performance levels with the proposed optical computation approach, the reconfiguration of the system for the planned task is crucial. In another study, we have shown that with wavefront shaping and by optimizing a few tens of parameters, the reconfiguration of the system could improve the performance of the optical computer significantly. However, the optimization follows a blackbox approach and limited in the number of programming parameters that can be controlled. More….
SEMESTER PROJECT: Sustainable resins for 3D printing
3D printing reduces time from the design board to the finalized product and can cut down on waste from slow iterative product development. However, most 3D printers use non-recyclable, non-degradable materials to print. Additionally, these inks and resins incorporate harsh chemicals like organic solvents. There is a need to make 3D printing more sustainable. Vegetable-oil based resins appear as an alternative to synthetic acrylic resins commonly used in light-based 3D printers. Recently, engineers have functionalized commercially available vegetable oils (like those found at the supermarket) into reactive inks without harsh organic solvents for light-based 3D printers. In this semester project the student will adapt these degradable vegetable-oil based resins to a tomographic volumetric printer. More….
SEMESTER/MASTER PROJECT: Advance additive manufacturing instrument development
Melt electro writing (MEW) is an emerging high-resolution additive manufacturing technique for fibers, membranes and 3D objects in the micrometer range. MEW is related to solution electrospinning and allows the fabrication of continuous fibers with diameters of 1 – 50 µm, this is up to 10x smaller than for most other additive manufacturing techniques. Current and future applications of MEW include biomedical sciences, sensors, the textile industry, filtration technologies and many more. More…
SEMESTER/MASTER PROJECT: On-the-fly feedback for tomographic additive manufacturing
In tomographic volumetric additive manufacturing, objects are polymerized within a rotating vial containing a liquid or gel resin.