Whoops, I just realized that I failed to qualify that statement properly. CasADi [https://web.casadi.org/], released in 2013, uses adjoint sensitivities to compute gradients through ODE solutions using symbolic (but still reverse-mode) differentiation.

I should have said something like: We were the first to implement adjoint sensitivities in a modern, tracing-based autodiff framework suitable for machine learning [HIPS autograd]. But I messed up and should have given credit to Joel Andersson for being first, my apologies.