students fall 2024

Author: yi-zhang

index.md

@@ -9,7 +9,7 @@ page_title: Computational Biology Lab @ Duke
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 ### Yi Zhang, Ph.D.
-**Yi is an Assistant Professor at [Duke University][Duke] in the [Department of Neurosurgery][DukeNeurosurgery] and [Department of Biostatistics and Bioinformatics][DukeBB].** Yi obtained PhD in Bioengineering with [Dr. Jun S. Song][SongLab] at [University of Illinois at Urbana-Champaign][UIUC]. She joined [Dr. X. Shirley Liu][LiuLab]'s lab and with co-mentors [Dr. Myles Brown][MylesLab] and [Dr. Xihong Lin][LinLab], as a Research Fellow at [Department of Data Science, Dana-Farber Cancer Institute][DFCI] and [Harvard University School of Public Health][HSPH]. She has been developing integrative genomic methods to identify functional gene regulatory mechanisms behind disease-associated human genetic variants. She has also been developing machine learning methods that leverage large-scale single-cell datasets to understand cell states in tumor microenvironment, and also machine learning methods for spatial transcriptomics data. Ongoing work includes statistical and deep learning modeling of single-cell and spatial transcriptomics, statistical models that combines statistical genetics and functional genomics, to understand heterogenous complex diseases.
+**Yi is an Assistant Professor at [Duke University][Duke] in the [Department of Neurosurgery][DukeNeurosurgery] and [Department of Biostatistics and Bioinformatics][DukeBB].** Yi obtained PhD in Bioengineering with [Dr. Jun S. Song][SongLab] at [University of Illinois at Urbana-Champaign][UIUC]. She joined [Dr. X. Shirley Liu][LiuLab]'s lab and with co-mentors [Dr. Myles Brown][MylesLab] and [Dr. Xihong Lin][LinLab], as a Research Fellow at [Department of Data Science, Dana-Farber Cancer Institute][DFCI] and [Harvard University School of Public Health][HSPH]. She has been developing integrative genomic methods to identify functional gene regulatory mechanisms behind disease-associated human genetic variants. She has also been developing machine learning methods that leverage large-scale single-cell datasets to understand cell states in tumor microenvironment, and also machine learning methods for spatial transcriptomics data. Ongoing work includes statistical and deep learning modeling of single-cell and spatial transcriptomics, statistical models for genetics and functional genomics, to understand heterogenous complex diseases.
 
 
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 - We lead development of computational, statistical, and machine learning methods that leverage large-scale and multi-omic data to understand cancer initiation, progression, and therapeutic responses. 
 - We foster an interdisciplinary, inclusive, and supportive environment. We welcome computational biologists, bioinformaticians, computer scientists, MDs, immunologists, and oncologists. 
 - We are enthuastic collaborators to work on real-world biological and translational genomics problems. 
-- <u>Postdocs</u> or PhD soon-to-be who work on computational biology, bioinformatics, cancer genomics, single-cell omics, spatial transcriptomics, machine learning, genetic variant function, and biological questions in cancer or tumor microenvironment - Welcome to contact and join us! If possible please include (1) CV or Resume (2) A short research statement describing previous/ongoing work and proposed research and interest (3) 1-3 representative publications (published, accepted, or preprint) (4) Three names of references and contact information. Thanks! (Job postings to come)
-- <u>Prospective students</u> interested in our research are encouraged to apply through Duke graduate programs: Computational Biology and Bioinformatics ([CBB] - PhD), Biostatsistics (PhD & MS). (Or co-mentoring for other Duke programs!) Graduate admission decisions are made by program committee. If contacting for interest, please include (1) CV or Resume (2) Short description of research experiences and interest (3) Three names of references and contact information. We will also be available for CBB & Biostats students to rotate soon.
+- <u>Postdocs</u> or PhD soon-to-be who work on computational biology, bioinformatics, cancer genomics, single-cell omics, spatial transcriptomics, machine learning, genetic variant function, and biological questions in cancer or tumor microenvironment - Welcome to contact and join us! If possible please include (1) CV or Resume (2) A short research statement describing previous/ongoing work and proposed research and interest (3) 1-3 representative publications (published, accepted, or preprint) (4) Three names of references and contact information. 
+- <u>Prospective students</u> interested in our research are encouraged to apply through Duke graduate programs: Computational Biology and Bioinformatics ([CBB] - PhD), Biostatsistics (PhD & MS). Graduate admission decisions are made by program committee. MS students from Biomedical Engineering, Computer Science, ECE are welcome for research interns/assistantships. If contacting for interest, please include (1) CV or Resume and if available (2) Short description of research experience and interest (3) Three names of references. Thanks!
 
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@@ -50,7 +50,7 @@ We are core members of the Brain Tumor Omics Program ([BTOP](https://neurosurger
 
 Single-cell genomics data provide high-dimensional measurement of gene expression for each cell. *How do we mathematically describe a cell? How do we find cell types or cell states most relavant to biology? How do we integrate the power of the large number of cell data points to understand what roles each cells are playing in tumors?* We develop computational methods leveraging machine learning models, large-scale single-cell genomics data, and multi-omic datatypes like spatial transcriptomics and multiome. Currently, we are particular interested in low-dimensional methods and deep learning models with interpretability. 
 
