Julian LaNeve

- Placed 1st in global data science competition with over 6,000 undergraduate, masters, and PhD students, winning $100,000 cash prize
- Recommended an introduction of trade restrictions based on specialization in different types of plastic waste treatment to counter the global pollution crisis

https://www.correlation-one.com/blog/the-data-open-championship-2021
https://newsroom.haas.berkeley.edu/mfe-student-on-winning-citadel-data-open-championship-team/

- Created report and video (NDA protected) to characterize illegal drug usage populations and behaviors
- Identified drug abuse likelihood across multiple demographic characteristics (age, race, education, income, etc.)
- Utilized a network analysis to identify gateway drugs leading to high risk illegal drug abuse
- Placed first out of ~90 competitors across several schools based on individual and team performance

- Created 12 minute video demonstrating and exploring the effects of particle movement
- Calculated energy costs associated with moving an agent on the x/y axis according to the energy function E = 1/y
- Discovered insights on behavior near y-axis for discretely and continuously moving agents
- Generalized energy costs associated with moving along any continuous curve and numerically solved integrals using Python
- Identified characteristics of most efficient agent paths according… Show more

- Created 12 minute video demonstrating and exploring the effects of particle movement
- Calculated energy costs associated with moving an agent on the x/y axis according to the energy function E = 1/y
- Discovered insights on behavior near y-axis for discretely and continuously moving agents
- Generalized energy costs associated with moving along any continuous curve and numerically solved integrals using Python
- Identified characteristics of most efficient agent paths according to polynomial movement curves
- Won first place as an individual against predominantly four-person teams; award presented at the 2021 Texas MAA Conference Show less

- Utilized concepts of expected value, variance, and probability in 8 rounds of market making against other participants; placed first out of ~100 competitors from top universities
- Accurately adjusted markets according to “news” events (e.g. if making a market around GPA, a news event could be “50% of participants have a GPA over 3.8”)
- Additionally won the Flow Traders sub-competition based on individual performance against all ~100 other competitors in identifying positive EV trades… Show more

- Utilized concepts of expected value, variance, and probability in 8 rounds of market making against other participants; placed first out of ~100 competitors from top universities
- Accurately adjusted markets according to “news” events (e.g. if making a market around GPA, a news event could be “50% of participants have a GPA over 3.8”)
- Additionally won the Flow Traders sub-competition based on individual performance against all ~100 other competitors in identifying positive EV trades under uncertainty Show less

- Produced 18 page report in one week on arbitrage availability in European soccer markets in team of 4
- Inferred European soccer team skills using match score outcomes
- Identified and characterized betting arbitrage using linear/logistic regressions
- Clustered time series data into regimes of arbitrage availability in Python
- 1 of ~100 students accepted into predominantly graduate and doctoral student competition based on resume, entrance exam, and short answer responses

- Created fully functioning web application in 24 hours enabling small farmers access to financing options by representing their produce as a digital blockchain asset
- Utilized React, JavaScript, Node, and wasp to create full stack web application
- Invited to pitch to a panel of venture capitalists and the London Business School’s Women in Business Conference

- Built web application in 24 hours where users can input preferential living conditions and be recommended ideal cities based on custom implementation of k-nearest neighbors
- Utilized Python, Pandas, and Dash to create an interactive and visually appealing user interface

- Created report and video (NDA protected) identifying causes and results of fatigue in Canadian women’s rugby teams
- Synthesized large data set (>10GB) of player movement sampled at 1s intervals into usable features
- Recommended player fatigue mitigation strategies and potential risk factors to minimize fatigue-related injuries

Received fellowship for funding to pursue interdisciplinary project in finance and statistics. Presented to 6 students out of entire 6,500 undergraduate population.