Data & Science with Glen Wright Colopy

Data & Science with Glen Wright Colopy

By Glen Wright ColopyScienceTechnologyMathematics
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Data & Science with Glen Wright Colopy episodes

  • Keith O’Rourke | The Logic of Statistics

    Keith O'Rourke | The Logic of Statistics

    Dr. Keith O'Rourke talks about the logical reasoning behind statistical modeling. Topics include mathematical vs scientific reasoning, whether science has become too stats focused, and vice versa.

    Watch it on...

    Youtube: https://youtu.be/FqE4ROHBKpY
    Podbean: https://dataandsciencepodcast.podbean.com/e/keith-o-rourke-the-logic-of-statistics/

     

    Topic List:

    0:00 - The logic of statistics

    0:30 - What is scientific statistics?
    5:15 - The logic of statistics and CS Pierce
    9:15 - Role of representation in statistics: explicit vs implicit
    14:13 - Diagrammatic Reasoning
    18:45 - Why is modeling counterfactual?
    19:33 - How can statisticians become better scientists?
    28:40 - Science is hard
    31:24 - Computational approaches to learning
    42:00 - Learning through metaphor
    46:28 - Diagrammatic representations vs math
    48:40 - Is science too statistics-focussed? 
    59:35 - Is statistics sufficiently science-focussed? 
    1:08:40 - Scientific Debate

     

    #statistics #datascience #science 

    1 hr 14 min
  • Jack Fitzsimons | Evil Models: Hiding Malware in Neural Networks

    Jack Fitzsimons | Evil Models: Hiding Malware in Neural Networks

    Did you know that it's possible to hide malware in neural networks? Actually, you can hide malware in many statistical models. This is the subject of two recently-published papers (aptly titled "EvilModel" & "EvilModel 2.0"). Dr. Jack Fitzsimons makes it easy to understand how this is done, using techniques that began long before computers.  

     

    Watch or listen on... 

    Youtube: https://youtu.be/QBnk8ogL8Nk
    Podbean: https://dataandsciencepodcast.podbean.com/e/jack-fitzsimons-evil-models-hiding-malware-in-neural-networks/

    52 min
  • Scott Cunningham | Causal Inference (The Mixtape)

    Scott Cunningham | Causal Inference (The Mixtape)

    Scott Cunningham (Baylor University) discusses the ideas of his book "Causal Inference: The Mixtape". Topics include trusting inference in the absence of counterfactuals and the challenges of apply scientific methods to social phenomena. 

    Watch it on...

    YouTube: https://youtu.be/yNaCudDVTkY
    Podbean: https://dataandsciencepodcast.podbean.com/e/scott-cunningham-causal-inference-the-mixtape/

    0:00 - COMING UP...

    0:35 - What makes it into the mixed tape?
    7:10 - Coding to learn
    11:15 - More people are expected to work with data & code
    12:50 - Design vs program vs estimators
    20:40 - Causation with zero correlation
    27:00 - Optimization make everything endogenous
    28:45 - The hospital example
    29:30 - Credible scientific discovery vs motivated discovery
    39:55 - Different meanings of causality
    43:30 - The impossible counterfactual 
    47:00 Counterfactual nihilism
    49:20 Social experiments / Defund the police
    53:35 - Skepticism about the science of social phenomena
    1:05:20 - The Italian crime example
    1:16:30 - Scientific debate

     

    1 hr 21 min
  • Eric Daza | Important Ideas in Causal Inference


    Eric Daza | Important Ideas in Causal Inference

    YouTube: https://youtu.be/K5nsSMJVIT0


    Andrew Gelman and Aki Vehtari wrote a paper titled, "What are the most important statistical ideas of the past 50 years?". The first idea in the list is "counterfactual causal inference". Eric Daza (Evidation Health) walks us through the main ideas of the Gelman & Vehtari paper, drawing examples from several fields, including medical & healthcare statistics. 

    Topics

    0:00 - Coming up...Correlation vs Causation
    1:20 - Most important statistical ideas over the last 50 years
    6:10 - Counterfactual Causal Inference
    9:40 - Assumptions Change between Applied Domains
    21:10 - Propensity Score Methods
    25:15 - Transportability of Scientific Results 
    26:30 - People don't want generalizable results
    32:00 - Generic Computation Algorithms
    37:00 - Reweighting
    43:57 - Matching Methods
    58:20 - Medical Data is Higher Dimensional that we think.
    1:00:15 - Is a Trial Population Representative? 
    1:10:35 - Causal Models in the Future
    1:18:45 - Apostates Welcome
    1:21:45 - Scientific Debate

     

     

    1 hr 24 min
  • Ruda Zhang | Gaussian Process Subspace Regression

    Ruda Zhang | Gaussian Process Subspace Regression

    Ruda Zhang (Duke University) walks us through "Gaussian Process Subspace Regression for Model Reduction" by Zhang, Mak, and Dunson.

    To keep the topic interesting for both the early career & advanced audience we recap key points at a high level so that no one gets lost.

     

    This episode involves a presentation, so you may prefer to watch the YouTube version here: https://youtu.be/IPtqUUG4XcY

     

    Ruda's website: https://ruda.city/

    The paper: https://arxiv.org/abs/2107.04668

    1 hr 10 min
  • Ruda Zhang | Math-Science Duality

    Ruda Zhang | Math-Science Duality

    Watch it on...

