Leonard Apeltsin's guest appearances

Leonard Apeltsin

The Alldus Podcast - AI in Action

Datacast

Believe you can because you can!

Georg Hildebrand

Gregor Lammel

Chelsea Weidman Burke

Rouzbeh Gerami

Jan Saputra Muller

Hans Ritschl

Justin DoBosh

Dagmar Schuller

Peter Diehl

Dirk Hofmann

Ulla Kruhse-Lehtonenm

Frank Herfert

Andreas Helbig

Martin Loetzsch

Thomas Karbe

OwlTail

AI is not only the future. Many industries and companies use AI techs today. If your goal is to overcome competitors in the long run, then adapting faster is key. Leonard Apeltsin shares his valuable insights. Watch or listen to the entire episode to know a lot more. Wanna get more traffic? Submit your request…

AI is not only the future. Many industries and companies use AI techs today. If your goal is to overcome competitors in the long run, then adapting faster is key. Leonard Apeltsin shares his valuable insights. Watch or listen to the entire episode to know a lot more. Wanna get more traffic? Submit your request…

216. Data Science And Machine Learning With Leonard Apeltsin

Jacob Shiff

Today's guest is Jacob Shiff, Co-Founder &amp; COO and Leonard Apeltsin, Head of Data Science at Anomaly. Founded in 2020, Anomaly is tackling the challenge of fraud, waste and abuse (FWA) in healthcare, a problem costing over $300 billion per year in the US. They are building software that uses analytics, ML and proprietary data to catch and prevent improper healthcare payments for payers and employers. Anomaly brings together top engineers and data scientists, deep payment integrity domain experts, and experienced payer and benefits leaders. In the episode, Jacob and Leonard will tell you about: What inspired them to set up Anomaly, The impact they are making within Healthcare Finances, Applying AI &amp; Data Science to help solve these problems, Key steps on bringing predictive models into production, Challenges they need to overcome working with data, Skills they look for when hiring Data Scientists, &amp; What excites them for the future of Anomaly

Today's guest is Jacob Shiff, Co-Founder &amp; COO and Leonard Apeltsin, Head of Data Science at Anomaly. Founded in 2020, <a href="https://www.findanomaly.com/" rel="noopener nofollow" target="_blank">Anomaly</a> is tackling the challenge of fraud, waste and abuse (FWA) in healthcare, a problem costing over $300 billion per year in the US. They are building software that uses analytics, ML and proprietary data to catch and prevent improper healthcare payments for payers and employers. Anomaly brings together top engineers and data scientists, deep payment integrity domain experts, and experienced payer and benefits leaders. In the episode, Jacob and Leonard will tell you about: What inspired them to set up Anomaly, The impact they are making within Healthcare Finances, Applying AI &amp; Data Science to help solve these problems, Key steps on bringing predictive models into production, Challenges they need to overcome working with data, Skills they look for when hiring Data Scientists, &amp; What excites them for the future of Anomaly

E165 Jacob Shiff, Co-Founder & COO and Leonard Apeltsin, Head of Data Science at Anomaly

