## Leadership

### Director

Ulisses Braga-Neto

Professor

Department of Electrical and Computer Engineering

*Machine learning; Statistical signal processing; Applications in engineering and science. *

## Steering Committee

Ulisses Braga-Neto (ECE)

Nick Duffield (TAMIDS/ECE)

Narasimha Reddy (TEES/ECE)

## Lab Members

Raymundo Arroyave

Professor

Department of Materials Science and Engineering

*Materials Science; Numerical Methods*.

Raktim Bhattacharya

Professor

Department of Aerospace Engineering

*Uncertainty quantification in SciML; Convex optimization; Information fusion in manifolds;**Asynchronous algorithms for exascale computation; Applications in astrodynamics and flight mechanics.*

Yalchin Efendiev

Professor

Department of Mathematics

*Multiscale methods; Porous media; Model reduction; Numerical analysis; Data-driven model reduction; Uncertainty quantification*.

Eduardo Gildin

Professor

Department of Petroleum Engineering

*Reservoir simulation; Model order reduction.*

Diego Marcondes

Visiting Post-Doctoral Researcher

Department of Electrical and Computer Engineering

*Statistical Learning Theory, Mathematical Morphology, Physics-Informed Neural Networks, Operator Learning*.

Lisa Perez

Associate Director

Texas A&M High Performance Research Computing

*High performance computing; Quantum chemistry; Molecular dynamics; Scientific machine learning; AI/ML.*

Jean Ragusa

Professor

Department of Nuclear Engineering

*Scientific computing (solution techniques, algorithms); Uncertainty quantification; Model-order reduction applied to complex radiation/multiphysics problems.*

Lifan Wang

Professor

Department of Physics & Astronomy

*Time-domain astronomy; Astronomical site surveys; Machine learning in astronomy; Supernovae; Astronomical polarimetry; Wide-field astronomical surveys; Cosmological parameters*.

## Former Lab Members

Dehao Liu

Post-Doctoral Researcher

Department of Materials Science and Engineering

TAMIDS

*Computational materials science; Physics-informed machine learning; Scalable versatile Bayesian optimization; Additive manufacturing.*

Levi McClenny

Research Assistant

Department of Electrical Engineering

*Physics-informed deep learning, Physics-explainable AI; PINNs; Materials science.*

Ming Zhong

Assistant Research Scientist

TAMIDS

*Numerical ODE/PDE; Modeling *and simulation; PINN/PI-GP; Inverse problems; Data recovery.