Graduate Students | People | Engineering Sciences & Applied Mathematics

Ethan Baxter

Parameter identifiability and estimation for rate-based models of synaptic plasticity (Advisor: James Fitzgerald).

Colton Bryant

Using an overset grid scheme to study the dynamics of particles confined to fluid interfaces. (Advisors: David Chopp and Michael Miksis)

Christina Catlett

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Developing experimental design methods to address identifiability issues in sparse model selection (SINDy) as applied to synthetic biological systems (Advisor: Niall Mangan).

Nan Ding

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Modeling firing responses of Drosophila thermosensory neurons based on gene expression profiles (Advisors: William Kath and Marco Gallio)

Deirdre Edward

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Using a combination of artificial intelligence methods (e.g. machine learning, natural language processing) and legal analysis to characterize social and environmental justice issues in the transportation industry (Advisor: Amanda Stathopoulos).

Gonzalo Ferrandez Quinto

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Mathematical modeling of active particles confined within droplets in the Stokes flow regime (Advisor: Petia Vlahovska).

Paul Ge

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Jingting Gao

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Design of computer simulations with probability and applied analysis (Advisor: Chang-Han Rhee).

Ruoming Gong

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Nonlinear analysis and math modeling with application in astrophysics and social systems (Advisor: Daniel M. Abrams).

Zoey Ho

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Addison Howe

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Dynamics of cell differentiation in the gastrulating mouse embryo (Advisor: Madhav Mani)

Benjamin Hyatt

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Achieving efficient and robust numerical simulations of astrophysical and geophysical fluid flows via numerical analysis of timestepping stability in spectral methods (Advisor: Daniel Lecoanet)

Sho Kawakami

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Investigation of motility and shape fluctuations of droplets containing active particles. (Advisor: Petia Vlahovska)

My research is about deep learning, neural networks, focused on computer vision and natural language processing (Advisor: Diego Klabjan).

Tao Li

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Use physics-inspired or machine learning methods to describe dynamics of gene expressions, such as under the condition of aging or small environmental variations. (Advisor: Madhav Mani)

Sungsoo Lim

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I work on machine learning applications in health care and autonomous parking. I recently completed a project on implementation and application of Monte Carlo Tree Search (MCTS) to feature acquisition with focus on medical diagnosis application. I am currently working on development and implementation of federated MCTS with application to autonomous parking (Advisor: Diego Klabjan).

Xinyi Liu

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Mathematical modelling of tissue deformation appearing in the esophageal diseases using continuum mechanics theories, dynamical systems theory and numerical simulations. (Advisor: Neelesh Patankar).

Cameron Mackenzie

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David Martin

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Maximilian Mattessich

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Statistical modelling of biological systems from sequence data, focusing on machine learning and maximum entropy models.(Advisor: Rosemary Braun)

Siqiao Mu

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My research involves designing and analyzing algorithms at the intersections of reinforcement learning, federated learning, and nonconvex optimization (Advisor: Diego Klabjan).

Sophia Nelson

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(Advisor: Neelesh Patankar)

Anh Nguyen

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Gene network reconstruction methods, graph theory (Advisor: Diego Klabjan).

Liam O'Connor

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Nonlinear optimization of physics-based simulations (Advisor: Daniel Lecoanet)

Tom Overman

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I design and analyze machine learning algorithms with a focus on federated learning (Advisor: Diego Klabjan).

Mingjie Pei

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(Advisor: David Chopp)

Connor Puritz

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Modeling and analysis of single cell RNA sequencing data (Advisor: Rosemary Braun).

Noah Roselli

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Designing computer vision models and algorithms to detect, locate, and specify degraded tissue states in medical imaging of the urethra. Mapping gene expression and modeling physical behavior to understand underlying mechanics in different esophageal disease phenotypes (Advisor: Neelesh Patankar).

Michael Rushka

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Algorithms for machine learning (Advisor: Diego Klabjan).

Bennet Sakelaris

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Using modeling approaches to investigate the neural basis of learning and memory, specifically by exploring the mechanisms and properties of the olfactory system (Advisor: Hermann Riecke).

Adam Sandler

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Decomposing biomedical data into components and groups for analysis. More specificly, using Bayesian statistics in order to compute the Tucker decomposition of tensors (Advisor: Diego Klabjan).

Aaron Scheiner

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Mathematical modeling of firefly (P. malaccae) synchronization built on a framework of nonlinear dynamics and coupled oscillators. Developing computer vision algorithms for tracking the identities and flashing patterns of individual fireflies in a swarm (Advisor: Daniel Abrams).

Elisheva Siegfried

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Investigating the dynamics of nonlinear systems with an emphasis on population dynamics (Advisors: Alvin Bayliss and Vladimir Volpert).

Divjyot Singh

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Richard Suhendra

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Mathematical modeling of fruit fly navigation and behaviour (Advisor: William Kath, Marco Gallio).

Kumar Utkarsh

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I develop mathematical models to understand pain crisis events in Sickle Cell Disease. The modelling approach draws from Stochastic Processes, Dynamical Systems and Machine Learning. (Advisor: Daniel Abrams)