Data Skeptic

By Kyle Polich

Technology

What's Data Skeptic about?

The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.

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LAST EPISODE

Black Friday

11.26.2021

The retail holiday “black Friday” occurs the day after Thanksgiving in the United States. It’s dubbed this because many retail companies spend the first 10 months of the year running at a loss (in the red) before finally earning as ...

Data Skeptic episodes:

11.26.2021 Black Friday The retail holiday “black Friday” occurs the day after Thanksgiving in the United States. It’s dubbed this because many retail companies spend the first 10 months of the year running at a loss (in the red) before finally earning as ...
11.22.2021 Aligning Time Series on Incomparable Spaces Alex Terenin, Postdoctoral Research Associate at the University of Cambridge, joins us today to talk about his work "Aligning Time Series on Incomparable Spaces."
11.15.2021 Comparing Time Series with HCTSA Today we are joined again by Ben Fulcher, leader of the Dynamics and Neural Systems Group at the University of Sydney in Australia, to talk about hctsa, a software package for running highly comparative time-series analysis.
11.08.2021 Change Point Detection Algorithms Gerrit van den Burg, Postdoctoral Researcher at The Alan Turing Institute, joins us today to discuss his work "An Evaluation of Change Point Detection Algorithms."
11.01.2021 Time Series for Good Bahman Rostami-Tabar, Senior Lecturer in Management Science at Cardiff University, joins us today to talk about his work "Forecasting and its Beneficiaries."
10.25.2021 Long Term Time Series Forecasting Alex Mallen, Computer Science student at the University of Washington, and Henning Lange, a Postdoctoral Scholar in Applied Math at the University of Washington, join us today to share their work "Deep Probabilistic Koopman: Long-term Time-Series Forecasting Under Periodic Uncertainties."
10.17.2021 Fast and Frugal Time Series Forecasting Fotios Petropoulos, Professor of Management Science at the University of Bath in The U.K., joins us today to talk about his work "Fast and Frugal Time Series Forecasting."
10.11.2021 Causal Inference in Educational Systems Manie Tadayon, a PhD graduate from the ECE department at University of California, Los Angeles, joins us today to talk about his work “Comparative Analysis of the Hidden Markov Model and LSTM: A Simulative Approach.”
10.04.2021 Boosted Embeddings for Time Series Sankeerth Rao Karingula, ML Researcher at Palo Alto Networks, joins us today to talk about his work “Boosted Embeddings for Time Series Forecasting.” Works Mentioned Boosted Embeddings for Time Series Forecasting by Sankeerth Rao Karingula, Nandini Ramanan, Rasool Tahmasbi, Mehrnaz ...
09.27.2021 Change Point Detection in Continuous Integration Systems David Daly, Performance Engineer at MongoDB, joins us today to discuss "The Use of Change Point Detection to Identify Software Performance Regressions in a Continuous Integration System". Works Mentioned The Use of Change Point Detection to Identify Software Performance Regressions ...
09.20.2021 Applying k-Nearest Neighbors to Time Series Samya Tajmouati, a PhD student in Data Science at the University of Science of Kenitra, Morocco, joins us today to discuss her work Applying K-Nearest Neighbors to Time Series Forecasting: Two New Approaches.
09.13.2021 Ultra Long Time Series Dr. Feng Li, (@f3ngli) is an Associate Professor of Statistics in the School of Statistics and Mathematics at Central University of Finance and Economics in Beijing, China. He joins us today to discuss his work Distributed ARIMA Models for Ultra-long ...
09.06.2021 MiniRocket Angus Dempster, PhD Student at Monash University in Australia, comes on today to talk about MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification, a fast deterministic transform for time series classification. MINIROCKET reformulates ROCKET, gaining a 75x ...
08.30.2021 ARiMA is not Sufficient Chongshou Li, Associate Professor at Southwest Jiaotong University in China, joins us today to talk about his work Why are the ARIMA and SARIMA not Sufficient.
08.23.2021 Comp Engine Ben Fulcher, Senior Lecturer at the School of Physics at the University of Sydney in Australia, comes on today to talk about his project Comp Engine. Follow Ben on Twitter: @bendfulcher For posts about time series analysis : @comptimeseries comp-engine.org
08.16.2021 Detecting Ransomware Nitin Pundir, PhD candidate at University Florida and works at the Florida Institute for Cybersecurity Research, comes on today to talk about his work “RanStop: A Hardware-assisted Runtime Crypto-Ransomware Detection Technique.” FICS Research Lab - https://fics.institute.ufl.edu/ LinkedIn - https://www.linkedin.com/in/nitin-pundir470/
