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Sunia Tanweer

Assistant Research Scientist

tanweer@umich.edu

Office Information:

Office Number: 1868 EH

Applied Mathematics; Mathematical Biology; Mathematics; Research Scientist

Education/Degree:

Michigan State University East Lansing, MI, USA
Dual PhD in Mechanical Engineering, and Computational Mathematics Sept 2022 ‑ May 2026
CGPA: 4.0/4.0 | Advisor: Dr Firas A. Khasawneh
Awarded NSF Frontera Computational Science Fellowship for 2025‑2026—worth ∼ $50000 to use Frontera supercomputer at TACC UT Austin
Thesis: Analyzing Dynamical Systems with Topological Data Analysis, Stochastic Theory and Machine Learning
Main courses: Numerical Methods of Differential Equations, Numerical Linear Algebra, Mathematical Foundations of Data Science, Stochastic Processes, Analysis of Stochastic Processes, Nonlinear Dynamics, Parallel Computing, Scientific Machine Learning, Computational Statistics, Deep Learning, Data Structures, MLOps, Computational Optimization
National University of Sciences and Technology (NUST) Islamabad, Pakistan
Bachelor's in Mechanical Engineering Sept 2017 ‑ May 2021
CGPA: 3.95/4.00 – Summa Cum Laude (awarded President’s Gold Medal for Academic Excellence)
US State Department’s fully funded merit‑based Global UGRAD Semester Exchange Scholarship for 6th semester at University of Wyoming
(Laramie, WY, USA)—worth over $25000. Selected out of 14000+ applicants from all over Pakistan. Mentioned in President’s Honor Roll

Sunia Tanweer develops intelligent algorithms designed to extract actionable insight from complex, high-dimensional systems. Her work combines topology, dynamical systems, and statistical learning to build models that remain robust in the presence of noise, stochasticity, and nonlinear behavior. She has applied these approaches across domains including neuroscience, artificial intelligence, and engineering, with a focus on problems where traditional models struggle to generalize or provide reliable interpretation. At the Intelligent Algorithms Core, she is focused on translating foundational research into scalable tools that can be deployed in real-world bioscience settings. Her goal is to develop algorithms that not only deliver strong predictive performance but also enable decision-making by revealing underlying structure such as transitions, stability, and failure modes. She is particularly interested in building cross-disciplinary platforms with commercialization potential, bridging academic innovation with industry and startup ecosystems.