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Transcriptional regulatory networks, composed of regulatory proteins and their target genes, control many aspects of cell development and physiology. One important class of regulatory proteins are transcription factors (TFs), which bind to DNA in a sequence specific manner to regulate gene expression. In the human genome, TFs often bind in clusters, i.e. two or more DNA binding sites in close proximity to each other. However, the binding of TFs to clusters of sites is not well understood.

This project considers the problem of automatically converting questions and claims in natural language to potentially complex SQL queries over a domain-specific database. By "complex," we mean that the queries are analytical in nature, often involving aggregation and subqueries, as opposed to focusing on retrieval, which traditional natural language querying interfaces are designed for.

We investigate how to generate the most meaningful "factlets" from Duke University Basketball game statistics.

Distributed denial of service (DDoS) attack has grown to a major network security thread due to the recent prosperity of DDoS-for-hire services and IoT botnets. Despite of the various efficient and deployable commercial and academic network layer DDoS protection architecture, the thread remains active since the focus of DDoS attack has switched from network bandwidth to application layer resources, which can hardly be defended under current defense solutions. In this paper, we present Adjusted deficit round robin (ADRR) algorithm as a solution to application layer DDoS attack.

As robot autonomy increases, new challenges occur in human-robot interaction (HRI) studies. Investigations on HRI involving different levels of autonomy (LOA) benefit both the operator and system performance. In order to provide a high level of semi-autonomous control in related lab experiments, this project develops a human-robot system featuring waypoint control. The system consists of an RGB-D camera, a robotic arm, and a Graphical User Interface (GUI).

Poor accuracy has always been a challenge to neurosurgery procedures. The conventional method for procedures like External ventricular drain (EVD) highly relies on the personal experience and familiarity with neuroanatomical features of the surgeon. Without guidance, this method often yields poor results because of the anatomical variations in individual patients. The purpose of this project is to explore the feasibility of enhancing operating accuracy of neurosurgery procedures like EVD insertions with external tracking solutions and a wearable augmented-reality holographic device.