Name : Amaljith Chandroth Kalliyadan Degree : PhD Topic: Tunable metamaterial photonic structures for electro-optic and energy saving devicee Summary Ofir Yaish PhD Prof. Yaron Orenstein and Dr. Nir Shlezinger subject: Computational modeling and analysis of biological systems based on high-throughput data

Name : Amaljith Chandroth Kalliyadan Degree : PhD Name of the Supervisor : Professor. Ibrahim Abdulhalim Topic: Tunable metamaterial photonic structures for electro-optic and energy saving devices Summary Metamaterial (MTM) devices hold significant potential across various fields, including sensing, security, screening, and telecommunications. Liquid crystals (LCs) are particularly promising for tuning these devices due to their strong electro-optic response and ability to infiltrate nano- and micro-scale structures. Previous studies have demonstrated that metamaterial-based systems can achieve achromatic performance, wide viewing angles, and rapid switching, as only thin LC layers are required. In this research, I will report on several designs and some built devices: (i) A Thick dielectric subwavelength structure filled with LC showing that the switching speed increases by two orders of magnitude due to designing the electromagnetic field confinement in a region where the LC switches faster; (ii) A metal-insulator-metal (MIM) stacked MTM device for interfacial solar photothermal conversion. The structure comprises a top MTM metal layer for wide-angle light trapping, a middle dielectric spacer for resonant mode enhancement, and a bottom reflective tungsten layer to nullify transmission. It exhibits broadband, wide-angle absorption and polarization-independent characteristics, as well as a remarkable photothermal conversion efficiency in the 400-2400nm spectral range. An average absorption of ≈77% is theoretically and experimentally achieved. The device has shown a promising photothermal conversion efficiency of 73%, an evaporation rate (ER) above 10 L/h.m2 under moderate sunlight conditions, with the potential to double under optimized conditions, showing that it is a strong candidate for efficient water desalination and wastewater treatment; (iii) An enhanced optical transmission (EOT) structure combined with LC layer for fast and broadband tuning. Ofir Yaish PhD Prof. Yaron Orenstein and Dr. Nir Shlezinger subject: Computational modeling and analysis of biological systems based on high-throughput data Summary : The rapid growth of high-throughput experimental assays has opened unprecedented opportunities for understanding complex biological systems, but it has also introduced new challenges in data modeling and interpretation. In this seminar, I will present computational frameworks I developed for modeling and analyzing genomic systems, focusing on two distinct but complementary directions. First, I will discuss our work on CRISPR/Cas9 off-target prediction, where we generated the most comprehensive datasets of off-target sites with bulges to date and developed SWOffinder, a combinatorial search algorithm, alongside advanced machine-learning models that achieved state-of-the-art prediction accuracy across both in vitro and in cellula data. This study highlights the power of integrating large-scale data with tailored algorithms to address limitations in genome-editing safety and design. However, predictive modeling alone is insufficient for fully uncovering biological mechanisms. Therefore, in the second part, I will present F-MoDA (Fourier-based Motif Discovery in Attribution maps), a novel computational method for extracting regulatory motifs from deep-learning attribution maps. F-MoDA leverages signal processing and clustering to discover motifs more accurately, concisely, and efficiently than existing approaches. Together, these works demonstrate how computational modeling, combined with interpretable machine learning, can both enhance practical applications, such as genome editing, and advance our understanding of fundamental biological processes.