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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).

Human exposure to atmospheric aerosols is increasingly recognized as the leading global environmental determinant of health and longevity. However, ground-based monitoring remains sparse in many regions of the world. Deep learning offers immense potential to advance understanding of air quality by leveraging large satellite remote sensing datasets. However, the spatial heterogeneity and autocorrelation of ground-based measurements pose challenges to the training and testing of algorithms for true out of sample prediction. Thus, algorithms benefit from additional process-based constraints from a chemical transport model and targeted ground-based measurements of aerosol chemical composition.  This talk will highlight recent advances in applying deep learning by building on satellite remote sensing, global modeling, and ground-based measurements to improve understanding of air quality and planetary health from global toward urban scales

In this talk, we will demonstrate how the celebrated connection between Ricci curvature, optimal transport, and geometric inequalities such as the Brunn-Minkowski inequality, extends to the setting of general Lagrangians on weighted manifolds. As examples, we will consider classical (mechanical and electromagnetic) Lagrangians on Riemannian manifolds. In particular, we will state a generalization of the horocyclic Brunn-Minkowski inequality to complex hyperbolic space of arbitrary dimension, and a new Brunn-Minkowski inequality for contact magnetic geodesics on odd-dimensional spheres.

Identifying microbial genomic factors that underlie important phenotypes is a key goal of microbiological and microbiome research. Current approaches for this goal, however, cannot tap into the wealth of genomic information in a systematic manner, particularly in microbes that are not well-characterized. I will present a new conceptual and methodological approach for analyzing microbial communities using multi-sample sequence graphs. Our results demonstrate that this approach captures sequence and variant information more accurately than traditional approaches, provides graphs that are more suitable for comparative analyses, and is computationally tractable. I will end by demonstrating an application for predicting gut colonization trajectories of Vancomycin-resistant Enterococcus. Overall, our results underscore the value of graph-based frameworks for comparative metagenomic analyses.