Research Trajectory

This is a quick summary of my research trajectory.

As of September 5, 2021 — What exactly are my top 5 research interests?

  1. The intersection of public administration and technology
  2. Changes and uses in encryption technology
  3. Sentiment analysis and modern polling methodologies
  4. General uses cases for machine learning
  5. The ethical use of large language or foundational models

A quick update on my research interests from March 2, 2022:

  • Public administration
    • Local government administration
    • The intersection of public administration and technology
    • How technology influences government
    • How government uses technology
  • Changes and uses in encryption technology
    • Encryption and society
    • Quantum encryption
  • Sentiment analysis and modern polling methodologies
    • Automated sentiment analysis
    • Sentiment analysis and machine learning
    • Modern polling methods
    • The breakdown of polling
  • General uses cases for machine learning
    • Common API use cases within the ML space
    • General ML use cases compared
    • My general look at MLOps open source code
    • A review of MLOps Github repos
  • The ethical use of large language or foundational models
    • Language models and society
    • The intersection of technology and modernity
    • Oversupply of information (flooding)

My 5 year writing plan as of March 3, 2022:

  • Year 1 – Heavy ML focus for the rest of 2022
    • Finish writing a collected series of ML/AI essays on Substack and combine them into a manuscript, “The Lindahl Letter: On Machine Learning Year Two.” This manuscript should include both years one and two. 
      • Weekly Substack posts
      • Manuscript generation at the end of the year
      • Will need to be edited by a professional before the print edition goes live
    • Rework last years speaking engagement talks into academic papers. This could be one combined paper or potentially 5 different papers depending on how the initial effort shapes up.
      • “What is ML Scale? The Where and the When of ML Usage.”
      • “The ML scale problem: Thinking about where and when to use ML, ROI models, synthetic data, repeatable frameworks, and teams.”
      • “Applied ML ROI – Understanding ML ROI from different approaches at scale.”
      • “Demystifying Applied ML – Building Frameworks & Teams to Operationalize ML at Scale.”
      • “Figuring out applied ML: Building frameworks and teams to operationalize ML at scale. V3”
    • Rerun the MLOps Github research and turn that content into a paper
  • Year 2 – For 2023 I want to pivot into studying sentiment analysis and modern polling methodologies. At this point, I will have written 104 essays on ML/AI and should probably refocus on a specific topic that is material to ML/AI, but adjacent to it as an area of research. It’s possible by 2023 that quantum computing will be a huge topic for research and will end up getting some attention as well.
    • Automated sentiment analysis paper
    • Sentiment analysis and machine learning essays for Substack
    • Modern polling methods essays for Substack
    • The breakdown of modern polling paper
  • Year 3 – 2024 will include a return to writing about local government administration and technology. It will be 20 years since earning my master of public administration degree. By this time my writing should be as crisp and focused as it will ever be and my perspective on technology will be well considered from my previous work on ML/AI. 
    • Technology and local government administration
    • The intersection of public administration and technology
    • How technology influences the practice of governing 
    • How government uses ML/AI technology
  • Year 4 – 2025 will probably be the year where quantum computing has broken down modern encryption frameworks. 
    • Changes and uses in encryption technology
    • Encryption and society
    • Quantum encryption
  • Year 5 – 2026 is going to be a year where my backlog should be highly full. The previous 4 years of this writing plan should have created a ton of leftover writing works.
    • A reflective work on ML/AL
    • Did open source MLOps technology survive?
    • Did the serverless trend pan out in the cloud?