Manil Maskey


Manil Maskey is a Senior Research Scientist with the National Aeronautics and Space Administration (NASA). He also leads the Advanced Concepts team, within the Inter Agency Implementation and Advanced Concepts at the Marshall Space Flight Center and Science Mission Directorate’s Artificial Intelligence initiative at NASA HQ.  His research interests include computer vision, visualization, knowledge discovery, cloud computing, and data analytics. Dr. Maskey's career spans over 21 years in academia, industry, and government. Dr. Maskey is an adjunct faculty at the UAH Atmospheric Science department, a senior member of Institute of Electrical and Electronics Engineers (IEEE), chair of the IEEE Geoscience and Remote Sensing Society (GRSS) Earth Science Informatics Technical Committee, member of American Geophysical Union (AGU) and AGU Fall Meeting Planning Committee, member of European Geosciences Union (EGU), and member of Association for Advancement of Artificial Intelligence (AAAI).

Muthukumaran Ramasubramanian


Muthukumaran Ramasubramanian received the M.S. degree incomputer science from the University of Alabama in Huntsville (UAH), where heis currently pursuing the Doctorate degree in computer science. He is also aComputer Science Researcher and leads the Machine Learning Team forNASA–Interagency Implementation and Advanced Concepts Team, UAH. His workfocuses on using deep-NLP techniques to surface novel relationships from largecorpora of text and to deploy deep learning solutions to detecting earthscience phenomena on a global scale. His research interests include machinelearning, big data, computer vision, and scalable cloud services.

Iksha Gurung


Iksha Gurung is a Computer Scientist working with University of Alabama in Huntsville, supporting National Aeronautics and Space Administration Inter-Agency Implementation of Advanced Concepts Team (NASA-IMPACT). He leads the development and machine learning team in NASA-IMPACT.  His projects include applying machine learning to Earth science phenomena studies and scaling the solutions to production.

Sujit Roy


Dr. Sujit Roy works as a Computer Scientist Level VI at NASA’s Interagency Implementation and Advanced Concept Teams (IMPACT), where he leads the development of foundational models for analyzing satellite imagery and enhancing weather forecasting, resulting in practical multiple scientific applications. Prior to his tenure at NASA IMPACT, Dr. Roy contributed to the field of Explainable AI at the University of Manchester. He received his PhD in Computer Science from Ulster University in collaboration with the Indian Institute of Technology Kanpur, India. In his PhD, he contributed to the domain of Computational Neuroscience by developing algorithms for Advancing MEG- and EEG-Based Decoding of Motor Imagery for Practical Brain-Computer Interfaces. He has experience of 10 years in Research and Development in the field of machine learning and deep learning. He is also a Co-founder of BrainAlive Research Pvt Ltd. His professional repertoire spans deep learning, brain-computer interfaces, and image processing. Dr. Roy is particularly focused on advancing computer vision, video/image processing, explainable artificial intelligence, signal processing/synthesis, and reinforcement learning.

Lecture content

Large-Scale AI for Geosciences

This two-day school curriculum is designed to provide comprehensive, hands-on training in two distinct yet critical areas of AI foundation models for geosciences: Geospatial Foundation Models on Day 1, and Large Language Models for Science on Day 2. The lesson aims to equip participants with the skills needed to understand, develop, and apply these models in various steps of geoscience research lifecycle and practical scenarios.

Day 1: Geospatial Foundation Models (5 June, 2024)

09:30 - 11:00 (CEST)

Introduction to Geospatial Foundation Models

11:00 - 11:30 (CEST): Break

11:30 - 13:00 (CEST)

Datasets, Model Pretraining and Fine-Tuning: 

  • Dataset overview for foundation model
  • Downstream use cases
  • Pretraining
  • Fine-tuning

13:00 - 14:30 (CEST): Lunch

14:30 - 15:30 (CEST)

Hands-on fine-tuning: participants will have access to specific data, or can choose to bring their own data.

15:30 - 16:00 (CEST) Break

16:00 - 17:00 (CEST)

Hands on deployment: 

  • Deploy to cloud environment
  • Host an API
  • Verify

Day 2: Large Language Models (LLMs) for Science (6 June, 2024)

09:30 - 10:30 (CEST)

Introduction to LLMs for Science

  • Overview of LLMs
  • LLM for Science research lifecycle
  • How NASA Science is using LLMs

11:00 - 11:30 (CEST) Break

11:30 - 13:00 (CEST)

Data Preparation and Model Training

  • Preparing datasets: scientific literature, reports, metadata etc.
  • System needs
  • Overview of model training
  • Hands on: Parameter Efficient Fine-Tuning on prem with provided data.

13:00 - 14:30 (CEST) Lunch

14:30 - 15:30 (CEST)

Advanced Techniques in Model Inferencing for Science

  • Deploying fine-tuned model for science
  • Evaluation

15:30 - 16:00 (CEST) Break

16:00 - 17:00 (CEST)

Hands-on exercise of using LLM for various tasks including data analysis, compliance checking, and natural language querying for geospatial data.