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Ravi Krishna joins us today to talk about his recent work on a differentiable NAS framework for ads CTR prediction. He discussed what CTR prediction is about and why his NAS framework helps in building neural networks for better ads recommendation. Listen to learn about methodology, related literature and his results. Click for additional show notes Thanks to our sponsor: https://astrato.io Astrato is a modern BI and analytics platform built for the Snowflake Data Cloud. A next-generation live query data visualization and analytics solution, empowering everyone to make live data decisions.
Effectively managing a large budget of pay per click advertising demands software solutions. When spending multi-million dollar budgets on hundreds of thousands of keywords, an effective algorithmic strategy is required to optimize marketing objectives. In this episode, Nathan Janos joins us to share insights from his work in the ad tech industry. Click for additional show notes Thanks to our sponsor! https://wandb.com/ The developer-first MLOps platform. Build better models faster with experiment tracking, dataset versioning, and model management.
Data Skeptic: Ad Tech

Data Skeptic: Ad Tech

2022-06-1842:241

Increasingly, people get most if not all of the information they consume online. Alongside the web sites, videos, apps, and other destinations, we’re consistently served advertisements alongside the organic content we search for or discover. Targetted ads make it possible for you to discover relevant new products you might otherwise not have heard about. Targetting can also open a pandora’s box of ethical considerations. Online advertising is a complex network of automated systems. Algorithms controlling algorithms controlling what we see. This season of Data Skeptic will focus on the applications of data science to digital advertising technology. In this first episode in particular, Kyle shares some of his own personal experiences and insights working in pay-per-click marketing. Click for additional show notes    
Our mobile phones generate an incredible amount of data inbound and outbound. In today’s episode, Nishant Kishore, a PhD graduate of Harvard University in Infectious Disease Epidemiology, explains how mobility data from mobile phones can be captured and analysed to understand the spread of infectious diseases. Click here for additional show notes Thanks to our sponsor! https://neptune.ai/ Log, store, query, display, organize, and compare all your model metadata in a single place
Haywire Algorithms

Haywire Algorithms

2022-06-0633:331

The pandemic changed how we lived. And this had a ripple effect on the performance of machine learning models. Ravi Parikh joins us today to discuss how the pandemic has affected the performance of machine learning models in clinical care and some actionable steps to fix it. Click here for additional show notes Thanks to our sponsor: Astera Centerprise is a no-code data integration platform that allows users to build ETL/ELT pipelines for modern data warehousing and analytics.
Carly Lupton-Smith joins us today to speak about her research which investigated the consistency between household and county measures of school reopening. Carly is a doctoral researcher in Biostatistics at Johns Hopkins Bloomberg School of Public Health. Listen to know about her findings. Click here for additional show notes on our website! Thanks to our sponsor!ClearML is an open-source MLOps solution users love to customize, helping you easily Track, Orchestrate, and Automate ML workflows at scale. Astera Centerprise is a no-code data integration platform that allows users to build ETL/ELT pipelines for modern data warehousing and analytics.  
Modern Data Stacks

Modern Data Stacks

2022-05-2634:331

Today, we are joined by Alexander Thor, a Product Manager at Vizlib, makers of Astrato. Astrato is a data analytics and business intelligence tool built on the cloud and for the cloud. Alexander discusses the features and capabilities of Astrato for data professionals. Visit our website for additional show notes!  
Emoji as a Predictor

Emoji as a Predictor

2022-05-2321:25

Emojis are arguably one of the most effective ways to express emotions when texting. In today’s episode, Xuan Lu shares her research on the use of emojis by developers. She explains how the study of emojis can track the emotions of remote workers and predict future behavior. Listen to find out more!
On the show today, Fabian Braesemann, a research fellow at the University of Oxford, joins us to discuss his study analyzing the gig economy. He revealed the trends he discovered since remote work became mainstream, the factors causing spatial polarization and some downsides of the gig economy. Listen to learn what he found.
On the show today, we interview Mouhamed Abdulla, a professor of Electrical Engineering at Sheridan Institute of Technology. Mouhamed joins us to discuss his study on remote teaching and learning in applied engineering. He discusses how he embraced the new approach after the pandemic, the challenges he faced and how he tackled them. Listen to find out more. Click here for additional show notes on our website! Thanks to our sponsor! https://neptune.ai/ Log, store, query, display, organize, and compare all your model metadata in a single place  
Remote Productivity

Remote Productivity

2022-05-0929:47

It is difficult to estimate the effect on remote working across the board. Darja Šmite, who speaks with us today, is a professor of Software Engineering at the Blekinge Institute of Technology. In her recently published paper, she analyzed data on several companies' activities before and after remote working became prevalent. She discussed the results found, why they were and some subtle drawbacks of remote working. Check it out!   Click here for additional show notes on our website!
We explore this complex question in two interviews today.  First, Kasey Wagoner describes 3 approaches to remote lab sessions and an analysis of which was the most instrumental to students.  Second, Tahiya Chowdhury shares insights about the specific features of video-conferencing platforms that are lacking in comparison to in-person learning. Click here for additional show notes on our website! Thanks to our sponsor!ClearML is an open-source MLOps solution users love to customize, helping you easily Track, Orchestrate, and Automate ML workflows at scale.  
In this episode, we speak with Abdullah Kurkcu, a Lead Traffic Modeler. Abdullah joins us to discuss his recent study on the effect of COVID-19 on bicycle usage in the US. He walks us through the data gathering process, data preprocessing, feature engineering, and model building. Abdullah also disclosed his results and key takeaways from the study. Listen to find out more.  Click here for additional show notes on our website. Thanks to our sponsor!Astrato is a modern BI and analytics platform built for the Snowflake  Data Cloud. A next-generation live query data visualization and analytics solution, empowering everyone to make live data decisions.    
Today, we are joined by Jennifer Jacobs and Nadya Peek, who discuss their experience in teaching remote classes for a course that is largely hands-on. The discussion was focused on digital fabrication, why it is important, the prospect for the future, the challenges with remote lectures, and everything in between. Click here for additional show notes on our website! Thanks to our sponsor! https://neptune.ai/ Log, store, query, display, organize, and compare all your model metadata in a single place
Today, we are joined by Denae Ford, a Senior Researcher at Microsoft Research and an Affiliate Assistant Professor at the University of Washington. Denae discusses her work around remote work and its culminating impact on workers. She narrowed down her research to how COVID-19 has affected the working system of software engineers and the emerging challenges it brings.     Click here to access additional show notes on our website!   Thanks to our sponsor!  Weights & Biases : The developer-first MLOps platform. Build better models faster with experiment tracking, dataset versioning, and model management.  
Quantum K-Means

