Aspen Center for Physics

2022 Colloquia

Thursdays at 3 p.m. MDT, 9 p.m. UTC

Click Here To Access the Zoom Talks



  • June 2
    Fundamental Physics from Galaxy Surveys
    Speaker: Mikhail Ivanov,  Institute for Advanced Study
    The distribution of galaxies on large scales is a sensitive probe of cosmological physics. In particular, the structure of this distribution depends on properties of dark matter and the dynamics of the early universe. Understanding this dependence, however, is a challenging task because the observed galaxy distribution is modulated by a variety of non-linear effects. I will present innovative theoretical tools that have allowed for a systematic analytic description of these effects. These tools play a central role in a new program of extracting cosmological information from galaxy surveys. I will share some results of this program from my independent analysis of the public data from the Baryon acoustic Oscillation Spectroscopic Survey. These results include the measurement of the Hubble constant as well as constraints on new physics and the early Universe. Finally, I will discuss the main challenges of this program and possible synergies with machine learning.
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  • June 9
    Accelerating first-principles calculations of the structure of matter with machine learning
    Speaker: Phiala Shanahan, MIT
    Close-in “hot planets” present a new opportunity for enriching our understanding of atmospheric dynamics of all planets. Not only are they presently the most well observed exoplanets, subject to an unusual forcing arrangement (i.e., steady irradiation on the same side of the planet throughout its orbit, leading to “perpetual day and night sides”), the dynamics on these planets is also unlike that on any of the solar system planets. Moreover, characterizing the flow pattern and temperature distribution on the extrasolar planets is critical for reliable interpretation of data currently being collected, as well as of data from large missions soon to come online (e.g., JWST and Ariel). In this talk, the flow structures (e.g., storms and jets) and the variability they induce on a large class of exoplanets, known as “hot-Jupiters”, will be discussed.
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  • June 16
    Machine learning for biological sequence design with therapeutic applications
    Speaker: Lucy Colwell, Cambridge University
    Prediction of protein function from sequence is a central challenge that allows us to discover new proteins with specific functional properties. Experimental and computational labels can be used to train and validate machine learning models that predict protein function directly from sequence. I will present deep learning models that accurately predict the presence and location of functional domains within protein sequences, adding hundreds of millions of annotations to public databases. Furthermore, experimental breakthroughs enable data on the relationship between sequence and function to be rapidly acquired. However, the cost and latency of wet-lab experiments require methods that find good sequences in few experimental rounds, where each round contains large batches of sequence designs. In this setting, I will discuss model-based optimization approaches that take advantage of sample inefficient methods to find diverse sequence candidates for experimental evaluation. The potential of this approach is illustrated through the design and experimental validation of proteins and peptides for therapeutic applications.
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  • June 23
    LIGO: a 1/10 scale model of Cosmic Explorer
    Speaker: David Shoemaker, MIT Kavli Institute
       The field of gravitational-wave astronomy has demonstrated its ability to provide insights into gravitation in the extremes of nature, as well as its ability to complement photon and particle astronomy and astrophysics. With roughly 100 events since the first observation in 2015, the approach to the instrumentation and its ability to deliver more science when upgraded even incrementally is demonstrated.
        The field is now formulating observatory concepts for a significant step in sensitivity – ten times that of the current instruments. This will bring to the order of 103 more sources into reach, in addition to improving the resolution for nearby sources and increasing the sensitive range in frequency and thus variety of sources.
        We will describe the US vision for the next generation gravitational-wave observatory, Cosmic Explorer, and call on the experience to date with LIGO to provide a sense of the feasibility and the path to realization for this major undertaking.

  • June 30
    How Materials Can Learn How to Function
    Speaker: Andrea Liu, University of Pennsylvania
  • Artificial neural networks learn via optimization where a loss function is minimized by a computer to achieve the desired result. But physical networks, such as mechanical spring networks or flow networks, have no attached processors to perform the optimization, so they cannot minimize such a loss function. What such systems do automatically minimize is their elastic energy (mechanical networks) or the dissipated power (flow network). I will describe how these natural physical processes can be harnessed to teach systems how to perform machine learning tasks such as classification, as well as functions inspired by biology. For example, the ability of proteins (e.g. hemoglobin) to change their conformations upon binding of an atom (oxygen) or molecule, or the ability of the brain’s vascular network to send enhanced blood flow and oxygen to specific areas of the brain associated with a given task. This learning strategy has recently been implemented in electrical circuits.


  • July 7
    New Rules: Quantum Circuits, Cellular Automata, Complexity and Chaos
    Speaker: Austen Lamacraft, University of Cambridge
  • Many of you will have played with cellular automata such as Conway's Game of Life. These are model systems in which complex and chaotic behaviors emerge from simple dynamical rules.

    Motivated by quantum computation, physicists have in recent years begun to study quantum circuits, which are in some way a quantum analogue of cellular automata. In this talk, I'll discuss some of the similarities and differences between these two classes of systems, and what they can teach us about classical and quantum dynamics more generally.


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  • September 15
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