Graduate Students
William Annan
Email: annanwe@clarkson.edu
Website: https://sites.google.com/clarkson.edu/williamannan/about?authuser=0
Research: My Ph.D. research involves developing mathematical models to study the progression of Rhegmatogenous Retinal Detachment (RRD) and its effects on the dynamics of rod outer segment renewal. The goal is to predict the optimal time frame for treatment to prevent blindness.
Advisors: Prof. Diana White and Prof. Emmanuel Asante-Asamani
Research assistantship: As part of an NSF/NIH funded project, I am utilizing the vertex modeling framework to investigate pattern formation in fruit flies. The objective is to comprehend the impact of long-range communication through filopodia on pattern spacing.
Emmanuel Atindama
Email: atindaea@clarkson.edu
Website: https://sites.google.com/view/emmanuelatindama-exploremath
Emmanuel works with Prof. Prashant Athavale on mathematical image processing problems. In particular, he is working on reconstructing electron backscatter diffraction (EBSD) maps using weighted total variation (TV) flow and machine learning algorithms. His interests are mathematical and statistical image processing and data science. His research on the EBSD restoration was partly supported by NIST grant 70NANB21H047.
Wisdom K. Attipoe
Email: attipowk@clarkson.edu
Wisdom works with Professor Emmanuel Asante-Asamani on developing efficient Exponential Time Discretization schemes for solving reaction-diffusion equations, which have vast applications in drug manufacturing, cancer progression, and the spread of epidemics. He is also working on an NSF/NIH funded project to create machine-learning models that classify the organization of bristle cells on the thorax of fruit flies.
Arun Barkhanda
Email: barkhaa@clarkson.edu
Arun works with Prof. Prashant Athavale on methods for AI-guided image denoising, machine learning, variational analysis, and partial differential equations.
Ethan Bartiromo
Email: bartiret@clarkson.edu
Ethan is interested in applications of ordinary and partial differential equations, as well as modeling through probability. He enjoys the beauty of rigorous mathematical proofs and strives to think of innovative ways to solve math problems.
S. H. Dinuka Sewwandi De Silva
Email: desilvs@clarkson.edu
Dinuka works with Professor Emmanuel Asante-Asamani on Modeling bleb-based chemotaxis in confined eukaryotic cells. She is also working on an NSF/NIH funded project to model the formation of bristle cell patterns in fruit flies.
Mackenzie Dalton
Email: daltonma@clarkson.edu
Mackenzie works with Professors Emmanuel Asante-Asamani and James Greene on understanding the progression of type 1 diabetes using a mechanistic modeling approach.
Sathsara Dias
Email: diassl@clarkson.edu
Website: https://www.diassl.com/
Advisors: Prof. Marko Budišić, Prof. Brian Helenbrook, Prof. Pat Piperni
Research: My research during my Ph.D. focused on data-driven methods for model- and data-order reduction, including Koopman/Dynamic Mode Decomposition and Proper Orthogonal Decomposition, applied to dynamical systems. My expertise extends into data science, where I employ deep learning, machine learning, and statistical techniques to derive insights from complex data sets.
Uresha Dias
Email: diaski@clarkson.edu
I am Uresha Dias, with a keen interest in Mathematical Biology. I am involved in research with Professor Greene focused on learning collective behaviors in nature theoretically. Apart from that I am engaged in a research project funded by CREST, focusing on quantifying the impacts of space on antibiotic resistance evolution by considering various factors.
Sucharitha Dodamgodage
Email: dodamgss@clarkson.edu
LinkedIn: www.linkedin.com/in/sucharitha-dodamgodage-6451aa1b1
Sucharitha is a Lawrence ’57 and Antoinette Delaney Ignite Research Fellow at Clarkson University (2022-2024). She collaborates with Prof. Sumona Mondal from the Department of Mathematics and Dr. Shanatanu Sur from the Department of Biology at Clarkson University. Her research interest centers around the advancement of statistical methodology for modeling microbiome data, addressing the inherent challenges posed by compositional data structures. By leveraging the Dirichlet distribution, their research group aims to provide powerful tools for unraveling the intricate dynamics of microbial communities and their implications for human and environmental health and disease.
