Our Leadership Team

The SFL Scientific leadership team brings passion and deep experience in delivering custom data science solutions to businesses worldwide. Their experience in high-tech consulting and analytical modeling is what makes SFL a different kind of company—the kind that creates actionable and lasting impact for large corporations, small and medium-size businesses, as well as internal and outsourced service providers in your organization. 


Michael Segala, Ph.D., Co-Founder, CEO

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 span four common types of data across major industry sectors: time-series analysis, natural language processing, machine vision, and consumer/market data. 

Michael is also an expert in the wearable and consumer electronic space, developing a sleep apnea classification tool using time-series audio data that enabled the product's FDA approval. He built a real-time object detection platform using state-of-the-art deep learning to detect and track a variety of objects in live video streams. This work launched the client’s flagship product and created a $14 million USD yearly revenue stream.

Michael has created a set of machine learning models to predict vulnerabilities in various Cybersecurity related scenarios, including developing time-series classification algorithms to detect distributed denial of services attacks.

Under his direction, SFL developed a framework to predict fraudulent household energy consumption from data of an international energy provider, as well as examining behavioral forecasting, customer segmentation, and the necessary business intelligence tools in today's digital economy. He has created a Bayesian framework that enabled a top-tier pharmaceutical company to statistically model their manufacturing process and predict future capacity. In the financial and services sector, he has lead the effort to implement custom end-to-end stock market prediction and portfolio optimization platforms for PE firms. 

Daniel Ferrante, Ph.D., Co-Founder, CDO

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. During this time, Dr. Ferrante was also responsible for the implementation and oversight for high performance computing infrastructure and other distributed computing systems, 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 data warehousing, data engineering, and quantitative big image analysis (60000×55000 pixels @ 0.5μm/pixel resolution) to study brain-wide connectivity in large >1.5PB datasets. Applications included disease modeling, segmentation, and cell-detection in various animal species as well as Topological Data Analysis to identify autism in the SFARI dataset for the Simons Foundation, and modeling the statistical evolution of brain regions. His experience with quantitative big image analysis serves as a backbone for work in the healthcare space, especially digital histopathology and the medical imaging space.

He is an expert in applied and computational methods, machine vision, and analytical modeling of complex systems. His recent projects include developing a state-of-the-art computer vision framework to classify and segment several objects within high-resolution satellite images; the detection framework can be applied to a host of infrastructure and development questions and provide insights on a national or global scale. 

Miscellaneous projects include predicting cybersecurity vulnerabilities, an AI chatterbot platform with a built-in recommendation system, and 3D modeling of houses using photographs.

He is fluent in English, Spanish, and Portuguese.

Michael Luk, Ph.D., Co-Founder, CTO

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 Corporation, where he developed machine learning algorithms to model and analyze yield metrics. During this time, 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 modeling. 

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 summarization; 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, mathematical sciences, and is fluent in English, Cantonese, and French.

Chief Operating Officer

Alexander Tolpygo, Chief Operating Officer

Alexander is a biomedical engineer and statistician by training and has been affiliated and consulted for public and government institutions such as Cold Spring Harbor Laboratory, Brookhaven National Laboratory, The Henry Jackson Foundation, RIKEN BSI, and the NIH/NINDS.

He has assembled experimental and computational teams and has secured and managed +$20M in private and grant investments. He is passionate about the healthcare industry, point of care solutions, and modernizing inefficiencies and medical diagnoses. He is an expert in high-throughput digital pathology and whole-slide imaging, creating brain-wide connectivity projects and advising a broad spectrum of scientists, while developing frameworks and algorithms for image registration, cell detection, and segmentation. As an engineer, he strives to deliver faster, more efficient, and cost-effective strategies during the life cycle of any corporate engagement.