Learning Hierarchical Semantic Segmentations of LIDAR Data. - David Dohan; Brian Matejek; Thomas A. Funkhouser (3DV, 2015)

Compresso: Efficient Compression of Segmentation Data for Connectomics. - Brian Matejek; Daniel Haehn; Fritz Lekschas; Michael Mitzenmacher; Hanspeter Pfister (MICCAI (1), 2017)

Anisotropic EM Segmentation by 3D Affinity Learning and Agglomeration. - Toufiq Parag; Fabian Tschopp; William Grisaitis; Srinivas C. Turaga; Xuewen Zhang; Brian Matejek; Lee Kamentsky; Jeff W. Lichtman; Hanspeter Pfister (CoRR, 2017)

Scalable Interactive Visualization for Connectomics. - Daniel Haehn; John Hoffer; Brian Matejek; Adi Suissa-Peleg; Ali K. Al-Awami; Lee Kamentsky; Felix Gonda; Eagon Meng; William Zhang; Richard Schalek; Alyssa Wilson; Toufiq Parag; et al. (Informatics, 2017)

Efficient Correction for EM Connectomics with Skeletal Representation. - Konstantin Dimitriev; Toufiq Parag; Brian Matejek; Arie E. Kaufman; Hanspeter Pfister (BMVC, 2018)

Biologically-Constrained Graphs for Global Connectomics Reconstruction. - Brian Matejek; Daniel Haehn; Haidong Zhu; Donglai Wei 001; Toufiq Parag; Hanspeter Pfister (CVPR, 2019)

Synapse-Aware Skeleton Generation for Neural Circuits. - Brian Matejek; Donglai Wei 001; Xueying Wang 002; Jinglin Zhao; Kálmán Palágyi; Hanspeter Pfister (MICCAI (1), 2019)

Commercial Visual Analytics Systems-Advances in the Big Data Analytics Field. - Michael Behrisch 001; Dirk Streeb; Florian Stoffel; Daniel Seebacher; Brian Matejek; Stefan Hagen Weber; Sebastian Mittelstädt; Hanspeter Pfister; Daniel A. Keim (IEEE Trans. Vis. Comput. Graph., 2019)

Two Stream Active Query Suggestion for Active Learning in Connectomics. - Zudi Lin; Donglai Wei 001; Won-Dong Jang; Siyan Zhou; Xupeng Chen; Xueying Wang 002; Richard Schalek; Daniel R. Berger; Brian Matejek; Lee Kamentsky; Adi Suissa-Peleg; Daniel Haehn; et al. (ECCV (18), 2020)

AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions. - Donglai Wei 001; Kisuk Lee; Hanyu Li; Ran Lu; J. Alexander Bae; Zequan Liu; Lifu Zhang; Márcia dos Santos; Zudi Lin; Thomas D. Uram; Xueying Wang 002; Ignacio Arganda-Carreras; et al. (MICCAI (1), 2021)

Algorithmic Tools for Understanding the Motif Structure of Networks. - Tianyi Chen; Brian Matejek; Michael Mitzenmacher; Charalampos E. Tsourakakis (ECML/PKDD (2), 2022)

Scalable Biologically-Aware Skeleton Generation for Connectomic Volumes. - Brian Matejek; Tim Franzmeyer; Donglai Wei 001; Xueying Wang 002; Jinglin Zhao; Kálmán Palágyi; Jeff W. Lichtman; Hanspeter Pfister (IEEE Trans. Medical Imaging, 2022)

Direct Amortized Likelihood Ratio Estimation. - Adam D. Cobb; Brian Matejek; Daniel Elenius; Anirban Roy; Susmit Jha (AAAI, 2024)

Non-Invasive Stress Monitoring From Video. - Akshata Tiwari; Brian Matejek; Daniel Haehn (ISBI, 2024)

Resource-Constrained Heuristic for Max-SAT. - Brian Matejek; Daniel Elenius; Cale Gentry; David Stoker; Adam D. Cobb (CoRR, 2024)

Addressing Uncertainty in LLMs to Enhance Reliability in Generative AI. - Ramneet Kaur; Colin Samplawski; Adam D. Cobb; Anirban Roy; Brian Matejek; Manoj Acharya; Daniel Elenius; Alexander M. Berenbeim; John A. Pavlik; Nathaniel D. Bastian; Susmit Jha (CoRR, 2024)

Melanoma Detection with Uncertainty Quantification. - Sanghyuk Kim; Edward Gaibor; Brian Matejek; Daniel Haehn (ISBI, 2025)

SAFE-NID: Self-Attention with Normalizing-Flow Encodings for Network Intrusion Detection. - Brian Matejek; Ashish Gehani; Nathaniel D. Bastian; Daniel J. Clouse; Bradford J. Kline; Susmit Jha (Trans. Mach. Learn. Res., 2025)

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding. - Trilok Padhi; Ramneet Kaur; Adam D. Cobb; Manoj Acharya; Anirban Roy; Colin Samplawski; Brian Matejek; Alexander M. Berenbeim; Nathaniel D. Bastian; Susmit Jha (CoRR, 2025)