08.09.2021 GANs in Finance Florian Eckerli, a recent graduate of Zurich University of Applied Sciences, comes on the show today to discuss his work Generative Adversarial Networks in Finance: An Overview.
08.02.2021 Predicting Urban Land Use Today on the show we have Daniel Omeiza, a doctoral student in the computer science department of the University of Oxford, who joins us to talk about his work Efficient Machine Learning for Large-Scale Urban Land-Use Forecasting in Sub-Saharan Africa.
07.26.2021 Opportunities for Skillful Weather Prediction Today on the show we have Elizabeth Barnes, Associate Professor in the department of Atmospheric Science at Colorado State University, who joins us to talk about her work Identifying Opportunities for Skillful Weather Prediction with Interpretable Neural Networks. Find more ...
07.19.2021 Predicting Stock Prices Today on the show we have Andrea Fronzetti Colladon (@iandreafc), currently working at the University of Perugia and inventor of the Semantic Brand Score, joins us to talk about his work studying human communication and social interaction. We discuss the ...
07.12.2021 N-Beats Today on the show we have Boris Oreshkin @boreshkin, a Senior Research Scientist at Unity Technologies, who joins us today to talk about his work N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting. Works Mentioned: N-BEATS: Neural Basis ...
07.06.2021 Translation Automation Today we are back with another episode discussing AI in the work field. AI has, is, and will continue to facilitate the automation of work done by humans. Sometimes this may be an entire role. Other times it may automate ...
06.28.2021 Time Series at the Beach Shane Ross, Professor of Aerospace and Ocean Engineering at Virginia Tech University, comes on today to talk about his work “Beach-level 24-hour forecasts of Florida red tide-induced respiratory irritation.”
06.21.2021 Automatic Identification of Outlier Galaxy Images Lior Shamir, Associate Professor of Computer Science at Kansas University, joins us today to talk about the recent paper Automatic Identification of Outliers in Hubble Space Telescope Galaxy Images. Follow Lio on Twitter @shamir_lior
06.16.2021 Do We Need Deep Learning in Time Series Shereen Elsayed and Daniela Thyssens, both are PhD Student at Hildesheim University in Germany, come on today to talk about the work “Do We Really Need Deep Learning Models for Time Series Forecasting?”
06.11.2021 Detecting Drift Sam Ackerman, Research Data Scientist at IBM Research Labs in Haifa, Israel, joins us today to talk about his work Detection of Data Drift and Outliers Affecting Machine Learning Model Performance Over Time. Check out Sam's IBM statistics/ML blog at: ...
05.31.2021 Darts Library for Time Series Julien Herzen, PhD graduate from EPFL in Switzerland, comes on today to talk about his work with Unit 8 and the development of the Python Library: Darts.
05.24.2021 Forecasting Principles and Practice Welcome to Timeseries! Today’s episode is an interview with Rob Hyndman, Professor of Statistics at Monash University in Australia, and author of Forecasting: Principles and Practices.
05.21.2021 Prequisites for Time Series Today's experimental episode uses sound to describe some basic ideas from time series. This episode includes lag, seasonality, trend, noise, heteroskedasticity, decomposition, smoothing, feature engineering, and deep learning.
05.07.2021 Orders of Magnitude Today’s show in two parts. First, Linhda joins us to review the episodes from Data Skeptic: Pilot Season and give her feedback on each of the topics. Second, we introduce our new segment “Orders of Magnitude”. It’s a statistical game ...
05.03.2021 They're Coming for Our Jobs AI has, is, and will continue to facilitate the automation of work done by humans. Sometimes this may be an entire role. Other times it may automate a particular part of their role, scaling their effectiveness. Unless progress in AI ...
04.26.2021 Pandemic Machine Learning Pitfalls Today on the show Derek Driggs, a PhD Student at the University of Cambridge. He comes on to discuss the work Common Pitfalls and Recommendations for Using Machine Learning to Detect and Prognosticate for COVID-19 Using Chest Radiographs and CT ...
04.19.2021 Flesch Kincaid Readability Tests Given a document in English, how can you estimate the ease with which someone will find they can read it? Does it require a college-level of reading comprehension or is it something a much younger student could read and understand? ...
04.09.2021 Fairness Aware Outlier Detection Today on the show we have Shubhranshu Shekar, a Ph. D Student at Carnegie Mellon University, who joins us to talk about his work, FAIROD: Fairness-aware Outlier Detection.
