The Office of Emergency Preparedness and Safety will conduct a practice lockdown exercise on Tuesday, Nov. 3, at 2:20 p.m. A lockdown is an emergency protocol designed to secure and protect individuals who are near an immediate threat. This procedure is used when it may be more dangerous to evacuate a building than to remain indoors.

At 2:20 p.m., HMC will initiate the exercise with an announcement sent via Mudd Alert, which you will receive through your HMC email (and by text message if you’ve chosen to opt in for text notifications using the Mudd Alert Registration form.

The goal of this exercise is to practice securing yourself in your current location should an emergency situation arise. The exercise will conclude no later than 2:30 p.m. with a second Mudd Alert notification indicating that it has ended. If you do not receive the first or second Mudd Alert messages, please proceed with initiating and concluding the exercise on your own wherever you are on campus during this timeframe. Community members will not be asked to evacuate during this lockdown exercise.

For more information, please contact Kelly Salazar, emergency preparedness and safety manager.

Students, faculty, and staff are invited to connect with President Nembhard this semester to share ideas, discuss projects and initiatives, talk about the College’s strategic plan, raise issues affecting the community, or simply connect.

To request a meeting, please choose a preferred slot on this link. Submitting a request does not guarantee an appointment. A member of the President’s Office will follow up with next steps.

Please note:

• Groups should be limited to no more than five people.
• Requests are reviewed as they are received and are subject to change.

Unlike most machine learning applications, robotics involves physical constraints that make off-the-shelf learning challenging. Difficulties in large-scale data collection and training present a major roadblock to applying today’s data-intensive algorithms. Robot learning has an additional roadblock in evaluation: every physical space is different, making results across labs inconsistent.

Two common assumptions of the robot learning paradigm limit data efficiency. First, an agent typically assumes isolated environments and no prior experience. Second, agents frequently rely on vision alone to learn tasks. Victoria Dean will present work that lifts these two assumptions, improving the data efficiency of robot learning by leveraging multimodality (images and audio) as well as pretraining. She will then introduce a real-world manipulation benchmark for evaluating the generalization of policy learning methods via shared data and hardware.

Finally, Dean will turn to another robot learning application at a larger scale: optimizing heating and cooling systems in our buildings. Buildings account for nearly 40% of carbon emissions in the U.S., yet most building control policies fail to generalize beyond a single building. She will discuss ongoing work to benchmark generalization via the Smart Buildings Challenge, highlighting how machine learning can tackle pressing sustainability challenges.

Speaker

Victoria Dean is a teaching professor in the Department of Computer Science at Princeton University. Her research interests are at the intersection of machine learning and robotics, investigating generalization, benchmarking and democratization. She teaches across introductory computer science, machine learning and ethics and enjoys implementing real-world projects in my courses. Dean is passionate about teaching and mentoring undergraduate students, with an emphasis on improving diversity, equity, and inclusion.

Prior to joining Princeton, Dean was an assistant professor of computer science at Olin College of Engineering. She did her PhD at CMU’s Robotics Institute, advised by Abhinav Gupta and supported by the NSF GRFP and Siebel Scholars. She got her B.S. in computer science from MIT and spent time at DeepMind, Waymo, Deep Genomics, Counsyl, Google and Coursera.

Human Resources is excited to continue our Fall Learning Series in partnership with Optum, featuring the theme: “Thriving Through Change: Building Resilience in Today’s Workplace.” These live, instructor-led virtual sessions (via WebEx) are designed to provide practical tools and insights to support your professional and personal well-being through engaging and relevant topics. Each session will be recorded so you can revisit the content at your convenience.

Advance registration is required to attend the live session.

Iris is a new computing resource for the HMC community, filling the gap between a personal computer and a high-performance computing cluster. Through custom virtual machines, Iris offers flexibility in the software and configuration available for a project. This gives researchers another option for building a computing environment around their workflow. In this session, Nic Dodds, research computing specialist, will introduce Iris, explain where it fits among HMC’s research computing resources, and discuss potential uses and how to get started.

In December 2022, for the first time in human history, a fusion reaction in the laboratory produced more energy than the lasers that started it. That single result—decades in the making—didn’t just answer a physics question. It reshaped how the United States thinks about nuclear deterrence, energy, and what’s possible at the frontier of high-energy-density science. Kim Budil, director of Lawrence Livermore National Laboratory, will discuss this breakthrough and how this fits into the broader national security missions at Lawrence Livermore National Laboratory and beyond.

This talk is part of Professor Tom Donnelly’s course Fusion: A Brighter Future. Speakers will share their expertise on a variety of topics that relate to fusion.

