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Jun 3, 2020 • 54min

Episode 33: Domain Randomization in Robotics with Josh Tobin

Josh Tobin is the founder and CEO of a stealth machine learning startup. Previously, Josh worked as a deep learning & robotics researcher at OpenAI and as a management consultant at McKinsey. He is also the creator of Full Stack Deep Learning (fullstackdeeplearning.com), the first course focused on the emerging engineering discipline of production machine learning. Josh did his Ph.D. in Computer Science at UC Berkeley, advised by Pieter Abbeel.
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May 21, 2020 • 1h 34min

Episode 32: Economics, Data For Good and AI Research with Sara Hooker

Sara Hooker is a researcher at Google AI doing deep learning research on reliable explanations of model predictions for black-box models. Her main research interests gravitate towards interpretability, model compression, and security. In 2014, she founded Delta Analytics, a non-profit dedicated to bringing technical capacity to help non-profits across the world use machine learning for good. She grew up in Africa, in Mozambique, Lesotho, Swaziland, South Africa, and Kenya. Her family now lives in Monrovia, Liberia.
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Apr 18, 2020 • 1h 8min

Episode 31: From Quantum Computing to Epidemic Modeling with Colleen Farrelly

Colleen M. Farrelly is a data scientist whose experience spans biotech, healthcare, pharma, marketing, finance, operations, edtech, manufacturing, and disaster logistics. Her research focuses mostly on the intersection of topology/geometry, machine learning, statistics, and quantum computing. She's passionate about poetry, surfing, Gators football, and socioeconomics.
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Mar 27, 2020 • 1h 15min

Episode 30: Data Science Evangelism with Parul Pandey

Parul Pandey is a Data Science Evangelist at H2O.ai. She combines Data Science, evangelism and community in her work. Her emphasis is to break down the data science jargon for the people. Prior to H2O.ai, she worked with Tata Power India, applying Machine Learning and Analytics to solve the pressing problem of load sheddings in India. She is also an active writer and speaker and has contributed to various national and international publications including Towards Data Science, Analytics Vidhya, and KDNuggets and Datacamp.
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Mar 13, 2020 • 1h 18min

Episode 29: From Bioinformatics to Natural Language Processing with Leonard Apeltsin

Dr. Leonard Apeltsin is a research fellow at the Berkeley Institute for Data Science. He holds a Ph.D. in Biomedical Informatics from UCSF and a BS in Biology and Computer Science from Carnegie Mellon University. Leonard was a Senior Data Scientist & Engineering Lead at Primer AI, a machine learning company that specializes in using advanced Natural Language Processing Techniques to analyze terabytes of unstructured text data. As a founding team-member, Leonard helped expand the Primer AI team from four employees to over 80 people. Outside of Data Science and ML, Leonard enjoys scuba diving, salsa dancing, and making short documentary films.
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Mar 1, 2020 • 58min

Episode 28: Excelling in Data Analytics with Vincent Tatan

Vincent Tatan is a Data and Technology enthusiast with relevant working experiences from Google LLC, Visa Inc., and Lazada to implement microservice architectures, business intelligence, and analytics pipeline projects. Vincent is a native Indonesian with a record of accomplishments in problem-solving with strengths in Full Stack Development, Data Analytics, and Strategic Planning. He has been actively consulting Singapore Management University's Business Intelligence and Analytics Club, guiding aspiring data scientists and engineers from various backgrounds, and opening up his expertise for businesses to develop their products. Vincent also opens up his one on one mentorship service to coach on landing your dream Data Analyst/Engineer Job at Google, Visa, or other large tech companies.
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Jan 24, 2020 • 1h 1min

Episode 27: Feature Engineering with Ben Fowler

Ben Fowler has been in the field of data science for over five years. In his current role at Southeast Toyota Finance, Ben leads the end to end model development process to solve the problem of interest. Ben holds a Master of Science in Data Science from Southern Methodist University, graduating in 2017. Following graduation, Ben has been a guest speaker to the SMU program multiple times. Additionally, Ben has spoken at the PyData Miami 2019 and PyData LA 2019 Conferences and has spoken multiple times at the West Palm Beach Data Science Meetup.
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Jan 8, 2020 • 50min

Episode 26: From Cognitive Neuroscience To Reinforcement Learning with Arthur Juliani

Arthur Juliani is a Senior Machine Learning Engineer at Unity Technologies, where he has worked as a founding member of the ml-agents GitHub project as well as the leader of the Obstacle Tower project. He is also currently a Ph.D. candidate in Cognitive Neuroscience at the University of Oregon, where he studies computation models of spatial representation learning in humans.
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Dec 22, 2019 • 56min

Episode 25: Algorithmic Trading with Alexandr Honchar

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.
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Dec 9, 2019 • 1h 12min

Episode 24: From Actuarial Science to Machine Learning with Mael Fabien

Former actuarial scientist Mael Fabien discusses his journey to machine learning, including co-founding Wanago and pursuing a Master's degree in Actuarial Science. He explains his deep learning research project on emotion recognition and his involvement in teaching machine learning. The podcast also covers his internship experiences in data science, projects in text transformation and machine learning applications, and the exploration of reinforcement learning in robotics and games.

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