Conference Presentations

Watch talks, workshops, and technical presentations from industry conferences

Talks & Workshops

Learn from conference presentations at WBS, QuantMinds, and other industry events. Our talks cover AAD implementation strategies, performance benchmarks, and real-world case studies.

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Automatic IFT for Live Risk Transition

QuantMinds presentation demonstrating how Automated Implicit Function Theorem enables transition from overnight batch risk to real-time Live Risk without multi-year IT transformation.

Key Takeaways

  • Transform overnight batch risk into real-time Live Risk
  • Automatic differentiation of calibration routines using AIFT
  • No hidden variable tracking or large Jacobian computation required
  • QuantLib integration achieving 20ms pricing + Greeks for 1000 swaps

Technical Highlights

  • Automated Implicit Function Theorem (AIFT) for solver differentiation
  • Works with exact-fit and nearly-exact calibrations
  • No codebase refactoring required
  • Checkpointing approach without going into solvers
  • Published in Risk.NET 2022 by Goloubentsev, Lakshtanov, and Piterbarg
Watch Talk
conferencelive-riskIFT
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WBS Talk: AAD for Longstaff-Schwartz American Monte Carlo

Conference talk on AAD for Longstaff-Schwartz American Monte Carlo with memory-efficient implementation.

Key Takeaways

  • Pathwise + regression AAD
  • Memory only 2x primal
  • Better stability than bumping
Watch Talk
conferenceamerican-monte-carloLSM
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WBS2025: Greeks for Autocallables

Dmitri Goloubentsev

Presentation from the 21st WBS Quantitative Finance Conference in Palermo demonstrating production-ready autocallable Greeks calculation with 99.8% cloud cost reduction.

Key Takeaways

  • Production-ready autocallable Greeks methodology
  • How to achieve 99.8% cloud cost reduction
  • Smoothing techniques for discontinuous payoffs
  • Complete open-source implementation

Technical Highlights

  • Pathwise Greeks with smoothing
  • Automatic Adjoint Differentiation
  • Open-source methodology
View Slides
presentationWBS2025autocallables
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ING Analysis: Cross-Gamma Performance Comparison

Stephan Bosch

ING comparison of bumping-over-AAD for second-order cross-gamma (CVA portfolio).

Key Takeaways

  • Scalability of tape vs code-gen AAD for large Hessians

Technical Highlights

  • Code-gen linear vs tape quadratic
  • Bumping-over-AAD unlocks massive reuse
View Slides
presentationINGcross-gamma
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ING Presentation: AAD Engine Scalability Analysis

Stephan Bosch, ING

ING presentation comparing AAD engine scalability for Monte Carlo and higher-order sensitivities.

Key Takeaways

  • Linear vs quadratic scaling in AAD engines
  • When code-generation wins

Technical Highlights

  • Code-generation AAD scales linearly
  • Vectorization critical for MC
View Slides
presentationWBS2023ING
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WBS2023: Expected Shortfall with AAD

Svetlana Borovkova

Presentation on Expected Shortfall and AAD computational costs.

Key Takeaways

  • AAD application to regulatory risk metrics
View Slides
presentationWBS2023expected-shortfall

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Dive deeper into the technology

How AADC Works

AADC extracts a computational graph (DAG) from existing analytics at runtime and compiles it to optimized machine code, achieving 100-1000× for Python and 6-100× for C++ with automatic derivatives.

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Performance Benchmarks

Independent benchmarks demonstrating AADC's performance advantages over traditional AAD approaches, GPU implementations, and ML frameworks.

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