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An investigation into algorithms for generalized, dynamic sentiment tracking in natural language is reported. Little progress has been made in the automatic analysis of literary fiction. However, accurate recognition and robust modeling of text's emotional content would allow for large scale analysis of the ever increasing number of digitized books in addition to other applications such as sorting, searching, and making book recommendations. But the challenge of human-like reading of these books not only includes computational hurdles but inherent ambiguity.

The system described here, if properly implemented, might be used to help student engineers teach themselves how to access an information store, such as a library, and how to specify a search request.

First-order methods for convex and nonconvex optimization have been an important research topic in the past few years. This talk studies and develops efficient algorithms of first-order type, to solve a variety of problems. We first focus on the widely studied gradient-based methods in composite convex optimization problems that arise extensively in compressed sensing and machine learning.

Zero-knowledge proofs (ZKPs) enable a prover to convince a verifier that a given statement is true without revealing anymore information other than the fact the statement is true. Although this high-level description makes it sound simple, zero-knowledge proofs open up multiple use cases with regards to private transactions, verifiable outsourced computation, and much more. zkSNARKs are a particularly useful ZKP construction as the proof is very small and very fast to verify no matter the problem size.