Integrated Calendar

Atmospheric lidars are crucial remote sensing tools in aerosol andclimate research. A pulsed time-of-flight lidar continuously samplesvertical atmospheric profiles across day and night, yielding aspatiotemporal atmospheric map. However, lidar analysis ischallenged by low signal-to-noise ratios, sunlight interference, andthe need for frequent calibration.To address these challenges, our work begins by developing theAtmospheric Lidar Data Augmentation (ALiDAn) framework tosimulate spatiotemporal and multiwavelength lidar data underdiverse atmospheric and system conditions. Using ALiDAn's data,we identify limitations and flaws in standard processing methods.To address these, we develop a Maximum Likelihood Estimation(MLE) approach tailored to lidar's photon-counting Poissonstatistics. This significantly improves signal analysis, especiallyduring the daytime, allowing for more frequent and accurateretrievals.With this enhanced signal estimation, we also reveal unexpectedflaws in the standard calibration approach. Therefore, we propose anovel learning-based lidar calibration that uses spatiotemporalmeteorological and lidar data. This approach helps address lowsignal-to-noise ratios, keeps calibration more updated than thecurrent operational method, and achieves higher accuracy onsimulated data. These advancements enable continuous, reliableaerosol retrievals and offer insights and tools to guide futureadvancement in lidar analysis.This talk presents several key advancements from my PhD research,conducted under the supervision of Prof. Yoav Y. Schechner at theAndrew and Erna Viterbi Faculty of Electrical & ComputerEngineering, the Technion.

Nucleotide variation in gene regulatory elements is a major determinant of phenotypes including morphological diversity between species, human variation and human disease. Despite continual progress in the cataloging of these elements, little is known about the code and grammatical rules that govern their function. Deciphering the code and their grammatical rules will enable high-resolution mapping of regulatory elements, accurate interpretation of nucleotide variation within them and the design of sequences that can deliver molecules for therapeutic purposes. To this end, we are using massively parallel reporter assays (MPRAs), including capture-C based MPRAs, to simultaneously test the activity of thousands of gene regulatory elements and their target promoter in parallel. Regulatory elements can also serve as therapeutic targets. By targeting regulatory elements via CRISPR activation (CRISPRa), we show that they can be used to rescue a variety of haploinsufficient diseases (having -50% dosage reduction due to having only one functional allele). In addition, utilizing CRISPRa to engineer adipocytes and adipose organoids to outcompete tumors for nutrients, we show that they can be used as a novel cancer therapy, termed Adipose Manipulation Transplantation (AMT).