The Community Coffee Break precedes the lecture by Brandon Sorbom, chief science officer and co-founder of Commonwealth Fusion Systems. He has been recognized by MIT Technology Review (35 Innovators under 35) and TIME magazine (World’s Most Influential Rising Stars). Gather with the Mudd community before enjoying an inspiring program that explores the frontier of fusion energy, the ethical responsibilities of technological innovation and Sorbom’s entrepreneurial journey.

At a time when the world is looking for bold solutions to combat climate change, Sorbom is leading a revolution in clean energy. From creating a tabletop fusion device as an undergraduate to leading the ARC Reactor design study as a grad student, Sorbom has exemplified the transformative power of curiosity, rigorous science and a deep commitment to global stewardship.

The lecture begins at 11 a.m.

Machine Learning often relies on massive amounts of data to capture relevant patterns. In contexts where experts are performing highly-skilled tasks, however, large datasets can be hard to create, and seemingly small miscalculations on the model’s part can be disproportionately problematic.

Sarah Jobalia, a Stanford University PhD candidate, will focus on the design of virtual 3D hair as a case study to show how grounding AI pipelines in cultural, scientific and social knowledge produces more realistic, diverse and reliable results. Virtual hair, which is both computationally complex and culturally important to represent faithfully, is central to adding depth and nuance to the stories we see on screen. In her research, she draws upon interdisciplinary insights to define strand texture as the various distinctive patterns that are formed by forces internal to a hair strand, presenting a novel taxonomy for rigorously and concisely describing texture. Mirroring social understandings of hair, Jobalia disentangles this texture from the hair’s overall style, which describes where the strands fall in relation to the head. Through learning and generating texture and style separately, she highlights two fundamental but separate aspects of hair, enabling generation that can capture the essence of desired hairstyles. This both yields fast generation of the curly and afro-textured hair types that previous works struggle to capture and allows for intuitive user interaction and compatibility with existing artist workflows. Broadly, these insights show how grounding AI tools in existing knowledge not only improves results but allows tools to support, rather than replace, experts.

Speaker

Sarah Jobalia is a PhD candidate in Stanford University’s Computer Science Department, advised by Ron Fedkiw. Her research centers on building more accessible tools for animation artists to represent characters of all backgrounds on screen. Specifically, through the study of hair- both on- and off-screen, Jobalia uses a combination of computer graphics and machine learning to automatically generate 3D hair styles from just a few images of any subject. By focusing on how hair type affects the way hair looks and moves, she builds tools that are equitably representative of all styles.

A Human Exposition of Result #138 (Subset Sum in Time O(2^{0.49n}), What That Means, and How We Got There)

On October 6, 2026, OpenAI announced that an internal AI model had made progress on hundreds of open problems in mathematics and theoretical computer science. Rough preprints of the results were released to the public in a GitHub repository and mathematicians have spent the subsequent weeks verifying, explaining, simplifying, and building upon them.

Tim Randolph will focus on Result #138, a faster exact algorithm for the NP-Complete Subset Sum problem. He will summarize the history of prior work on exact algorithms for this problem, including the genesis of several important ideas and the runtime “barrier” at time 2^{0.5n}. He will then outline how these ingredients are combined in the OpenAI proof and the simultaneous work of Li and Ye.

Speaker

Tim Randolph is an assistant professor of computer science at Harvey Mudd College. Prior to Harvey Mudd, he received his PhD in the Theory of Computation Group at Columbia University.

How does a computer scientist build a research career around the problems people actually face in the field? Brianna B. Posadas ’13 will share her personal journey to becoming an assistant professor and describe how a background in human-computer interaction, user-centered design and computing education led to agricultural technology research at Cal Poly Pomona.

Posadas will highlight several projects that connect computing, agriculture and community-engaged research: designing usable security practices for greenhouse workers; collaborating with Pitzer College on a California Energy Commission project investigating agrivoltaic systems and the integration of solar energy production with agriculture; developing sensor-based technologies for monitoring cows; and using agricultural datasets to create more meaningful and applied experiences in computer science courses.

Together, these projects illustrate how agricultural contexts can motivate new computing research questions while creating opportunities for interdisciplinary collaboration, student mentorship and teaching innovation. The talk will conclude with reflections on developing an academic identity, pursuing research grounded in real-world communities and building a career that connects human-centered computing with agriculture and student learning.

Speaker

Brianna B. Posadas, PhD, is an assistant professor in the Department of Computer Science at California State Polytechnic University, Pomona. An engineering alumna, she earned an M.S. in agricultural and biological engineering and a PhD in computer and information science and engineering from the University of Florida, where she was the first Latina to earn a doctorate from the department. Before joining Cal Poly Pomona, she was a Computing Innovation Postdoctoral Fellow and later an assistant professor at Virginia Tech.

