Meeko Oishi

Meeko Oishi

University of New Mexico

H-index: 28

North America-United States

About Meeko Oishi

Meeko Oishi, With an exceptional h-index of 28 and a recent h-index of 20 (since 2020), a distinguished researcher at University of New Mexico, specializes in the field of Human-in-the-loop systems, Stochastic reachability, Stochastic optimal control, Non-parametric learning, motor control in Parkin.

His recent articles reflect a diverse array of research interests and contributions to the field:

Classification of Human Learning Stages via Kernel Distribution Embeddings

Stochastic Optimal Control For Gaussian Disturbances with Unknown Mean and Variance Based on Sample Statistics

Chance constrained stochastic optimal control for linear systems with a time varying random control matrix

Efficient Estimation of the Human Circadian Phase via Kalman Filtering

Convexified Open-Loop Stochastic Optimal Control for Linear Systems with Log-Concave Disturbances

Probabilistic verification of ReLU neural networks via characteristic functions

Data-driven stochastic optimal control using kernel gradients

Blamelessly Optimal Control For Polytopic Safety Sets

Meeko Oishi Information

University

Position

Professor Electrical and Computer Engineering

Citations(all)

3260

Citations(since 2020)

1508

Cited By

2383

hIndex(all)

28

hIndex(since 2020)

20

i10Index(all)

62

i10Index(since 2020)

39

Email

University Profile Page

Google Scholar

Meeko Oishi Skills & Research Interests

Human-in-the-loop systems

Stochastic reachability

Stochastic optimal control

Non-parametric learning

motor control in Parkin

Top articles of Meeko Oishi

Classification of Human Learning Stages via Kernel Distribution Embeddings

IEEE Open Journal of Control Systems

2024/1/1

Meeko Oishi
Meeko Oishi

H-Index: 21

Neera Jain
Neera Jain

H-Index: 12

Stochastic Optimal Control For Gaussian Disturbances with Unknown Mean and Variance Based on Sample Statistics

2023/12/13

Meeko Oishi
Meeko Oishi

H-Index: 21

Chance constrained stochastic optimal control for linear systems with a time varying random control matrix

2023/5/31

Ali Bidram
Ali Bidram

H-Index: 16

Meeko Oishi
Meeko Oishi

H-Index: 21

Efficient Estimation of the Human Circadian Phase via Kalman Filtering

2023/7/24

Convexified Open-Loop Stochastic Optimal Control for Linear Systems with Log-Concave Disturbances

IEEE Transactions on Automatic Control

2023/6/9

Probabilistic verification of ReLU neural networks via characteristic functions

2023/6/6

Data-driven stochastic optimal control using kernel gradients

2023/5/31

Blamelessly Optimal Control For Polytopic Safety Sets

arXiv preprint arXiv:2304.06625

2023/4/13

Stochastic Reachability of Discrete-Time Stochastic Systems via Probability Measures

arXiv preprint arXiv:2304.00598

2023/4/2

Chance constrained stochastic optimal control based on sample statistics with almost surely probabilistic guarantees

arXiv preprint arXiv:2303.16981

2023/3/29

Meeko Oishi
Meeko Oishi

H-Index: 21

Physics-informed kernel embeddings: Integrating prior system knowledge with data-driven control

arXiv preprint arXiv:2301.03565

2023/1/9

Introduction to the Special Section on Selected Papers from ICCPS 2021

2023/1/6

Advanced Quantitative Approaches to Unveil Social Inequities in Educational Contexts

2023

Fast tuning of observer-based circadian phase estimator using biometric data

Heliyon

2022/12/1

Approximate Stochastic Optimal Control for Linear Time Invariant Systems with Heavy-tailed Disturbances

arXiv preprint arXiv:2210.09479

2022/10/17

Meeko Oishi
Meeko Oishi

H-Index: 21

Distribution steering for discrete-time linear systems with general disturbances using characteristic functions

2022/6/8

Approximate quantiles for stochastic optimal control of LTI systems with arbitrary disturbances

2022/6/8

Meeko Oishi
Meeko Oishi

H-Index: 21

Data-driven chance constrained control using kernel distribution embeddings

2022/5/11

SOCKS: A stochastic optimal control and reachability toolbox using kernel methods

2022/5/4

Meeko Oishi
Meeko Oishi

H-Index: 21

State-based confidence bounds for data-driven stochastic reachability using Hilbert space embeddings

Automatica

2022/4/1

See List of Professors in Meeko Oishi University(University of New Mexico)

Co-Authors

academic-engine