Demo Data from SIGIR 2026 / reviewers in the wild / expert
Hunter Nisonoff
dblp:313/9902
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Generative modeling
diffusion model |
0.9 | 1 | 2025 | Unlocking Guidance for Discrete State-Space Diffusion and Flow Models · ICLR 2025 |
Machine learning › Generative modeling › normalizing flow
discrete flow model |
0.9 | 1 | 2025 | 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 |
0.9 | 1 | 2025 | Unlocking Guidance for Discrete State-Space Diffusion and Flow Models · ICLR 2025 |
Machine learning › Generative modeling
normalizing flow |
0.9 | 1 | 2025 | Unlocking Guidance for Discrete State-Space Diffusion and Flow Models · ICLR 2025 |
Bioinformatics and computational biology
synthetic biology |
0.8 | 1 | 2024 | GENTANGLE: integrated computational design of gene entanglements · Bioinform. 2024 |
Bioinformatics and computational biology › molecular informatics › cheminformatics
molecule generation |
0.3 | 1 | 2025 | Unlocking Guidance for Discrete State-Space Diffusion and Flow Models · ICLR 2025 |
Bioinformatics and computational biology
genomics |
0.2 | 1 | 2024 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Unlocking Guidance for Discrete State-Space Diffusion and Flow ModelsabstractGenerative 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 |
ICLR | 1 |
| 2024 | GENTANGLE: integrated computational design of gene entanglementsabstractSUMMARY: 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 |