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Back in early May, I flew to North Carolina to interview Alex Rubinsteyn (left) and Benjamin Vincent (right). Together, they run the Personalized Immunotherapy Research Lab (PIRL) at UNC-Chapel Hill. It would be difficult for me to emphasize the degree to which Alex and Benjamin are in a league of their own amongst almost any other researcher I have ever met, demonstrating a degree of translational ambition, scientific insight, clarity of thought, and just genuine kindness that one rarely sees. And each time I stumble across a person like this, I try my best to get them in front of a camera. This is the first interview I’ve ever done where I traveled specifically for a guest(s), and I’m very, very glad I did it.
We’re in an interesting moment with cancer vaccines. Between Sid Sijbrandij’s ‘founder mode’ journey on his cancer (which involved cancer vaccines), and the dog cancer vaccine story, a lot of eyes are being directed here. As luck would have it, Alex and Benjamin have spent a fairly high fraction of their career in this exact field, and have contributed to some of the most foundation work in it: MHCflurry 2.0, OpenVax, LENS, and plenty of other work. In fact, Alex actually helped run a neoantigen cancer vaccine trial a few years back! So obviously, they’d make for great conversation here. We talked for three hours about this area, eventually stretching beyond cancer vaccines and discussing the grander field of personalized immunotherapy. This is comfortably the longest episode I’ve ever recorded, and I suspect we could have gone for an hour longer.
Finally: this is a complicated subject, and we really get into the details. Because of it, it may be worth skimming a companion cancer vaccine essay I released a month back. Many subjects from the article are discussed + expanded upon in the podcast. Here is the essay: https://www.owlposting.com/p/how-to-build-a-cancer-vaccine-and
Timestamps:
(00:00:26) Introduction
(00:01:26) What cancer vaccines are, and the "easy living drug" dream
(00:04:34) TAAs vs neoantigens — and why the field switched
(00:08:18) MHC, HLA, and neoantigens, defined
(00:12:57) The 100,000 MHCs per cell and the information-theoretic problem
(00:15:28) Why a cancer can't just drop its MHC
(00:20:18) Immunopeptidomics: why predicting antigens isn't knowing them
(00:28:57) How many targets you actually need, and the HLA-loss problem
(00:30:22) Can a tumor mutate its own MHC?
(00:31:34) What are tumor-associated antigens and cancer-testis antigens?
(00:34:09) The thymus, AIRE, and why T cells were never trained against CTAs
(00:37:35) Why "normal" proteins is still an unfinished reference problem
(00:41:05) The short list of T-cell killing success stories
(00:43:20) Did the old vaccine literature just need Keytruda?
(00:47:31) Why not cut out the middleman and engineer the T cells?
(00:50:09) Autologous vs affinity-enhanced TCRs, and a database-matched middle path
(00:54:23) Can we TCR-T and CAR-T in-vivo?
(01:02:08) Why whole-tumor and lysate vaccines never worked
(01:04:46) Immunodominance, or why a great vaccine can still do nothing
(01:10:38) Can you predict immunogenicity with ML?
(01:12:24) What you tell the FDA for hyperpersonalized drugs
(01:14:14) How secret is the computational pipeline?
(01:17:40) Why the field desperately needs real benchmarking
(01:21:57) Moderna and BioNTech: are the success stories real?
(01:30:04) Tumor mutational burden, and scraping the bottom for glioblastoma
(01:36:46) If cancer is so heterogeneous, why does any of this work?
(01:38:40) The hyper-optimistic case: metastatic disease and antigen spreading
(01:43:00) Antigenic drift, driver variants, and the 2030s adaptive vaccine
(01:48:02) Why cell therapy at all, instead of antibodies?
(01:52:43) Neoantigen vaccines as a categorical departure for the FDA
(02:00:03) The antigen-selection safety logic, and why it's flawed
(02:04:42) Running investigator initiated trials (which exist in the US!)
(02:19:40) The tragedy of the commons in cancer vaccine trials
(02:22:38) How Ben and Alex met (on Twitter!)
