prev health aiml @apple MD candidate Case Western
Joined August 2021
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Connor retweeted
Earlier this year, I came across a peculiar email written by the late Lewis Sheiner, a UCSF physician who built the mathematical methods for safely testing drug doses in people. This field is called "pharmacometrics", and it remains a cornerstone of modern drug development.
In this 20-year-old email, he emphatically assigns credit for the foundation of this work to an *economist* and *quantitative trader* named Barr Rosenberg -- who, driven by a personal tragedy involving a mis-dosed medication, had reached out to him. How did an economist get entangled in the math of drug dosing?!
This mystery seemed too fascinating to ignore. So I spent this summer tracing the story backward, speaking with Barr's former colleagues, clinical pharmacologists, programmers, and even the first director of the FDA's Center for Drug Evaluation and Research, Carl Peck(!) to learn about Barr's unlikely contributions to this important field.
This is the story of Barr Rosenberg, the pioneering pharmacometrics software -- NONMEM -- that his math inspired, and the curious inner workings of how a ragtag team of clinicians, statisticians, and programmers shaped the way we test drugs today: "Borrowing Strength".
Connor retweeted
In this era of artificial intelligence, it is becoming urgent to distinguish human art from what machines produce. There is an ontological difference, even before an aesthetic one, between art and what a machine can generate through statistical calculation based on millions of images created by others. Algorithms lack the spark of humanity. For this reason, the Church wishes to renew an alliance with artists and cultural institutions to safeguard our humanity.
Connor retweeted
Biosecurity is the next frontier of AI security. We built SOTA monitors so agents can do more biology, safely.
Our monitors outperform frontier model safeguards with fewer refusals on dual-use tasks. They’re fast, real-time, and robust to adversarial attacks. 🧵
Connor retweeted
As believers of open research, we are disappointed to see Anthropic silently degrading Fable 5 for AI development
"Any topic related to building pretraining pipelines, distributed training infrastructure, or ML accelerator design... may have limited effectiveness through Claude via methods such as prompt modification, steering vectors, or parameter-efficient fine-tuning."
Not only do they get to decide what you use LLMs for in research, but this also enables them to silently intervene in your research without you knowing.
This sets a dangerous precedent. If a model refuses openly, users can understand the boundary. If a model falls back to another model, users can still evaluate the difference. But if a model silently modifies or weakens its own answers while still pretending to help, researchers lose the ability to know whether a failed result came from their own idea, their implementation, or an invisible intervention by the model provider.
That is not safety. Safety policies should be transparent, auditable, and user-visible.
On top of that, the people most harmed by this are not the largest labs with massive teams and proprietary infrastructure. It is the independent researchers, academic groups, startups, and open-source builders who rely on public tools to compete, innovate, and pioneer AI for everyone else.
Connor retweeted
After AlphaGo, the skill of human Go players noticeably improved. I suspect we will see a similar pattern in math.
Connor retweeted
We need a new clinical paradigm for AI algorithms that find incidental risk factors on a scan with AUC of 0.8 because these are (1) going to become more common and (2) don't have a clear path to commercial viability.
New newsletter: THIS IS HOW WE CURE PANCREATIC CANCER
There are 3 broad reasons why pancreatic cancer is so hard to treat:
1. It often starts w a gene mutation that doctors thought was “undruggable”
2. It is largely “invisible” to the immune system by evading, and even turning down, T cell response
3. It’s asymptomatic until it’s too late—and radiologists can’t even see it on scans until it’s lethal
We’re making progress along all three categories.
Today’s post explains how—and how AI-assisted radiology could make a huge difference
open.substack.com/pub/derekt…
Connor retweeted
🚨 BREAKING: @CMSGov is issuing billing guidance that allows physicians to get paid for deprescribing work for the first time.
Connor retweeted
Alex on why AI drug discovery companies need to generate novel data to succeed:
"AI models based on the research that's available is a lot of garbage in and garbage out."
"A lot of the recorded literature is actually incorrect. There's been tons of studies that show if you go try to replicate the experiments that are in the literature, you don't even get the same results."
"The AI companies that I believe are gonna be most set up for success are the companies with a novel way to generate science tokens that don't exist in the public domain."
Alex Karnal (@alex_karnal) is the most talented bio and healthcare investor I've ever met.
He's spent 20 years in the industry and says 2025 was the single most exciting year he's seen. The start of a once-in-a-lifetime, trillion-dollar revolution in public health.
He explains how few people realize we already have the medicines to prevent our deadliest diseases. The problem is that almost no one takes them.
There's a population of people born with a mutation that means their bodies don't produce a protein called PCSK9. Their lifetime risk of cardiovascular disease is 88% lower than yours. Pharma turned that genetic advantage into a drug. It's been approved for years, but the number of people taking it is still vanishingly small.
Partly because high cholesterol is a silent killer. You feel nothing, right up until you have a heart attack. And partly because the health system makes it punishingly hard to stay on a preventive drug like a PCSK9 inhibitor. In other words, the medicine works, but the system around it doesn't.
That's what's starting to change, and in this episode, Alex explains why.
We discuss the "health stack" he believes can add a decade to most lives, why oral GLP-1s are breaking every adoption record in pharma, peptides and citizen pharmacology, and what AI is doing to drug discovery.
I wish I had an "Alex" for every interesting topic. We've been having versions of this conversation for over five years, and every single one is as clear and as useful as this one.
Enjoy!
Timestamps:
0:00 Intro
1:00 The State of Modern Medicine
5:00 Designing the Modern Health Stack
12:17 The GLP-1 Inflection Point
19:18 The Biological Mechanisms of GLP-1
30:36 Overcoming Frictions in Healthcare
34:19 Cardiovascular Disease
44:04 Addressing Alzheimer's
47:04 The Future of Cancer
57:33 Drug Discovery
1:05:25 AI and Scientific Super Intelligence
1:14:40 Citizen Pharmacology and the Peptide Movement
1:18:13 Background and Career Journey
1:31:09 Braidwell's Investment Approach
1:33:30 The Kindest Thing
Connor retweeted
In honor of 50 years of Apple, we're sharing - for the first time ever - Don Valentine's original 1977 memo for Sequoia's investment into Apple Computer. #Apple50
now you only honor your medicine rotation if you bill at a resident level
Connor retweeted
It’s almost like this whole brand / generic thing is a great structure to incentive r&d while ensuring broad affordability on a reasonable timeline.
Connor retweeted
Since people are talking about cutting-edge cancer treatments, it is good to remember that in many states patients do not receive targeted therapies that they are approved for. There's a lot of alpha in getting healthcare in the US to look like it does in MA, HI, and NY.
Connor retweeted
Evo 2 is out in Nature today, showing that genome language models can predict and design across the full complexity of life, from phages to eukaryotes.
A few surprises from the project, including how ignoring trillions of nucleotides was key to getting a good model. 🧵
Connor retweeted
A Visual Guide to DNA Sequencing.
Learn how different DNA sequencing technologies work, from Sanger sequencing to Illumina to nanopores. (Complete with illustrations!)
Written by @evandeturk. Illustrated by @EllaWD_PhD.
Connor retweeted
If you simply let people keep their own money and get a true catastrophic plan, the median American would have around $2M in real dollars by retirement. That covers lifetime healthcare costs for 90% of people with no government involvement.
Connor retweeted
Predicting cell state in previously unseen conditions such as disease or in response to a drug has typically required retraining for each new biological context. Today, Arc is releasing Stack, a foundation model that learns to simulate cell state under novel conditions directly at inference time, no fine-tuning required.
