Where I express personal thoughts in words
Salma Bakouk is the CEO and co-founder of Sifflet, a Full Data Stack Observability platform. Before Sifflet, Salma was an Executive Director at Goldman Sachs in Sales & Trading in Asia, leading key Data & Analytics initiatives. Salma holds an Engineering Degree from École Centrale Paris in Applied Mathematics and a Master's in Statistics and Data Science.
Nick Handel is Transform's CEO and Co-Founder. Before Transform, Nick was Head of Data at Branch International in the micro-lending space.
Before Branch, Nick held a variety of roles at Airbnb, both as a Data Scientist & Product Manager. He was on the Growth team that founded the Experiences product and the Data Platform team. His work includes launching Airbnb's ML platform, Zipline, building the company's data science team, helping with the company's initial international expansion, and leading the data science team that launched Airbnb's Trips product.
Before joining Airbnb, Nick was a research economist at BlackRock. He is an avid trail runner, climber, skier, and adventurer, much of the time with his dog Huckleberry.
Leigh-Marie Braswell is an investor at Founders Fund.
Before joining Founders Fund, she was an early engineer & the first product manager at Scale AI, where she originally built & later led product development for the LiDAR/3D annotation products, used by many autonomous vehicles, robots, and AR/VR companies as a core step in their machine learning lifecycles. She also has done software development at Blend, machine learning at Google, and quantitative trading at Jane Street.
She is originally from Alabama, graduated from MIT, and loves to play poker, run long distances, and scuba dive.
Einat Orr is the CEO and Co-founder of Treeverse, the company behind lakeFS, an open-source platform that delivers resilience and manageability to object-storage-based data lakes. She received her PhD. in Mathematics from Tel Aviv University in optimization in graph theory. Einat previously led several engineering organizations, most recently as the CTO at SimilarWeb.
Emeli Dral is a Co-founder and CTO at Evidently AI, a startup developing tools to analyze and monitor the performance of machine learning models. Earlier, she co-founded an industrial AI startup and served as the Chief Data Scientist at Yandex Data Factory. She led over 50 applied ML projects for various industries - from banking to manufacturing. Emeli is also a data science lecturer at St. Petersburg State Management School and Harbour.Space University. She is a co-author of the Machine Learning and Data Analysis curriculum at Coursera with over 100,000 students. She also co-founded Data Mining in Action, the largest open data science course in Russia.
Fabiana Clemente is a Data Scientist with a background that ranges from Business Intelligence to Big Data Development and IoT architecture. Throughout her professional career, she has been leading state-of-the-art projects in global companies and startups. She has an academic background in Applied Maths, and MSc in Data Management combined with nano degrees in Deep Learning and Secure and Private AI.
As YData’s Co-Founder, she combines Data Privacy with Deep Learning as her main field of work and research, with the mission to unlock data with privacy by design. She also aims to inspire more women to follow her steps and join the tech community.
Luis Serrano is a Quantum AI Research Scientist at Zapata Computing. He is the author of the book Grokking Machine Learning and maintains a popular YouTube channel to explain machine learning in pedestrian terms. Luis has previously worked in machine learning at Apple and Google, and at Udacity as the head of content for AI and data science. He has a Ph.D. in mathematics from the University of Michigan, a master's and bachelor's from the University of Waterloo, and worked as a postdoctoral researcher in mathematics at the University of Quebec at Montreal.
Meta-learning, also known as learning how to learn, has recently emerged as a potential learning paradigm that can learn information from one task and generalize that information to unseen tasks proficiently. During this quarantine time, I started watching lectures on Stanford’s CS 330 class on Deep Multi-Task and Meta Learning taught by the brilliant Chelsea Finn. As a courtesy of her lectures, this blog post attempts to answer these key questions:
Why do we need meta-learning?
How does the math of meta-learning work?
What are the different approaches to design a meta-learning algorithm?
In this post and those to follow, I will be walking through the creation and training of recommendation systems, as I am currently working on this topic for my Master Thesis. Part 4 looks into the nitty-gritty mathematical details of matrix factorization, arguably the most common baseline model for recommendation system research these days.
Alexandr Honchar is an AI practitioner and entrepreneur, made in Ukraine, living in Italy, working worldwide. He has a master's degree in mathematics from the University of Verona, Italy, and has been working in the AI field for the last 7 years. He has grown from a data scientist role to a founder and tech leader role for several companies. Lately, he founded Neurons Lab - an AI boutique where he pushes AI frontiers and builds, which are unique for the culture of freedom and creativity with other strong technical experts. Apart from business, he blogs at Medium about recent AI advances and gives talks at conferences and meetups across Europe.
