This gathering is a vital opportunity for us to reflect on our collective achievements and discuss the strategic opportunities and plans taking shape this year. During this community-wide update, we will cover several key initiatives, including an exciting first look at our plans for launching the leadership phase of our upcoming fundraising campaign.

Why is Quantum Mechanics So Hard to Describe?: A Computational Lens on the Problem

Abstract

Why is quantum mechanics so hard to describe? In this talk, Chinmay Nirkhe will argue that this mystery can be explained through a computational lens. Nirkhe will introduce a perspective on quantum mechanics inspired by theoretical computer science and complexity theory, asking questions like: How hard is it to describe a quantum state? How hard is it for a (quantum) computer to certify properties of physical systems like local Hamiltonians? These questions naturally lead to QMA, the quantum analogue of NP (from complexity theory), where proofs themselves are quantum states and familiar ideas like verification and efficiency take on surprising new meanings.

Nirkhe will sketch recent research ideas (by him and the community) showing how quantum states can hide information in ways that defeat classical reasoning, how circuit depth and query complexity limit what we can learn about them, and what differentiates quantum proofs and states from their classical counterparts. The goal of this seminar is to invite thinking about quantum mechanics and physics from a new computational vantage point.

Join the Office of Human Resources in celebrating Employee Appreciation Day, a time to recognize and thank our dedicated employees for their hard work, commitment and contributions to our community.

Human Resources is excited to introduce a new learning series: The HR Compass – Chart Your Course. This series is intended to empower every staff member to spark innovation, influence outcomes, harness digital tools, build resilience, take accountability, coach for growth and navigate change with clarity and confidence across Harvey Mudd.

The “Digitally Aware” session will help staff members adopt the right tools, automate wisely and use data to simplify work and solve real problems.

Advance registration is required to attend the live virtual session. Access information will be shared with registered participants prior to the session.

“Computation for Climate: Modeling Agriculture Under Uncertainty”

Abstract

Climate mitigation in agriculture is ultimately a modeling problem: how do we turn complex biology and sparse data into reliable decisions? Soil carbon and greenhouse gas emissions arise from nonlinear processes, incomplete records, and uncertain futures. Converting those dynamics into decision-ready tools requires integrating computer science, statistics, and domain science in ways that are rigorous, transparent, and scalable.

David LeBauer will describe his path from ecology to building climate modeling systems that support governments and startups working on carbon and greenhouse gas accounting in managed lands. He chose to build a career at the intersection of open-source software, quantitative modeling and public policy.

LeBauer will introduce the computational architecture behind these systems in accessible terms: how biological processes are encoded in models, how Bayesian calibration constrains parameters using noisy data, how ensemble simulations and sensitivity analysis propagate and decompose uncertainty, and how software design and open science practices determine whether tools are interpretable, reproducible, transparent and scalable for real-world decision-making.

The broader message is that impactful climate work requires scientific depth, disciplined software engineering, and a commitment to transparency and reproducibility, and that there are multiple viable paths to building such a career.

Speaker

David LeBauer (PhD, earth system science, UC Irvine) is a scientist and consultant working at the boundary of ecology, statistics, and scientific computing. He builds open, reproducible modeling and data infrastructures that couple process-based ecosystem models, field measurements, and Bayesian inference to make ecological predictions transparent, scalable, and decision-relevant. He is founder of The LeBauer Approach and Modeling Lead and Project Manager for California’s statewide cropland carbon monitoring and modeling initiative. Previously, he founded the Data Science team at the University of Arizona and led model calibration and validation at Indigo Ag in support of carbon credit protocols. He is creator and long-time co-lead of the open-source PEcAn ecosystem modeling platform and has led open data and cyberinfrastructure efforts supporting model–data synthesis, forecasting, and data-intensive agricultural research.

The Office of Human Resources continues its Spring Learning Series in partnership with Optum, featuring the theme: “Ignite Your Impact: Creating Wellness, Joy and Purpose at Work.” These monthly, live, instructor-led virtual sessions (via WebEx) are designed to help you bring energy, motivation, and meaning into your professional and personal life through engaging and practical topics. Each session will be recorded so you can revisit the content at your convenience.

During the spring semester, President Nembhard’s office hours are open to all students, faculty and staff. These sessions provide an opportunity to discuss projects, ideas for the College, the strategic plan, issues affecting the community and other topics.

To request a meeting, please choose a preferred slot on the Doodle. Submitting a request does not guarantee an appointment, and a member of the President’s Office will follow up to confirm next steps.

Please note:
• Groups should be limited to no more than five people.
• Requests are reviewed and confirmed on a first-come, first-served basis and are subject to change.

The IA Lab for Emerging Health Tech
in Partnership with the Physics Department Present:
Light, Life, and the Lessons In Between: Building a Career In Industry
Iva Maxwell
Director of Sequencing Development, Roche

Iva Maxwell earned her B.A. in physics at Mount Holyoke College and her Ph.D. in Applied Physics from Harvard University, where she worked on ultrafast optics, materials science, and biophysics. She has worked at a number of biotech companies, including Meso Scale Diagnostics and Singular Genomics. She is now the Director of Sequencing System Development at Roche.

The Cabinet is hosting informal coffee breaks in spring 2026 for students, faculty and staff. These coffee breaks will be attended by most of the Cabinet team and will provide an opportunity for the community to share ideas, express concerns and highlight successes.

All are welcome to stop by as schedules allow.

“An Unlikely of Events: My Journey to Batch Means Estimators”

Abstract

Noé Vidales, an assistant professor at Cal Poly Pomona, introduces his research on efficient multivariate variance estimators for serially correlated data. A family of partial-overlapping batch means estimators is introduced, which offers a tradeoff between computational and variance efficiency. The family is indexed by a shift parameter which determines the overlap between batches and is a generalization of non-overlapping batch means and overlapping batch means. Through the talk, Vidales aims to connect his research to his journey to statistics with two goals: clarifying abstract concepts and showcasing a non-traditional path to academia.

Multi-layer transformer models form the backbone of modern deep learning, yet little mathematical work details their benefits and deficiencies as compared with other architectures. This makes it difficult to answer practical and fundamental questions about the transformer architecture: What powers increase with model depth? Can alternative architectures improve efficiency without sacrificing expressivity? Clayton Sanford presents a communication-based theoretical framework for understanding the representational capabilities and limitations of multi-layer transformers. These results imply that parallelizability is a key property of the standard transformer that other architectures cannot easily replicate. Stanford contextualizes these results within the broader conversation about the challenges of developing a principled theory of neural networks and share opinions on how theoretical computer science can remain relevant to their study.

Speaker

Clayton Sanford is a senior research scientist at Google, where he works on distillation and pretraining for Gemini. He has a PhD in computer science from Columbia University and studied machine learning theory with advisors Rocco Servedio and Daniel Hsu. His research focuses on the theoretical capabilities of neural architectures, particularly transformers.