My research interests lie broadly in artificial intelligence and
machine learning, including the applications in computer vision and
natural language processing. I am very lucky to have Prof.
Adriana Kovashka and Prof.
Rebecca Hwa as my co-advisors.
(2021.11)I defended my thesis titled "Domain Robustness in Multi-modality Learning and Visual Question Answering".
(2021.6)Our BasisNet paper has been awarded best paper award for CVPR 2021 workshop: Efficient Deep Learning for Computer Vision. Many thanks to all my collaborators at Google: Li, Chun-Te, Andrey, Andrew, Brendan, Yukun, and my advisors at Pitt: Rebecca and Adriana!
(2021.3)I passed my thesis proposal and officially became a PhD candidate.
(2021.3)Our paper on domain robustness for visual question answering has been accepted to CVPR 2021.
(2020.8)I worked with Google Geo (Maps) as a research intern in the summer 2020.
(2020.1)I continued my work with Google Research as a part-time student researcher from January to May, 2020.
(2019.10)I attended ICCV 2019 at Seoul, Korea and presented our weakly-supervised object detection paper.
(2019.5)I came back to Google Research, Seattle as a research intern!
(2016.11) I received the Arts and Sciences Graduate Fellowship again for Spring 2017.
(2016.5) I received the Arts and Sciences Graduate Fellowship for Fall 2016.
Research
Publications
Video Timeline Modeling for News Story Understanding
Meng Liu, Mingda Zhang, Jialu Liu, Hanjun Dai, Ming-Hsuan Yang, Shuiwang Ji, Zheyun Feng, Boqing Gong.
To appear, Neural Information Processing Systems (NeurIPS), Track on Datasets and Benchmarks, December 2023.(Spotlight)
Train-Once-for-All Personalization
Hong-You Chen, Yandong Li, Yin Cui, Mingda Zhang, Wei-Lun Chao, Li Zhang. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2023.
(pdf)
If you are interested in my previous publications (before joining Google), click here...
How to Practice VQA on a Resource-limited Target Domain Mingda Zhang, Rebecca Hwa, Adriana Kovashka. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), January 2023.
(project)
(pdf)
(bibtex)
@InProceedings{Zhang_2023_WACV,
author = {Zhang, Mingda and Hwa, Rebecca and Kovashka, Adriana},
title = {How To Practice VQA on a Resource-Limited Target Domain},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {2023},
}
Learning to Overcome Noise in Weak Caption Supervision for Object Detection
Mesut Erhan Unal, Keren Ye, Mingda Zhang, Christopher Thomas, Adriana Kovashka, Wei Li, Danfeng Qin, Jesse Berent Transactions of Pattern Analysis and Machine Intelligence (TPAMI), 2022
(pdf)
(bibtex)
@article{unal2022learning,
title={Learning to Overcome Noise in Weak Caption Supervision for Object Detection},
author={Unal, Mesut Erhan and Ye, Keren and Zhang, Mingda and Thomas, Christopher and Kovashka, Adriana and Li, Wei and Qin, Danfeng and Berent, Jesse},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2022}
}
Abg-CoQA: Clarifying Ambiguity in Conversational Question Answering
Meiqi Guo, Mingda Zhang, Siva Reddy, Malihe Alikhani. Proceedings of the 3rd Conference on Automated Knowledge Base Construction (AKBC), October 2021.
(pdf)
(bibtex)
@InProceedings{Guo_2021_AbgCoQA,
author = {Guo, Meiqi and Zhang, Mingda and Reddy, Siva and Diab, Ahmad and Alikhani, Malihe},
title = {Abg-Co{QA}: Clarifying Ambiguity in Conversational Question Answering},
booktitle = {3rd Conference on Automated Knowledge Base Construction (AKBC)},
year = {2021}
}
Domain-robust VQA with Diverse Datasets and Methods but No Target Labels Mingda Zhang, Tristan Maidment, Ahmad Diab, Adriana Kovashka, Rebecca Hwa. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2021.
(project)
(pdf)
(poster)
(slides)
(arxiv)
(bibtex)
@InProceedings{Zhang_2021_Domain,
author = {Zhang, Mingda and Maidment, Tristan and Diab, Ahmad and Kovashka, Adriana and Hwa, Rebecca},
title = {Domain-robust VQA with Diverse Datasets and Methods but No Target Labels},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021}
}
BasisNet: Two-stage Model Synthesis for Efficient Inference Mingda Zhang, Chun-Te Chu, Andrey Zhmoginov, Andrew Howard, Brendan Jou, Yukun Zhu, Li Zhang, Rebecca Hwa, Adriana Kovashka. 4th Workshop on Efficient Deep Learning for Computer Vision (ECV21), Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshop, June 2021.(Best Paper Award)
(pdf)
(supplementary)
(slides)
(arxiv)
(bibtex)
@InProceedings{Zhang_2021_BasisNet,
author = {Zhang, Mingda and Chu, Chun-Te and Zhmoginov, Andrey and Howard, Andrew and Jou, Brendan and Zhu, Yukun and Zhang, Li and Hwa, Rebecca and Kovashka, Adriana},
title = {BasisNet: Two-Stage Model Synthesis for Efficient Inference},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2021}
}
Breaking Shortcuts by Masking for Robust Visual Reasoning
Keren Ye, Mingda Zhang, Adriana Kovashka. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), January 2021.