Posadas’s research connects human-centered computing, data science and agricultural technology. Across her work at Virginia Tech and Cal Poly Pomona, she has studied how farmers and agricultural workers adopt emerging technologies, including assistive technologies, precision-agriculture tools and AI-enabled systems. Her current projects include usable security for greenhouse workers, agrivoltaic systems through a collaboration with Pitzer College, sensor-based technologies for monitoring cows and the use of agricultural data in computer science education. Together, this work reflects her goal of helping engineers and computer scientists develop technologies that respond to the practical needs of agricultural communities.

Posadas is committed to fostering diversity, belonging and community in computing and higher education. She advises oSTEM at Cal Poly Pomona, organizes the annual Hispanics in Computing meeting and founded the Latino/Hispanic Organization of Graduate Students at the University of Florida.

Join us for HMC Day of Gratitude and Kudos! Take a moment to appreciate the colleagues who make a difference in our HMC community. Stop by for light refreshments, connect with others and pick up Kudos Cards to recognize a faculty or staff colleagues for their support, collaboration, dedication or everyday contributions.

Kudos Cards will be available at the event and are available in the Office of Human Resources in Kingston Hall.

Come by, grab a treat and share some gratitude and kudos!

TCCS SIS Implementation Director Elizabeth Morgan will present a preview of the faculty portal features in Anthology Student, which will launch in summer 2027. There will be more information, orientation and trainings in the spring and summer, but this introductory preview is a Bite of Learning you don’t want to miss.

This winter is projected to have one of the strongest El Niños on record, with increased odds of heavy rainfall, flooding, coastal erosion, and landslides in Southern California. In this talk, climate and mathematics professor Bobby Sanchez will provide an oceanographer’s perspective on how an El Niño forms, why rain and flooding are more likely to occur, and how worried you should be about Kelvin waves! The lecture will conclude with a Q&A.

Students, faculty, and staff are invited to connect with President Nembhard this semester to share ideas, discuss projects and initiatives, talk about the College’s strategic plan, raise issues affecting the community, or simply connect.

To request a meeting, please choose a preferred slot on this link. Submitting a request does not guarantee an appointment. A member of the President’s Office will follow up with next steps.

Please note:

• Groups should be limited to no more than five people.
• Requests are reviewed as they are received and are subject to change.

At the heart of our Milky Way galaxy lies a supermassive black hole called Sagittarius A* that is evolving on the timescale of mere minutes. Computer scientist Katie Bouman will present the methods and procedures used to produce the first images of Sagittarius A* as well as discuss future directions we are taking to leverage machine learning to sharpen our view of the black hole, including mapping its evolving environment in 3D.

It has been theorized for decades that a black hole will leave a “shadow” on a background of hot gas. However, due to its small size, traditional imaging approaches require an Earth-sized radio telescope. In this talk, Bouman discusses techniques developed to photograph a black hole using the Event Horizon Telescope, a network of telescopes scattered across the globe. Recovering an image from this data requires solving an ill-posed inverse problem which necessitates the use of image priors to reduce the space of possible solutions. Although we have learned a lot from these initial images already, remaining scientific questions motivate us to improve this computational telescope to see black hole phenomena still invisible to us. She will discuss approaches developed to incorporate data-driven diffusion model priors into the imaging process to sharpen the view of the black hole and understand the sensitivity of the image to different underlying assumptions. Additionally, she will discuss how her team has developed techniques that allow them to extract the evolving structure of our own Milky Way’s black hole over the course of a night. She will introduce Orbital Black Hole Tomography, which integrates known physics with a neural representation to map evolving flaring emission around the black hole in 3D for the first time.

Speaker

Katherine L. (Katie) Bouman is a professor in the Computing and Mathematical Sciences, Electrical Engineering and Astronomy departments at the California Institute of Technology. Her work combines ideas from signal processing, computer vision, machine learning and physics to find and exploit hidden signals for scientific discovery. Before joining Caltech, she was a postdoctoral fellow in the Harvard-Smithsonian Center for Astrophysics. She received her PhD in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT in EECS, and her bachelor’s degree in electrical engineering from the University of Michigan. She is a Rosenberg Scholar, Heritage Medical Research Institute Investigator, recipient of the Royal Photographic Society Progress Medal, Electronic Imaging Scientist of the Year Award, Sloan Fellowship, University of Michigan Outstanding Recent Alumni Award and co-recipient of the Breakthrough Prize in Fundamental Physics. As part of the Event Horizon Telescope Collaboration, she co-led the Imaging Working Group and acted as coordinator for papers concerning the first imaging of the M87* and Sagittarius A* black holes.