Statistics on Aneesh Sathe

Why Every Biotech Research Group Needs a Data Lakehouse

Tue, 29 Jul 2025 06:04:54 +0000

start tiny and scale fast without vendor lock-in

All biotech labs have data, tons of it. The problem is the same across scales. Accessing data across experiments is hard. Often data simply gets lost on somebody’s laptop with a pretty plot on a poster as the only clue it ever existed. The problem is almost insurmountable if you try to track multiple data types. Trying to run any kind of data management activity used to have large overhead. New technology like DuckDB and their new data lakehouse infrastructure, DuckLake, try to make it very easy to adopt and scale with your data. All while avoiding vendor lock-in.

Briefing: The State of Explainable AI (XAI) and its Impact on Human-AI Decision-Making

Thu, 24 Jul 2025 06:10:10 +0000

This post is a sloptraption, my silk thread in the CloisterWeb. The post was made with the help of NotebookLM. You can chat with the essay and the sources here: XAI NotebookLM Chat

I. Executive Summary #

The field of Explainable AI (XAI) aims to make AI systems more transparent and understandable, fostering trust and enabling informed human-AI collaboration, particularly in high-stakes decision-making. Despite significant research efforts, XAI faces fundamental challenges, including a lack of standardized definitions and evaluation frameworks, and a tendency to prioritize technical “faithfulness” over practical utility for end-users. A new paradigm emphasizes designing explanations as a “means to an end,” grounded in statistical decision theory, to improve concrete decision tasks. This shift necessitates a human-centered approach, integrating human factors engineering to address user cognitive abilities, potential pitfalls, and the complexities of human-AI interaction. Practical challenges persist in implementation, including compatibility, integration, performance, and, crucially, inconsistencies (disagreements) among XAI methods, which significantly undermine user trust and adoption.

AI: Explainable Enough

Wed, 23 Jul 2025 05:50:31 +0000

They look really juicy, she said. I was sitting in a small room with a faint chemical smell, doing one my first customer interviews. There is a sweet spot between going too deep and asserting a position. Good AI has to be just explainable enough to satisfy the user without overwhelming them with information. Luckily, I wasn’t new to the problem.

Coming from a microscopy and bio background with a strong inclination towards image analysis I had picked up deep learning as a way to be lazy in lab. Why bother figuring out features of interest when you can have a computer do it for you, was my angle. The issue was that in 2015 no biologist would accept any kind of deep learning analysis and definitely not if you couldn’t explain the details.

My Road to Bayesian Stats

Tue, 22 Jul 2025 06:32:55 +0000

By 2015, I had heard of Bayesian Stats but didn’t bother to go deeper into it. After all, significance stars, and p-values worked fine. I started to explore Bayesian Statistics when considering small sample sizes in biological experiments. How much can you say when you are comparing means of 6 or even 60 observations? This is the nature work at the edge of knowledge. Not knowing what to expect is normal. Multiple possible routes to a seen a result is normal. Not knowing how to pick the route to the observed result is also normal. Yet, our statistics fails to capture this reality and the associated uncertainties. There must be a way I thought.

Beyond the Dataset

Fri, 11 Jul 2025 05:41:24 +0000

On the recent season of the show Clarkson’s farm, J.C. goes through great lengths to buy the right pub. As with any sensible buyer, the team does a thorough tear down followed by a big build up before the place is open for business. They survey how the place is built, located, and accessed. In their refresh they ensure that each part of the pub is built with purpose. Even the tractor on the ceiling. The art is in answering the question: How was this place put together?

Jan. 8, 2025: Count your DIGITS! Drunk Bayesian

Wed, 08 Jan 2025 08:06:00 +0000

NVIDIA Project DIGITS #

Around 2015 I was putting together funds in academia. Convincing IT, senior professors, and finance that yes, it was worth giving me a LOT of cash to build a workstation with multiple GPUs.

…
“No, it isn’t for gaming.”
…
“Yes, it will change the world.”
…
“No, there are no university rules that hardware bought multiple invoices across multiple departments can’t be used in the same box.”
…
“Yes, I’m aware that all my individual quotes are just below the bureaucracy summoning purchase limits.”
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“Yes I tried random forest with the other stats and ML methods, this really is better. How do I know? Well…”

Jan. 7, 2025: Building Dwelling Thinking

Tue, 07 Jan 2025 14:00:00 +0000

Today’s product builders and data scientists shape the way people see the world. The analysis, plots, and UI we create are places where others dwell. Not merely occupy but live and harness the mental space we give them access to. Martin Heiddeger wrote Building Dwelling Thinking(archive.org) in his 1971 book, Poetry Language Thought.

Man’s relation to locations, and through locations to spaces, inheres in his dwelling. The relationship between man and space is none other than dwelling, strictly thought and spoken

Jan 4, 2025

Sun, 05 Jan 2025 00:30:49 +0000

Bayesian Thinking Talk (youtube) #

Talk details from Frank Harrell’s blog - includes slides

This beautiful talk about Bayesian Thinking by Frank Harrell should be essential material for scientists who are trained in frequentist methods. The talk covers the shortcomings of frequentist approaches, but more importantly the paths out of those quagmires are also shown.

Frank discusses his journey to Bayesian stats in this blog post from 2017 which is also in the next section.