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projects
Arbitrage-free neural-SDE market models
[ongoing] Currently working on combining neural networks with risk models based on classical stochastical differential equations(SDEs), for assessing risk profiles and hedging strategies.
Supervised by: Mayank Goel - BITS Pilani
Stock Price Forecasting
[ongoing] Optimising and improvising existing trading model which was based on LSTM and Random Forest.
Conducted comprehensive literature review, currently working on incorporating sentiment analysis and correlation-based stock grouping to improve the model accuracy.
Supervised by: J.K Sahoo - BITS Pilani
Optimising TSV placement in 3D NoC
[ongoing] Conducted extensive literature review of existing application-mapping and latency estimation techniques.
Currently simulating an application-specific 3D-NoC using Access Noxim and SystemC. Formulated a new loss function for variable TSVs which is being tested.
Supervised by: Kanchan Manna - BITS Pilani
publications
Comparing the Role of Spatially and Temporally capable Deep Learning Architectures in Rainfall Estimation: A Case Study over North East India Permalink
European Geosciences Union - General Assembly 2024, Vienna, Austria
Recommended citation: Handur-Kulkarni, A., Mehta, S., Ghatalia, A., and Anilkumar, R.: Comparing the Role of Spatially and Temporally capable Deep Learning Architectures in Rainfall Estimation: A Case Study over North East India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19394, https://doi.org/10.5194/egusphere-egu24-19394, 2024.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Discrete Structures in Computer Science
First Degree Teaching Assistant, Department of CSIS, 2023
Database Systems
First Degree Teaching Assistant, Department of CSIS, 2024
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Microprocessors and Interfacing
First Degree Teaching Assistant, Department of CSIS, 2024
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work
Machine Leaning Research Intern - NESAC
Part of a 7-member team. Developed and tested an ML algorithm to predict rainfall in north eastern states of India. Integrated Pytorch with the python API of Google Earth Engine cloud computing platform for data extraction/analytics. Implemented a UNET and an LSTM model tailored for rainfall prediction after literature review of some related papers. Tried various optimisers with different learning rates, used batch normalisation and kaiming initialisation to reduce losses and plotted the R2/RMSE losses and worked on accuracy metrics.