Show Notes:(2:18) Leonard discussed his undergraduate experience at Carnegie Mellon - where he studied Biology and Computer Science.(5:10) Leonard decided to pursue a Ph.D. in Bioinformatics at the University of California - San Francisco.(6:27) Leonard described his Ph.D. research that focused on finding hidden patterns in genetically-linked diseases.(9:42) Leonard went deep into clustering algorithms (Markov Clustering and Louvain) and their applications such as protein and news article similarity.(13:21) Leonard shared his story of starting a data science consultancy with various client startups.(17:58) Leonard discussed the interesting consulting projects that he worked on: from detecting plagiarism to predicting bill insurance.(22:04) Leonard shared practical tips to learn technical concepts.(23:23) Leonard reflected on his experience working with a string of startups including Accretive Health, Quid, and Stride Health.(26:06) Leonard is the founding team member of Primer AI, a startup that applies state-of-the-art NLP techniques to build machines that read and write, back in early 2015.(30:31) Leonard discussed the technical challenges to develop algorithms that power Primer’s products to scale across languages other than English.(34:28) Leonard unpacked his technical post "Russian NLP” on Primer’s blog.(38:17) Leonard talked about the advances in the NLP research domain that he is most excited about in 2020 (XLNet &gt;&gt;&gt; BERT).(41:10) Leonard discussed the challenges of scaling the data-driven culture across Primer AI as the company grows.(46:20) Leonard mentioned different use cases of Primer for clients in finance, government, and corporate.(51:41) Leonard talked about his decision to leave Primer and become a Data Science Health Innovation Fellow at the Berkeley Institute for Data Science.(54:30) Leonard went over applications of data science in healthcare that will be adopted widely in the next few years.(1:02:45) Leonard discussed his process of writing a book called “Data Science Bookcamp.”(1:07:21) Leonard revealed how he chose the case studies to be included in the book.(1:10:27) Closing segment.His Contact Info:LinkedInGoogle ScholarBerkeley Institute For Data ScienceHis Recommended Resources:Semi-Supervised LearningAssociation Rule LearningspaCy (Open-Source Library for Advanced NLP)fastText  (NLP library from Facebook)XLNet: Generalized Autoregressive Pretraining for Language UnderstandingBERT: Pretraining of Deep Bidirectional Transformers for Language UnderstandingFederated Learning with Differential Privacy: Algorithms and Performance AnalysisDifferential Privacy- Enabled Federated Learning for Sensitive Health DataOasis Labs and Dr. Dawn SongFitbit and Apple WatchWalter Pitts who invented neural networksPaul Werbos who invented back-propagationFei-Fei Li who constructed the ImageNet dataset“The Signal and The Noise” by Nate SilverYou can read the completed chapters of "Data Science Bookcamp" using the codes below:Permanent discount code: poddcast195 free eBook codes: dcdsprf-B373, dcdsprf-CA3B, dcdsprf-299E, dcdsprf-6E5, and dcdsprf-9660 (activated and will last for 2 months)

