Dr. Kaushik Ram is an investor at Yali Capital with nearly a decade of experience in the U.S. research ecosystem across life sciences, artificial intelligence, and data-driven technologies. He began his training at the University of California, San Diego, earning a bachelor’s degree from the Halıcıoğlu Data Science Institute and working in the chemistry department on advanced molecular modeling, including QM/MM and many-body simulation methods. He later worked with Dr. Ilya Zaslavsky at the San Diego Supercomputer Center on geospatial intelligence and spatially explicit modeling, building large-scale systems deployed during the COVID-19 response garnering critical acclaim from the research community. He earned a Ph.D. at The Scripps Research Institute in Bioinformatics and Machine Learning, completing the program in record time under the mentorship of Dr. Ali Torkamani and Dr. Chunlei Wu at the Scripps Research Translational Institute, founded by Dr. Eric Topol. His doctoral work focused on applying machine learning and integrative multi-omics approaches to rare-disease diagnostics, providing deep insight into translating complex biological data into scalable, defensible technology platforms. At Yali Capital, Kaushik focuses on deep-tech and life sciences investments, partnering with founders building science-led companies with global ambition.
I began my undergraduate years with a strong foundation in data, computation, and engineering. Working in the chemistry department at UC San Diego with Francesco Paesani was my first serious exposure to research. It taught me the value of first-principles thinking, rigor, and staying with problems that do not yield quick answers.
Around the same time, I worked on geospatial information systems at the San Diego Supercomputer Center with Ilya Zaslavsky. His insistence on rigorous work, refusal to tolerate shortcuts, and encouragement to take bold risks grounded in conviction had a lasting influence on me. That experience played a decisive role in my decision to pursue graduate training.
Despite coming from a data science background, I deliberately pivoted into biology. Graduate school was demanding. It involved long periods of uncertainty, failed approaches, and the challenge of bringing structure to noisy biological systems. The process forced patience, precision, and intellectual honesty. My work in integrative multi-omics reinforced a belief I still hold: real progress in deep technology is slow and non-linear, and earned through rigour.
Over time, my interests expanded from research to execution, from understanding hard problems to helping translate them into durable systems and companies. I moved into venture investing with clear intent, drawn to founders working at the frontiers of technology where timelines are long, outcomes uncertain, and conviction matters.
At Yali, I work closely with technically strong and grounded teams that value process, governance, and long-term thinking. I am motivated by founders who choose difficult paths, make principled trade-offs, and build with care rather than haste.
I am convinced that the coming decade will shape India’s role in global deep technology. The companies that endure will be built patiently, correctly, and without shortcuts.