(02:27:49) Founder-mode oncology and the rise of concierge cancer care
(02:31:50) How good is concierge oncology, really? The Sid index case
(02:40:03) Why old precision oncology was useless, and why now is different
(02:43:07) LLMs, and patients advocating for their own testing
(02:47:30) The molecular-testing trial that should exist
(02:48:50) Single-cell long-read as the one true assay
(02:49:56) What would you do with $100M equity-free?
(02:53:27) The automated box: tumor in, RNA therapeutic out
(02:55:58) Why identifying targets is the easy part
After having written long-form essays over a weirdly diverse number of areas of the life-sciences, I am increasingly confident in my status as someone who knows a little about a lot of things. But every now and then, you meet someone who casually reveals to you an entire subfield who, up until your conversation with them, you’d never even thought of before. This happened to me when I met Sterling a few months back. We met in the elevator as we were both leaving an event, and by the time we’d reached the bottom floor, the conversation had become so interesting that we stood in the lobby for an hour as I pestered him with more and more questions.
Sterling runs a company called Iku Bio. Iku ostensibly does something quite simple: it helps biologics manufacturers figure out what to feed their cells. This is called media optimization, and it is done in an astonishingly old-fashioned way. An engineer runs a handful of experiments in a benchtop bioreactor the size of a Fiji water bottle, waits days for analytical results, and repeats, maybe three or four times before timelines force them to stop searching.
Sterling’s solution was to use printed circuit boards (PCBs)—the same green wafers inside your phone and your microwave—as the substrate for microfluidic bioreactors. Because PCBs are made via lithography, you get complexity for free. Because they’re already mass-manufactured at planetary scale, you inherit sixty years of cost optimization. And because they’re literally designed to carry electrical signals, you can embed sensors directly into the thing rather than cramming them in after the fact.
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Neurotechnology? For Cancer? (Ben Woodington & Elise Jenkins)
This is an episode with Ben Woodington and Elise Jenkins, who are the cofounders of Coherence Neuro. The pitch for Coherence is as follows: a brain implant that treats cancer with electricity. When I first learned of the company in mid-2025, it was such an alien thesis that I instinctively wrote it off entirely. This surely isn’t clinically plausible at all, maybe it will be one day, but certainly not today.
Then, while I was in San Francisco, I met up with Nicole, Coherence’s chief of staff. After that, I was far more convinced that there was something real here, especially after she told me that the electricity ←→ cancer thesis already has some merit: Optune, an FDA-approved medical device developed by Novocure. This has been on the market for over a decade, and uses externally delivered alternating electric fields to treat glioblastoma. And it works! If Optune is consistently used, glioblastoma patients can live up to twice as long compared to chemotherapy alone. How does it work? Simple: the alternating electrical fields prevent fast-dividing cells from replicating by (specifically, mitotic spindle formation).
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What if we could grow human tissue by recapitulating embryogenesis? (Matthew Osman & Fabio Boniolo)
Wednesday, December 17, 2025 • Duration 02:01:18
This is an interview with Matthew Osman and Fabio Boniolo, the co-founders of Polyphron.
The thesis behind Polyphron is equal parts nauseating and exciting in how ambitious it is: growing ex-vivo tissue to use in organ repair.
And, truthfully, it felt so ambitious as to not be possible at all. When I had my first (of several) pre-podcast chats with Matt and Fabio to understand what they were doing, I expressed every ounce of skepticism I had about how this couldn’t possibly be viable. Everybody knows that complex tissue engineering is something akin to how fusion is viewed in physics; theoretically possible, but practically intractable in the near-term. What we can reliably grow outside of a human body are simple structures—bones, skin, cartilage—but anything beyond that is surely decades away.
But after the hours of conversation I’ve had with the team, I’ve began to rethink my position. As Eryney Marrogi lines out in his Core Memory article over Polyphron (https://www.corememory.com/p/exclusive-cracking-the-only-engineering), there is an engineering system that has reliably produced viable human tissue for eons: embryogenesis.
