I am a second year PhD student in the department of
Statistics
at
Boston University.
My PhD advisor is Professor
Uri Eden
and we are now working on neural signal processing and neural computation
for exploring and analyzing brain activity.
Previously, I obtained a BSc in Applied Mathematics in Taiwan and a M.S. in
Statistics
from
Indiana University
under the supervision of Professor
Stanley Wasserman.

I am now working as a statistical consultant in the MSSP Consulting Center at BU, also
guiding Master’s students in their consulting projects.

Contact Info:

Email: whsin@bu.edu
Office: MCS B46B
111 Cummington Mall
Boston, MA 02215 USA

Office Hours: By appointment

Teaching:

“Those that know, do. Those that understand, teach.” - Aristotle.

Current

PhD Mentor:

MSSP consulting program: MA676 - Statistics Practicum II, Spring 2020

MSSP consulting program: MA675 - Statistics Practicum I, Fall 2019

Teaching Fellow @ Boston University

MA115 - Statistics I, Spring 2019

MA124 - Calculus II, Fall 2018

Teaching Assistant @ Indiana University

STAT-S520 - Introduction to Statistics, Fall 2016, Spring 2017, Fall 2017, and
Spring 2018

Research:

I am particularly interested in statistical approaches to network analysis. Previously,
I worked with Professor Stanley Wasserman at Indiana University and focused on statistical
methodologies for social network analysis. After moving to Boston University, I began working
with Professor Uri Eden on statistical methods for analyzing neural activity.

Previous Work:

Driven by an interest in understanding human social and psychological behavior, I worked with
Professor Stanley Wasserman on statistical approaches for analyzing longitudinal network data
in social science. More specifically, we focused on the stochastic actor-oriented model (SAOM)
and the temporal exponential random graph model (TERGM) for network dynamics. Given that
the SAOM and the TERGM share a similar mathematical core, we would like to determine which
model is more consistent with dynamic network process and how stochastic process leads to
the differences by empirical comparison.