Siqi Wang

I am a 4th year Computer Science PhD student at Boston University, Image and Video Computing Group, where I'm fortunate to be advised by Prof. Bryan A. Plummer. Before joining BU, I was advised by Prof. Daniel Ritchie at Brown Visual Computing and received my Master degree in Computer Science in 2020. I received my Bachelor’s degree in Computer Science at Beihang University, Beijing, China in 2018.  /  CV  /  Google Scholar  /  Github  /  LinkedIn

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Teaching Fellow

My research interests fall within the umbrella of machine learning and computer vision. Currently, my primary focus centers on learning with noisy labels (LNL), with a particular emphasis on enhancing noise detection performance. Simultaneously, I actively engage with biomedical data, striving to advance machine intelligence within the biomedical domain.

A Unified Framework for Connecting Noise Modeling to Boost Noise Detection
Siqi Wang, Chau Pham, Bryan A. Plummer,
arXiv, 2023

In this work, we explore the integration of noise modeling and noise detection, proposing an interconnected structure with three crucial blocks: noise modeling, source knowledge identification, and enhanced noise detection using noise source-knowledge-integration methods.

CHAMMI: A benchmark for channel-adaptive models in microscopy imaging
Zitong Chen, Chau Pham, Siqi Wang, Michael Doron, Nikita Moshkov, Juan C. Caicedo, Bryan A. Plummer,
Advances in Neural Information Processing Systems (NeurIPS), 2023

We present a benchmark for investigating channel-adaptive models in microscopy imaging, which consists of 1) a dataset of varied-channel single-cell images, and 2) a biologically relevant evaluation framework. In addition, we adapted several existing techniques to create channel-adaptive models and compared their performance on this benchmark to fixed-channel, baseline models.

LNL+K: Learning with Noisy Labels and Noise Source Distribution Knowledge
Siqi Wang, Bryan A. Plummer,
arXiv, 2023

We introduce a new task called Learning with Noisy Labels and noise source distribution Knowledge (LNL+K), which assumes we have some knowledge about likely source(s) of label noise that we can take advantage of. By making this presumption, methods are better equipped to distinguish hard negatives between categories from label noise. In addition, this enables us to explore datasets where the noise may represent the majority of samples, a setting that breaks a critical premise of most methods developed for the LNL task.

Anchoring to Exemplars for Training Mixture-of-Expert Cell Embeddings
Siqi Wang, Manyuan Lu, Nikita Moshkov, Juan C. Caicedo, Bryan A. Plummer,
arXiv, 2021

Treatment ExemplArs with Mixture-of-experts (TEAMs), an embedding learning approach that learns a set of experts that are specialized in capturing technical variations in our training set and then aggregates specialist's predictions at test time.

Stylistic Compatibility Learning with Deep Neural Networks for Indoor Scene
Siqi Wang, Daniel Ritchie (advisor),
Master Project Report, 2020

A deep neural network with conditioning method to learn the scene style.

An Efficient Adaptive Algorithm for Removal of Impulse Noises
Siqi Wang, Tongyu Yue, Bo Lang,
International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2017

Adaptive Min-Max Average Filters (AMMAF) for the removal of impulse noises.

Efficient Segmentation for Region-based Image Retrieval Using Edge Integrated Minimum Spanning Tree
Yang Liu, Lei Huang, Siqi Wang, Xianglong Liu, Bo Lang,
International Conference on Pattern Recognition (ICPR), 2016

A RBIR-oriented image segmentation algorithm named Edge Integrated Minimum Spanning Tree (EI-MST).

Template from source code.