-<u>Related work</u>: [STHD: machine learning model for high-resolution spatial transcriptomics](https://www.biorxiv.org/content/10.1101/2024.06.20.599803v1) (Sun* and Zhang*#, BioRxiv 2024), *Software*: [STHD](https://github.com/yi-zhang/STHD).   [MetaTiME for TME cell states](https://www.nature.com/articles/s41467-023-38333-8) ( *Zhang et al. Nature communications* 2023). *Software*: [MetaTiME cell state annotator toolkit](https://github.com/yi-zhang/MetaTiME), [Cross-cohort training pipeline](https://github.com/yi-zhang/MetaTiMEpretrain). <u>Ongoing</u>: (1) Machine learning method for large-scale single-cell transcriptomics integration & interpretation; (2) Timecourse single-cell multiome (ATAC+RNA) modeling; (3) Spatial transcriptomics. 
+<u>Related work</u>: [STHD: machine learning model for high-resolution spatial transcriptomics](https://www.biorxiv.org/content/10.1101/2024.06.20.599803v1) (Sun* and Zhang*#, BioRxiv 2024), *Software*: [STHD for VisiumHD](https://github.com/yi-zhang/STHD).   [MetaTiME for TME cell states](https://www.nature.com/articles/s41467-023-38333-8) ( *Zhang et al. Nature communications* 2023). *Software*: [MetaTiME cell state annotator toolkit](https://github.com/yi-zhang/MetaTiME), [Cross-cohort training pipeline](https://github.com/yi-zhang/MetaTiMEpretrain). <u>Ongoing</u>: (1) Machine learning method for large-scale single-cell transcriptomics integration & interpretation; (2) Timecourse single-cell multiome (ATAC+RNA) modeling; (3) Spatial transcriptomics. 
 
 
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pages/group.md

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 ## Members
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 ##### Yi Zhang, PhD
-I'm an Assistant Professor at Duke University School of Medicine in the Department of Neurosurgery and Department of Biostatistics and Bioinformatics. I obtained BS from University of Science and Technology of China (USTC), and PhD in Bioengineering with Dr. Jun S. Song at University of Illinois at Urbana-Champaign (UIUC). I did postdoc at Department of Data Science, Dana-Farber Cancer Institute and Harvard University School of Public Health. I have been developing integrative genomic methods to identify functional gene regulatory mechanisms behind disease-associated human genetic variants. I have also recently been developing statistical, machine learning, and artificial intelligence methods that leverage large-scale single-cell datasets to understand cell states in tumor microenvironment. My ongoing work include deep learning modeling of single-cell and spatial transcriptomics, and statistical models that combines statistical genetics and functional genomics. For fun, I enjoy running, hiking, music and wildlife photography. 
+I'm an Assistant Professor at Duke University School of Medicine in the Department of Neurosurgery and Department of Biostatistics and Bioinformatics. I obtained BS from University of Science and Technology of China (USTC), and PhD in Bioengineering with Dr. Jun S. Song at University of Illinois at Urbana-Champaign (UIUC). I did postdoc at Department of Data Science, Dana-Farber Cancer Institute and Harvard University School of Public Health working with Shirley Liu, Myles Brown, and Xihong Lin. I have been developing integrative genomic methods to identify functional gene regulatory mechanisms behind disease-associated human genetic variants. I have also recently been developing statistical, machine learning, and artificial intelligence methods that leverage large-scale single-cell datasets to understand cell states in tumor microenvironment. My ongoing work include deep learning models of spatial transcriptomics, and statistical models for genetics and functional genomics. For fun, I enjoy running, hiking, music and wildlife photography. 
 
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 ##### Chuhanwen Sun
-My name is Chuhanwen (Silas) Sun, currently a visiting research intern in Yi's lab through Aug 2024, as part of my Master program study at Karolinska Institute. I graduated from Tsinghua University with degree in Life Sciences. I am eager to enhance my research capabilities, focusing specifically on data-driven disease research, particularly in the realms of disease diagnosis and treatment. In my free time, I enjoy basketball, gym working out and film photography. 
+My name is Chuhanwen (Silas) Sun, currently a visiting research intern in Yi's lab, as part of my Master program study at Karolinska Institute. I graduated from Tsinghua University with degree in Life Sciences. I am eager to enhance my research capabilities, focusing specifically on data-driven disease research, particularly in the realms of disease diagnosis and treatment. In my free time, I enjoy basketball, gym working out and film photography.
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+##### Xingyuan Zhang
+I am a rotation Computational Biology and Bioinformatics PhD student in Yi's lab. I obtained my BS in biotechnology from Peking University and my MS in Biostatistics from UNC Chapel Hill. My research interest is about using statistical modeling and machine learning algorithms to solve problems in spatial transcriptomics and other genomics problems related to human diseases. In my spare time, I love to play basketball, listening to classical music as well as watching movies.
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+##### Huiyue Li
+I am a first-year PhD student in Biostatistics at Duke University, working as a rotation student in Yi's lab since August 2024. Previously, I received my master's degree at Duke Biostatistics. My research interest is to develop statistical and computational methods for single-cell and spatial genomics. In my free time, I enjoy playing pickleball, cooking, and playing with my cat Charlie.
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+##### Tong Cheng
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+My name is Tong (Roxy) Cheng, and I am currently a first-year Master student in Biostatistics at Duke University. I hold a dual bachelor’s degree in mathematics and psychology from Boston University. I aim to develop impactful research projects that drive the creation of innovative medical treatments and therapies.  In my free time, I enjoy playing video games, such as League of Legends, cooking, and reading
 
