Hi there! I’m a Data Scientist and AI Researcher working at Inria Chile. I hold a Masters degree in Computer Science from KAIST and a Bachelor degree in Engineering from University of Chile.
My main interest is the development and application of deep learning as a tool for scientific discovery. Currently I’m working on problems from astronomy, biology and environmental sciences aiming to develop deep learning models for anomaly detection and forecasting tasks. I’m also working with LLMs from a scientific and engineering perspective.
Check it out these Inria projects in which I’m working on: OceanIA, GreenAI, EMISTRAL.
-
Reinforcement-learning robotic sailboats: simulator and preliminary results, 6th Robot Learning Workshop NeurIPS 2023.
-
Enhancing Writing Skills of Chilean Adolescents: Assisted Story Creation with LLMs, Generative AI for education (GAIED) Workshop NeurIPS 2023. Hernan Lira, Luis Martí, Nayat Sanchez-Pi.
-
A Graph Neural Network with Spatio-temporal Attention for Multi-sources Time Series Data: An application to Frost Forecast, 2022, Sensors. Hernan Lira, Luis Martí and Nayat Sanchez-Pi.
-
Searching for changing-state AGNs in massive datasets–I: applying deep learning and anomaly detection techniques to find AGNs with anomalous variability behaviours, code, 2021, The Astronomical Journal. Paula Sanchez-Saez, Hernan Lira, Luis Martí, Nayat Sanchez-Pi et al.
-
Frost forecasting model using graph neural networks with spatio-temporal attention, 2021, AI: Modeling Oceans and Climate Change Workshop at ICLR 2021. Hernan Lira, Luis Martí, Nayat Sanchez-Pi.
-
Simultaneous feature selection and heterogeneity control for SVM classification: An application to mental workload assessment, 2020, Expert Systems with Applications.
-
A Human-centric and Environment-aware Testing Framework for Providing Safe and Reliable Cyber-Physical System Services, 2020, Journal of Web Engineering. In-Young Ko et al.
-
Environment-aware and human-centric software testing framework for cyber-physical systems, 2019, International Conference on Web Engineering (ICWE). In-Young Ko et al.
-
Mental Workload Assessment in Smartphone Multitasking Users: A Feature Selection Approach using Physiological and Simulated Data, 2018, International Conference on Web Intelligence. Hernan Lira et al.
-
Using Psychophysiological Sensors to Assess Mental Workload During Web Browsing, 2018, Sensors.
-
Towards a continuous assessment of cognitive workload for smartphone multitasking users, 2017, The first international symposium on human mental workload, Dublin Institute of Technology.