04.05.2021 Life May be Rare Today on the show Dr. Anders Sandburg, Senior Research Fellow at the Future of Humanity Institute at Oxford University, comes on to share his work “The Timing of Evolutionary Transitions Suggest Intelligent Life is Rare.” Works Mentioned: Paper: “The Timing ...
03.29.2021 Social Networks Mayank Kejriwal, Research Professor at the University of Southern California and Researcher at the Information Sciences Institute, joins us today to discuss his work and his new book Knowledge, Graphs, Fundamentals, Techniques and Applications by Mayank Kejriwal, Craig A. Knoblock, ...
03.22.2021 The QAnon Conspiracy QAnon is a conspiracy theory born in the underbelly of the internet. While easy to disprove, these cryptic ideas captured the minds of many people and (in part) paved the way to the 2021 storming of the US Capital. This ...
03.15.2021 Benchmarking Vision on Edge vs Cloud Karthick Shankar, Masters Student at Carnegie Mellon University, and Somali Chaterji, Assistant Professor at Purdue University, join us today to discuss the paper "JANUS: Benchmarking Commercial and Open-Source Cloud and Edge Platforms for Object and Anomaly Detection Workloads" Works Mentioned: ...
03.05.2021 Goodhart's Law in Reinforcement Learning Hal Ashton, a PhD student from the University College of London, joins us today to discuss a recent work Causal Campbell-Goodhart’s law and Reinforcement Learning. "Only buy honey from a local producer." - Hal Ashton Works Mentioned: “Causal Campbell-Goodhart’s law ...
03.01.2021 Video Anomaly Detection Yuqi Ouyang, in his second year of PhD study at the University of Warwick in England, joins us today to discuss his work “Video Anomaly Detection by Estimating Likelihood of Representations.”Works Mentioned: Video Anomaly Detection by Estimating Likelihood of Representations ...
02.22.2021 Fault Tolerant Distributed Gradient Descent Nirupam Gupta, a Computer Science Post Doctoral Researcher at EDFL University in Switzerland, joins us today to discuss his work “Byzantine Fault-Tolerance in Peer-to-Peer Distributed Gradient-Descent.” Works Mentioned: https://arxiv.org/abs/2101.12316 Byzantine Fault-Tolerance in Peer-to-Peer Distributed Gradient-Descent by Nirupam Gupta and Nitin ...
02.15.2021 Decentralized Information Gathering Mikko Lauri, Post Doctoral researcher at the University of Hamburg, Germany, comes on the show today to discuss the work Information Gathering in Decentralized POMDPs by Policy Graph Improvements. Follow Mikko: @mikko_lauri Github https://laurimi.github.io/
02.05.2021 Leaderless Consensus Balaji Arun, a PhD Student in the Systems of Software Research Group at Virginia Tech, joins us today to discuss his research of distributed systems through the paper “Taming the Contention in Consensus-based Distributed Systems.” Works Mentioned “Taming the Contention ...
01.29.2021 Automatic Summarization Maartje ter Hoeve, PhD Student at the University of Amsterdam, joins us today to discuss her research in automated summarization through the paper “What Makes a Good Summary? Reconsidering the Focus of Automatic Summarization.” Works Mentioned “What Makes a Good ...
01.22.2021 Gerrymandering Brian Brubach, Assistant Professor in the Computer Science Department at Wellesley College, joins us today to discuss his work “Meddling Metrics: the Effects of Measuring and Constraining Partisan Gerrymandering on Voter Incentives". WORKS MENTIONED: Meddling Metrics: the Effects of Measuring ...
01.15.2021 Even Cooperative Chess is Hard Aside from victory questions like “can black force a checkmate on white in 5 moves?” many novel questions can be asked about a game of chess. Some questions are trivial (e.g. “How many pieces does white have?") while more computationally ...
01.11.2021 Consecutive Votes in Paxos Eil Goldweber, a graduate student at the University of Michigan, comes on today to share his work in applying formal verification to systems and a modification to the Paxos protocol discussed in the paper Significance on Consecutive Ballots in Paxos. ...
01.01.2021 Visual Illusions Deceiving Neural Networks Today on the show we have Adrian Martin, a Post-doctoral researcher from the University of Pompeu Fabra in Barcelona, Spain. He comes on the show today to discuss his research from the paper “Convolutional Neural Networks can be Deceived by ...
12.25.2020 Earthquake Detection with Crowd-sourced Data Have you ever wanted to hear what an earthquake sounds like? Today on the show we have Omkar Ranadive, Computer Science Masters student at NorthWestern University, who collaborates with Suzan van der Lee, an Earth and Planetary Sciences professor at ...
12.22.2020 Byzantine Fault Tolerant Consensus Byzantine fault tolerance (BFT) is a desirable property in a distributed computing environment. BFT means the system can survive the loss of nodes and nodes becoming unreliable. There are many different protocols for achieving BFT, though not all options can ...

