The purpose of this program is to help faculty more efficiently complete contained research tasks such as creating figures, scraping websites, merging datasets, etc. If you are looking for long-term, research-support help we recommend you work with your divisional admin to identify and hire your own part-time research assistant (see here for details on the hiring process).
We have a team of research assistants who can help with most general data-analytic tasks:
- Max Terouanne is especially good at data analytics and visualization, time-adaptive quantile regression, and Machine Learning.
- Meijia Chen is especially good at python, machine learning, and time series analysis.
Costs
- The Dean’s Office is subsidizing the cost of these research assistants.
- We will charge your costar $100 for each 10 hours of RA time that you use. These charges will be processed a few times each semester (vs immediately). You are responsible for keeping track of your costar balance.
Scheduling RAs
Faculty can schedule no more than 50 hours of RA time per semester. This scheduling rule is not built into the software; we ask that faculty self-regulate. If you require more than 50 hours of RA time, please write to Khaled Hamdy to ask for an exception.
To reserve an RA's time, please take the following steps:
- You must schedule the RA's time each week. They can not work unless you schedule their time.
- Click the blue, "Schedule an Appointment" button in the sidebar to the right.
- Select the name of the RA with whom you would like to work.
- Select a five-hour increment of time. Note that all RA's are instructed to indicate that they are available on Sunday from 8am until 4pm (10 hours of work a week). The RA will not necessarily be working these hours – we give them discretion over when they work and from where. The scheduling platform requires that they select a specific date and time and so we instruct them to choose these hours as a default.
- When you book an appointment with an RA they will receive an email with your name and contact information. The RA will write to arrange an initial meeting within 48 hours of receiving the appointment request.
Data Analysts, Spring 2023
Max Terouanne
Appointment Date: October 20, 2022 - May 15, 2023
Hours Per Week: 10
Max Terouanne is currently enrolled in the M.S. program in Business Analytics at the SEAS and Business School, Columbia University. Before enrolling in the program, he worked as a research intern in Energy Trading at ENGIE Global Markets and as a finance data analyst at Kering, a luxury group. He earned his undergraduate degree in Engineering from CentraleSupelec, France. His technical strengths include the following programming languages: Python, R, MATLAB and DAX (Power BI). He has experience with the following data science techniques: general Machine Learning methods, Auto-Regressive and Time-Adaptive Regression Models and Deep Learning. He has extensive experience working with the following packages: pandas, scikit-learn, plotly, scipy, seaborn and keras. He has some experience with the following statistical approaches: t and ANOVA tests (and GLM in general). In the past, he has worked with the following databases: meteorological observations and predictions and financial series from the European Energy Market (studying correlations between renewables productions and power real-time spot prices), internal finance data from Kering.
He is especially good at data analytics and visualization, time-adaptive quantile regression and Machine Learning in general but can help with most general, data-analytic tasks. He hopes to get a job in the Energy sector or the Music (or more broadly the Media and Entertainment) industry after graduating. See here for his resume.
Meijia Chen
Appointment Date: October 20, 2022 - May 15, 2023
Hours Per Week: 10
Meijia Chen is currently enrolled in the M.S. program in Operations Research at the School of Engineering, Columbia University. Before enrolling in the program, she interned as a quantitative researcher at Shenwanhongyuan Securities. She earned her undergraduate degree in Computational Finance and Financial Technology from City University of Hong Kong. Her technical strengths include the following programming languages: Python, C++, and R. She has experience with the following data science techniques: machine learning, deep learning. She has extensive experience working with the following packages: Numpy, Sklearn, Pandas, etc. She has some experience with the following statistical approaches: time series analysis, regression. In the past, she has worked with the following databases: Bloomberg, Wind, WRDS. She is especially good at python, machine learning, and time series analysis but can help with most general, data-analytic tasks. She hopes to pursue a doctoral degree in financial technology after graduating. See here for her resume.