Nicholas Ho @eigenNick
PhD Student @CMUCompBio | Generative AI and Biology @GenbioAI | Opinions are my own nickdst.github.io #1 in Innovation Joined June 2021-
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🌎👩🔬 For 15+ years biology has accumulated petabytes (million gigabytes) of🧬DNA sequencing data🧬 from the far reaches of our planet.🦠🍄🌵 Logan now democratizes efficient access to the world’s most comprehensive genetics dataset. Free and open. doi.org/10.1101/2024.0…
imagine getting beaten by a chinese food delivering company at training an LLM (along with a banger tech report btw)
first i thought scaling laws originated in OpenAI (2020) then i thought they came from Baidu (2017) now i am enlightened: Scaling Laws were first explored at Bell Labs (1993)
We were able to reproduce the strong findings of the HRM paper on ARC-AGI-1. Further, we ran a series of ablation experiments to get to the bottom of what's behind it. Key findings: 1. The HRM model architecture itself (the centerpiece of the paper) is not an important factor.…
A common takeaway from "the bitter lesson" is we don't need to put effort into encoding inductive biases, we just need compute. Nothing could be further from the truth! Better inductive biases mean better scaling exponents, which means exponential improvements with computation.
Recently, we used contextualized.ml to go beyond physical limits in biology and medicine, inferring n-of-1 models for 7997 of patients and generating models of unseen diseases on-demand. This hints at how to develop accurate and personalized biological simulators like AIDO.
Recently, we used contextualized.ml to go beyond physical limits in biology and medicine, inferring n-of-1 models for 7997 of patients and generating models of unseen diseases on-demand. This hints at how to develop accurate and personalized biological simulators like AIDO.
Lots of hype around multimodal FMs, virtual cells (and labs?), all-atom design...I really think core algorithms (not just scale/integration) will solve the next problems in AIxBio. Take Transition Path Sampling: models transitions for dynamics, optimization, and cell fate. 👇…
@davidycli IMO it's not just data the field needs - rarely is supervised/correlation learning what we want to achieve, yet it's all everyone uses. So now we have structure prediction models that don't learn any physics and break down when there isn't a close evolutionary analogue.. .. we…
1/ How can we model each tumor’s unique biology instead of relying on one-size-fits-all approaches? In our latest blog post, we highlight findings from the recent PNAS paper with GenBio AI Research Scientist @probablybots and Co-Founder and Chief Scientist @ericxing, showing how…
My amazing PhD student Wendy Yang @muyu_wendy_yang is graduating this summer & seeking industry R&D roles! She's published in ISMB, Nature Methods, and interned at Genentech. Strong in #AI/#ML for gene regulation. Looking for top AI+bio talent? Contact Wendy: [email protected]
My amazing PhD student Wendy Yang @muyu_wendy_yang is graduating this summer & seeking industry R&D roles! She's published in ISMB, Nature Methods, and interned at Genentech. Strong in #AI/#ML for gene regulation. Looking for top AI+bio talent? Contact Wendy: [email protected]
If you think the "mean baseline" is formidable, you should meet it's older and stronger sibling, the weighted mean baseline.
1/ 🚀 Introducing AIDO.StructureDiffusion: A generative model for structural protein design—enabling high-quality, controllable generation of monomers, complexes, and antibodies. 🧵
Design Of Intrinsically Disordered Region Binding Proteins @ScienceMagazine 🚀 New paper published from David Baker!🚀 A groundbreaking computational approach has been developed to design proteins that can specifically bind to intrinsically disordered regions (IDRs), which are…
The Transformer–SSM retrieval gap is driven by just a few heads! SSMs lag on tasks like MMLU (multiple-choice) and GSM8K (math) due to in-context retrieval challenges. But here’s the twist: just a handful of heads handle retrieval in both architectures. What we found 👇 1/
(1/n) Can we disentangle the effects of multiple covariates (e.g., sex, age, disease) to predict multiple counterfactual outcomes at the single-cell level? We introduce CellDISECT, a causal generative model that addresses this by learning to generate synthetic counterfactuals…
Current multimodal single-cell integration methods act as black boxes, lacking meaningful interpretability. We introduce NetworkVI, a VAE that performs integration via gene-gene interactions and the Gene Ontology for biologically grounded analysis. biorxiv.org/content/10.110…
Excited to share #AlphaGenome, a start of our AlphaGenome named journey to decipher the regulatory genome! The model matches or exceeds top-performing external models on 24 out of 26 variant evaluations, across a wide range of biological modalities.1/6
A useful but inconvenient razor: Given two equally-evidenced propositions, the one more damaging to my ego is more likely to be true.
We know Attention and its linear-time variants, such as linear attention and State Space Models. But what lies in between? Introducing Log-Linear Attention with: - Log-linear time training - Log-time inference (in both time and memory) - Hardware-efficient Triton kernels
Honored to share a major thread of my PhD research, out now in PNAS. We address a core issue with how models are used for scientific discovery. Models are so important that they define the entire scientific process... 1/n
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gfdsxcvgdxn @hdhshdgga
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Carla Luciana Padilla... @Carlalpf91
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Han Guo @HanGuo97
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Peter Koo @pkoo562
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Jiayan(Yoshii) Ma @JiayanMa_Yoshii
79 Followers 384 Following (Epi)Genomics+AI Northwestern University Bioinformatics University of California San Diego, Bioinformatics 2021. Former President of UBIC 马嘉彦(Ma Jiayan/ま よしひこ)
Md Toki Tahmid @MdTokiTahmid64
35 Followers 283 Following Incoming PhD student (Fall 2025) @ Princeton, CS; Research domain: Computation Biology🧬
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Jacob Buckman @jacobmbuckman
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