Ivy Lu's guest appearances

Ivy Lu

HumAIn Podcast - Artificial Intelligence, Data Science, Developer Tools, and Technical Education

湾区文化沙龙

Nikita Shamgunov

Chris Butler

Mike Robbins

Lorna Davis

Jake Porway

Stephen Miller

Robert Scoble

John Spence

Babak Hodjat

Vasco Pedro

Lex Sokolin

Jordan Tigani

Magnus Carlsen

Thor Ernstsson

Alex Beard

OwlTail

Ivy Lu: How To Organize Data Science Teams and Data Science Projects for Startups [Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSIvy Lu is the head of data science and machine learning at Oxygen. Ivy's onboarding marked the launch of Oxygen’s banking platform. She has bachelor's degree in Geographical Information System from Peking University, a Ph.D in Earth Systems and Geoinformation Science and a Master's degree in Geographic Information Science and Cartography both from George Mason University. Episode Links:  Ivy Lu’s LinkedIn: https://www.linkedin.com/in/ivy9lu/ Ivy Lu’s Twitter: https://twitter.com/oxygenbanking Ivy Lu’s Website: https://www.blog.oxygen.us/ Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter: https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (00:00) – Introduction(01:42) – I joined Capital One as a data scientist after my graduation from George Mason University with a PhD in Geographic Information Science. After I moved to the west coast, I joined Apple. So, at Apple, I work on an anti-fraud team where we fight against all kinds of fraud and abuse within the whole Apple ecosystem to bring trust and safety to the Apple customers. Both experiments helped me prepare for my new challenge at Oxygen as a FinTech company. So, that's my career , how I passed from the traditional banking industry to a large technology company. And now I'm at the spin hat company Oxygen. (04:05) – A collaboration challenge, since you are the only one and only data scientist on the team, basically, you are collaborating with so many different teams and departments: from operations to marketing customer support or product features. So, you need to collaborate with every single one in the different departments and understand their needs, understand their pain. That also comes related to the first challenge. Collaboration comes with prioritization.(06:57) – Data science teams should be positioned as the foundation and the cross team within the whole organization. So for each line of the business, data scientists should have domain knowledge about the problem that they are trying to deal with(09:20) – I collaborate with our fraud team to set up a lot of protections in the core sets. We collaborate with different fraud vendors on how to set up all the parameters, all the controls in place in the fraud vendors for our sign up status. After the sign up flow is pretty under control, I built a preliminary machine learning model for the fraudsters, to detect fraudsters after sign up for the behaviors they show with our card.(14:48) – I see these days, as data scientists it may require different skills than before. Nowadays, maybe, coding skills are not required anymore with such a good tool for data scientists and for machine learning engineers. But, ultimately, I still think the important thing is the study section background on the machine learning algorithm, the deep understanding of the machine learning algorithms. Also what's important is the deep understanding of the problem they're solving.(17:41) – There are two types of team structure. One is like the data science team belongs to one centralized team and then people may wear multiple hats. So, one day you may work on project A, then another day and work on project B, versus another one that is more embedded.(20:33) – We launched a new product called Elements. So we are now offering four tiers of the product, with increasing cashback with different saving APRs, as well as other retail and travel benefits like priority pass, launch access, reimbursements, like digital subscriptions, like Netflix, and the Peloton Digital.  (23:08) – We are going to raise our series B soon and a series B is all about metrics. Whether your company is going to be sustainable, what's your retention, what's your user growth. So a lot of KPIs and the metrics you send show to not only our internal business, but also to work presents for our VC.Advertising Inquiries: https://redcircle.com/brandsPrivacy &amp; Opt-Out: https://redcircle.com/privacy