What if you could recapitulate this process? What if you could naturally get cells to arrange themselves into higher-order structures, by following the exact chemical guidelines that are laid out during embryo development? And, most excitedly, what if you didn’t need to understand any of these overwhelmingly complex development rules, but could outsource it all to a machine-learning system that understood what set of chemical perturbations are necessary at which timepoints?
This does not exist today, but Polyphron has given early proof points that is possible. In their most recent finding, which we talk about on the podcast, their models have discovered a distinct set of chemical perturbations that force developing neurons to arrange themselves with a specific polarity: just shy of 90°, arranged like columns. This is obviously still a simple structure—still a difficult one to create, given that even an expert could not arrive to that level of polarity—but it represents proof that you can use computational methods to discover the chemical instructions that guide tissue self-assembly.
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We don't know what most microbial genes do. Can genomic language models help? (Yunha Hwang, Ep #7)
Rush is augmenting drug discovery for all scientists with machine-driven superintelligence.
LatchBio is building agentic scientific tooling that can analyze a wide range of scientific data, with an early focus on spatial biology. Clip on them in the episode.
If you’re at all interested in sponsoring future episodes, reach out!
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This is an interview with Yunha Hwang, an assistant professor at MIT (and co-founder of the non-profit Tatta Bio). She is working on building and applying genomic language models to help annotate the function of the (mostly unknown) universe of microbial genomes.
There are two reasons you should watch this episode.
One, Yunha is working on an absurdly difficult and interesting problem: microbial genome function annotation. Even for E. coli, one of the most studied organisms on Earth, we don’t know what half to two-thirds of its genes actually do. For a random microbe from soil, that number jumps to 80-90%. Her lab is one of the leading groups working to apply deep learning to solving the problem, and last year, released a paper that increasingly feels foundational within it (with prior Owl Posting podcast guest Sergey Ovchinnikov an author on it!). We talk about that paper, its implications, and where the future of machine learning in metagenomics may go.
And two, I was especially excited to film this so I could help bring some light to a platform that she and her team at Tatta Bio has developed: . There’s been a lot of discussion online about AI co-scientists in the biology space, but I have increasingly felt a vague suspicion that people are trying to be too broad with them. It feels like the value of these tools are not with general scientific reasoning, but rather from deep integration with how a specific domain of research engages with their open problems. SeqHub feels like one of the few systems that mirrors this viewpoint, and while it isn’t something I can personally use—since its use-case is primarily in annotating and sharing microbial genomes, neither of which I work on!—I would still love for it to succeed. If you’re in the metagenomics space, you should try it out!
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Bringing organ-scale cryopreservation into existence (Hunter Davis, Ep #6)
Monday, November 24, 2025 • Duration 01:54:51
Sponsor note: the supporter of this video is rush.cloud. If you are at all involved with doing preclinical drug discovery and would benefit from computational tools, you should check out their platform + beautiful website here: rush.cloud.
If you’re at all interested in working together for future episodes, reach out!
This is an interview with Hunter Davis, the CSO and co-founder (alongside Laura Deming) of Until Labs, which you may also know by its prior name, Cradle. They are a biotech startup devoted to organ-scale cryopreservation. They raised a $58M Series A back in September 2025, and are backed by Founders Fund (especially interesting!), Lux Ventures, and others.
In this interview, we mainly talk about the engineering and scientific difficulties in the cryopreservation field, including some background details on their September 2024 progress report on neural slice rewarming, how they characterize tissue damage in their attempts to do kidney cryopreservation, the potential economics of future cryopreservation protocols, and lots more.
Can machine learning enable 100-plex cryo-EM structure determination? (Ellen Zhong, Ep #5)
Monday, November 10, 2025 • Duration 01:40:18
Sponsor note: I am extremely happy to announce my first commercial, service-oriented sponsor: rush.cloud. I’ve been doing these podcasts entirely through very kind philanthropic graces, which is very nice, but I’d ideally like to be helping someone when they sponsor me. And now I have that! So, if you are at all involved doing preclinical drug discovery and would benefit from computational tools, you should check out their platform + beautiful website here: rush.cloud.
Ellen Zhong is perhaps one of the only people in the ML x bio field to have created an entirely new subfield of research during her PhD: the application of deep-learning to cryo-EM particle images.
