Gajendra Deshpande
Gajendra Deshpande is a distinguished professional with an M.Tech. in Computer Science and Engineering from Visvesvaraya Technological University, Belagavi, along with a PG Diploma in Cyber Law and Cyber Forensics from the National Law School of India University, Bengaluru. He founded and currently manages Theta Dynamics Private Limited in Belagavi.
Deshpande is renowned for his extensive contribution to the tech community, having delivered over 100 talks and conducted more than 25 workshops at various esteemed international conferences, including JuliaCon 2023 at MIT, USA, EuroPython Ireland, PyCon MEA Dubai, PyCon APAC Japan, PyData Global, and many more across Europe, Asia and the USA. His expertise has guided teams to victory in the Smart India Hackathon and National Security Hackathon five times.
As an active member of PyCon India, Deshpande has played crucial roles, such as leading the Program Committee in 2021 and serving as the Mentorship Lead in 2023. He has been instrumental in organizing FOSSCon India 2019 and BelPy conferences. His commitment extends to various professional bodies, serving as the Founding chair of Belagavi ACM chapter, Served as Vice Chair of the IEEE Young Professionals Affinity Group, Bangalore Section, and an Execom Member of IEEE Bangalore section. He is a Fellow Member of the Royal Statistical Society UK and maintains memberships with OWASP, the British Computer Society, and Senior member of ACM. Deshpande has significantly contributed to Python, Julia, and FOSS Conferences by reviewing proposals, mentoring speakers, engaging in discussions, and organizing events.
Sessions
Satellite-based air quality products (e.g., NO₂, PM2.5/AOD, CO) are valuable for environmental monitoring but often have coarse resolution and significant gaps, especially under cloudy conditions. This tutorial guides participants through the end-to-end process of generating high-resolution air quality maps from coarse-resolution satellite data using AI/ML techniques. The tutorial features practical exercises utilizing Python's robust ecosystem (Xarray, Rasterio, scikit-learn, TensorFlow/Keras, PyTorch, GeoPandas, Folium, etc.), enabling participants to produce accurate, validated, and interactive maps suitable for local-level air quality assessments.
In this we discuss the Development of an AI/ML (Artificial Intelligence/Machine Learning) model to generate fine spatial resolution air quality map from coarse resolution satellite data. It utilizes existing python-based ML libraries such as numpy, pandas, xarray, rasterio, GDAL, scikit-learn, tensorflow, keras, torch, xgboost geopandas, cartopy, folium, matplotlib, seaborn, sentinelhub, earthpy, opencv-python, scikit-image, joblib, netCDF4, and h5py. The developed model was validated with unseen independent data too.
The experiment has following objectives:
1) To utilize large satellite data having gaps under cloudy conditions.
2) To select suitable ML algorithm and ensure optimal fitting of ML model for desired accuracy
3)To validate model output with unseen independent data
Usage: To enhance air quality knowledge, Sharpen focus at local level.
Users: Researchers and government bodies monitoring/working on air quality assessment.
Available Solutions (reasons for not using them): Individual components are available, comprehensive and proven solution does not exist.
Outcome of the project: Fine resolution air quality map of NO2
Before modern engineering tools, designers and engineers built clay models to visualize, test, and refine ideas before creating real systems. Similarly, digital twins serve as virtual counterparts to physical objects, enabling simulation, optimization, and monitoring throughout an object's lifecycle. Leveraging real-time data from sensors, digital twins empower industries to predict behavior, improve efficiency, and enhance decision-making.
In this talk, we will explore the lifecycle of digital twins, their types (Component, Product/Asset, System, and Process Twins), and their transformative advantages. With Python's unparalleled capabilities, we will demonstrate how to build digital twins using specialized packages such as PyTwin and DT-CNS (Digital Twin-Oriented Complex Networked Systems) and integrate these into industrial frameworks like OpenUSD. Python’s flexibility, robust libraries, and ease of use make it the ideal choice for creating impactful digital twin applications.