Binhai Zhu - US grants

DMS-0138065
Binhai Zhu

In this project, we investigate a three-dimensional geometric
problem that originated in neural maps, which model the
motions of neurons so as to understand the human behavior.
The problem is defined as follows: given a neuron, which is
modeled as a polyhedron, compute a minimum set of (minimal)
cylindrical segments to approximate the neuron. We plan to
design a good approximation for this problem (i.e., the error
between the cylindrical segments and the neuron is small).
We also plan to have a good implementation, build a prototype
system, and perform extensive empirical studies. Practically,
a solution to this problem will have a great impact in
computational biology. Theoretically, this problem generalizes
the problem of finding a single line stabbing a set of balls
in 3D (and has never been seriously studied to the best of our knowledge).

Modeling the motions of human neurons is an important problem
in biology, especially in understanding human behavior under
different circumstances. To do that, we first need to model
a single neuron, which is very much like a tree-shaped
polyhedron, using a set of cylindrical segments. Different
cylindrical segments of different radii have different
functionality, so the union of the cylindrical segments
should be as close to the neuron as possible. In practice
this problem is estimated manually by technicians in
the computational biology community. The process is
time-consuming and error-prone. Our research will focus
on automating this process with computers and a successful
solution will have deep influence in the computational
biology community. We will train two graduate students
throughout this project.

In this project, based on applications in protein structure alignment
and its visualization, we will investigate several fundamental problems
regarding the discrete Frechet distance. These include improving the
computation time for protein structure alignment by taking into account
practical conditions (e.g. the lengths of the segments connecting neighboring
alpha-carbon atoms are almost uniform), and simultaneously simplifying a pair
of chains for realistic visualization, etc. Based on these theoretical
investigations, we also plan to build a prototype software system, available
in the public domain, for these applications.

Protein structure alignment is an important and fundamental technique used to
understand the evolution of proteins and to predict their functionality.
This understanding is crucial for the identification of the cause of certain
diseases and for the development of new drugs. Here we propose a new approach
based on the discrete Frechet distance to improve the quality and speed of
protein structure alignment. Our proposal will consider fundamental questions
with regard to this technique. The resulting software tools will be accessed
by the public for education and research. This project will help support
and train several graduate students at Montana State University.

Models and Algorithms for Genome Evolution (MAGE) is an international meeting addressing the interdisciplinary area of genome evolution. MAGE is also celebrating the 50-year career of David Sankoff, a pioneer in computational genomics and biology. The community of computational evolutionary biologists is composed of mathematicians, computer scientists, statisticians, physicists and biologists, all willing to contribute to a better knowledge of the history of life on earth. The plurality of backgrounds yields a plurality of approaches, theoretical and empirical. The organizers represent a diverse international set of researchers at different stages of their careers. While the core of senior and early-career researchers have been invited and accepted, these funds will enable up to 20 students and postdocs to participate in the conference. In addition to the presentations and daily poster sessions, the meeting will also provide input to a book to be published by Springer in its series Computational Biology.

Vehicular networks are becoming increasingly popular. To make them truly useful, irrelevant information exchanges among vehicles should to be eliminated to avoid unnecessary driver distraction. This project aims to tackle this fundamental problem, wherein what information is delivered to which vehicle(s) is intelligently determined. The project will study the closeness between vehicles based their interactions, in the form of information exchange, so a driver can determine whether a received message is relevant based on the closeness information. Because information is filtered by a vehicle's close 'friends', the amount of irrelevant information it receives will be reduced, and thus efficient information dissemination is achieved. The research will produce an efficient information dissemination system that complements and enhances existing intelligent transportation systems, connected vehicles, and vehicular telematics. The project will also include efforts to deploy the system to offer a better information provision service to drivers. Two PhD students and several undergraduate students will be trained in this project.

The researchers propose to use interactions between vehicles to estimate their closeness, and most importantly, to determine what data should be delivered to which vehicle(s) based on the closeness information. The key to their approach is constructing a vehicular social network (VSN) that enables drivers to integrate their social network with vehicular network. The list of points of interest (POIs) that a vehicle visited is considered its genome, and vehicles with similar genetic features are considered initially connected in a VSN. These connections are then cultivated by the interactions among vehicles. With positive, negative, and uncertain interactions, the closeness between two vehicles having direct interactions is modeled as a Dirichlet distribution. For vehicles that have no direct interactions, their closeness is inferred from the social network between them. The PIs will design a polynomial-time solution to addressing the massive closeness assessment problem, i.e., computing the closeness from a driver to all others in a VSN. The researchers also propose an efficient algorithm for the all-pair closeness assessment problem, i.e., computing the closeness of any pair of vehicles in a VSN. A cloud-hosted service is proposed to coordinate social connection construction, VSN maintenance, closeness assessment, and information dissemination.

This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at institutions of higher education across Montana. Over its one-year duration, this project will: build collaboration and infrastructure among college and university computing faculty and other stakeholders in Montana; identify the needs and gather data on the underrepresentation of computer science identity, especially for women and American Indian students; and support funding 20 students from across Montana for two Visit Days, where they will learn about graduate degrees in computing in Montana. The data gathered will be useful for setting up standards for scholarships in a later stage. Intellectually, the research results identify reasons preventing low-income, women, and American Indian students from pursuing computing degrees and careers. Broadly, this project will initiate a systematic process to increase computing populations in Montana.<br/><br/>The overall goal of this project is to increase STEM degree completion of low-income, high-achieving students with demonstrated financial need. As a planning project for a larger S-STEM Track 3 future project (whose goal is to increase computer science students at all levels in Montana), the goals of this project are to advance understanding of the reasons preventing Montanan students, especially minorities, from choosing computing as careers, and certainly, and to involve student participation to help build their career. New measures, like persistence intentions (i.e., how likely a student is to continue his/her career in computing) will be used to collect data which will help setting up standards on scholarships for the corresponding students in a later stage. The project will also strengthen collaboration for computing personnel in Montana and will attract student participation by supporting 20 out-of-town undergraduate students pursuing graduate degrees on two Visit Days. In addition to all of these, one of the outcomes of this project is a larger Track-3 proposal.<br/><br/>This project is funded by NSF’s Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of low-income academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.