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."
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.?
Sankeerth Rao Karingula, ML Researcher at Palo Alto Networks, joins us today to talk about his work ?Boosted Embeddings for Time Series Forecasting.?
Boosted Embeddings for Time Series Forecasting
by Sankeerth Rao Karingula, Nandini Ramanan, Rasool Tahmasbi, Mehrnaz Amjadi, Deokwoo Jung, Ricky Si, Charanraj Thimmisetty, Luisa Polania Cabrera, Marjorie Sayer, Claudionor Nunes Coelho Jr
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".
The Use of Change Point Detection to Identify Software Performance Regressions in a Continuous Integration System
by David Daly, William Brown, Henrik Ingo, Jim O?Leary, David BradfordSocial Media
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.
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 Time Series.
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 improvement on larger datasets with essentially the same performance. In this episode, we talk about the insights that realized this speedup as well as use cases.
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.
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
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/
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.
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.
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 from the Barnes Research Group on their site.
Weather is notoriously difficult to predict. Complex systems are demanding of computational power. Further, the chaotic nature of, well, nature, makes accurate forecasting especially difficult the longer into the future one wants to look. Yet all is not lost!
In this interview, we explore the use of machine learning to help identify certain conditions under which the weather system has entered an unusually predictable position in it?s normally chaotic state space.
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 paper Look inside. Predicting Stock Prices by Analyzing an Enterprise Intranet Social Network and Using Word Co-Occurrence Networks.
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.
N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting
By Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio
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 a particular part of their role, scaling their effectiveness.
Carl Stimson, a Freelance Japanese to English translator, comes on the show to talk about his work in translation and his perspective about how AI will change translation in the future.
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.?
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
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??
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: http://www.research.ibm.com/haifa/dept/vst/ML-QA.shtml
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.
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.
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.
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 show in which participants must identify the true statistic hidden in a list of statistics which are off by at least an order of magnitude. Claudia and Vanessa join as our first contestants. Below are the sources of our questions.
Heightshttps://en.wikipedia.org/wiki/Willis_Tower https://en.wikipedia.org/wiki/Eiffel_Tower https://en.wikipedia.org/wiki/GreatPyramidof_Giza https://en.wikipedia.org/wiki/InternationalSpaceStation
Bird StatisticsBirds in the US since 2000 Causes of Bird Mortality
Amounts of Data
Our statistics come from this post
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 inexplicably halts, the tasks done by humans vs. machines will continue to evolve. Today?s episode is a speculative conversation about what the future may hold.
Co-Host of Squaring the Strange Podcast, Caricature Artist, and an Academic Editor, Celestia Ward joins us today! Kyle and Celestia discuss whether or not her jobs as a caricature artist or as an academic editor are under threat from AI automation.Mentions https://squaringthestrange.wordpress.com/ https://twitter.com/celestiaward The legendary Dr. Jorge Pérez and his work studying unicorns Supernormal stimulus International Society of Caricature Artists Two Heads Studios
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 Scans.
Help us vote for the next theme of Data Skeptic!
Vote here: https://dataskeptic.com/vote
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?
While these questions are useful to ask, they don't admit a simple answer. One option is to use one of the (essentially identical) two Flesch Kincaid Readability Tests. These are simple calculations which provide you with a rough estimate of the reading ease.
In this episode, Kyle shares his thoughts on this tool and when it could be appropriate to use as part of your feature engineering pipeline towards a machine learning objective.
For empirical validation of these metrics, the plot below compares English language Wikipedia pages with "Simple English" Wikipedia pages. The analysis Kyle describes in this episode yields the intuitively pleasing histogram below. It summarizes the distribution of Flesch reading ease scores for 1000 pages examined from both Wikipedias.
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.
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.?
?The Timing of Evolutionary Transitions Suggest Intelligent Life is Rare.?by Andrew E Snyder-Beattie, Anders Sandberg, K Eric Drexler, Michael B Bonsall
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, and Pedro Szekley.
?Knowledge, Graphs, Fundamentals, Techniques and Applications?by Mayank Kejriwal, Craig A. Knoblock, and Pedro Szekley
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 is a contemporary conspiracy which came into existence and grew in a very digital way. This makes it possible for researchers to study this phenomenon in a way not accessible in previous conspiracy theories of similar popularity.
This episode is not so much a debunking of this debunked theory, but rather an exploration of the metadata and origins of this conspiracy.
This episode is also the first in our 2021 Pilot Season in which we are going to test out a few formats for Data Skeptic to see what our next season should be. This is the first installment. In a few weeks, we're going to ask everyone to vote for their favorite theme for our next season.
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"
?JANUS: Benchmarking Commercial and Open-Source Cloud and Edge Platforms for Object and Anomaly Detection Workloads.?
by: Karthick Shankar, Pengcheng Wang, Ran Xu, Ashraf Mahgoub, Somali ChaterjiSocial Media
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
?Causal Campbell-Goodhart?s law and Reinforcement Learning?by Hal AshtonBook
?The Book of Why?by Judea PearlPaper
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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
by: Yuqi Ouyang, Victor Sanchez
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.?
