Seminars

David Persson presents Polar Express, a method for polar decomposition and the matrix sign function motivated by the Muon neural-network optimizer. It uses matrix multiplications suited to GPUs and adapts each polynomial update through a minimax problem to reduce worst-case error. The talk covers convergence, finite-precision implementation in bfloat16 and validation-loss improvements when training GPT-2 on FineWeb data. This is an in-person PACM IDeAS seminar at Princeton.

Richard L. Huganir discusses research on the molecular mechanisms that regulate glutamate receptors, the brain's major excitatory neurotransmitter receptors, and thereby modulate communication between neurons.

José Molina Mora presents an integrative study of the core stress-response mechanisms, or perturbome, of Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus. Transcriptomic data are analysed using machine learning, systems biology, functional enrichment and ortholog comparisons to prioritise stress-associated genes. Experimentally determined protein structures are combined with AlphaFold predictions where structural data are missing. Molecular docking and further computational analyses then identify preliminary compounds and conditions that might inhibit selected targets. These results are a proof of concept awaiting experimental validation. The talk connects gene-expression analysis with structural modelling for antimicrobial research and is aimed at researchers, graduate students and professionals in microbiology, bioinformatics and related biomedical fields.

Emily Jacobs will examine how endocrine transitions such as the circadian cycle, menstrual cycle, pregnancy, and menopause reshape the human brain. The talk combines precision brain imaging with a review of research, policy, and funding efforts intended to close persistent gaps in women's brain-health evidence.

Olivia Di Matteo introduces quantum computing and compilation, examining how the translation from high-level quantum programs to efficient hardware instructions becomes a bottleneck as applications scale. The seminar identifies parts of the quantum software stack where machine learning and artificial intelligence can improve this process. It presents the group’s work using graph-based reinforcement learning to optimize quantum circuits, then considers outstanding challenges and research questions for scalable quantum software.

Rebecca Swanson draws on more than a decade of teaching at the Colorado School of Mines to examine both partial and complete flipped-classroom approaches. Examples span Calculus II, Linear Algebra, Foundations of Advanced Mathematics and Topology. The discussion addresses how different designs supported learning, how they extended to courses with multiple sections, and how implementation challenges were handled. It treats flipping as a flexible set of instructional choices, allowing teachers to adapt the approach to their own aims and constraints instead of adopting a single uniform model.

Reproducible research can turn scientific observations into reliable, reusable knowledge. This talk presents ReproNim and related community infrastructure for sharing data and computational workflows. It examines versioned, re-executable analysis at scale using OpenNeuro, together with harmonization and querying across dozens of research sites. ReproLakes and ReproPonds combine public and private metadata stores with FAIR and reproducible practices to improve discovery and reuse. The discussion addresses both technical obstacles and research-culture challenges, offering practical approaches for making shared scientific resources more useful and accelerating cumulative discovery.

Petar Suman explores how higher-order statistics of the cosmic microwave background connect inflationary theory to observations. Although single-field inflation explains the origin of primordial fluctuations, its particle-physics mechanism remains uncertain. The first part examines the cosmological-collider programme: massive fields during inflation can leave oscillatory signatures in primordial non-Gaussianity, allowing measurements to constrain the masses and spins of early-universe particles. The second treats inflation as an open quantum system. An open effective field theory describes dissipation produced when the inflaton interacts with an environmental bath; joint analysis of the CMB power spectrum and bispectrum can then constrain those dynamics. Together, these approaches use precise cosmological data to test complex inflationary models at energies inaccessible to terrestrial accelerators.

This webinar explores computational and structure-based vaccine design, comparing prefusion stabilisation of vaccine antigens with epitope scaffolding onto non-antigen protein backbones. It discusses the advantages and limitations of both strategies. No background in computational protein design is required; familiarity with vaccine antigens and viral glycoproteins is helpful. Free online webinar; advance registration and details: https://www.ebi.ac.uk/training/events/strategies-computational-vaccine-design-prefusion-stabilisation-epitope-focussing/

Stephanie Gil studies reliable coordination in robot networks facing malicious information and ordinary environmental uncertainty. Communication signals provide physical evidence of trustworthiness that is difficult to forge; the cy-trust framework turns that evidence into probabilistic trust estimates and weights neighboring agents accordingly. For consensus and distributed optimization, the analysis establishes almost-sure convergence with bounded departures from nominal performance even when malicious agents form a majority of a node’s neighbors, exceeding the classical Byzantine threshold. Theory and hardware experiments support these guarantees. For natural uncertainty, real-time sensing is incorporated into rollout-based reinforcement learning, reweighting possible futures. Applications include fleet routing under random demand and Project CETI’s autonomous robotic rendezvous with sperm whales at sea. The talk closes with directions for combining trust and long-horizon sequential decisions to retain resilience when planning data may be corrupted.

