Vikram Krishnamoorthy

Vikram's been working a wet lab since middle school, In high school, he designed and ran experiments that led to recognition as an Intel Science Talent Search Semifinalist. He graduated from the University of Pennsylvania’s Life Sciences and Management dual degree program with a bachelor’s from Wharton with dual concentrations in finance and healthcare management, and a bachelor’s in biology with honors. His undergrad research experience ranged from high throughput CRISPR knockout screening in iPSCs to studying lncRNAs in Arabidopsis. At college, he founded Etcetera Bio, Here, he designed an automated nano-injection system to precisely deliver genetic payloads of arbitrary size into cell nuclei using Carbon Nanopipettes. The vision was to enable engineering of highly sophisticated cell therapies with genetic logic and safety switches. He funded R&D with a small amount of dilutive funding from Pear VC and grant funding from various sources including Y Combinator’s Startup School Grant. Through this startup, he found his way to Biotech Without Borders and the community bio movement

Preethi Krishnamoorthy

Preethi is a computer scientist with experience in Artificial Intelligence, particularly LLMs. Her path into computer science was through computational biology, where her first research experience involved developing machine learning models for classifying breast cancer biopsy images using deep neural networks and techniques like K-Means clustering and Random Forest – techniques that are relevant again as we start using protein embeddings for finding candidates and classification. She’s currently continuing to hone her skills as a junior studying CS and Robotics at Carnegie Mellon University’s School of Computer Science, where she’s involved in everything from engineering prosthetic hands to designing wearables. In addition to her academic background, an entire summer spent implementing end to end pipelines for fine-tuning and evaluating LLMs has honed a skillset that is quite valuable for this project. This type of work, writing new differentiable potential functions for diffusion and fine-tuning embedding models for classification, often requires going beyond the documentation and diving into the research papers behind the transformers, so we’re glad to have someone with that skillset on this team.

Patrik D'haeseleer

Patrik D'haeseleer is a research scientist at anational laboratory, where he has worked on computational biology of biofuels and host-pathogen systems. He has a PhD in Computer Science from UNM developing early tools for large scale gene expression analysis, and did a postdoc at the Church Lab in Harvard on systems biology. He has 4+ years experience using state-of the-art protein structure prediction and design tools such as TrRosetta, RoseTTAFold, AlphaFold, ESMFold, TRDiffusion. Patrik co-founded Counter Culture Labs 10 years ago, a community lab for biotechnology in Oakland, where he currently serves as Secretary, leads community projects, and teaches computational biology, microbiology, synthetic biology, and art & science classes.