Recent biological studies have been revolutionized in scale and granularity by multiplex and
high-throughput assays. Profiling cell responses across several experimental parameters …

Due to the inception of Web 2.0 and freedom to facilitate the dissemination of information,
sharing views, expressing opinions with regards to current world level events, services …

We analyze Twitter data relating to the COVID-19 pandemic using dynamic topic modeling
techniques to learn topics and their prevalence over time. Topics are learned using four …

Due to its superior processing power in fields like text, picture, and audio processing, deep
learning (DL) is becoming more and more popular. The exponential growth and extensive …

The precedent is a textual citation of prior court decisions. This undoubtedly offers great
value in a common-law-based judicial system where courts are bound to their previous …

By representing documents as mixtures of topics, topic modeling has allowed the successful
analysis of datasets across a wide spectrum of applications ranging from ecology to …

Tensor factorizations have been widely used for the task of uncovering patterns in various
domains. Often, the input is time-evolving, shifting the goal to tracking the evolution of …

Many physical phenomena can be described by nonlinear partial differential equations
(PDEs). Yet, analytic solutions are oftentimes unavailable, and lab experiments can be time …

Temporal text data, such as news articles or Twitter feeds, often comprises a mixture of long-
lasting trends and transient topics. Effective topic modeling strategies should detect both …

With the ever-increasing access to data, one of the greatest challenges that remains is how
to make sense out of this abundance of information. In this dissertation, we propose three …