Moises Baly
Verified Expert in Engineering
Machine Learning Developer
Cambridge, MA, United States
Toptal member since August 8, 2016
Moises is a software engineer who's interested in data exploration and analysis. He enjoys most working on complex problems that involve data structures, non-trivial algorithms, and mathematics. Moises' top priority is having a rock-solid team because if the people around him are great, anything can be accomplished. He also values transparency, honesty, and clarity of ideas.
Portfolio
Experience
Availability
Preferred Environment
Windows
The most amazing...
...thing I've coded was a kernel density estimate function for spatial scoring of places based on distance.
Work Experience
Software Engineer
Spatially
- Designed and implemented scalable/distributed spatial computation engines. As a member of the Core Engineering team, contributed to the development of the spatial-aware infrastructure.
- Spearheaded the migration from a conventional spatial back-end infrastructure (PostgreSQL, PostGIS) to a NoSQL architecture sitting on top of technologies such as GeoWave, Cassandra, Spark, and others.
- Developed micro-services for querying data sets counting millions of records.
- Responsible for scaling and productizing the company's spatial research prototypes developed by our team of data scientists.
Software Engineer
ATOS Inc.
- Worked within ATOS e-payment services, specifically in a back-end project for financial entities and banks.
- Assisted the team leader with project time management. Prepared and delivered technical demos to distributed teams in India.
- Developed a web application for the monitoring of interbank movements of compensation and fraud.
Experience
Scientific Calculator and Simple Linear Equation Solver
The calculator is written in Scala using sbt. To run the project, at least Scala version 2.11.8 is required and sbt version 0.13.11.
2D Bin Packing Library for Irregular Pieces
https://github.com/mses-bly/2D-Bin-PackingThe 2D bin-packing problem consists of a given a set of 2D pieces with unknown form or shape; we have to place them in a series of rectangular bins minimizing the material used. In other words, place all the pieces in as few bins as possible.
The problem is NP-hard, thus there is no guarantee that an algorithm will provide an optimal solution, although we can compare different solutions in terms of how many bins an algorithm has used for a particular set of pieces. This project tries a few heuristics and approximations in order to solve the problem with a reasonable amount of computational effort; given that a brute-force approach to the problem would take virtually an infinite amount of time.
Android with Geolocation API Integration | Sample code
https://github.com/mses-bly/AndroPlacesWeka on Spark | Proof of Concept
https://github.com/mses-bly/WekaOnSparkEducation
Master's Degree in Computer Science
Cornell University - Ithaca, New York (USA)
European Engineering Degree in Computer Science
Polytechnic University of Valencia - Valencia, Spain
Skills
Libraries/APIs
Pandas, NumPy, Scikit-learn, Spark ML, JTS Topology Suite
Tools
Amazon Elastic MapReduce (EMR), Git, GeoTools, Apache Maven, SBT, ScalaTest, IPython Notebook, PyCharm, IntelliJ IDEA, Spark SQL, Eclipse IDE
Languages
Scala, Python, Java, SQL, Go, HTML, CSS
Frameworks
Apache Spark, Play, Flask, JUnit, Hibernate, Apache Tapestry, Django REST Framework
Paradigms
Functional Programming, Spatial Databases, Object-relational Mapping (ORM), Scrum, Object-oriented Programming (OOP), Kanban
Platforms
Linux, Android, Java EE, Oracle, Windows, Apache Kafka
Storage
JSON, Oracle 11g, Cassandra, PostgreSQL, DB, MongoDB
Other
Machine Learning, Data Structures, GeoJSON, Apache Cassandra, Computational Geometry, Spatial Statistics, Apache Accumulo
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