The Weekly Regression

In this episode, we are joined by Enrico Santus, a Data Science Leader in Pharmacovigilance at Bayer.

Enrico talks about his project that uses NLP to detect adverse events on Twitter. We discuss how he developed this project as a way to measure sentiment surrounding the COVID-19 pandemic.  Also, breakdown how using such a large pool of data like Twitter can come with its own challenges including the influence and proliferation of fake news.

Finally, Enrico and I think about what the future has in store for Healthcare and how AI/ML will undoubtedly play an influential role!

Follow Enrico on LinkedIn HERE [https://www.linkedin.com/in/esantus/]

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In this episode, we are joined by Mark Myou, a Senior Data Scientist at NVIDIA and the host of the Caribbean Data Science podcast.

Mark breaks down how world-class recommendation engines are conceptualized and built to grow your business. Using examples from companies like Google, Netflix, and Amazon, we discuss what happens under the hood of powerful recommendation and search engines including talking about the latest developments in deep learning for language modeling.  Finally, Mark and I think about what the future of search engines will look like. From suggested searches to conversational AI, the possibilities are endless!  

Caribbean Data Science Podcast [https://www.youtube.com/channel/UCrYyWKKNcrabk02nK-WyGsg]

Festival of Big Data and Cloud Free Registration [https://www.festivalofdataandcloud.com/join-the-free-webinar]

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LinkedIn HERE [https://www.linkedin.com/company/the-weekly-regres 

In this episode, we are joined by Farhana Faruqe, a Data Scientist at Children’s National Health System and a Ph.D. candidate in Human-Technology Collaboration at The George Washington University.

Farhana outlines the various applications and developments in AI Ethics including Explainable AI and Trustworthy AI. We talk about what it means for a model or algorithm to be trustworthy and how we can mitigate risk at different stages of the data science pipeline. Finally, Farhana summarizes her recent publication “Monitoring Trust in Human-Machine Interactions for Public Sector Applications” where she measures people's trust in Artificial Intelligence and Machine Learning. 

Farhana Faruqe LinkedIn [https://www.linkedin.com/in/farhana-faruqe-98884013/]

Episode 9 w/ Larry Medsker HERE [https://theweeklyregression.buzzsprout.com/1630165/8286326-ep09-designing-ethical-ai-systems-with-dr-larry-medsker]

Follow the TWR Podcast on:
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In this episode, we are joined by Matt Barr, the CTO of mySidewalk.

Matt talks about mySidewalk's comprehensive community data library and the platform's ability to not only aggregate and display public data but also run analytics from neural networks to geospatial analysis. We also discuss how analytics and data science can have a positive impact in improving community development, decrease response times, and optimize resource management. Finally, Matt underscores the importance of forging your own path in data science and focusing on the domain you want to make an impact in.

mySidewalk Website: https://mysidewalk.com/

Follow the TWR Podcast on:
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In this episode, we are joined by Pedro Uria, a Data Scientist at AMPSIGHT.

Pedro talks about his work in geospatial object detection and building an end-to-end machine learning pipeline. We discuss every part of the pipeline such as data collection & labeling, building & training computer vision models, and productizing & containerizing models.  Additionally, we address the challenges of using geospatial and satellite data from data augmentation to model evaluation.

Follow the TWR Podcast on:
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In this episode, we are joined by Alaeddine Ayadi, a Data Scientist at LVMH.

Alaeddine talks about using Data Science and Machine Learning to enhance and personalize the customer experience using a multitude of digital and data touchpoints.  He outlines how the luxury retail and goods sector is ripe for digital transformation, especially in a post-COVID era.  Additionally, we discuss the importance of understanding the business use case before applying data science and machine learning techniques so that nothing compromises the main objective, the customer experience

Find Alaeddine on LinkedIn HERE [https://www.linkedin.com/in/ayadi-alaeddine/]

Follow the TWR Podcast on:
Instagram HERE [https://www.instagram.com/theweeklyregression/]
Twitter HERE [https://twitter.com/TheWeeklyReg]
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In this episode, we are joined by Larry Medsker, a Professor at The George Washington University and the founder/co-editor in chief of the AI and Ethics journal.

Larry talks about the importance of ethics in Machine Learning and Data Science. He outlines the novel idea of "Ethics by Design", where ethics is considered at the same time the AI system is under construction. Additionally, we discuss the role that government agencies play in industry and whether there should be stricter controls on how AI systems are built, trained, and deployed. 

Check out the AI and Ethics Journal HERE [https://www.springer.com/journal/43681]

Follow the TWR Podcast on:
Instagram HERE [https://www.instagram.com/theweeklyregression/]
Twitter HERE [https://twitter.com/TheWeeklyReg]
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In this episode, we are joined by Erik Edmonds, a Computer Vision Engineer at Caterpillar Inc.

  Erik talks about how he embeds AI models into hardware that is used to power autonomous vehicles used in mining operations based in Australia. We have a great discussion about how CV Engineers differ from traditional Machine Learning  Engineers, and we talk about the various constraints that need to be addressed when developing AI on the Edge.  Additionally, we go over examples at Apple and Google where embedded AI is used to run commonly used applications and frameworks.

  Check out other projects by Erik HERE [https://erik-edmonds.github.io/]

  Follow the TWR Podcast on:
  Instagram HERE [https://www.instagram.com/theweeklyregression/]
  Twitter HERE [https://twitter.com/TheWeeklyReg]
  LinkedIn HERE [https://www.linkedin.com/company/the-weekly-regres]

In this episode, we are joined by Tavia Binger, a Senior Health Data Analyst at U.S. News and World Report.

COVID-19 shocked the healthcare system, and data collected on COVID-19 shows massive disparities in treatment, infection, and death rates for minority groups, people of color, and those in lower-income brackets. Tavia, who helps create the U.S. News Best Hospitals Ranking, talks about her team's endeavor to reassess how we value and rank hospital and healthcare systems by introducing their novel Health Equity Index (HEI). She discusses the data sources used to calculate the index and why taking health equity into account helps both hospitals and patients.

For more information about the HEI:
HEI Webinar: HERE [https://www.usnews.com/news/live-events/webinar-us-news-health-equity-index-measuring-hospital-performance-on-health-equity]
Disparities in AVR: HERE [https://health.usnews.com/health-care/best-hospitals/articles/race-and-risk-post-surgery]

Check out the TWR Podcast on:
Instagram HERE [https://www.instagram.com/theweeklyregression/]
Twitter HERE [https://twitter.com/TheWeeklyReg]
LinkedIn HERE [https://www.linkedin.com/company/the-weekly-regres]

In this episode, we are joined by Sanjog Sharma, a Customer Engineer at Google.

  What is the cloud? Why are so many companies and practitioners turning to it to solve their big data problems? Sanjog does an amazing job at describing exactly what the cloud is and why is transforming businesses around the world. He also talks about his role of advising which Google Cloud products his client should use to best solve their problem. We also have a great discussion about the future of big data analytics and cloud computing.  Finally, Sanjog underscores the fact that with all the tools and roles out in the data world, it's easier than ever to forge your own path in the field. 

  Check out the TWR Podcast on:
  Instagram HERE [https://www.instagram.com/theweeklyregression/]
  Twitter HERE [https://twitter.com/TheWeeklyReg]
  LinkedIn HERE [https://www.linkedin.com/company/the-weekly-regres]