Teachers & Mentors


Dr. Marek Gagolewski is a Professor at the Polish Academy of Sciences and Warsaw University of Technology, doing researching on and teaching Data Science, Big Data, and Machine Learning. He teaches introductory and advanced courses in R, Python, and C++, and supervises PhD and MSc students in Computer and Data Science. He is the author of best-selling books on Python and R programming and many R packages, including the famous stringi package. Marek holds a PhD in Computer Science from the Polish Academy of Sciences, specializing in data aggregation, fusion, and mining, as well as computational statistics and uncertainty modeling.


Jakub works as a Senior Data Scientist at New Yorker where he works on various project mainly related to computer vision and object detection. Previously he worked in the Fintech sector. Jakub holds a masters degree in biomedical signal processing. He is passionate about practical, hands-on deep learning, as well as about python development and bringing deep learning into production systems.


Qingchen is an award-winning data scientist with rigorous training in machine learning, artificial intelligence, statistics, and econometrics. He is one of only 87 grandmasters (as of mid-2017) on the Kaggle data science competition platform (ranked 14th out of 52,000+ active competitors on Kaggle), and he also has significant experience in software engineering (C++, Java, Python). Currently he is working on research and development of data-driven solutions to problems in digital marketing.


Daniel is the Dean of Data Science Retreat (DSR). He is passionate about machine learning, teaching, and food. At DSR/DLR, he teaches scalable machine learning on Spark, which has led to him to write a library to make Spark Dataframes behave more like Pandas and HandySpark.

He’s written a really good series of posts on how to understand deep learning from first principles. You can find Daniel on LinkedInGitHub, and Twitter.


Jose Quesada is the founder and director of DSR and AI Deep Dive. He did his PhD work at UC Boulder and then at Carnegie Mellon. Jose’s work in machine learning goes back 20 years. He optimizes business value through radical uses of deep learning. He has mentored and directed more than 150 machine learning portfolio projects, some of which resulted in startups, and others that ended up becoming non-profits with significant social impacts. His goal is to demonstrate that a single person or a small team can have an enormous impact thanks to open source and pre-trained models. One doesn’t need to be a big corporation to make a difference.

Dr. Paco Tornay

Paco is the Dean of Data Science at AI Deep Dive. He has always been passionate about learning. Teaching is just an extension of his own learning process, and is for that reason a part of that passion. Teaching has been his day job for over 20 years now as a professor at the University of Granada. Some of his other interests include programming and language learning. He has learned a few programming languages since he began with C, and is comfortable speaking five different human languages. His interest in neural networks started during the time of the legendary PDP book edited by D.E. Rumelhart and J.L. McClelland. Paco has kept up with the research in the area ever since, and has applied NN models over the years in several practical applications.


Gilberto Titericz

Gilberto is a machine learning expert holding a USA O-1 Visa, and has been ranked #1 out of 78,200 active data scientists at Kaggle for more than 2 years (https://www.kaggle.com/titericz). He is a 14x prize winner and has ranked in the top 1% 29x in competitions.

Kelvin Lwin

After spending nearly a decade at UC Berkeley, Kelvin decided to repay his debt to the public education system by helping build UC Merced. He spent seven years teaching 4,500 students across 55 classes while redesigning the undergraduate Computer Science curriculum. He is now busy designing curricula at NVIDIA’s Deep Learning Institute (DLI) to democratize access to the latest technologies across many disciplines, industries, and geographies. Kelvin helped DLI reach over 100,000 developers worldwide directly and in collaboration with Udacity and Coursera/Deeplearning.ai. He continues to search for ways to leverage AI to solve the Paradox of Progress.