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Anya von Diessl
Mathematician and computer scientist building AI systems and founding ventures.
Contact Me
Mathematician and computer scientist building AI systems and founding ventures.
Education
Stanford University
B.S. Mathematics
Stanford University
M.S. Computer Science
Artificial Intelligence Track
My work spans machine learning research at Stanford, production software engineering at Google, and founding ventures in fintech and education.
With a rigorous foundation in mathematics and computer science, I combine AI research and full-stack engineering with firsthand experience shaping product strategy, understanding customer needs, and bringing technology to market.
Selected Research
Exploring uncertainty, biological systems, and what machine learning can reveal.
Stanford Translational AI Lab
Deep learning for medical imaging, with probabilistic inference to study uncertainty and identify meaningful biomarkers.
Conducted interdisciplinary research uniting artificial intelligence, probabilistic modeling, and computational medicine. Developed and deployed deep learning architectures for computer vision and medical imaging, integrating probabilistic inference to quantify uncertainty and extract high-fidelity biomarkers. Leveraged Bayesian and data-driven modeling frameworks to improve diagnostic prediction, enhance model interpretability, and advance precision healthcare through statistically robust AI systems.
Stanford
Contrastive learning and ChromBPNet-based models to investigate chromatin accessibility across disease states.
Developed a contrastive deep learning model to identify regulatory differences in chromatin accessibility across normal, tumor, and metastatic states. By leveraging CNN architectures like ChromBPNet, applied advanced machine learning techniques to enhance the precision of sequence-to-function predictions, with a focus on refining computational models for disease progression, particularly in thyroid cancer.
Stanford University
Bayesian inference and variational optimization to uncover hidden structure and improve model identifiability and robustness.
Conducted research on latent-variable and probabilistic models for uncertainty quantification in high-dimensional, noisy datasets. Developed and implemented Bayesian inference frameworks and variational optimization algorithms to uncover hidden structure and mitigate estimation bias in predictive modeling. Leveraged stochastic process theory, information-theoretic measures, and causal inference techniques to enhance model identifiability, interpretability, and robustness in complex real-world systems.
Entrepreneurship
Founding ventures has taken me from technical architecture to the conversations that shape a useful product.
Experience & Perspective
My work spans research, production software, and teaching. I enjoy understanding difficult systems, building with them, and helping others do the same.
Teaching Assistant, CS227B: General Game Playing
Assisted in teaching a graduate-level AI course on the design of autonomous agents capable of strategic reasoning in novel environments. Guided students in applying methods from automated reasoning, symbolic knowledge representation, adversarial and heuristic search, resource-bounded planning, and algorithmic game theory to develop general-purpose intelligence systems that learn and execute strategies from formal game descriptions.
Teaching Assistant, CS105
Served as an instructor for Stanford CS105, teaching Python and front-end development while mentoring students through technical projects and problem-solving. One of very few third-year undergraduate students selected by the Stanford Computer Science Department to serve as a teaching assistant.
Software Engineering Intern, Google Ads
Developed a high-performance, production-scale interface for a real-time fraud detection platform within Google Ads, leveraging TypeScript, HTML, and CSS to engineer modular, scalable, and latency-optimized components. Collaborated with cross-functional teams to translate complex fraud analytics pipelines and anomaly detection workflows into intuitive, data-rich visual systems, enhancing usability, reliability, and decision speed across a platform processing billions of ad transactions daily.
Engineering
A few of the models, agents, and systems I’ve built.
Technical Foundation
The languages, libraries, and analytical methods I use across my research and engineering.
Hover & drag to explore
Core Stack
Python · TypeScript · PyTorch · Hugging Face
Bayesian Inference · Deep Learning · Optimization · Full-Stack Engineering
Get in Touch
Have any questions? Reach out to me from this contact form and I will get back to you shortly.