(pdf)
(supplementary)
(bibtex)
@InProceedings{Ye_2021_Shortcut,
author = {Ye, Keren and Zhang, Mingda and Kovashka, Adriana},
title = {Breaking Shortcuts by Masking for Robust Visual Reasoning},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {2021}
}
Story Completion with Explicit Modeling of Commonsense Knowledge Mingda Zhang, Keren Ye, Rebecca Hwa, Adriana Kovashka. Minds vs. Machines: How far are we from the common sense of a toddler?, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshop, June 2020.
(pdf)
(recording)
(bibtex)
@InProceedings{Zhang_2020_Story,
author = {Zhang, Mingda and Ye, Keren and Hwa, Rebecca and Kovashka, Adriana},
title = {Story Completion With Explicit Modeling of Commonsense Knowledge},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}
}
Monitoring ICU Mortality Risk with A Long
Short-Term Memory Recurrent Neural Network
Ke Yu*, Mingda Zhang*, Tianyi Cui*, Milos Hauskrecht. (*equal contributions) Proceedings of Pacific Symposium on Biocomputing (PSB), January 2020.(Oral)
(pdf)
(bibtex)
@InProceedings{Yu_2019_Monitoring,
author = {Yu, Ke and Zhang, Mingda and Cui, Tianyi and Hauskrecht, Milos},
title = {Monitoring ICU Mortality Risk with A Long Short-Term Memory Recurrent Neural Network},
booktitle = {Proceedings of Pacific Symposium On Biocomputing (PSB)},
month = {January},
year = {2020}
}
Interpreting the Rhetoric of Visual Advertisements
Keren Ye, Narges Honarvar Nazari, James Hahn, Zaeem Hussain, Mingda Zhang, Adriana Kovashka. Transactions of Pattern Analysis and Machine Intelligence (TPAMI), 2019.
(pdf)
(bibtex)
@Article{Ye_2019_Interpreting,
author = {Ye, Keren and Nazari, Narges Honarvar and Hahn, James and Hussain, Zaeem and Zhang, Mingda and Kovashka, Adriana},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
title = {Interpreting the Rhetoric of Visual Advertisements},
year = {2021},
volume = {43},
number = {4},
pages = {1308-1323},
doi = {10.1109/TPAMI.2019.2947440}
}
Cap2Det: Learning to Amplify Weak Caption
Supervision for Object Detection
Keren Ye, Mingda Zhang, Adriana Kovashka, Wei Li, Danfeng Qin, Jesse Berent. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2019.
(pdf)
(supplementary)
(code)
(arxiv)
(bibtex)
@InProceedings{Ye_2019_Cap2Det,
author = {Ye, Keren and Zhang, Mingda and Kovashka, Adriana and Li, Wei and Qin, Danfeng and Berent, Jesse},
title = {Cap2Det: Learning to Amplify Weak Caption Supervision for Object Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}
Equal But Not The Same: Understanding the Implicit Relationship Between Persuasive Images and Text Mingda Zhang, Rebecca Hwa, Adriana Kovashka. Prceedings of the British Machine Vision Conference (BMVC), September 2018.(Spotlight)
(project)
(dataset)
(pdf)
(slides)
(recording)
(arxiv)
(bibtex)
@InProceedings{Zhang_2018_Equal,
author = {Zhang, Mingda and Hwa, Rebecca and Kovashka, Adriana},
title = {Equal But Not The Same: Understanding the Implicit Relationship Between Persuasive Images and Text},
booktitle = {Proceedings of the British Machine Vision Conference (BMVC)},
month = {September},
year = {2018}
}
Automatic Understanding of Image and Video Advertisements
Zaeem Hussain, Mingda Zhang, Xiaozhong Zhang, Keren Ye, Chris Thomas, Zuha Agha, Nathan Ong, Adriana Kovashka. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017.(Spotlight)
(project)
(pdf)
(poster)
(slides)
(recording)
(arxiv)
(bibtex)
@InProceedings{Hussain_2017_Automatic,
author = {Hussain, Zaeem and Zhang, Mingda and Zhang, Xiaozhong and Ye, Keren and Thomas, Christopher and Agha, Zuha and Ong, Nathan and Kovashka, Adriana},
title = {Automatic Understanding of Image and Video Advertisements},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}
Multistep DNA-templated Synthesis using a
Universal Template
Yizhou Li, Peng Zhao, Mingda Zhang,
Xianyuan Zhao, Xiaoyu Li. Journal of the American Chemical Society (JACS), 135,
17727-17730 (2013).
DNA-directed Formation of Peptide Bond: A Model
Study toward DNA-programmed Peptide Ligation
Chi Zhang, Yizhou Li, Mingda Zhang,
Xiaoyu Li. Tetrahedron , 68, 5152-5156 (2012).
Detection of Bond Formations by DNA-programmed
Chemical Reactions and PCR Amplification
Yizhou Li, Mingda Zhang, Chi Zhang,
Xiaoyu Li. Chemical Communications, 48, 9513-9515 (2012).
Experience
Work
(2022.6 - now) Software Engineer at Google Research, New York City, NY
(2021.12 - 2022.6) Software Engineer at Google Cloud AI and Industry Solutions, New York City, NY
(2020.5 - 2020.8) Research Intern at Google Geo, (Virtually at) New York City, NY
(2020.1 - 2020.5) Part-time Student Researcher at Google Research, (Remotely from) Pittsburgh, PA
(2019.5 - 2019.8) Research Intern at Google Research, Seattle, WA
(2018.5 - 2018.8) Ph.D. Software Engineering Intern at Google Research, Seattle, WA