Browse School of Medicine | Stanford Profiles

BioI'm a Stanford Data Science Fellow and postdoc with Rhiju Das at the Department of Biochemistry. I build lab-in-the-loop AI for RNA biology, pairing deep learning with wet-lab experiments at scale.

I did my PhD in Computer Science at the University of Cambridge with Pietro Liò, on geometric deep learning for molecular design. I built gRNAde, the first 3D generative model for RNA, and validated it in the wet lab as a visiting researcher in Phil Holliger's group at the MRC LMB. I've also interned at Prescient Design (Genentech) and FAIR Chemistry (Meta AI), and my work has been recognized by the Qualcomm Innovation Fellowship and the A*STAR National Science Scholarship.

Contact Info
chaitjo@stanford.edu

Other Names:

Chaitanya Joshi

Israel Juarez Contreras

Postdoctoral Scholar, Biochemistry

Current Research and Scholarly InterestsSterols are the most abundant lipid in the plasma membrane. Their structure is deeply conserved, built though a long iterative evolutionary process whose end products are the topology of the fused steroid ring system and the structure of the aliphatic tail extending from it. Together these let the molecule pack tightly against the acyl chains of neighboring lipids, which is how sterols reinforce the membrane and set its fluidity. This same interaction produces a second effect. Sterols associate preferentially with saturated lipids, particularly sphingolipids, and that preference sorts the bilayer into ordered domains, often called lipid rafts, which concentrate certain proteins and exclude others.

The Bloch hypothesis holds that the sterol biosynthetic pathway was progressively selected for membrane function, with each step yielding a molecule better suited to the bilayer than the one before it. Fluidity has historically been taken as the property under selection, but it is not the only one. Rebuilding ergosterol biosynthesis stepwise in living yeast showed that domain formation imposes its own demands, and that the two properties are not optimized by the same modifications. The pathway alternates between them, arriving at structures that regulate fluidity and organization together rather than either alone. A further design principle follows from this. The pairing between a sterol and the acyl chain length of its partner sphingolipid is highly specific. Replacing the native pathway in yeast with cholesterol biosynthesis abolished the domains ergosterol supports, since ergosterol pairs with the very long acyl chains of fungal sphingolipids while cholesterol pairs with the shorter chains of mammalian membranes.

These principles, observed in fungi, carry direct consequences for mammals, where cholesterol occupies two distinct pools. One is structural, held in complex with sphingolipids and other lipids. The other is a residual fraction, free or accessible that carries out essential roles in signaling and homeostasis. Accessible cholesterol is defined operationally, by what a probe can bind, but what it corresponds to physiochemically remains open. My central goal is to define accessible cholesterol through a more rigorous biophysical lens and connect that definition to the machinery in cells.

Contact Info
ijuarezc@stanford.edu

Sharada Kalanidhi

Director of Data Science, Biochemistry - Genome Center

Current Role at StanfordParaphrasing the mathematician Alexander Grothendieck: the essential thing is to pose problems in the right framework.

Sharada is developing a new field, Mathematical Medicine, which applies pure mathematical frameworks to genomic and multi-omic data for quantitative, personalized diagnosis. This approach explores alternatives to prevailing cohort-based statistical paradigms, particularly in complex clinical cases that have resisted standard methods.

After more than a decade of research and close collaboration with biochemists at the Stanford Genome Technology Center (Dept. of Biochemistry), Sharada concluded that the mathematics currently used for multi-omic diagnosis is inadequate for the level of biological and clinical complexity being attempted. Her conclusion echoes the perspective of the mathematician Mikhail Gromov: “This area does not yet exist. It will have to be invented.” Mathematical Medicine represents one possible construction of such an area. This approach is aligned in spirit with the philosophy of the late mathematician Jim Simons: "We don't start with models. We start with data. We don't have any preconceived notions." Mathematical Medicine lets the data speak for itself.

This field is focused on the development of an intermediate translation layer between cohort-based statistical models and individualized multi-omic diagnosis and clinical decision-making. Without this mathematical layer, the clinical adoption of multi-omic data- particularly for complex cases- has been limited. As a result, many complex, multi-system conditions remain undiagnosed or misdiagnosed for long periods, delaying effective treatment and, in some cases, allowing disease processes to worsen. Additionally, what is learned from rare and extreme cases proves highly informative for the rest of the population.

Further information on this field, including opportunities for early philanthropic partnerships, is available at: https://mathmed-2026.web.app/

Kavita Karmarkar

High School Student, Biochemistry - Genome Center
Staff, Biochemistry - Genome Center

Preston Kellenberger

Ph.D. Student in Biochemistry, admitted Autumn 2025

BioI was raised in Saint Louis County, MO and completed my undergraduate degree in Biochemistry at The University of Missouri – Columbia. I’m most excited by translational problems that can be addressed through the structural understanding and engineering of biological molecules. At Stanford, I look forward to joining a collaborative community that spans broad scientific disciplines, and to contributing to research that advances human health. I love spending my extra time playing the drums, and I have served as a snare drummer for the world-class Madison Scouts and Cavaliers drum corps.

Contact Info
pkellenb@stanford.edu

Chaitan Khosla

Wells H. Rauser and Harold M. Petiprin Professor and Professor of Chemistry and, by courtesy, of Biochemistry

Current Research and Scholarly InterestsResearch in this laboratory focuses on problems where deep insights into enzymology and metabolism can be harnessed to improve human health.

For the past two decades, we have studied and engineered enzymatic assembly lines called polyketide synthases that catalyze the biosynthesis of structurally complex and medicinally fascinating antibiotics in bacteria. An example of such an assembly line is found in the erythromycin biosynthetic pathway. Our current focus is on understanding the structure and mechanism of this polyketide synthase. At the same time, we are developing methods to decode the vast and growing number of orphan polyketide assembly lines in the sequence databases.

For more than a decade, we have also investigated the pathogenesis of celiac disease, an autoimmune disorder of the small intestine, with the goal of discovering therapies and related management tools for this widespread but overlooked disease. Ongoing efforts focus on understanding the pivotal role of transglutaminase 2 in triggering the inflammatory response to dietary gluten in the celiac intestine.

Peter S. Kim

Virginia and D. K. Ludwig Professor of Biochemistry

Current Research and Scholarly InterestsOur research focuses on developing new strategies for vaccine creation. We also aim to generate vaccines targeting infectious agents that have eluded efforts to date. We integrate experimental approaches with protein language models to guide artificial evolution and enable efficient antibody and protein engineering. Our interdisciplinary approach aims to address critical global health challenges.

Maia Kinnebrew

Postdoctoral Scholar, Biochemistry

Contact Info
mkinnebr@stanford.edu

Silvana Maria Konermann

Assistant Professor of Biochemistry

BioSilvana is an Assistant Professor of Biochemistry at Stanford and Executive Director and Core Investigator at Arc Institute. Her research laboratory aims to understand the molecular pathways that drive the development of Alzheimer’s disease using next-generation functional genomics, with the long-term goal of developing rationally targeted therapeutics for neurodegenerative disorders. She received her Ph.D. in Neuroscience from MIT. Silvana’s pioneering work on tools to directly perturb the transcriptomic landscape of the cell using CRISPR has been recognized by her faculty appointment as a Chan Zuckerberg Biohub Investigator and Hanna Gray Fellow of the Howard Hughes Medical Institute.