Quantum K-Means

2022-04-1139:521

In this episode, we interview Jonas Landman, a Postdoc candidate at the University of Edinburg. Jonas discusses his study around quantum learning where he attempted to recreate the conventional k-means clustering algorithm and spectral clustering algorithm using quantum computing.  Click here to access additional show notes on our website!
K-Means in Practice

K-Means in Practice

2022-04-0430:55

K-means is widely used in real-life business problems. In this episode, Mujtaba Anwer, a researcher and Data Scientist walks us through some use cases of k-means. He also spoke extensively on how to prepare your data for clustering, find the best number of clusters to use, and turn the ‘abstract’ result into real business value. Listen to learn.  Click here to access additional show notes on our website! Thanks to our sponsor! ClearML is an open-source MLOps solution users love to customize, helping you easily Track, Orchestrate, and Automate ML workflows at scale.
Building a fair machine learning model has become a critical consideration in today’s world. In this episode, we speak with Anshuman Chabra, a Ph.D. candidate in Computer Networks. Chhabra joins us to discuss his research on building fair machine learning models and why it is important. Find out how he modeled the problem and the result found. Click here to access additional show notes on our webiste! Thanks to our sponsor! https://astrato.io Astrato is a modern BI and analytics platform built for the Snowflake Data Cloud. A next-generation live query data visualization and analytics solution, empowering everyone to make live data decisions.
Many people know K-means clustering as a powerful clustering technique but not all listeners will be as familiar with spectral clustering. In today’s episode, Sibylle Hess from the Data Mining group at TU Eindhoven joins us to discuss her work around spectral clustering and how its result could potentially cause a massive shift from the conventional neural networks. Listen to learn about her findings. Visit our website for additional show notes Thanks to our sponsor, Weights & Biases
Breathing K-Means

Breathing K-Means

2022-03-1442:55

In this episode, we speak with Bernd Fritzke, a proficient financial expert and a Data Science researcher on his recent research - the breathing K-means algorithm. Bernd discussed the perks of the algorithms and what makes it stand out from other K-means variations. He extensively discussed the working principle of the algorithm and the subtle but impactful features that enables it produce top-notch results with low computational resources. Listen to learn about this algorithm.
Comments (19)

Vassili Savinov

great episode, cant wait to hear next one. Thanks!

Jun 28th
Reply

DemonDogs

Description?

Jan 23rd
Reply

ncooty

@6:00: The threshold for statistical significance does not "depend on the outcome." It raises a red flag even to hear someone say that, especially the host of a "data science" podcast. (Of course, if he knew what he was talking about, he'd be a "statistician" instead.) He might more accurately have said that any such estimate of the minimum sample size depends on the number of planned comparisons and the assumed effect size for each measured effect. Confusion about this should disqualify someone from hosting such a podcast.

Aug 23rd
Reply

ncooty

@2:19: Too much interpretation as if respondents were randomly sampled. Respondents self-selected.

Aug 23rd
Reply

Antonio Andrade

thanks so much for sharing the results

Aug 12th
Reply

ncooty

@1:03: It doesn't "beg the question"; it "raises the question." To "beg the question" is to commit a logical fallacy in which one assumes the conclusion.

Jun 15th
Reply

Benjamin Weckerle

Is the spin-off / journal club podcast on castbox?

Jun 2nd
Reply

Platte Gruber

KILLER intro, awesome work!

Jan 8th
Reply (1)

Marco Gorelli

"I find it stunning that people don't do that. The only thing I can think of is that there's just the lack of focused time. There's so many things we could spend our time on now we spend a little on all of them and we don't have depth that we need. A lot of people will come to a conference or something like that just to be away from work and only focus on one thing. Unfortunately they also bring their phone and completely break that paradigm. " ouch

Dec 8th
Reply

Bhavul Gauri

Brilliantly put!

Aug 27th
Reply

Akshay Shirsath

Thoughtful episode.

Jun 8th
Reply (1)

Achint Verma

A very very high level introduction to Kalman Filters. You could have talked about the matrices.

Mar 29th
Reply

Troy Kirin

Golden, thanks for this!

Mar 19th
Reply

Vannucci Santos

Why the guy talking about ethics was so evasive?

Dec 25th
Reply

Anna Malahova

I love everything about this podcast channel! It is easy to listen to, easy to understand without data science background, interesting topics and examples of situations where to apply. Very enjoyable and entertaining delivery. Informative show notes that help you to recall what the episode was about even after a while. Really can't think about any downsides. I am listening to all episodes starting from early days like an audiobook, love how music into evolved over time.

Nov 24th
Reply

Abdul Wahab Abrar

What about using Deep Learning techniques directly and integrate it with Neuroscience

Feb 12th
Reply

Giancarlo Vercellino

rilevante dal 23esimo minuto

Dec 12th
Reply
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