Chaoyi Zhang presents a method for observing distributed social-brain networks in awake marmosets during natural social interaction. Prefrontal, cingulate, striatal, thalamic and amygdala circuits are implicated in autism and other neuropsychiatric conditions, but have rarely been measured together during genuine primate social behavior. The platform replaces part of the skull with an acoustically transparent implant, enabling volumetric functional ultrasound measurements of cortical and subcortical activity at approximately 150-micrometre resolution. A whole-brain vascular reference and automated registration permit comparisons across roughly 50 atlas-defined regions, sessions and animals. Experiments show that a live social partner produces stronger and more extensive activity than video of the same partner. The talk considers applying this approach to genetically tractable, transgenic marmoset models of autism risk genes to identify altered network signatures and evaluate their potential as circuit-level biomarkers.

Intermediate-band solar cells seek to absorb photons below the host band gap and increase current without sacrificing voltage. This talk examines submonolayer CdTe quantum dots embedded in ZnCdSe matched to InP, avoiding a harmful wetting layer and permitting hundreds of absorbing layers. Recent growth results show deeper photoluminescence associated with larger dots and improved intermediate-band placement. A complementary inverse-design study optimizes ZnCdSe/ZnCdMgSe distributed Bragg reflectors to recycle photons over 1–1.8 micrometres and incidence angles of 0–70 degrees. Total absorption guides optimization, with designs showing broad reflectivity and tolerance to fabrication errors.

Why do dorsal raphe serotonin neurons respond to reward, surprise, salience and uncertainty in ways that resist a single explanation? This talk proposes that serotonin activity encodes prospective value: a prediction of cumulative future reward. The model is motivated by the unusually strong and persistent spike-frequency adaptation of these neurons, which may support efficient and accurate predictions. It reproduces important qualitative features of serotonergic responses that earlier models miss and predicts measured in vivo firing rates more accurately than competing accounts. By treating previously separate responses as manifestations of one prospective value code, the work connects serotonin physiology to its computational roles in learning and behaviour.

Open biological data resources connect complementary evidence about proteins involved in infection. Using influenza haemagglutinin, this seminar demonstrates sequence and functional annotations in UniProt, three-dimensional cryo-EM maps in EMDB, and experimental and predicted structures in PDBe and the AlphaFold Protein Structure Database. Participants will learn to browse, retrieve and interpret these data and follow connections between the resources. The session also considers how accessible biological data supports infection research and pandemic preparedness. It is aimed at students and researchers in protein biology, structural biology and bioinformatics; basic knowledge of sequence and structure formats is useful.

The talk explores a quantitative connection between Huygens’s legacies in wave optics and classical mechanics. Optical polarization, coherence and entanglement are related to mechanical quantities including centre of mass and moment of inertia. Distributions of classical point masses provide a representation of nonseparability in optical fields, and the correspondence extends to quantum states of two qubits. These mappings offer an intuitive way to examine entanglement and the shared mathematical structure of mechanics, classical optics and quantum physics.

Gang Chen delivers MIT.nano's annual Dresselhaus Lecture, drawing on research that spans electron and phonon transport, thermoelectric energy conversion, microelectronic thermal management, desalination and data-center cooling.

Alice Mark examines faculty participation when mathematics departments gradually coordinate their courses. The session considers who influences the process, whether instructors can opt out, how coordination affects teaching assignments, and what it means to ask colleagues to support a shared approach. It focuses on maintaining constructive discussion when faculty agree to coordinate but differ over implementation. Participants will explore these questions and develop strategies relevant to their own course-coordination efforts, rather than receive a prescribed set of answers.

Cagla Eroglu presents research on how astrocytes regulate synaptic connectivity during development, function and plasticity. The seminar examines molecular and cellular mechanisms that shape neural circuits using imaging, mouse models and complementary experimental approaches.

Nathan Alexander describes an introductory statistics curriculum that combines logic, theory and computation to develop critical quantitative analysis. Open-source tools support an interdisciplinary process in which students define concepts and propositions, then apply statistics to social questions. The approach aims to strengthen technical skills alongside critical reasoning. The talk also introduces the structure of the critstats package and its role in a community framework for building informational data systems that help users investigate particular histories, issues and challenges.

The brain adjusts its internal activity to direct visual and auditory attention and regulate responses to external stimuli. Appropriate endogenous activity may support normal cognition and behaviour, while its modulation can also produce dysfunction. Beginning with mathematical models of general anaesthesia, this talk examines how changes in noisy endogenous activity can fragment functional brain organization and account for loss of consciousness. Anaesthesia experiments in ferrets support the theoretical result. Further experimental examples are discussed to show how underlying internal noise may help explain observed transitions between brain states.