Aaron Molt
@AaronMolt
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We were in contention for Best Paper!
In his second talk in the same (!) session, @AaronMolt presents our novel metric for comparing 2-wise samples: #pickme - this work is based on his Bachelor's thesis. By the way, Aaron will be looking for a PhD position in 2026, so talk to him! #SPLC25
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In his second talk in the same (!) session, @AaronMolt presents our novel metric for comparing 2-wise samples: #pickme - this work is based on his Bachelor's thesis. By the way, Aaron will be looking for a PhD position in 2026, so talk to him! #SPLC25
Our work "Pick Me: Judging Sample Quality with Binary Decision Diagrams" has been accepted into @splcconf'25 🎉. This work is based on the Bachelor's Thesis of my amazing student @AaronMolt (who will be looking for a PhD position soon 😉). Details and preprint below ⬇️ #SPLC25
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First talk of @AaronMolt at #SPLC25! Aaron is presenting our work towards enabling efficient deterministic BDD compilation - this time around through eliminating atomic sets during preprocessing.
Aaand the extended version got accepted into #SPLC25 🎉 Title "Improving Deterministic BDD Compilation with Counting-Free Computation of Atomic Sets" Joint work with @AaronMolt who will also present it at the conference. Abstract ⤵️
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Aaand the extended version got accepted into #SPLC25 🎉 Title "Improving Deterministic BDD Compilation with Counting-Free Computation of Atomic Sets" Joint work with @AaronMolt who will also present it at the conference. Abstract ⤵️
It has been more than 20 years since @sergio_segura2 proposed to exploit atomic sets for preprocessing feature models. Over the holidays, we came up with "A Fast Counting-Free Algorithm for Computing Atomic Sets in Feature Models" Paper: https://t.co/QlitK648pu w/ @Aargon06
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Our work "Pick Me: Judging Sample Quality with Binary Decision Diagrams" has been accepted into @splcconf'25 🎉. This work is based on the Bachelor's Thesis of my amazing student @AaronMolt (who will be looking for a PhD position soon 😉). Details and preprint below ⬇️ #SPLC25
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It has been more than 20 years since @sergio_segura2 proposed to exploit atomic sets for preprocessing feature models. Over the holidays, we came up with "A Fast Counting-Free Algorithm for Computing Atomic Sets in Feature Models" Paper: https://t.co/QlitK648pu w/ @Aargon06
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