Ivy Lu: How To Organize Data Science Teams and Data Science Projects for Startups [Audio] Podcast:<a href="https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS" rel="nofollow" target="_blank"> Play in new window</a> |<a href="https://www.listennotes.com/podcasts/humain-podcast-artificial-intelligence-data-fBRret2PTiU/" rel="nofollow" target="_blank"> Download</a>Subscribe:<a href="https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5yZWRjaXJjbGUuY29tLzk5MTEzZjI0LTJiZDEtNDMzMi04Y2QwLTMyZTA1NTZjOGJjOQ" rel="nofollow" target="_blank"> Google Podcasts</a> |<a href="https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS" rel="nofollow" target="_blank"> Spotify</a> |<a href="https://www.stitcher.com/show/humain" rel="nofollow" target="_blank"> Stitcher</a> | <a href="https://tunein.com/podcasts/Technology-Podcasts/HumAIn-p1224678/" rel="nofollow" target="_blank">TuneIn</a> | <a href="https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9" rel="nofollow" target="_blank">RSS</a>Ivy Lu is the head of data science and machine learning at Oxygen. Ivy's onboarding marked the launch of Oxygen’s banking platform. She has bachelor's degree in Geographical Information System from Peking University, a Ph.D in Earth Systems and Geoinformation Science and a Master's degree in Geographic Information Science and Cartography both from George Mason University. Episode Links:  Ivy Lu’s LinkedIn: <a href="https://www.linkedin.com/in/ivy9lu/" rel="nofollow" target="_blank">https://www.linkedin.com/in/ivy9lu/</a> Ivy Lu’s Twitter: <a href="https://twitter.com/oxygenbanking" rel="nofollow" target="_blank">https://twitter.com/oxygenbanking</a> Ivy Lu’s Website: <a href="https://www.blog.oxygen.us/" rel="nofollow" target="_blank">https://www.blog.oxygen.us/</a> Podcast Details: Podcast website: <a href="https://www.humainpodcast.com" rel="nofollow" target="_blank">https://www.humainpodcast.com</a> Apple Podcasts: <a href="https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009" rel="nofollow" target="_blank">https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009</a> Spotify: <a href="https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS" rel="nofollow" target="_blank">https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS</a> RSS: <a href="https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9" rel="nofollow" target="_blank">https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9</a> YouTube Full Episodes: <a href="https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag" rel="nofollow" target="_blank">https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag</a> YouTube Clips: <a href="https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos" rel="nofollow" target="_blank">https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos</a> Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: <a href="https://www.patreon.com/humain/creators" rel="nofollow" target="_blank">https://www.patreon.com/humain/creators</a> – Twitter: <a href="https://twitter.com/dyakobovitch" rel="nofollow" target="_blank">https://twitter.com/dyakobovitch</a> – Instagram: <a href="https://www.instagram.com/humainpodcast/" rel="nofollow" target="_blank">https://www.instagram.com/humainpodcast/</a> – LinkedIn: <a href="https://www.linkedin.com/in/davidyakobovitch/" rel="nofollow" target="_blank">https://www.linkedin.com/in/davidyakobovitch/</a> – Facebook: <a href="https://www.facebook.com/HumainPodcast/" rel="nofollow" target="_blank">https://www.facebook.com/HumainPodcast/</a> – HumAIn Website Articles: <a href="https://www.humainpodcast.com/blog/" rel="nofollow" target="_blank">https://www.humainpodcast.com/blog/</a> Outline: Here’s the timestamps for the episode: (00:00) – Introduction(01:42) – I joined Capital One as a data scientist after my graduation from George Mason University with a PhD in Geographic Information Science. After I moved to the west coast, I joined Apple. So, at Apple, I work on an anti-fraud team where we fight against all kinds of fraud and abuse within the whole Apple ecosystem to bring trust and safety to the Apple customers. Both experiments helped me prepare for my new challenge at Oxygen as a FinTech company. So, that's my career , how I passed from the traditional banking industry to a large technology company. And now I'm at the spin hat company Oxygen. (04:05) – A collaboration challenge, since you are the only one and only data scientist on the team, basically, you are collaborating with so many different teams and departments: from operations to marketing customer support or product features. So, you need to collaborate with every single one in the different departments and understand their needs, understand their pain. That also comes related to the first challenge. Collaboration comes with prioritization.(06:57) – Data science teams should be positioned as the foundation and the cross team within the whole organization. So for each line of the business, data scientists should have domain knowledge about the problem that they are trying to deal with(09:20) – I collaborate with our fraud team to set up a lot of protections in the core sets. We collaborate with different fraud vendors on how to set up all the parameters, all the controls in place in the fraud vendors for our sign up status. After the sign up flow is pretty under control, I built a preliminary machine learning model for the fraudsters, to detect fraudsters after sign up for the behaviors they show with our card.(14:48) – I see these days, as data scientists it may require different skills than before. Nowadays, maybe, coding skills are not required anymore with such a good tool for data scientists and for machine learning engineers. But, ultimately, I still think the important thing is the study section background on the machine learning algorithm, the deep understanding of the machine learning algorithms. Also what's important is the deep understanding of the problem they're solving.(17:41) – There are two types of team structure. One is like the data science team belongs to one centralized team and then people may wear multiple hats. So, one day you may work on project A, then another day and work on project B, versus another one that is more embedded.(20:33) – We launched a new product called Elements. So we are now offering four tiers of the product, with increasing cashback with different saving APRs, as well as other retail and travel benefits like priority pass, launch access, reimbursements, like digital subscriptions, like Netflix, and the Peloton Digital.  (23:08) – We are going to raise our series B soon and a series B is all about metrics. Whether your company is going to be sustainable, what's your retention, what's your user growth. So a lot of KPIs and the metrics you send show to not only our internal business, but also to work presents for our VC.Advertising Inquiries: <a href="https://redcircle.com/brands" rel="nofollow" target="_blank">https://redcircle.com/brands</a> Privacy &amp; Opt-Out: <a href="https://redcircle.com/privacy" rel="nofollow" target="_blank">https://redcircle.com/privacy</a>

