Alessandro Palma
@ale__palmaa
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PhD student at @HelmholtzMunich and @TU_Muenchen | ML and computational biology
Joined June 2022
RT @martenlienen: Real data is noisy but HiPPO assumes it's clean. Our UnHiPPO initialization resists noise with implicit Kalman filtering….
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RT @SchuchardtJan: How private is DP-SGD for self-supervised training on sequences?.Our #ICML2025 spotlight shows that it can be very priva….
arxiv.org
Many forms of sensitive data, such as web traffic, mobility data, or hospital occupancy, are inherently sequential. The standard method for training machine learning models while ensuring privacy...
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RT @SikkemaLisa: Analyzing your single-cell data by mapping to a reference atlas? Then how do you know the mapping actually worked, and you….
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RT @lucrevaleriani: Happy to be in Boston to present nf-core/tumourevo pipeline, a joint effort from all the Cancer Data Science Lab at @Un….
nf-co.re
Analysis pipleine to model tumour clonal evolution from WGS data (driver annotation, quality control of copy number calls, subclonal and mutational signature deconvolution)
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RT @andrea_dittadi: I meant to tweet my ICLR papers before the conference, but figured I'd wait until it was comically late. If you’ve got….
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Looking forward to meeting everyone during the poster sessions!.
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Aside from realistic single-cell generation, we showcase our model in applied settings, such as data augmentation and batch correction. Looking forward to meeting everyone during the poster sessions!.
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We extend classifier-free guidance in Flow Matching to the multi-attribute setting, enabling control of the generation process using multiple covariates, each with its guidance weight.
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CFGen uses latent Flow Matching to generate discrete and multi-modal single-cell data across biological settings. We use discrete decoders for each modality to generate the corresponding data characteristics from flexible noise models.
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We will present our work “Multi-Modal and Multi-Attribute Generation of Single Cells with CFGen” at #ICLR2025. Meet us tomorrow at 10 am, Hall 3, poster #17!. This is joint work with @TillRichter6 hanyi manuel @AlexanderTong7 @andrea_dittadi @fabian_theis
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RT @KHrovatin: To figure out what it takes to make flow matching models solve single-cell biology questions, I discussed with Alessandro Pa….
hrovatin.github.io
Models that fit like water in a vessel.
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RT @mo_lotfollahi: (1/8) IMPA is published now in @NatureComms. (a) It can generate phenotypic cell painting/microscopy data images under u….
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RT @fabian_theis: 1/🚀 Excited to share RegVelo, our new computational model combining RNA velocity with gene regulatory network (GRN) dynam….
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RT @Dominik1Klein: 1/6 Looking for neural estimators of entropic #OptimalTransport or simply cool applications of #FlowMatching? Excited by….
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RT @SchuchardtJan: Deep learning with differential privacy can protect sensitive information of individuals. But what about groups of multi….
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RT @_yji_: Do you run functional assays? Wish you could get more results without having to scale?. If you’re not using Prophet, you’re leav….
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RT @geisler_si: We introduce Spatio-Spectral GNNs (S²GNNs) – an effective modeling paradigm via the synergy of spatially and spectrally par….
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RT @janxengelmann: Mixed Models with Multiple Instance Learning (MixMIL) received an Oral & Outstanding Student Paper award at @aistats_con….
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RT @LucaEyring: Dealing with undesired distribution shifts in unpaired translation tasks? Our #ICLR2024 paper shows how to mitigate them le….
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