I'm Derek, a professor at NYU, teaching Machine Learning in Financial Engineering, and the founder of Sov.ai.

• Previously a researcher at University of Cambridge, Oxford University, and the Alan Turing Institute.
• Developing applications to optimize asset management and alpha discovery for large quantitative funds.

Everything listed here is available under Unlicense

My research has been used by large institutional banks and quantitative hedge funds (see SSRN).

• DeltaPy (12,409 Downloads) — First tabular data augmentation package in Python (market data) [code][report]
• PandaPy (21,163 Downloads) — Pandas alternative that mimics ‘Structs’ in the C Language (market data) [code][report]
• AtsPy (53,147 Downloads) — First automated time series package in Python (alternative data) [code][report]
• DataGene (4,246 Downloads) — First package assessing dataset similarity (market data) [code][report]
• MLAM (35,409 Downloads) — First repository for machine learning in asset management (market data) [code][report]

Other packages under this license include MTSS-GAN [code][report], the first multivariate conditional time series generator, FairPut [code][report], a FAIR package using LightGBM, and PandasVault [code], an advanced Pandas repository.

We're seeking to collaborate with motivated, independent PhD graduates or doctoral students on approximately seven new projects in 2024. If you’re interested in contributing to cutting-edge investment insights and data analysis, please get in touch! This could be in colaboration with a university or as independent study.

Sov.ai is at the forefront of integrating advanced machine learning techniques with financial data analysis to revolutionize investment strategies. We are working with three of the top 10 quantitative hedge funds, and with many mid-sized and boutique firms.

Our platform leverages diverse data sources and innovative algorithms to deliver actionable insights that drive smarter investment decisions.

By joining Sov.ai, you'll be part of a dynamic research team dedicated to pushing the boundaries of what's possible in finance through technology. Before expressing your interest, please be aware that the research will be predominantly challenging and experimental in nature.

We offer a wide range of projects that cater to various interests and expertise within machine learning and finance. Some of the exciting recent projects include:

• Predictive Modeling with GitHub Logs: Develop models to predict market trends and investment opportunities using GitHub activity and developer data.
• Satallite Data Analysis: Explore non-traditional data sources such as social media sentiment, satellite imagery, or web traffic to enhance financial forecasting.
• Data Imputation Techniques: Investigate new methods for handling missing or incomplete data to improve the robustness and accuracy of our models.

Please visit docs.sov.ai for more information on public projects that have made it into the subscription product. If you already have a corporate sponsor, we are also happy to work with them.

• Innovative Environment: Engage with the latest technologies and methodologies in machine learning and finance.
• Collaborative Team: Work alongside a team of experts passionate about driving innovation in investment insights.
• Flexible Projects: Tailor your research to align with your interests and expertise, with the freedom to explore new ideas.
• Experienced Researchers: Experts previously from NYU, Columbia, Oxford-Man Institute, Alan Turing Institute, and Cambridge.
• Post Research: Connect with alumni that has moved on to DRW, Citadel Securities, Virtu Financial, Akuna Capital, HRT.

If you’re excited about leveraging your expertise in machine learning and finance to drive impactful research and projects, we’d love to hear from you! Please reach out to us at [email protected] with your resume and a brief description of your research interests.

Join us in shaping the future of investment insights and making a meaningful impact in the world of finance!

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