    Youtube: https://youtu.be/GoDwen-RGZg
    Podbean: https://dataandsciencepodcast.podbean.com/e/ruda-zhang-math-science-duality/

    Statistics is thought to reside at the interface of science and mathematics. Ruda Zhang (Duke University) discusses the friction at this interface and the role that both mathematical formalism & observational/data-driven intuition play in scientific discovery. A great topic for anyone interested in statistics' role in scientific discovery.

    #datascience #ai #science #mathematics


    Topic List
    00:00 COMING UP...
    2:44 Ruda Zhang's compendium of cool ideas + a Gaussian process PSA
    7:08 Is intuition undervalued in scientific research?
    10:16 Mathematics vs observational science. Rigor vs intuition.
    14:07 Intuition & discovery precedes mathematical rigor
    21:58 Mathematics vs empirical science & the complexity of induction
    30:24 Abstract thinking & the cost/benefit of discovery
    37:25 The efficient frontier / Pareto Front of knowledge
    42:55 Pragmatism and competence
    50:24 Math /science dualism
    1:15:52 AI making scientific discoveries
    1:19:15 Statistical & scientific debate

    1 hr 23 min
  • Simon Mak | Integrating Science into Stats Models

    Simon Mak | Integrating Science into Stats Models

    #statistics #science #ai

    It’s a common dictum that statisticians need to incorporate domain knowledge into their modeling and the interpretation of their results. But how deeply can scientific principles be embedded into statistical models? Prof. Simon Mak (Duke University) is pushing this idea to the limit by integrating fundamental physics, physiology, and biology into both the models and model inference. This includes Simon’s joint work with Profs. David Dunson and Ruda Zhang (also of Duke University).

    Scientific reasoning AND stats. What more could we ask for?

    Enjoy!

    Watch it on....

    YouTube: https://youtu.be/bUbZO7R4z40

    Podbean: https://dataandsciencepodcast.podbean.com/e/simon-mak-integrating-science-into-stats-models/

     

    00:00 - COMING UP….Scientists & Statisticians

    02:09 - Introduction - Integrating scientific knowledge into AI/ML
    06:08 - How much domain knowledge is sufficient?
    09:15 - Choosing which prior knowledge to integrate into a model
    14:49 - Black box & gray box optimization
    19:50 - Non-physics examples of integrating scientific theory into ML models
    22:45 - Scientific principles & modeling at different scales
    27:20 - Correlation is one just way of modeling linkage
    36:37 - Conditional independence & different-fidelity experiments
    39:40 - Innovation vs incorporation of known information in the model
    42:52 - Aortic stenosis example
    52:49 - Which mathematics can be used to represent scientific knowledge
    57:09 - How to acquire scientific domain knowledge
    1:02:45 - Complementary approaches to integrating science
    1:06:48 - Gaussian process & integrating priors over functions
    1:12:48 - A topic for statisticians and scientists to debate:science-based vs data-based learning.

    Simon Mak's Webpage: https://sites.google.com/view/simonmak/home

     

    1 hr 20 min
  • Martin Goodson | Practical Data Science & The UK’s AI Roadmap

    Martin Goodson | Practical Data Science & The UK's AI Roadmap

    #ai #datascience #startups

    Martin Goodson (Evolution AI) describes the key aspects of the UK's AI Roadmap & responses to the document by members of the Royal Statistical Society. In particular, Martin describes the disconnect between the priorities of AI startups and industry practitioners on one side, and government and academia on the other. Martin also outlines which skills early career data scientists should focus on while in school versus after entering the workforce.

    Also available on....

    YouTube: https://youtu.be/T9qRl6Hclhg

     

    Topic List

    0:00 COMING UP: Scientific culture & AI

    1:25 The UK AI Roadmap

    8:44 Who is a data science “practitioner”? 

    12:53 Data science in AI startups

    20:36 Is there a disconnect between practitioners & academia?

    25:09 Key skills for new data science graduates

    32:03 Coding & production level data science

    39:30 Learning the right data analysis skills at the course-level. 

    45:32 AI leadership

    58:40 AI from academia & OpenSource initiatives

    1:05:37 Large institutions' impact on the AI field

    1:08:24 Back to the UK AI roadmap  

    1:12:16 Building an AI community 

    1:13:15 AI in our lifetime: Moonshots & realistic goals

    1:14:31 Scientific debate

    1 hr 17 min
  • Jack Fitzsimons | Data Security, Privacy, & Artificial Intelligence

    Dr. Jack Fitzsimons (Oblivious AI) gives a high-level introduction to the technologies that can either exploit or protect your data privacy. If you'd like to survey the landscape of data privacy-preserving technologies (from someone who's building the tech) this is a good place to start!

    #datascience #privacy #ai

     

    0:00 - Coming up...

    3:24 - Introduction
    6:20 - Data privacy and privacy enhancing technologies  
    13:00 - History of privacy enhancing technologies
    19:54 - Differential privacy: Hiding the influence of a single data point
    22:52 - Trading data utility for data privacy
    38:32 - Tracking algorithms and how they decide user preferences
    42:04 - Preserving privacy: Anonymizing data & VPNs
    50:17 - Exploration vs Exploitation: Combining best of multiple domains to tackle problems
    54:13 - Federated learning, input and output privacy of data
    58:45 - Balancing data privacy vs data-driven personalization
    1:05:50 - What should data scientists/statisticians debate?

    1 hr 15 min

About Data & Science with Glen Wright Colopy

From the publisher's feed

Data and Science with Glen Wright Colopy is a podcast covering critical scientific reasoning, particularly from a data science / machine learning / statistics perspective. Episodes typically focus on understanding of how to be better scientists and critical thinkers for the practical purpose of being a better data scientists.