Paul Dougall
Email: dougalpe@clarkson.edu
Paul works with Prof. Joe Skufca and Prof. Shantanu Sur on machine-learning applications within bioinformatics. More specifically, improving interpretable models and feature space-informed model selection for the prediction of antimicrobial resistance using whole genome sequencing.
Daniel T. Fuller
Email: fullerdt@clarkson.edu
Daniel works with Prof. Sumona Mondal and Prof. Shantanu Sur on statistical applications in bioinformatics.
Olaoluwa Ogunleye
Email: ogunleoa@clarkson.edu
LinkedIn: https://www.linkedin.com/in/olaoluwa-ogunleye/
Olaoluwa (Ola) works with Prof Guangming Yao on using homotopy methods to solve nonlinear multiobjective optimization problems and radial basis functions for PDEs. He spends his free time playing soccer, learning algorithms and data structures, and developing my technical skills.
Nipuni Senani de Silva Rammini
Email: desilvr@clarkson.edu
Nipuni works with Professor Greene on collective motion behavior modeling using data-driven techniques. She is interested in utilizing data-driven interference methods to understand dynamical systems. She is a deep-learning enthusiast and an anime lover.
Sorunke Olasunkanmi Samuel
Email: sorunkos@clarkson.edu
LinkedIn: www.linkedin.com/in/olasunkanmi-sorunke
Quote: To every problem in mathematics there is always a solution.
I am Olasunkanmi Samuel (B.sc Mathematics and Statistics) from University of Lagos, Akoka, and I am currently doing my PhD Program in the field of Applied Statistics at Clarkson university. I will be working with Prof. Mohammed Meysami on the application of spatial statistics and data analysis on Crime. The Objective of this research interest is to apply spatial statistics and data analysis in combating crime through crime identification,crime awareness, Evacuation (Satellite visualization, Images and Drones), Crime Mapping and Crime pattern analysis using complex statistical methods.
Thevasha Sathiyakumar
Email: sathiyt@clarkson.edu
LinkedIn: www.linkedin.com/in/thevasha-sathiyakumar-812658100
Thevasha works with Prof. Marko Budišić, Prof. Sumona Mondal and Prof. Shantanu Sur on merging statistics and applied topology with applications to cancer cell dynamics. Her doctoral dissertation employs a functional hypothesis test of the time-varying persistence homology in order to capture coherence in movement of cancer cells as they respond to environmental stimuli. Apart from the main thesis work, she is also involved in biostatistics projects with a focus of using data mining and statistical methods for analyzing microbiome data and public health data.
Chamodi Wijenayake
Email: wijenach@clarkson.edu
LinkedIn: https://www.linkedin.com/in/chamodi-wijenayake-651a65176/
I prefer to be addressed as Chamodi, and I work on identifying new spatial scan methods for cluster detection with Dr. Mohammad Meysami. In addition to my academic pursuits, recently I have a keen interest in improving strategic thinking through chess and on exploring new strategies in personal development. I always like to engage with professionals who are enthusiastic in exploring new concepts in their respective fields.
Ibraheem Abiodun Yahayah
Email: yahayai@clarkson.edu
LinkedIn: Linkedin.com/in/yahayah
Yahayah Ibraheem Abiodun (PhD, Applied Mathematics ) BSc, Mathematics, University of Ilorin, Nigeria; MSc, Financial and Actuarial Mathematics, Wroclaw University of Science and Technology, Poland; MSc, Financial Engineering, WorldQuant University. Ibraheem has a background as a Risk Analyst at Credit-Suisse and Fund Accountant at StateStreet enriching his academic pursuit.
Ibraheem will be working with Professor Guangming Yao diving into the realm of numerical methods using radial basis functions (RBFs) to solve various types of partial differential equations including elliptic PDEs and other time dependent PDEs. Beyond academia and industry, my passions extend to teaching, exploring new realms of knowledge, and forging meaningful connections with fellow enthusiasts. As a mantra, I believe every problem has a solution provided it is well-defined.