Planning with Large Language Models

• I am focusing on the planning capabilities of large language models under the mentorship of Eric Malmi within the Gemini team led by Aliaksei Severyn.

Board Games and Large Language Models

• I work on advancing the extent to which LLMs can understand, plan, and reason about board games as a part of DeepMind’s board games effort.

Lead a team of five data scientists.

Behavioral Credit Scoring:

• Built a PySpark gradient-boosting model to predict consumer default risk probability, achieving a market-leading Gini metric results of up to 75%.

ML-Driven Marketing Campaign:

• Devised a data-driven campaign for promoting a heart disease drug to doctors on behalf of a top pharmaceutical company, which led to a 10% sales increase during A/B testing.
• Statistical analysis was conducted using Statsmodels, SciPy, and Python plotting packages.

Personalized Newsletter and E-Commerce Recommender Systems:

• Constructed a hybrid recommender system combining content-based and collaborative filtering, which achieved a 1.5% click-through rate during the proof-of-concept phase.
• Utilized Databricks, Python, PyTorch, and AWS in the technology stack.

Delivery Delay Estimation:

• Developed a customer support system for a shopping mall during the COVID-19 pandemic, which predicted delivery delays using a time-series ARIMA model supplemented with supervised learning techniques.
• The technology stack comprised Pandas, NumPy, and Sklearn.

Systemic Risk Model Execution Efficiency:

• Created a parallel version of a hill climber heuristic that made the optimization problem practically tractable. The heuristic’s runtime was limited to 3 minutes and, on average, generated solutions within 5% of the optimum, with the reported worst-case being 15% for tractable test-set instances.
• Employed a technology stack encompassed Python, CPLEX, and OR-Tools.

Treasury Department Cash Traceability:

• Constructed an uncollateralized debt tracking system by amalgamating diverse daily feeds to generate comprehensive firm-wide reports within seconds.
• Employed Q/kdb+, Python, PyQ kernel, and SQL for the development.

E-Trading Execution Limits Calibration:

• Fine-tuned an in-house model to prevent real-time executions during high-risk scenarios, employing a statistical analysis of e-trading clients.
• Utilized Pandas for the calibration process.

Listed derivatives liquidity:

• Created proof-of-concept for data-driven liquidation model based on intraday futures trading.
• Utilized Q/kdb+ for data processing.