Invited Talks and Presentations, Oral Exams, and Defenses
Error Correction for Connectomics
Invited Talk at BioImage Computing @ CVPR 2019, June, 2019.
Synapse-Aware Skeleton Generation for Neural Circuits
Invited Poster at Max Planck Institute--Howard Hughes Medical Institute Connectomics Conference, April, 2019.
Segmentation of Electron Micrscopy Images in Connectomics
Qualifying Exam at Harvard University, May, 2018.
Learning Global Features for Neuron Reconstruction in EM Images
Master's Defense at Princeton University, May, 2016.
Publications
Z. Lin, D. Wei, W.D. Jang, S. Zhou, X. Chen, X. Wang, R. Schalek, D. Berger, B. Matejek, L. Kamentsky, A. Suissa-Peleg, D. Haehn, T. Jones, T. Parag, J.W. Lichtman, and H. Pfister
Two Stream Active Query Suggestion for Active Learning in Connectomics
In Proceedings of European Conference on Computer Vision (ECCV), August, 2020.
@article{lin2020twostream,
    title={Two Stream Active Query Suggestion for Active Learning in Connectomics},
    author={Lin, Zudi and Wei, Donglai and Jang, Won-Dong and Zhou, Siyan and Chen, Xupeng and Wang, Xueying and Schalek, Richard and Berger, Daniel and Matejek, Brian and Kamentsky, Lee and Peleg, Adi and Haehn, Daniel and Jones, Thouis R. and Parag, Toufiq and Lichtman, Jeff and Pfister, Hanspeter},
    booktitle = {European Conference on Computer Vision (ECCV)},
    month = {August},
    year = {2020}
}
B. Matejek, D. Wei, X. Wang, J. Zhao, K. Palágyi, and H. Pfister
Synapse-Aware Skeleton Generation for Neural Circuits
In Springer: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), October, 2019.
@inproceedings{matejek2019synapse,
    title={Synapse-Aware Skeleton Generation for Neural Circuits},
    author={Matejek, Brian and Wei, Donglai and Wang, Xueying and Zhao, Jinglin and Pal{\'a}gyi, K{\'a}lm{\'a}n and Pfister, Hanspeter},
    booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
    pages={227--235},
    year={2019},
    organization={Springer}
}
B. Matejek, D. Haehn, H. Zhu, D. Wei, T. Parag, and H. Pfister
Biologically-Constrained Graphs for Global Connectomics Reconstruction
In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June, 2019.
@inproceedings{matejek2019biologically,
    title={Biologically-constrained graphs for global connectomics reconstruction},
    author={Matejek, Brian and Haehn, Daniel and Zhu, Haidong and Wei, Donglai and Parag, Toufiq and Pfister, Hanspeter},
    booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
    pages={2089--2098},
    year={2019}
}
K. Dmitriev, T. Parag, B. Matejek, A. Kaufman, and H. Pfister
Efficient Correction for EM Connectomics with Skeletal Representation
In British Machine Vision Conferemce (BMVC), September, 2018.
@article{dmitriev122018efficient,
    title={Efficient correction for em connectomics with skeletal representation},
    author={Dmitriev12, Konstantin and Parag, Toufiq and Matejek, Brian and Kaufman12, Arie E and Pfister, Hanspeter},
    journal={British Machine Vision Conferemce (BMVC)},
    year={2018}
}
M. Behrisch, D. Streeb, F. Stoffel, D. Seebacher, B. Matejek, S. Hagen Weber, S. Mittelstädt, H. Pfister, and D. Keim
Commercial Visual Analytics Systems - Advances in Big Data Analytics Field
In IEEE Transactions on Visualization and Computer Graphics (TVCG), July, 2018.
@article{behrisch2018commercial,
    title={Commercial visual analytics systems--advances in the big data analytics field},
    author={Behrisch, Michael and Streeb, Dirk and Stoffel, Florian and Seebacher, Daniel and Matejek, Brian and Weber, Stefan Hagen and Mittelstaedt, Sebastian and Pfister, Hanspeter and Keim, Daniel},
    journal={IEEE Transactions on Visualization and Computer Graphics},
    volume={25},
    number={10},
    pages={3011--3031},
    year={2018},
    publisher={IEEE}
}
B. Matejek, D. Haehn, F. Lekschas, M. Mitzenmacher, and H. Pfister
Compresso: Efficient Compression of Segmentation Data For Connectomics
In Springer: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), September, 2017.
@inproceedings{matejek2017compresso,
    title={Compresso: Efficient compression of segmentation data for connectomics},
    author={Matejek, Brian and Haehn, Daniel and Lekschas, Fritz and Mitzenmacher, Michael and Pfister, Hanspeter},
    booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
    pages={781--788},
    year={2017},
    organization={Springer}
}
D. Haehn, J. Hoffer, B. Matejek, A. Suissa-Peleg, A. Al-Awami, L. Kamentsky, F. Gonda, E. Meng, W. Zhang, R. Schalek, A. Wilson, T. Parag, J. Beyer, V. Kaynig, T. Jones, J. Tompkin, M. Hadwiger, J.W. Lichtman, and H. Pfister
Scalable Interactive Visualization for Connectomics
In MDPI Informatics, August, 2017.
@inproceedings{haehn2017scalable,
    title={Scalable interactive visualization for connectomics},
    author={Haehn, Daniel and Hoffer, John and Matejek, Brian and Suissa-Peleg, Adi and Al-Awami, Ali K and Kamentsky, Lee and Gonda, Felix and Meng, Eagon and Zhang, William and Schalek, Richard and others},
    booktitle={Informatics},
    volume={4},
    number={3},
    pages={29},
    year={2017},
    organization={Multidisciplinary Digital Publishing Institute}
}
T. Parag, F. Tschopp, W. Grisaitis, S.C. Turaga, X. Zhang, B. Matejek, L. Kamentsky, J.W. Lichtman, and H. Pfister
Anisotropic EM Segmentation by 3D Affinity Learning and Agglomeration
arXiv preprint arXiv:1707.08935, July, 2017.
@article{parag2017anisotropic,
    title={Anisotropic EM segmentation by 3D affinity learning and agglomeration},
    author={Parag, Toufiq and Tschopp, Fabian and Grisaitis, William and Turaga, Srinivas C and Zhang, Xuewen and Matejek, Brian and Kamentsky, Lee and Lichtman, Jeff W and Pfister, Hanspeter},
    journal={arXiv preprint arXiv:1707.08935},
    year={2017}
}
D. Dohan, B. Matejek, and T. Funkhouser
Learning Hierarchical Semantic Segmentation of LIDAR Data
In IEEE International Conference of 3D Vision, October, 2015.
@inproceedings{dohan2015learning,
    title={Learning hierarchical semantic segmentations of LIDAR data},
    author={Dohan, David and Matejek, Brian and Funkhouser, Thomas},
    booktitle={2015 International Conference on 3D Vision},
    pages={273--281},
    year={2015},
    organization={IEEE}
}
Independent Research, Written Exams, and Theses
Segmentation of Electron Microscopy Images for Connectomics
Qualifying Exam at Harvard University, May, 2018.
Learning Global Features for Neuron Reconstruction in EM Images
Master's Thesis at Princeton University, May, 2016.
Detecting Objects Using Google Street View Data
Independent Research at Princeton University, December, 2013.
A Computational Analysis of Arbitrage Opportunities in Sports Gambling
Independent Research at Princeton University, May, 2013.