Frank Schäfer

I am a postdoc in the Julia Lab at the Massachusetts Institute of Technology (MIT). Previously: PhD in physics in the Bruder group within the “Quantum Computing and Quantum Technology” PhD school at the University of Basel.


20:30 UTC

StochasticAD.jl: Differentiating discrete randomness

07/26/2023, 8:30 PM — 9:00 PM UTC
32-D463 (Star)

Automatic differentiation (AD) is great: use gradients to optimize, sample faster, or just for fun! But what about coin flips? Agent-based models? Nope, these aren’t differentiable... or are they? StochasticAD.jl is an open-source research package for AD of stochastic programs, implementing AD algorithms for handling programs that can contain discrete randomness.

15:40 UTC

Differentiation of discontinuities in ODEs arising from dosing

07/28/2023, 3:40 PM — 3:50 PM UTC
32-D463 (Star)

In this talk, we present continuous-adjoint sensitivity methods for hybrid differential equations (i.e., ordinary or stochastic differential equations with callbacks) modeling explicit and implicit events. The methods are implemented in the SciMLSensitivity.jl package. As a concrete example, we consider the sensitivity analysis of dosing times in pharmacokinetic models. We discuss different options for the automatic differentiation backend.

20:00 UTC

Convex Optimization for Quantum Control in Julia

07/28/2023, 8:00 PM — 8:10 PM UTC

Feedback control policies for quantum systems often lack performance targets and certificates of optimality. Here, we will show how bounds on the best possible control performance are readily computable for a wide range of quantum control problems by means of convex optimization using Julia's optimization ecosystem. We discuss how these bounds provide targets and certificates to improve the design of quantum feedback controllers.

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Pumas AIQuEra Computing Inc.Relational AIJeffrey Sarnoff

Bronze sponsors

Jolin.ioBeacon BiosignalsMIT CSAILBoeing

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