How To Organize Data Science Teams and Data Science Projects for Startups with Ivy Lu at Oxygen

20世纪初期，量子物理为自然科学哲学界带来了颠覆性的震荡和冲击。关于量子物理及其所蕴涵的哲学，在其他地区的沙龙中已有很多期活动对此做过深入的研究和探讨。本次沙龙作为湾区文化沙龙的试水内测，将简要介绍一点量子物理的基本概念和解释，然后针对每种解释推荐和介绍一些优秀的科幻作品。希望参加本期沙龙的朋友能吃下一些没看过的精彩科幻作品的安利，并且在以后欣赏科幻作品的时候能联系到量子物理的这些概念，也许会获得更愉悦的观赏体验。Disclaimer：主讲人本身不是物理专业出身，讲义中的物理科学内容全部来自于曹天元所著的《上帝掷骰子吗？量子物理史话》。所以本期的重点在于科幻作品小型安利交流会，而非科学概念细节研究。主讲人：Ivy LuIvy，曾任北大元火漫画社副社，喜欢奇幻文学、跑团、ACG，现为一名普通的程序员&amp;汉化组修图&amp;漫画翻译，修有漫画《夜访吸血鬼》、《龙枪编年史·秋暮之巨龙》、《美国众神》，译有《坟场之书》图像小说、《星际争霸：前线》漫画和《权力的游戏HBO官方指南幕后及艺术设定》，有两只猫。我们的微信公众号：湾区文化沙龙

20世纪初期，量子物理为自然科学哲学界带来了颠覆性的震荡和冲击。关于量子物理及其所蕴涵的哲学，在其他地区的沙龙中已有很多期活动对此做过深入的研究和探讨。本次沙龙作为湾区文化沙龙的试水内测，将简要介绍一点量子物理的基本概念和解释，然后针对每种解释推荐和介绍一些优秀的科幻作品。希望参加本期沙龙的朋友能吃下一些没看过的精彩科幻作品的安利，并且在以后欣赏科幻作品的时候能联系到量子物理的这些概念，也许会获得更愉悦的观赏体验。 Disclaimer：主讲人本身不是物理专业出身，讲义中的物理科学内容全部来自于曹天元所著的《上帝掷骰子吗？量子物理史话》。所以本期的重点在于科幻作品小型安利交流会，而非科学概念细节研究。 主讲人：Ivy Lu Ivy，曾任北大元火漫画社副社，喜欢奇幻文学、跑团、ACG，现为一名普通的程序员&amp;汉化组修图&amp;漫画翻译，修有漫画《夜访吸血鬼》、《龙枪编年史·秋暮之巨龙》、《美国众神》，译有《坟场之书》图像小说、《星际争霸：前线》漫画和《权力的游戏HBO官方指南幕后及艺术设定》，有两只猫。 我们的微信公众号：湾区文化沙龙

【湾区文化沙龙 （内测）】量子物理与科幻作品推介 by Ivy Lu