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Luciano Di Palma

Ph.D. Student

CEDAR - Inria Saclay

Biography

Luciano Di Palma is a Ph.D. student in Machine Learning and Data Management at CEDAR, a joint research team between École Polytechnique and Inria Saclay. His main research interest is designing scalable, intelligent analytics systems with strong theoretical performance guarantees and leveraging state-of-the-art machine learning and statistical techniques.

Interests

  • Machine Learning
  • Scalable Systems
  • Statistical Modeling

Education

  • Ph.D. in Active Learning and Data Exploration, 2017–2021

    Ecole Polytechnique and Inria Saclay

  • M.Sc. in Data Science, 2016–2017

    Université Paris-Saclay

  • Cycle Ingénieur Polytechnicien, 2014–2016

    École Polytechnique

  • B.Sc. in Physics, 2011–2013

    University of São Paulo

Publications

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New Algorithms and Optimizations for Human-in-the-Loop Model Development

In this thesis, we propose a human-in-the-loop framework for efficient model development over large data sets. In this framework, we …

AIDEme: an Active Learning based System for Interactive Exploration of Large Datasets

There is an increasing gap between the fast growth of data and the limited human ability to comprehend data. Consequently, there has …

A Factorized Version Space Algorithm for "Human-In-the-Loop" Data Exploration

While active learning (AL) has been recently applied to help the user explore a large database to retrieve data instances of interest, …

Optimization for Active Learning-based Interactive Database Exploration

There is an increasing gap between the fast growth of data and the limited human ability to comprehend data. Consequently, there has …

Experience

 
 
 
 
 

Data Platform Engineer Intern

Wildlife Studios

Apr 2017 – Aug 2017 São Paulo, Brazil

During my time at Wildlife, I was part of their Data Engineering team, which is responsible for all data-related tasks in the company. More precisely, I had the opportunity to work on several impactful projects, including the:

  • Design and implementation of a data pipeline for real-time processing, storage, and visualization of several metrics of interest, such as the number of app crashes, new installs, and active online users.

  • Installation, configuration, and benchmarking of the company’s first Spark cluster, enabling engineers to run data processing tasks and train Machine Learning models over terabytes of user data.

  • Analysis of the number of impressions, clicks, and installs of users across multiple games and AdNets, leading to an improvement in user targeting and an increase in the installs-to-impressions ratio.

 
 
 
 
 

Visiting Research Scholar

UC Berkeley

Mar 2016 – Jul 2016 California, United States

Applied concepts of Reinforcement Learning and Neural Networks techniques to develop an Adaptive Traffic Lights controller capable of efficiently choosing good control options for vehicle flux improvement. More precisely, I have:

  • Surveyed the State-of-the-Art in Neural Networks and Reinforcement Learning.

  • Designed and trained an adaptive traffic lights controller capable of adapting the distribution of green time over different lanes with varying vehicle throughput. The controller was trained using Reinforcement Learning techniques and Neural Networks.

  • Empirically proved that our adaptive controller is more efficient than fixed-time controllers in managing vehicle traffic, achieving a 25% reduction on the average number of queued vehicles.

Skills

Python

Java

Angular