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Aaditya Ojha

AI/ML Engineer at TEL | ECE at Carnegie Mellon University

United States

2+ years of work experience

Tokyo Electron
May 20263 mosPresent

Bayesian Optimization for 3DI Simulations
Carnegie Mellon University
Feb 20266 mosPresent

Remote

Designed synthetic sparse matrices reflecting realistic sparsity patterns and topologies to represent large-scale physical systems, then conducted a runtime performance study of GPU-based sparse matrix factorization for nonlinear circuit and power-system simulations. Implemented and benchmarked linear solves from Newton–Raphson iterations using NVIDIA cuDSS and cuSolver, focusing on sparse LU factorization of the Jacobian matrices. Compared GPU sparse factorization to a chord-based approach with a precomputed inverse, transforming solves into dense matrix–vector multiplications to leverage GPU parallelism. Analyzed runtime between repeated sparse factorizations and dense operations, highlighting the impact of fixed versus iteration-varying Jacobians on simulation performance.
Department of Electrical and Computer Engineering at Carnegie Mellon University
Jan 20267 mosPresent

Supported instruction for a core ECE course (~150 students) covering circuit analysis, electronic devices, computer architecture, and signals/data transmission. Led a 5-student recitation section and held office hours to reinforce foundational concepts and assist with coursework.
New York State Department of Health
Nov 20231 yr 10 mosAug 2025

Albany, New York, United States

Designed and implemented a Python-based bioinformatics pipeline for automated serogroup identification of Neisseria meningitidis, reducing diagnostic turnaround time and improving analytical reproducibility. The workflow integrated sequence processing, quality control, and serogroup classification to support rapid laboratory-based surveillance. Developed analytical dashboards to orchestrate script execution and generate heatmaps visualizing genomic similarity across bacterial isolates. These tools enabled comparative analysis, cluster detection, and real-time data interpretation to support outbreak investigation and public health response efforts.

Education

Carnegie Mellon University

Bachelor of Science - BS, Electrical and Computer Engineering

Carnegie Mellon University

20254 yrs2029

Bethlehem Central High School

20214 yrs2025

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Headline

AI/ML Engineer at TEL | ECE at Carnegie Mellon University

Location

United States

Work experience

AI/ML Engineering Intern at Tokyo Electron, May 2026 - Present · 3 mos, Bayesian Optimization for 3DI Simulations

Undergraduate Researcher at Carnegie Mellon University, Feb 2026 - Present · 6 mos, Remote, Designed synthetic sparse matrices reflecting realistic sparsity patterns and topologies to represent large-scale physical systems, then conducted a runtime performance study of GPU-based sparse matrix factorization for nonlinear circuit and power-system simulations. Implemented and benchmarked linear solves from Newton–Raphson iterations using NVIDIA cuDSS and cuSolver, focusing on sparse LU factorization of the Jacobian matrices. Compared GPU sparse factorization to a chord-based approach with a precomputed inverse, transforming solves into dense matrix–vector multiplications to leverage GPU parallelism. Analyzed runtime between repeated sparse factorizations and dense operations, highlighting the impact of fixed versus iteration-varying Jacobians on simulation performance.

Teaching Assistant - Introduction to Electrical and Computer Engineering at Department of Electrical and Computer Engineering at Carnegie Mellon University, Jan 2026 - Present · 7 mos, Supported instruction for a core ECE course (~150 students) covering circuit analysis, electronic devices, computer architecture, and signals/data transmission. Led a 5-student recitation section and held office hours to reinforce foundational concepts and assist with coursework.

Research Intern at New York State Department of Health, Nov 2023 - Aug 2025 · 1 yr 10 mos, Albany, New York, United States, Designed and implemented a Python-based bioinformatics pipeline for automated serogroup identification of Neisseria meningitidis, reducing diagnostic turnaround time and improving analytical reproducibility. The workflow integrated sequence processing, quality control, and serogroup classification to support rapid laboratory-based surveillance. Developed analytical dashboards to orchestrate script execution and generate heatmaps visualizing genomic similarity across bacterial isolates. These tools enabled comparative analysis, cluster detection, and real-time data interpretation to support outbreak investigation and public health response efforts.

Education

Bachelor of Science - BS, Electrical and Computer Engineering, Carnegie Mellon University

Bethlehem Central High School

Aaditya Ojha - AI/ML Engineering Intern in United States | Tal