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Hunter Nisonoff

dblp:313/9902 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2025
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Generative modeling · 100%
Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%

Topics — the 7 heaviest of 9, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Generative modeling
diffusion model
0.912025
Unlocking Guidance for Discrete State-Space Diffusion and Flow Models · ICLR 2025
Machine learning › Generative modeling › normalizing flow
discrete flow model
0.912025
Unlocking Guidance for Discrete State-Space Diffusion and Flow Models · ICLR 2025
Machine learning › Generative modeling › diffusion model › discrete diffusion model
discrete state-space diffusion
0.912025
Unlocking Guidance for Discrete State-Space Diffusion and Flow Models · ICLR 2025
Machine learning › Generative modeling
normalizing flow
0.912025
Unlocking Guidance for Discrete State-Space Diffusion and Flow Models · ICLR 2025
Bioinformatics and computational biology
synthetic biology
0.812024
GENTANGLE: integrated computational design of gene entanglements · Bioinform. 2024
Bioinformatics and computational biology › molecular informatics › cheminformatics
molecule generation
0.312025
Unlocking Guidance for Discrete State-Space Diffusion and Flow Models · ICLR 2025
Bioinformatics and computational biology
genomics
0.212024
GENTANGLE: integrated computational design of gene entanglements · Bioinform. 2024

Methods — techniques the papers use, named apart from their topics

continuous-time markov processes · 0.9continuous-time markov process · 0.9computational design pipeline · 0.8
YearPublicationVenuePosition
2025 Unlocking Guidance for Discrete State-Space Diffusion and Flow Models
abstract
Generative models on discrete state-spaces have a wide range of potential applications, particularly in the domain of natural sciences. In continuous state-spaces, controllable and flexible generation of samples with desired properties has been realized using guidance on diffusion and flow models. However, these guidance approaches are not readily amenable to discrete state-space models. Consequently, we introduce a general and principled method for applying guidance on such models. Our method depends on leveraging continuous-time Markov processes on discrete state-spaces, which unlocks computational tractability for sampling from a desired guided distribution. We demonstrate the utility of our approach, Discrete Guidance, on a range of applications including guided generation of small-molecules, DNA sequences and protein sequences.
Hunter Nisonoff, Junhao Xiong, Stephan Allenspach, Jennifer Listgarten
ICLR1
2024 GENTANGLE: integrated computational design of gene entanglements
abstract
SUMMARY: The design of two overlapping genes in a microbial genome is an emerging technique for adding more reliable control mechanisms in engineered organisms for increased stability. The design of functional overlapping gene pairs is a challenging procedure, and computational design tools are used to improve the efficiency to deploy successful designs in genetically engineered systems. GENTANGLE (Gene Tuples ArraNGed in overLapping Elements) is a high-performance containerized pipeline for the computational design of two overlapping genes translated in different reading frames of the genome. This new software package can be used to design and test gene entanglements for microbial engineering projects using arbitrary sets of user-specified gene pairs. AVAILABILITY AND IMPLEMENTATION: The GENTANGLE source code and its submodules are freely available on GitHub at https://github.com/BiosecSFA/gentangle. The DATANGLE (DATA for genTANGLE) repository contains related data and results and is freely available on GitHub at https://github.com/BiosecSFA/datangle. The GENTANGLE container is freely available on Singularity Cloud Library at https://cloud.sylabs.io/library/khyox/gentangle/gentangle.sif. The GENTANGLE repository wiki (https://github.com/BiosecSFA/gentangle/wiki), website (https://biosecsfa.github.io/gentangle/), and user manual contain detailed instructions on how to use the different components of software and data, including examples and reproducing the results. The code is licensed under the GNU Affero General Public License version 3 (https://www.gnu.org/licenses/agpl.html).
Jose Manuel Martí, Chloe Hsu, Charlotte Rochereau, Chenling Xu, Tomasz Blazejewski, Hunter Nisonoff, Sean P. Leonard, Christina S. Kang-Yun, Jennifer Chlebek, Dante P. Ricci, Dan Park, Harris Wang, Jennifer Listgarten, Yongqin Jiao, Jonathan E. Allen
Bioinform.6