Older episodes

Data Skeptic

What's Data Skeptic about?

Download our free app to listen on your phone

Data Skeptic episodes:

Black Friday

Aligning Time Series on Incomparable Spaces

Comparing Time Series with HCTSA

Change Point Detection Algorithms

Time Series for Good

Long Term Time Series Forecasting

Fast and Frugal Time Series Forecasting

Causal Inference in Educational Systems

Boosted Embeddings for Time Series

Change Point Detection in Continuous Integration Systems

Applying k-Nearest Neighbors to Time Series

Ultra Long Time Series

MiniRocket

ARiMA is not Sufficient

Comp Engine

Detecting Ransomware

GANs in Finance

Predicting Urban Land Use

Opportunities for Skillful Weather Prediction

Predicting Stock Prices

N-Beats

Translation Automation

Time Series at the Beach

Automatic Identification of Outlier Galaxy Images

Do We Need Deep Learning in Time Series

Detecting Drift

Darts Library for Time Series

Forecasting Principles and Practice

Prequisites for Time Series

Orders of Magnitude

They're Coming for Our Jobs

Pandemic Machine Learning Pitfalls

Flesch Kincaid Readability Tests

Fairness Aware Outlier Detection

Life May be Rare

Social Networks

The QAnon Conspiracy

Benchmarking Vision on Edge vs Cloud

Goodhart's Law in Reinforcement Learning

Video Anomaly Detection

Fault Tolerant Distributed Gradient Descent

Decentralized Information Gathering

Leaderless Consensus

Automatic Summarization

Gerrymandering

Even Cooperative Chess is Hard

Consecutive Votes in Paxos

Visual Illusions Deceiving Neural Networks

Earthquake Detection with Crowd-sourced Data

Byzantine Fault Tolerant Consensus

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