Past Speakers

Sajal Chakroborty

Where: Stratton Hall 202
When: 12:00 pm to 1:00 pm

Topic: Statistical Learning and Some Applications

Abstract: Statistical learning has been an active field of research for a long time. It lies at the intersection of Mathematical Statistics, Bayesian Statistics, Machine Learning and Data Science. I will briefly discuss my previous and current research experiences in this area, along with some future research plans. Finally, I will briefly share my graduate life experience.

Charlotte (Charly) Fowler
Assistant Professor

Where: Stratton Hall 202
When: 12:00 pm to 1:00 pm

Topic: Analysis of mobile health studies and a biostatistician’s journey

Abstract: In this talk, I will briefly explore how we can draw inferences from data collected by wearable devices. Specifically, I will demonstrate the use of functional data analysis to identify sleep patterns observed from wrist actigraphy data. Afterwards, I will discuss my personal journey in choosing biostatistics and pursuing a career in academia.

Dana Ferranti

Topic: Regularized Stokeslets, early career mathematician lessons, and related thoughts

Abstract: I completed my mathematics PhD at Tulane University in August 2023 and started my current postdoc position at WPI a few weeks later advised by Dr. Olson. I’ll give a friendly introduction to my research (numerical methods for Stokes flow) and talk a little bit about some lessons I’ve learned over the last several years. 

Randy Paffenroth

Topic: Neural networks and the relevance of dynamical systems in studying them

Abstract: Neural networks (NN) can be divided into two broad categories, recurrent and non-recurrent and they are often treated as distinct families of machine learning algorithms. In this talk we will argue that there is a closer relationship between these two types of neural networks than is normally appreciated and both can often be studied from the perspective of dynamical systems.

Joseph Fehribach

Topic: Kirchhoff graphs and some applications

Abstract: Abstract: Neural networks (NN) can be divided into two broad categories, recurrent and non-recurrent and they are often treated as distinct families of machine learning algorithms. In this talk we will argue that there is a closer relationship between these two types of neural networks than is normally appreciated and both can often be studied from the perspective of dynamical systems.

Carly Thorp

Topic: Working as a statistician in the corporate world and then transitioning into academia as a professor of practice

Guanying Peng

Topic: Applying techniques from nonlinear PDEs and calculus of variations to understand physical systems

Abstract: I'm interested in applying comprehensive techniques from nonlinear PDEs and calculus of variations to understand complex singularity structures in certain physical systems, including superconductors, liquid crystals, thin film blisters, convection pattern formations and some systems described by hyperbolic conservation laws. The studies of these systems are highly interdisciplinary. The mathematical studies of such problems require the development of new mathematical tools, and these studies further foster the fundamental understanding in related fields of sciences.

Nicole Buczkowski

Topic: Advantages and challenges of utilizing nonlocal operators in modeling

Sarah Olson

Topic: Biological fluid dynamics and its connections to computing, analysis, and optimization

Adam Sales

Topic: Using educational technology data to estimate what works in education, how it works, and for whom

Qingshuo Song

Topic: Stochastic control and its potential connections to the Navier-Stokes equations

Francesca Bernardi

Topic: Understanding particles and fluid transport at the microscale

Nathan Uricchio

Topic: Powerset operators and their connection to matroids

Sam Walcott

Topic: Bridging the gap between molecular and cellular biology with mathematical models