Thilina Weerathunga
Vice President - Model Analysis, Change And Innovation
Dallas, Texas, United States
Summary
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Senior🎓
Top SchoolThilina Weerathunga is a quantitatively-driven finance leader and Ph.D. physicist with 10 years of experience applying advanced data science and machine learning to risk, stress testing, and systematic trading. As Vice President in Citi’s Model Analysis, Change and Innovation group he oversees validation and lifecycle management for a diverse portfolio of models while coordinating across trading, quant, and regulatory teams. He combines hands-on implementation skills (Python, MATLAB, SQL, C# integration) with experience building execution layers and mid-frequency trading strategies at startups. His background in gravitational-wave detection research gives him a strong foundation in signal processing and statistical rigor that he applies to financial model performance and governance. Based in Dallas, he also hires and mentors junior quants, embedding reproducible practices into production workflows. Colleagues rely on him to translate complex quantitative methods into auditable, regulator-ready solutions.
10 years of coding experience
5 years of employment as a software developer
Master’s Degree, Physics, Master’s Degree, Physics at The University of Texas Rio Grande ValleyUniversity of Texas at San AntonioB.Sc, Computational Physics, B.Sc, Computational Physics at University of Colombo
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (24)
lstm-neural-networks10
stock-price-prediction10
rnn-tensorflow10
predict10
rnn-model10
market-prices10
recurrent9
stock9
recurrent-neural-networks9
lstm9
keras8
amazon8
deep-learning6
code-generation6
machine-learning6
Programming languages (2)
Github contributions (5)
ShihanUTSA/AIND-Recognizer
Jul 2017 - Jan 2025
Contributions:16 pushes, 1 branch in 7 years 7 months
ShihanUTSA/Time-series-prediction-using-a-Recurrent-Neural-Network
Oct 2017 - Jul 2020
Recurrent Neural Network architecture to predict financial time series/text generation. Code was executed using Amazon EC2 GPU instance.
Contributions:11 commits, 9 pushes, 1 branch in 2 years 9 months
code-generationpythonamazon-ec2predictnetwork-architecture
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