Show Notes:<ul><li>(2:18) Leonard discussed his undergraduate experience at <a href="https://www.cmu.edu/" rel="nofollow" target="_blank">Carnegie Mellon</a> - where he studied Biology and Computer Science.</li><li>(5:10) Leonard decided to pursue a <a href="https://bioinformatics.ucsf.edu/degree-program/research-areas" rel="nofollow" target="_blank">Ph.D. in Bioinformatics at the University of California - San Francisco</a>.</li><li>(6:27) Leonard described his Ph.D. research that focused on finding hidden patterns in genetically-linked diseases.</li><li>(9:42) Leonard went deep into clustering algorithms (<a href="https://micans.org/mcl/" rel="nofollow" target="_blank">Markov Clustering</a> and <a href="https://neo4j.com/docs/graph-algorithms/current/algorithms/louvain/" rel="nofollow" target="_blank">Louvain</a>) and their applications such as protein and news article similarity.</li><li>(13:21) Leonard shared his story of starting a data science consultancy with various client startups.</li><li>(17:58) Leonard discussed the interesting consulting projects that he worked on: from detecting plagiarism to predicting bill insurance.</li><li>(22:04) Leonard shared practical tips to learn technical concepts.</li><li>(23:23) Leonard reflected on his experience working with a string of startups including <a href="http://www.r1rcm.com/" rel="nofollow" target="_blank">Accretive Health</a>, <a href="https://quid.com/" rel="nofollow" target="_blank">Quid</a>, and <a href="https://www.stridehealth.com/" rel="nofollow" target="_blank">Stride Health</a>.</li><li>(26:06) Leonard is the founding team member of <a href="https://primer.ai/" rel="nofollow" target="_blank">Primer AI</a>, a startup that applies state-of-the-art NLP techniques to build machines that read and write, back in early 2015.</li><li>(30:31) Leonard discussed the technical challenges to develop algorithms that power Primer’s products to scale across languages other than English.</li><li>(34:28) Leonard unpacked his technical post "<a href="https://primer.ai/blog/Russian-Natural-Language-Processing/" rel="nofollow" target="_blank">Russian NLP</a>” on Primer’s blog.</li><li>(38:17) Leonard talked about the advances in the NLP research domain that he is most excited about in 2020 (XLNet &gt;&gt;&gt; BERT).</li><li>(41:10) Leonard discussed the challenges of scaling the data-driven culture across Primer AI as the company grows.</li><li>(46:20) Leonard mentioned different use cases of Primer for clients in finance, government, and corporate.</li><li>(51:41) Leonard talked about his decision to leave Primer and become a Data Science Health Innovation Fellow at the <a href="https://bids.berkeley.edu/people/leonard-apeltsin" rel="nofollow" target="_blank">Berkeley Institute for Data Science</a>.</li><li>(54:30) Leonard went over applications of data science in healthcare that will be adopted widely in the next few years.</li><li>(1:02:45) Leonard discussed his process of writing a book called “<a href="https://www.manning.com/books/data-science-bookcamp" rel="nofollow" target="_blank">Data Science Bookcamp</a>.”</li><li>(1:07:21) Leonard revealed how he chose the case studies to be included in the book.</li><li>(1:10:27) Closing segment.</li></ul>His Contact Info:<ul><li><a href="https://www.linkedin.com/in/leonard-apeltsin-baaa732/" rel="nofollow" target="_blank">LinkedIn</a></li><li><a href="https://scholar.google.com/citations?user=gJyyaZsAAAAJ&amp;hl=en" rel="nofollow" target="_blank">Google Scholar</a></li><li><a href="https://bids.berkeley.edu/people/leonard-apeltsin" rel="nofollow" target="_blank">Berkeley Institute For Data Science</a></li></ul>His Recommended Resources:<ul><li><a href="http://www.cs.cmu.edu/~10701/slides/17_SSL.pdf" rel="nofollow" target="_blank">Semi-Supervised Learning</a></li><li><a href="https://en.wikipedia.org/wiki/Association_rule_learning" rel="nofollow" target="_blank">Association Rule Learning</a></li><li><a href="https://spacy.io/" rel="nofollow" target="_blank">spaCy</a> (Open-Source Library for Advanced NLP)</li><li><a href="https://research.fb.com/fasttext/" rel="nofollow" target="_blank">fastText</a>  (NLP library from Facebook)</li><li><a href="https://github.com/zihangdai/xlnet" rel="nofollow" target="_blank">XLNet: Generalized Autoregressive Pretraining for Language Understanding</a></li><li><a href="https://arxiv.org/abs/1810.04805" rel="nofollow" target="_blank">BERT: Pretraining of Deep Bidirectional Transformers for Language Understanding</a></li><li><a href="https://arxiv.org/abs/1911.00222" rel="nofollow" target="_blank">Federated Learning with Differential Privacy: Algorithms and Performance Analysis</a></li><li><a href="https://arxiv.org/abs/1910.02578" rel="nofollow" target="_blank">Differential Privacy- Enabled Federated Learning for Sensitive Health Data</a></li><li><a href="https://www.oasislabs.com/" rel="nofollow" target="_blank">Oasis Labs</a> and <a href="https://www.oasislabs.com/dawn-song" rel="nofollow" target="_blank">Dr. Dawn Song</a></li><li><a href="https://www.fitbit.com/" rel="nofollow" target="_blank">Fitbit</a> and <a href="https://www.apple.com/watch/" rel="nofollow" target="_blank">Apple Watch</a></li><li><a href="https://en.wikipedia.org/wiki/Walter_Pitts" rel="nofollow" target="_blank">Walter Pitts</a> who invented neural networks</li><li><a href="https://en.wikipedia.org/wiki/Paul_Werbos" rel="nofollow" target="_blank">Paul Werbos</a> who invented back-propagation</li><li><a href="https://en.wikipedia.org/wiki/Fei-Fei_Li" rel="nofollow" target="_blank">Fei-Fei Li</a> who constructed the ImageNet dataset</li><li>“<a href="https://www.amazon.com/Signal-Noise-Many-Predictions-Fail-but/dp/0143125087" rel="nofollow" target="_blank">The Signal and The Noise</a>” by Nate Silver</li></ul>You can read the completed chapters of "<a href="https://www.manning.com/books/data-science-bookcamp?a_aid=khanhnamle1994&amp;a_bid=ef120387" rel="nofollow" target="_blank">Data Science Bookcamp</a>" using the codes below:<ul><li>Permanent discount code: poddcast19</li><li>5 free eBook codes: dcdsprf-B373, dcdsprf-CA3B, dcdsprf-299E, dcdsprf-6E5, and dcdsprf-9660 (activated and will last for 2 months)</li></ul>

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