Online job boards are one of the central components of modern recruitment industry. With millions of candidates browsing through job postings everyday, the need for accurate, effective, meaningful, and transparent job recommendations is apparent more than ever. While recommendation systems are successfully advancing in variety of online domains by creating social and commercial value, the job recommendation domain is less explored. Existing systems are mostly focused on content analysis of resumes and job descriptions, relying heavily on the accuracy and coverage of the semantic analysis and modeling of the content in which case, they end up usually suffering from rigidity and the lack of implicit semantic relations that are uncovered from users' behavior and could be captured by Collaborative Filtering (CF) methods. Few works which utilize CF do not address the scalability challenges of real-world systems and the problem of cold-start. In this paper, we propose a scalable item-based recommendation system for online job recommendations. Our approach overcomes the major challenges of sparsity and scalability by leveraging a directed graph of jobs connected by multi-edges representing various behavioral and contextual similarity signals. The short lived nature of the items (jobs) in the system and the rapid rate in which new users and jobs enter the system make the cold-start a serious problem hindering CF methods. We address this problem by harnessing the power of deep learning in addition to user behavior to serve hybrid recommendations. Our technique has been leveraged by CareerBuilder.com which is one of the largest job boards in the world to generate high-quality recommendations for millions of users.

Motile cilia are a highly conserved organelle found on the exterior of many human cells. Cilia beat in rhythmic patterns to transport substances or generate signaling gradients. Disruption of these patterns is often indicative of diseases known as ciliopathies, whose consequences can include dysfunction of macroscopic structures within the lungs, kidneys, brain, and other organs. Characterizing ciliary motion phenotypes as healthy or diseased is an essential step towards diagnosing and differentiating ciliopathies. We propose a modular generative pipeline for the analysis of cilia video data so that expert labor may be supplemented for this task. Our proposed model is divided into three modules: preprocessing, appearance, and dynamics. The preprocessing module augments the initial data, and its output is fed frame-by-frame into the generative appearance model which learns a compressed latent representation of the cilia. The frames are then embedded into the latent space as a low-dimensional path. This path is fed into the generative dynamics module, which focuses only on the motion of the cilia. Since both the appearance and dynamics modules are generative, the pipeline itself serves as an end-to-end generative model. This thorough and versatile model allows experts to spend less time caught in the minutiae of cilia biopsy analysis, while also enabling new insights by quantifying subtle patterns that would be otherwise difficult to categorize.

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Tracking cell particles in 3D microscopy videos is a challenging task but is of great significance for modeling the motion of cells. Proper characterization of the cell's shape, evolution, and their movement over time is crucial to understanding and modeling the mechanobiology of cell migration in many diseases. One in particular, toxoplasmosis is the disease caused by the parasite Toxoplasma gondii. Roughly, one-third of the world's population tests positive for T. gondii. Its virulence is linked to its lytic cycle, predicated on its motility and ability to enter and exit nucleated cells; therefore, studies elucidating its motility patterns are critical to the eventual development of therapeutic strategies. Here, we present a computational framework for fast and scalable detection, tracking, and identification of T. gondii motion phenotypes in 3D videos, in a completely unsupervised fashion. Our pipeline consists of several different modules including preprocessing, sparsification, cell detection, cell tracking, trajectories extraction, parametrization of the trajectories; and finally, a clustering step. Additionally, we identified the computational bottlenecks, and developed a lightweight and highly scalable pipeline through a combination of task distribution and parallelism. Our results prove both the accuracy and performance of our method.