If you aren’t familiar with that field, I luckily have a 8,000~ word article covering it, which walks through a lot of Ellen’s papers. If you don’t have time to read something that grossly large, the general breakdown of the problem is as follows: cryo-EM can give you thousands of 2D views of a 3D protein from many different angles, from that data, can you discover what that 3D structure is? Ellen, who is a computer science professor at Princeton University, has spent her academic career investigating that question, and now has an entire lab at Princeton focused on that and related ones. Including, as the title mentions, the possibility of doing performing cryo-EM structure determination at ultra-high scales.
The current in-vogue thing to do for most longevity companies is to go for cellular reprogramming. As in, fill a cell with the right transcription factors needed to reduce epigenetic noise, restore mitochondrial dysfunction, and so on. I’ve written about the promise there before, it’s definitely an exciting field.
So, when I first met Alan— who told me that he was a longevity researcher — last October, I naively assumed he was also on the reprogramming train. But he told me that he was investigating something a bit different. His pitch was that, instead of reprogramming the cell to fix age-related damage, what if you just protected it from (genetic) insult first? It’s an obvious idea, but one that I’d never really deeply considered. He sold me on the concept, and I was very curious to hear what he’d do next to push it forwards.
A few months after our chat, he spun up a company to pursue this line of thinking: Permanence Bio, which develops molecules that stabilize/protect the genome. They are just about eight months old, but there are already some exciting results coming out. I’m a sucker for people doing ‘contrarian research in consensus fields’, and I immediately knew I wanted to have Alan on the podcast. He graciously agreed and, during my trip to SF last month, we sat down and talked for a few hours.
In this episode, we talk about why DNA protection is so important, what indications is it useful for, how to mentally conceptualize the idea of a molecule ‘stabilizing’ a genome, what it was like to raise money for a company pursuing such an out-of-distribution thesis, and lots more.
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What could Alphafold 4 look like? (Sergey Ovchinnikov, Ep #3)
How do you make a 250x better vaccine at 1/10 the cost? Develop it in India. (Soham Sankaran, Ep #2)
Monday, February 3, 2025 • Duration 02:15:07
This is an interview with Soham Sankaran, the founder and CEO of PopVax, an mRNA vaccine development startup.
Curiously, PopVax is based in India, specifically Hyderabad. This should be a surprise to most people in the field: we never really hear of interesting biotech research being done in a place that isn’t [US, Europe, East Asia].
It’s an extremely interesting success story from what feels like a very clear underdog. In this 2-hour podcast, we discuss everything from why so little biotech research gets done in India, a breakdown on what you care about in vaccine design (immunogens), how PopVax uses machine learning for precise immunogen design, how raising money for a vaccinology startup is going, and a lot more.
Timestamps and transcripts are below. Just as in my last episode, I’ve included a ‘jargon explanation’ as a quick primer for some of the subjects discussed in the episode.
Some final bits: the studio rental costs were kindly covered by Dylan Reid! Huge shout-out to him for making this episode possible. Also shout-out to Samarth Jajoo, Reha Mathur, and David Yang for some very helpful discussion about the Indian biotech scene. And, if you think PopVax is interesting, which has some articles on their results, (they are actively hiring), and can be reached at contact@popvax.com.
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The result is a device that costs $8 per experimental lane versus $20,000 for the nearest comparable microfluidic system. And there are many, many ways for to improve from here on out.
This conversation covers the full stack: what cell culture media actually is and why it’s so much more than sugar water, why biologics manufacturing has more in common with semiconductor fabs than chemistry labs, how Sterling arrived at PCBs, and at the end of the talk, why he thinks a fair bit of lab automation is “philosophically a crime.”
Timestamps
* [00:00:48] Introduction
* [00:01:26] What is Iku Bio?
* [00:05:00] Media optimization as the biggest lever
* [00:06:23] What actually is media?
