Add info an June 5th compiler research monthly meeting

.github/actions/spelling/allow/terms.txt

@@ -16,6 +16,7 @@ IIT
 JIT'd
 Jacobians
 Jurgaityt
+LHC
 LLMs
 LLVM
 LULESH
@@ -49,6 +50,7 @@ jthread
 linkedin
 llm
 llvm
+meetinglist
 microenvironments
 omp
 openmp

_data/meetinglist.yml

@@ -1,44 +1,80 @@
+- 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:
+        image: "https://compiler-research.org/images/team/JRembser.png"
+        name: "Jonas Rembser"
+        bio: |
+          Jonas Rembser is a particle physicist with a strong interest in data
+          science and software development. After working with the CMS
+          experiment for his PhD in particle physics, Jonas joined CERN in 2021
+          to work on the ROOT project. He is takes care of the statistical
+          analysis libraries in ROOT, such as RooFit and Minuit 2, as well as
+          the ROOT Python interface. His other academic interests include
+          differential programming and financial markets.
+      description: |
+        RooFit is a C++ software for statistical data modeling that is part of
+        ROOT. It is widely used by the High Energy Physics (HEP) community,
+        most notably the LHC experiments. RooFits core feature is building and
+        numerically minimizing likelihood functions for parameter estimation
+        (aka. *fitting*). The minimization can be greatly sped up by using
+        reverse mode (Automatic Differentiation) AD, in particular for fits
+        with many parameters, as typical for the LHC experiments. This
+        presentation will tell the story of how AD was integrated to RooFit
+        powered by [Clad](https://github.com/vgvassilev/clad/), the AD tool
+        developed by Compiler Research. We will cover the integration strategy,
+        explain the particularities of AD in the context of likelihood fits,
+        and present benchmark results. The presentation will wrap up by
+        explaining the current limitations and plans for the future. More
+        information on AD in RooFit with Clad can also be found in
+        [this blog post](https://root.cern/blog/roofit-ad/) on the ROOT
+        website.
+
 - name: "CaaS Monthly Meeting"
   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
@@ -48,7 +84,7 @@
   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:
@@ -73,7 +109,7 @@
   date: 2024-11-07 17:00:00 +0200
   time_cest: "17:00"
   connect: "[Link to zoom](https://princeton.zoom.us/j/91526749339?pwd=52MET9OQwUqI9nGgk3ko3XmTASQPuN.1)"
   label: caas_07Nov2024
   agenda:
     - title: "CppInterOp: Advancing Interactive C++ for High Energy Physics"
       speaker:
@@ -130,7 +166,7 @@
   connect: "[Link to zoom](https://princeton.zoom.us/j/94396923932?pwd=9MDrbK3nQgGYjqkEZILGO7ne4YczA9.1)"
   label: caas_11Jul2024
   agenda:
     - title: "Automatic Differentiation in Roofit for Fast and Accurate Likelihood Fits"
       speaker:
         image: "https://compiler-research.org/images/team/Vaibhav.jpg"
         name: "Vaibhav Thakkar"