Byzantine Fault-Tolerance in Peer-to-Peer Distributed Gradient-Descent
by Nirupam Gupta and Nitin H. Vaidya
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.
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.?
?Taming the Contention in Consensus-based Distributed Systems?
by Balaji Arun, Sebastiano Peluso, Roberto Palmieri, Giuliano Losa, and Binoy Ravindran
by Leslie Lamport
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.?
?What Makes a Good Summary? Reconsidering the Focus of Automatic Summarization.?
by Maartje der Hoeve, Juilia Kiseleva, and Maarten de Rijke
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".
Meddling Metrics: the Effects of Measuring and Constraining Partisan Gerrymandering on Voter Incentives
by Brian Brubach, Aravind Srinivasan, and Shawn Zhao
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 challenging questions can contribute interesting results in computational complexity theory.
In this episode, Josh Brunner, Master's student in Theoretical Computer Science at MIT, joins us to discuss his recent paper Complexity of Retrograde and Helpmate Chess Problems: Even Cooperative Chess is Hard.
Complexity of Retrograde and Helpmate Chess Problems: Even Cooperative Chess is Hard
by Josh Brunner, Erik D. Demaine, Dylan Hendrickson, and Juilian Wellman
1x1 Rush Hour With Fixed Blocks is PSPACE Complete
by Josh Brunner, Lily Chung, Erik D. Demaine, Dylan Hendrickson, Adam Hesterberg, Adam Suhl, Avi Zeff
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.
Works Mentioned :
Previous Episode on Paxos
On the Significance on Consecutive Ballots in Paxos by: Eli Goldweber, Nuda Zhang, and Manos Kapritsos
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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 Visual Illusions.?
Works Mentioned in Paper:
?Convolutional Neural Networks can be Decieved by Visual Illusions.? by Alexander Gomez-Villa, Adrian Martin, Javier Vazquez-Corral, and Marcelo Bertalmio
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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 Northwestern University, on the crowd-sourcing project Earthquake Detective.
Paper: Applying Machine Learning to Crowd-sourced Data from Earthquake Detective
by Omkar Ranadive, Suzan van der Lee, Vivan Tang, and Kevin Chao
Earthquake Detective: https://www.zooniverse.org/projects/vivitang/earthquake-detective
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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 scale to large network sizes.
Kyle shared some initial reactions to the announcement about Alpha Fold 2's celebrated performance in the CASP14 prediction. By many accounts, this exciting result means protein folding is now a solved problem.
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Above all, everyone wants voting to be fair. What does fair mean and how can we measure it? Kenneth Arrow posited a simple set of conditions that one would certainly desire in a voting system. For example, unanimity - if everyone picks candidate A, then A should win!
Yet surprisingly, under a few basic assumptions, this theorem demonstrates that no voting system exists which can satisfy all the criteria.
This episode is a discussion about the structure of the proof and some of its implications.
Works MentionedA Difficulty in the Concept of Social Welfare by Kenneth J. Arrow Three Brief Proofs of Arrows Impossibility Theorem by John Geanakoplos Thank you to our sponsors! Better Help is much more affordable than traditional offline counseling, and financial aid is available! Get started in less than 24 hours. Data Skeptic listeners get 10% off your first month when you visit: betterhelp.com/dataskeptic Let Springboard School of Data jumpstart your data career! With 100% online and remote schooling, supported by a vast network of professional mentors with a tuition-back guarantee, you can't go wrong. Up to twenty $500 scholarships will be awarded to Data Skeptic listeners. Check them out at springboard.com/dataskeptic and enroll using code: DATASK
As the COVID-19 pandemic continues, the public (or at least those with Twitter accounts) are sharing their personal opinions about mask-wearing via Twitter. What does this data tell us about public opinion? How does it vary by demographic? What, if anything, can make people change their minds?
Today we speak to, Neil Yeung and Jonathan Lai, Undergraduate students in the Department of Computer Science at the University of Rochester, and Professor of Computer Science, Jiebo-Luoto to discuss their recent paper. Face Off: Polarized Public Opinions on Personal Face Mask Usage during the COVID-19 Pandemic.
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Niclas Boehmer, second year PhD student at Berlin Institute of Technology, comes on today to discuss the computational complexity of bribery in elections through the paper ?On the Robustness of Winners: Counting Briberies in Elections.?
?On the Robustness of Winners: Counting Briberies in Elections.? by Niclas Boehmer, Robert Bredereck, Piotr Faliszewski. Rolf Niedermier
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Clement Fung, a Societal Computing PhD student at Carnegie Mellon University, discusses his research in security of machine learning systems and a defense against targeted sybil-based poisoning called FoolsGold.
The Limitations of Federated Learning in Sybil Settings
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WORKS MENTIONED:?Calibrating Noise to Sensitivity in Private Data Analysis? by Cynthia Dwork, Frank McSherry, Kobbi Nissim, Adam Smith "Issues Encountered Deploying Differential Privacy" by Simson L Garfinkel, John M Abowd, and Sarah Powazek "Randomness Concerns When Deploying Differential Privacy" by Simson L. Garfinkel and Philip Leclerc
Check out: https://simson.net/page/Differential_privacy
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