* [00:13:07] Fetal bovine serum and the move to synthetic media
* [00:15:10] Walk me through a media optimization workflow
* [00:18:49] Why biologics manufacturing is closer to semiconductors than chemistry
* [00:21:50] Matching the phase three batch and generics
* [00:24:12] The 200-dimensional search space
* [00:37:02] Printed circuit boards as a medium for microfluidics, and the utility of lithography
* [00:40:48] Anatomy of the Iku device
* [00:57:09] What sensors are on the device today?
* [01:01:36] How do you use the Iku device to perform media optimization?
* [01:14:44] Does media optimization survive scale-up?
* [01:24:32] $8/lane vs. $20,000/lane: the economic utility of Iku’s device
* [01:32:05] Why PCB microfluidics didn’t exist 10 years ago
* [01:39:24] Who is the customer?
* [01:43:14] What is the ultimate goal of Iku?
* [01:49:07] What does the validation evidence need to look like?
* [01:52:14] What would you do with $100M equity-free?
* [01:57:31] Lab automation is in a strange place right now
After this, Nicole connected me with Ben and Elise, the cofounders of the company. It was an incredible conversation. During it, I was informed that cancer cells behave eerily similar to neurons: hijacking neural pathways, attracting nerves into their microenvironment, and forming synaptic connections with surrounding tissue. Given this set of evidence, none of which felt particularly controversial, an easy logical leap is to ask the question: why can’t you throw neuromodulation at the tumor? Maybe not even just for treatment, but monitoring as well? Optune was a step in the right direction, yes, but surely it can be pushed even further.
So Coherence was born, the only (neurotechnology x oncology) company in existence. Ben and Elise met during their PhD’s at Cambridge, spinning up the startup with the belief that a modality long assumed to be exclusively for neurological conditions like Parkinson’s, epilepsy, and chronic pain, may have a profound role to play in cancer. And perhaps even conditions outside of it.
And during my last trip to San Francisco for JPM 2026, I had the honor to sit down with Ben and Elise to talk about it all.
We discuss this recent polarity result, what the machine-learning problems at Polyphron looks like, and the genuinely insane economics of the whole endeavour. The last of which is especially exciting; it is rare you hear biotech founders talk about ‘expanding the Total Addressable Market’, and actually believe them. But here, it is a genuine possibility if the Polyphron approach ends up working.
(00:02:37) Why replace tissue rather than the whole organ?
(00:10:34) Why not do simple stem/progenitor cell injections?
(00:13:51) Can organs repair themselves naturally?
(00:18:21) What does “structure” actually mean in tissue engineering?
(00:21:04) Why are skin and bone the only FDA-approved tissues today?
(00:23:45) What exactly are tissue scaffolds?
(00:27:52) Why are organoids a “dead end” for this field?
(00:35:08) The argument for recapitulating developmental biology
(00:40:28) Walk us through the Polyphron experimental loop
(00:47:56) Can you simulate morphogenesis with only small molecules?
(00:49:49) How large is the set of possible tissue scaffolds?
(00:52:32) How reliable are developmental atlases?
(00:56:45) What is the machine learning model actually optimizing for?
(01:04:04) Polyphron’s first big tissue engineering result: polarity
(01:15:33) What comes after polarity?
(01:17:09) Why is vascularization the hardest problem of tissue engineering?
(01:20:33) Why can’t you just wash angiogenesis factors over the tissue?
(01:22:25) How does the graft integrate with the host’s blood supply?
(01:25:45) How do you validate tissue function before implantation?
(01:29:01) How do you design a clinical trial for a biological pacemaker?
(01:37:01) The argument for being a pan-tissue company
(01:41:57) What are the biggest scientific and economic risks?
(01:45:23) Who are Polyphron’s competitors?
(01:47:07) Expanding the TAM beyond transplant lists
(01:52:28) Autologous vs. Allogeneic approaches
(01:55:07) Is a 3-year timeline to the clinic realistic?
(01:56:28) Cross-species translation
(01:58:05) What would you do with $100M equity free?
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Note: Thank you to latch.bio for sponsoring this episode!
LatchBio is building agentic scientific tooling that can analyze a wide range of scientific data, with an early focus on spatial biology. Check out their agent at agent.bio! Clip on them in the episode.
