Add slides for the monthly meeting talk for Xeus-Cpp-Lite

_data/meetinglist.yml

@@ -2,7 +2,7 @@
   date: 2025-08-07 17:00:00 +0200
   time_cest: "17:00"
   connect: "[Link to zoom](https://princeton.zoom.us/j/94431046845?pwd=D5i77Qb0PgfwwIubvbo2viEunne7eQ.1)"
   label: caas_07Aug2025
   agenda:
     - title: "Interpreting C++ in the web through Jupyter"
       speaker:
@@ -12,7 +12,7 @@
           Anutosh Bhat is a scientific software engineer at QuantStack, working
           at the intersection of compilers, Jupyter infrastructure, and
           WebAssembly. He contributes to projects such as LLVM, Xeus, and
           JupyterLite, and leads efforts to package them for Emscripten-Forge,
           a conda-based distribution for the browser.
       description: |
         While interpreted languages like Python and R dominate interactive data
@@ -26,11 +26,12 @@
         with the Jupyter ecosystem to enable serverless, client-side C++
         execution — unlocking a new path for scientific computing, education,
         and rapid prototyping entirely in the browser.
+      slides: /assets/presentations/Anutosh_Bhat_Xeus-Cpp-Lite.pdf
 - name: "CaaS Monthly Meeting"
   date: 2025-06-05 17:00:00 +0200
   time_cest: "17:00"
   connect: "[Link to zoom](https://princeton.zoom.us/j/94431046845?pwd=D5i77Qb0PgfwwIubvbo2viEunne7eQ.1)"
   label: caas_05June2025
   agenda:
     - title: "Differentiating RooFit likelihoods with Clad"
       speaker:
@@ -66,43 +67,43 @@
   date: 2025-05-15 17:00:00 +0200
   time_cest: "17:00"
   connect: "[Link to zoom](https://princeton.zoom.us/j/94431046845?pwd=D5i77Qb0PgfwwIubvbo2viEunne7eQ.1)"
   label: caas_15May2025
   agenda:
     - title: "Accelerating simulation-based inference in RooFit at LHCb with Clad"
       speaker:
         image: "https://gitlab.cern.ch/uploads/-/system/user/avatar/23455/avatar.png?width=192"
         name: "Jamie Gooding"
         bio: |
           Jamie Gooding is a 3rd year Doctoral Student at Technische Universitat
           (TU) Dortmund and SMARTHEP Early Stage Researcher, working on the LHCb
           experiment and currently completing a secondment with the ROOT
           team. His research focuses on developing real-time analysis techniques
           to enable studies of charge-parity and lepton-flavour violation in
           neutral B meson decays, with an additional interest in computational
           tools for statistical analyses in HEP. Prior to starting his doctoral
           studies at TU Dortmund, he completed a Physics MPhys (Hons) at the
           University of Manchester.        
       description: |
         Simulation-based inference (SBI) is a set of statistical inference
         approaches in which Machine Learning (ML) algorithms are trained to
         approximate likelihood ratios, e.g., as an alternative to the likelihood
         fits commonly performed in HEP analyses. A demonstrator has been 
         developed in which SBI is applied to extract parameters of interest from
         the kinematic and angular distributions of
         $B^0 \rightarrow D^{*-} \mu^+ \nu_\mu​$ decays in pseudodata samples
         generated with RapidSim representative of the datasets used in LHCb
         analysis. The SBI fit is constructed using the RooFit framework, to
         which enhanced Python interfaces were recently introduced. Dense Neural
         Networks (DNNs) were trained to distinguish between the Standard Model
         and New Physics scenarios for varying parameters of interest. This
         workflow incorporates automatic differentiation (AD) of the learned
         likelihoods, using the ROOT SOFIE framework to generate C++ code from
         the DNNs, from which gradient code is generated by source-code
         transformation AD with Clad. In this talk, the SBI fit is introduced and
         compared to the current state-of-the-art. Additionally, this talk
         discusses the functionality introduced to enable differentiation from
         likelihood through to ML classifier call and the impact of this
         functionality upon performance of the SBI fit.
       slides: "/assets/presentations/Jamie_G-RooFit_SBI.pdf"
     - title: Next meeting
       speaker: Vassil Vassilev
@@ -112,7 +113,7 @@
   date: 2024-12-12 17:00:00 +0200
   time_cest: "17:00"
   connect: "[Link to zoom](https://princeton.zoom.us/j/91526749339?pwd=52MET9OQwUqI9nGgk3ko3XmTASQPuN.1)"
   label: caas_12Dec2024
   agenda:
     - title: "2024 Compiler-Research Project Overview"
       speaker: