Matias Quintana

About Matias Quintana

Matias Quintana, With an exceptional h-index of 11 and a recent h-index of 11 (since 2020), a distinguished researcher at National University of Singapore, specializes in the field of urban data science, GeoAI, human-building interaction, intelligent environments.

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

Opening the Black Box: Towards inherently interpretable energy data imputation models using building physics insight

A data-driven agent-based model of occupants’ thermal comfort behaviors for the planning of district-scale flexible work arrangements

Creating synthetic energy meter data using conditional diffusion and building metadata

Filling time-series gaps using image techniques: Multidimensional context autoencoder approach for building energy data imputation

Leveraging campus-scale Wi-Fi data for activity-based occupant modeling in urban energy applications

Utilizing wearable technology to characterize and facilitate occupant collaborations in flexible workspaces

The Building Data Genome Directory–An open, comprehensive data sharing platform for building performance research

Ten questions concerning reinforcement learning for building energy management

Matias Quintana Information

University

Position

Ph.D Candidate

Citations(all)

484

Citations(since 2020)

472

Cited By

75

hIndex(all)

11

hIndex(since 2020)

11

i10Index(all)

14

i10Index(since 2020)

13

Email

University Profile Page

Google Scholar

Matias Quintana Skills & Research Interests

urban data science

GeoAI

human-building interaction

intelligent environments

Top articles of Matias Quintana

Opening the Black Box: Towards inherently interpretable energy data imputation models using building physics insight

Energy and Buildings

2024/5/1

A data-driven agent-based model of occupants’ thermal comfort behaviors for the planning of district-scale flexible work arrangements

Building and Environment

2024/4/9

Matias Quintana
Matias Quintana

H-Index: 5

Clayton Miller
Clayton Miller

H-Index: 14

Creating synthetic energy meter data using conditional diffusion and building metadata

arXiv preprint arXiv:2404.00525

2024/3/31

Filling time-series gaps using image techniques: Multidimensional context autoencoder approach for building energy data imputation

Applied Thermal Engineering

2024/1/5

Leveraging campus-scale Wi-Fi data for activity-based occupant modeling in urban energy applications

Journal of Physics: Conference Series

2023/11/1

Utilizing wearable technology to characterize and facilitate occupant collaborations in flexible workspaces

Journal of Physics: Conference Series

2023/11/1

Matias Quintana
Matias Quintana

H-Index: 5

Clayton Miller
Clayton Miller

H-Index: 14

The Building Data Genome Directory–An open, comprehensive data sharing platform for building performance research

Journal of Physics: Conference Series

2023/11/1

Ten questions concerning reinforcement learning for building energy management

Building and Environment

2023/8/1

Cohort comfort models—Using occupant’s similarity to predict personal thermal preference with less data

Building and Environment

2023/1/1

Introducing the Cool, Quiet City Competition: Predicting Smartwatch-Reported Heat and Noise with Digital Twin Metrics

2023/11/15

From Personal Comfort to District Performance: Using Smartwatch and WiFi Data for Occupant-Driven Operation

2023/11/15

Matias Quintana
Matias Quintana

H-Index: 5

Clayton Miller
Clayton Miller

H-Index: 14

Enhancing Classification of Energy Meters with Limited Labels using a Semi-Supervised Generative Model

2023/11/15

Towards smartwatch-driven just-in-time adaptive interventions (JITAI) for building occupants

2022/11/9

Clayton Miller
Clayton Miller

H-Index: 14

Matias Quintana
Matias Quintana

H-Index: 5

From model-centric to data-centric: a practical MPC implementation framework for buildings

2022/11/9

ComfortLearn: Enabling agent-based occupant-centric building controls

2022/11/9

Green Mark Certified Buildings Metadata from Singapore: Dataset

2022/11/6

Personal comfort models based on a 6‐month experiment using environmental parameters and data from wearables

Indoor air

2022/11

Smartwatch-based ecological momentary assessments for occupant wellness and privacy in buildings

arXiv preprint arXiv:2208.06080

2022/8/12

Cohort-based personal comfort models for HVAC occupant-centric control

2021/11/17

Matias Quintana
Matias Quintana

H-Index: 5

The second ACM SIGEnergy workshop on reinforcement learning for energy management in buildings & cities (RLEM)

2021/11/17

Zoltan Nagy
Zoltan Nagy

H-Index: 23

Matias Quintana
Matias Quintana

H-Index: 5

See List of Professors in Matias Quintana University(National University of Singapore)

Co-Authors

academic-engine