If you’re at all interested in sponsoring future episodes, reach out!
In this podcast, we talk about her research, what she did during her recent sabbatical at Generate:Biomedicines, her recent interest in areas beyond cryo-EM (cryo-ET and NMR specifically), and more!
Timestamps
[00:00:00] Introduction
[00:02:43] What does it mean to apply ML to cryo-EM?
[00:04:28] Ab initio reconstruction and conformational heterogeneity
[00:15:41] Can we do multiplex cryo-EM structure determination?
[00:22:19] Datasets in cryo-EM
[00:26:25] Why isn’t there a foundation model for cryo-EM particle analysis?
[00:33:07] How much practical usage is there of these cryo-EM models amongst wet-lab cryo-EM researchers?
[00:40:34] Where can things still improve?
[00:46:57] Has deep learning done something in cryo-EM that was previously impossible?
[00:48:22] Ellen’s experience in the cryo-EM field
[00:53:40] Deep learning in cryo-EM outside of structure determination
[00:57:32] 3D volume reconstruction versus residue assignment in cryo-EM
[01:00:26] What did Ellen do during her sabbatical at Generate Biomedicines?
[01:07:07] Ellen’s research in cryo-ET
[01:13:54] Ellen’s research in NMR
[01:21:05] How did Ellen get into the cryo-EM field?
[01:26:57] Why did Ellen go back to graduate school?
[01:32:17] What makes Ellen more confident about trusting an external cryo-EM paper?
Finally, Alan has a really great blog (something I mention in the video), and I wanted to attach a much longer article he’s written about the topic here.
[00:00:00] Teaser clip
[00:01:39] Introduction
[00:07:32] What is Permanence working on?
[00:11:48] What does DNA protection actually look like?
[00:27:12] Why is DNA protection not focused on as much?
[00:41:03] The utility of epigenetic clocks
[00:46:47] Do you need multimechanism approaches for longevity?
[00:51:58] Longevity outside of DNA protection
[00:55:57] What's going on inside of Permanence?
[01:05:54] How could Permanence fail?
[01:09:03] How do you stay optimistic?
[01:10:26] Why work on aging?
[01:15:26] What are you bearish on?
[01:19:12] Weirder types of aging beyond 110
[01:21:37] How did you decide on DNA protection and what else would you have done?
[01:25:27] What was it like raising money?
[01:31:48] What do you think of past cancer prevention trials?
[01:34:12] What does good wet-lab talent look like?
[01:37:02] What does your information diet look like?
[01:40:06] What's it like going from research to being a CEO?
[01:42:20] What happens after cancer prevention for Permanence?
As a result, Sergey’s work has gained a reputation for being something that is worth reading. But nobody has ever interviewed him before! Which was shocking for someone who was so pivotally important for the field.
So, obviously, I wanted to be the first one to do it. After an initial call, I took a train down to Boston, booked a studio, and chatted with him for a few hours, asking every question I could think of. We talk about his own journey into biology research, some issues he has with Alphafold3, what Alphafold4-and-beyond models may look like, what research he’d want to spend a hundred million dollars on, and lots more. Take a look at the timestamps to get an overview!
Final note: I’m extremely grateful to Asimov Press for helping fund the travel + studio time required for this episode! They are a non-profit publisher dedicated to thoughtful writing on biology and metascience, such as articles over synthetic blood and interviews with plant geneticists. I myself have published within them twice! I highly recommend checking out their essays at asimov.press, or reaching out to editors@asimov.com if you’re interested in contributing.
Timestamps:
[00:00:00] Highlight clips
[00:01:10] Introduction + Sergey's background and how he got into the field
[00:18:14] Is conservation all you need?
[00:23:26] Ambiguous vs non-ambiguous regions in proteins
[00:24:59] What will AlphaFold 4/5/6 look like?
[00:36:19] Diffusion vs. inversion for protein design
[00:44:52] A problem with Alphafold3
[00:53:41] MSA vs. single sequence models
[01:06:52] How Sergey picks research problems
[01:21:06] What are DNA models like Evo learning?
[01:29:11] The problem with train/test splits in biology
