Breaking Into AI for Science
A conversation with Alishba Imran of UC Berkeley BAIR, Arc Institute, CZ Biohub, Voltx
The biotech, chemicals, and materials industries offer so much potential to improve lives – whether through life-saving therapies, or breakthrough materials for a more sustainable planet.
But for many, entering these fields feels intimidating. Students face intense coursework and long hours in the lab. For professionals, pivoting into these domains can feel even harder.
That’s why I found Alishba Imran’s story so inspiring: an undergrad at University of California, Berkeley, she carved her own path through study groups, shadowing researchers, and cold outreach.
A deep learning researcher with Berkeley Artificial Intelligence Research (BAIR), Alishba now splits her time between completing her undergrad studies, and advancing AI for biology as a Research Fellow at the Arc Institute. She's also the author of a new book: "AI for Robotics: Toward Embodied and General Intelligence in the Physical World."
In my hour-long chat with Alishba, we explored:
How she bootstrapped her learning across biology and chemistry
What it takes to launch a deep-tech startup like Voltx
Alishba's work advancing cell biology with self-supervised learning
The promise – and hype – of robotics research today
And how nonprofits like the Arc Institute are reshaping the landscape for researchers.
Watch or Listen Now
If you’re curious about the future of AI, biology, and robotics, this episode’s for you.
Also available on:
Spotify: https://spoti.fi/3BzMqiy
Apple Podcasts: https://apple.co/40sQCu9
Additional Show Notes
Episode Links:
Alishba's LinkedIn https://www.linkedin.com/in/alishba-imran-/
Alishba's Twitter https://x.com/alishbaimran_/
"AI for Robotics" book https://link.springer.com/book/10.1007/979-8-8688-0989-7
Arc Institute https://arcinstitute.org/
UC Berkeley BAIR AI Lab https://bair.berkeley.edu/
Chan Zuckerberg Biohub https://www.czbiohub.org/
DynaCLR Paper https://arxiv.org/html/2410.11281v1
Biopunk Lab https://biopunklab.com/
Episode Chapters:
00:00 - Preview & Introduction
05:02 – Why AI for Science?
10:34 – Bootstrapping Your Own Learning
16:02 – Why Battery Testing Matters: Launching Voltx
20:19 – Batteries, Raw Earth Minerals: Incremental vs Transformative Chemistries
23:54 – The Business Side of Deep Tech Entrepreneurship
29:14 – The State of Battery Tech Today
30:50 – Working with Chan Zuckerberg Biohub
33:37 – DynaCLR For Contrastive Learning of Cell State Dynamics
36:21 – Why Self-Supervised + Contrastive Learning
41:06 – Generalizing to Different Cells Contexts With Embeddings
42:06 – Microscopy or ML Advancements for Unlocking Progress
43:54 – Managing Terabytes of Data
46:51 – Arc Institute: Collecting Largest Single Cell Dataset
48:40 – Role of Tech-Founder Funded Institute To Advance Foundational Work
50:41 – Book Launch! Merging Classical Robotics Methods With Cutting-Edge Deep Learning
53:42 – Robotics Progress: Hype vs Reality
57:09 – Robotics for Science
58:54 – Future Trends: Automation, Foundational Single Cell Models, Protein and Genome Language Models, Transcriptomics
01:00:41 – Pivoting into AI for Science Early, Mid, or Late Career
01:02:49 – Exchanging Ideas; DynaCLR Paper; AI for Robotics Book