Francesca Lazzeri, Ph.D., is an experienced scientist and machine learning practitioner with over 12 years of academic and industry experience. She is the author of several publications, including technology journals, conferences, and books. She currently leads an international team of cloud advocates and developers at Microsoft, managing an extensive portfolio of customers in the academic/education sector, and building intelligent automated solutions on the Cloud. Before joining Microsoft, she was a research fellow at Harvard University in the Technology and Operations Management Unit. She is also an advisory board member of the Global Women in Data Science (WiDS) initiative, a machine learning mentor at the Massachusetts Institute of Technology and Columbia University, and an active member of the AI community.

Patricia Thaine is a Computer Science Ph.D. Candidate at the University of Toronto and a Postgraduate Affiliate at the Vector Institute researching privacy-preserving natural language processing, with a focus on applied cryptography. Her research interests also include computational methods for lost language decipherment. She is the Co-Founder and CEO of Private AI, a Toronto- and Berlin-based startup creating a suite of privacy tools that make it easy to comply with data protection regulations, mitigate cybersecurity threats, and maintain customer trust.

Eugene is a data scientist and writer. He works at the intersection of consumer data & tech to build machine learning systems to help customers and writes about effective data science, learning, and career. He's currently an Applied Scientist at Amazon, helping users read more and get more out of reading. Previously, he led the data science team at Lazada (acquired by Alibaba in 2016), working on e-commerce ML systems (e.g., ranking, automation, fraud detection).

Matthew McAteer is a Machine learning Researcher at FOR.ai. Before this, he got his career started in biological aging, before moving on to the mission of figuring out ways in which machine learning could be used on large amounts of noisy biomedical data. He has worked for many companies as a machine learning engineer and researcher, ranging from small startups to Google's TensorFlow team. His work on subjects like security and safety in machine learning has also been showcased at top conferences like ICML.

Carl Osipov is the Chief Technology Officer of CounterFactual.AI - a boutique machine learning consultancy he co-founded with his friend from IBM. Previously, he held engineering and technical leadership roles at Google and IBM, on programs and projects across both United States and Europe, in the areas of machine learning, computational natural language processing, cloud computing, and big data analytics. Carl is also an inventor with six patents at USPTO and is an author of "Serverless Machine Learning in Action," a book from Manning Publishers, currently available as an ebook subscription and expected in print in early 2021.

Brian T. O'Neill is a designer, advisor, and founder of Designing for Analytics, an independent consultancy that helps companies turn analytics into indispensable decision support applications. For over 20 years, he has worked with companies including Dell EMC, Global Strategy Group, TripAdvisor, Fidelity, JP Morgan Chase, E-Trade, and several SaaS startups. He has spoken internationally, giving talks at O'Reilly Strata, Enterprise Data World, the International Institute for Analytics Symposium, Predictive Analytics World, and Boston College. Brian also hosts the highly-rated podcast, Experiencing Data, where he reveals the strategies and activities that product, data science, and analytics leaders are using to deliver valuable experiences around data. In addition to consulting, Brian is also a professional percussionist and has performed at Carnegie Hall and The Kennedy Center.

Luigi Patruno is a Data Scientist and the Founder of MLinProduction.com. He's currently the Director of Data Science at 2U, where he leads a team of data scientists and ML engineers in developing machine learning models and infrastructure to predict student success outcomes. Luigi founded MLinProduction.com to educate data scientists, ML engineers, and ML product managers about best practices for running machine learning systems in production. As a consultant for Fortune 500s and start-ups, Luigi helps companies utilize data science to create competitive advantages. He has taught graduate-level courses in Statistics and Big Data Engineering and holds a Masters in Computer Science and a BS in Mathematics.

Alexey lives in Berlin with his wife and son. He’s a software engineer with a focus on machine learning, currently working at OLX Group as a Lead Data Scientist. Alexey is a Kaggle master, and he wrote a couple of books. One of them is “Mastering Java for Data Science,” and now he’s working on another one — “Machine Learning Bookcamp.”

Ankit Jain is a senior research scientist at Uber AI Labs, the machine learning research arm of Uber. His work primarily involves the application of deep learning methods to a variety of Uber’s problems ranging from food delivery, fraud detection to self-driving cars. Previously, he worked in a variety of machine learning roles at Facebook, Bank Of America, and other startups. He co-authored a book on machine learning titled TensorFlow Machine Learning Projects. Additionally, he’s been a featured speaker in many of the top AI conferences and universities and has published papers in several top conferences like NeurIPs and ICLR. He earned his MS from UC Berkeley and BS from IIT Bombay.

Ari Morcos is a Research Scientist at Facebook AI Research working on understanding the mechanisms underlying neural network computation and function and using these insights to build machine learning systems more intelligently. In particular, Ari has worked on a variety of topics, including understanding the lottery ticket hypothesis, the mechanisms underlying common regularizers, and the properties predictive of generalization, as well as methods to compare representations across networks, the role of single units in computation, and on strategies to measure abstraction in neural network representations. Previously, he worked at DeepMind in London, and earned his Ph.D. in Neurobiology at Harvard University, using machine learning to study the cortical dynamics underlying evidence accumulation for decision-making.