-##### Seung Hyun Jin
-I am a first-year Duke Undergraduate student majoring in Computational Biology & Bioinformatics, Computer Science, Statistical Science. I am a research intern in Zhang lab starting summer 2024. My areas of interest converge in AI and ML in biological spaces, specifically, in cells. I am always intrigued excited to learn about applications of machine learning in different fields. 
 
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 ##### Zijia Tang
 
-My name is Zijia (Thomas) Tang, and I am a research intern in Yi’s lab starting June 2024. I am a freshman at Duke University starting in September 2024. My research interests lie in computational biology and machine learning. Previously, I delved into single-cell perturbation, employing Variational Autoencoders (VAEs) to predict cellular responses to perturbations. I’m eager to increase the interpretability of my previous work and learn ways to combine privileged information and new information found by neural networks. In my free time, I enjoy playing drums, exercising, and reading science fiction.
+My name is Zijia (Thomas) Tang, and I am a undergraduate research intern in Yi’s lab starting June 2024, majoring in Computer Science. I am a freshman at Duke University starting in September 2024. My research interests lie in computational biology and machine learning. Previously, I delved into single-cell perturbation, employing Variational Autoencoders (VAEs) to predict cellular responses to perturbations. I’m eager to increase the interpretability of my previous work and learn ways to combine privileged information and new information found by neural networks. In my free time, I enjoy playing drums, exercising, and reading science fiction.
 
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@@ -47,6 +69,6 @@ My name is Zijia (Thomas) Tang, and I am a research intern in Yi’s lab startin
 
 | Name | Role | Current status |
 | ----------- | ----------- | ----------- |
-| Yijia Alva Jiang | Master student, Harvard DBMI, Shirley Liu lab | Bioinformatics Analyst, Dana-Farber Cancer Institute |
+| Yijia Alva Jiang | Master student, Harvard DBMI, Shirley Liu lab | PhD student, UPenn |
 | Jack Jiajinlong Kang | Master student, Harvard CBQG, Shirley Liu lab | PhD student, MD Anderson Cancer Center |
 | Yingxiao Shi | PhD student, Harvard BBS, Shirley Liu lab | PhD student, Harvard & Dana-Farber Cancer Institute |

pages/join.md

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 - **We will be recruiting postdocs and Duke students**. Interested in computational biology, machine learning, bioinformatics, cancer genomics, single-cell multi-omics, spatial transcriptomics, human genetics, and immunology? - **Welcome to join us!**
 
-- <u>Postdocs</u> or PhD soon-to-be who work on computational biology, bioinformatics, cancer genomics, single-cell omics, spatial transcriptomics, machine learning, genetic variant function, and biological questions in cancer or tumor microenvironment - Welcome to contact to join us! If possible please include (1) CV or Resume (2) A short research statement describing previous/ongoing work and proposed research and interest (3) 1-3 representative publications (published, accepted, or preprint) (4) Three names of references. Thanks! (Job postings to come)
-- <u>Prospective students</u> interested in our research are encouraged to apply through Duke graduate programs: Computational Biology and Bioinformatics ([CBB] - PhD), Biostatsistics (PhD & MS). Graduate admission decisions are made by program committee. If contacting for interest, please include (1) CV or Resume (2) Short description of research experience and interest (3) Three names of references. We will also be available for CBB & Biostats students to rotate soon. Thanks!
+- <u>Postdocs</u> or PhD soon-to-be who work on computational biology, bioinformatics, cancer genomics, single-cell omics, spatial transcriptomics, machine learning, genetic variant function, single-cell epigenetics, and biological questions in tissue or tumor microenvironment - Welcome to contact to join us! If possible please include (1) CV or Resume (2) A short research statement describing previous/ongoing work and proposed research and interest (3) 1-3 representative publications (published, accepted, or preprint) (4) Three names of references. Thanks! 
+- <u>Prospective students</u> interested in our research are encouraged to apply through Duke graduate programs: Computational Biology and Bioinformatics ([CBB] - PhD), Biostatsistics (PhD & MS). Graduate admission decisions are made by program committee. MS students from Biomedical Engineering, Computer Science, ECE are welcome for research interns/assistantships. If contacting for interest, please include (1) CV or Resume (2) Short description of research experience and interest (3) Three names of references. Thanks!
 
 [CBB]: https://medschool.duke.edu/education/biomedical-phd-programs/computational-biology-and-bioinformatics-program