The current strategy for tackling drug resistance is akin to playing Whac-A-Mole: a new resistance mutation appears, and we scramble to address it, only to find that another mutation pops up elsewhere. This reactive approach to evolution often leaves us a step behind the pathogens we are fighting. My research group at Stanford aims to change this paradigm by focusing on two key areas: predictive evolution and advanced biological modeling. First, we are leveraging machine learning to predict and simulate the evolution of diseases more accurately. By understanding when and how a disease is likely to develop resistance to a particular drug, we can design protein therapeutics that are effective against future forms of that disease. Our objective is to outpace viral evolution, ensuring that drugs remain potent and relevant even as pathogens mutate. Second, we are developing more sophisticated models of biology to improve our ability to design and control biological systems. Understanding the molecular level alone is insufficient; most diseases involve complex interactions within systems at the cellular and organismal levels. Our latest advancements have enabled us to model biological processes across entire organisms, enhancing our capacity to create targeted therapies and preventive measures. My interest in research stems from my childhood fascination with technology and gadgets, particularly those seen in movies. While I admired characters like James Bond, what truly captivated me was the behind-the-scenes work of figures like Q, the scientist who developed the cutting-edge technology. Research has always seemed magical to me, pushing the boundaries of what is possible, yet it is grounded in reproducibility and consistency, allowing us to achieve breakthroughs repeatedly. This kind of work is immensely challenging and demands extensive training. Universities play a crucial role in fostering such expertise, as they provide the unique environment and resources needed to delve deeply into specialized fields like AI and biology. Institutions like Stanford are essential for nurturing the next generation of scientists with the skills required to tackle complex problems and drive innovation. Investing in scientific research yields significant returns. The economic value generated by new companies emerging from biomedical research far exceeds the cost of training the scientists behind these innovations. The benefits extend beyond financial gains, as advancements in biomedical research have the potential to improve human health and well-being on a global scale. Science, however, is often misunderstood. Some people view it as aligned with specific political agendas, but science is fundamentally data-driven. It requires the courage to face and accept data that may contradict our preconceived notions. Scientists are not all-knowing; much of what we do involves uncertainty and a continuous process of hypothesis testing and revision. Embracing this dynamic nature is key to advancing our understanding and making meaningful contributions to society. In summary, my research at Stanford aims to transform the way we handle drug resistance by using advanced predictive models and comprehensive biological systems analysis. These efforts not only enhance our ability to combat diseases but also underscore the importance of academic institutions in training the skilled scientists needed to drive innovation. Investing in such research is a wise choice, both economically and for the future of human health.