Our Leadership Team



Dr. Segala has years of experience leading projects that apply data science and mathematical modeling to solve complex problems.

For his doctoral thesis, he worked as part of the CERN team that discovered the Higgs Boson in 2013, which ultimately led to the Nobel Prize in Physics. After graduating from Brown University, he worked as a data science consultant at Tessella Inc. and a principal data scientist at Compete Inc. and Akamai Technologies. 

A small set of his recent projects include: a Bayesian framework that enabled a top-tier pharmaceutical company to statistically model their manufacturing process and predict future capacity; sleep apnea classification using time-series audio data; real-time object detection in video streams using deep learning; behavioral forecasting, customer segmentation, and a recommendation engine for the digital marketing sector; an end-to-end stock market prediction and portfolio optimization platform for a PE firm; and real-time performance monitoring of data collection.



Dr. Ferrante completed his Ph.D. in theoretical physics at
Brown University, winning the Physics Department's awards for Scholarship and for excellence in Teaching. During his career in academia, he worked on a wide variety of numerical and computational methods, including complex/imaginary Monte Carlo,  chaotic dynamical systems, multi-fractal systems, and stochastic differential equations. Dr. Ferrante was also responsible for the high performance computing infrastructure, from commission and hardware installation to security and system administration.

Prior to SFL, Dr. Ferrante was a Sloan-Swartz Fellow, and later a Data Science Manager, at Cold Spring Harbor Laboratory, modeling and analyzing big data in neuroscience. His work included quantitative
big image analysis (30000×25000 pixels @ 0.5μm/pixel resolution) to study brain-wide connectivity, the application of Topological Data Analysis to identify autism in the SFARI dataset, and the modeling the statistical evolution of brain regions.

His recent projects include predicting cybersecurity vulnerabilities, a real-time object detection platform for live video feeds (from which our client created a $4 million USD yearly product), an AI chatterbot platform with a built-in recommendation system, and 3D modelling of houses using photographs.

He is an expert in applied and computational methods, machine vision, and analytical modeling of complex systems.

He is fluent in English, Spanish, and Portuguese.



Dr. Luk studied theoretical physics at Imperial College London and Mathematics at the University of Cambridge before completing his doctorate in Particle Physics, winning Brown University's graduate award for the Physical Sciences in 2013.

After graduating, he worked as a Process Engineer at Intel Corp, where he developed machine learning algorithms to model and analyze yield metrics. From his time at Intel, he also gained vast experience in the "small-data" regime, having worked on analyses that guided the design for the next generation of computer chips. His work included projects related to anomaly detection of defects in manufacturing; machine vision clustering algorithms for SEM images; yield forecasting; and survival probability modelling.  

More recently he has completed successful projects on time-series modeling for identification of fraudulent energy consumption; Natural Language Processing (NLP), from relatively simple document classification to research-level abstractive document summarisation; and a large variety of market analyses.

His current interests lie in tackling a wide range of NLP and Artificial Intelligence projects. 

Dr. Luk is an expert at data analysis, electronics and mathematical sciences and is fluent in English, Cantonese and French.