update February 2024

404.html

@@ -6,5 +6,5 @@
 
 <div class="page">
   <h1 class="page-title">404: Page not found</h1>
-  <p class="lead">Sorry, we've misplaced that URL or it's pointing to something that doesn't exist. <a href="{{ site.baseurl }}">Head back home</a> to try finding it again.</p>
+  <p class="lead">Sorry, we've misplaced that URL or it's pointing to something that doesn't exist. <a href="{{ site.baseurl_root }}">Head back home</a> to try finding it again.</p>
 </div>

Gemfile.lock

@@ -1,21 +1,21 @@
 GEM
   remote: https://rubygems.org/
   specs:
-    addressable (2.8.4)
+    addressable (2.8.6)
       public_suffix (>= 2.0.2, < 6.0)
     colorator (1.1.0)
-    concurrent-ruby (1.2.2)
+    concurrent-ruby (1.2.3)
     em-websocket (0.5.3)
       eventmachine (>= 0.12.9)
       http_parser.rb (~> 0)
     eventmachine (1.2.7)
-    ffi (1.15.5)
+    ffi (1.16.3)
     forwardable-extended (2.6.0)
-    google-protobuf (3.23.0-x86_64-linux)
+    google-protobuf (3.25.2-x86_64-linux)
     http_parser.rb (0.8.0)
-    i18n (1.13.0)
+    i18n (1.14.1)
       concurrent-ruby (~> 1.0)
-    jekyll (4.3.2)
+    jekyll (4.3.3)
       addressable (~> 2.4)
       colorator (~> 1.0)
       em-websocket (~> 0.5)
@@ -50,20 +50,18 @@ GEM
     mercenary (0.4.0)
     pathutil (0.16.2)
       forwardable-extended (~> 2.6)
-    public_suffix (5.0.1)
-    rake (13.0.6)
+    public_suffix (5.0.4)
     rb-fsevent (0.11.2)
     rb-inotify (0.10.1)
       ffi (~> 1.0)
-    rexml (3.2.5)
-    rouge (4.1.0)
+    rexml (3.2.6)
+    rouge (4.2.0)
     safe_yaml (1.0.5)
-    sass-embedded (1.62.1)
-      google-protobuf (~> 3.21)
-      rake (>= 10.0.0)
+    sass-embedded (1.70.0-x86_64-linux-gnu)
+      google-protobuf (~> 3.25)
     terminal-table (3.0.2)
       unicode-display_width (>= 1.1.1, < 3)
-    unicode-display_width (2.4.2)
+    unicode-display_width (2.5.0)
     webrick (1.8.1)
 
 PLATFORMS

_config.yml

@@ -1,24 +1,15 @@
-# # Dependencies
-# markdown:         redcarpet
-# highlighter:      pygments
-
-# Setup
 title:            Shixing Wang
 tagline:          "Shixing Wang's Personal Website"
 description:      Shixing Wang works as a postdoc at the physics department of Washington University in St. Louis. He studies organelle biogenesis as the main job, while having several side projects and even more hobbies.
 url:              https://shixingwang.github.io
 baseurl_root:     ""
 baseurl:          ""
 permalink:        /:categories/:title
-
 author:
   name:           'Shixing Wang'
   url:            https://twitter.com/wang_shixing
-
 github:
   repo:           https://github.com/ShixingWang/shixingwang.github.io
-
-# i18n
 languages:                  ["en", "zh-CN"]
 exclude_from_localizations: ["public"]
 lang_name:

_i18n/en.yml

@@ -1,6 +1,7 @@
 global:
   title:       "Shixing Wang"
-  description: "Shixing Wang's personal website"
+  description: "Postdoctoral Research Associate<br>Washington University in St. Louis"
+  copyright:   "All rights reserved."
 titles:
   index:    Shixing Wang
   home:     Home

_i18n/en/_posts/2024-02-12-biophysics-journal-club-on-neural-networks-and-brain.md

@@ -0,0 +1,40 @@
+---
+layout: post
+category: courses
+---
+
+### Hierarchical neural circuits motivate deep convolutional neural networks (CNNs) 2012
+
+- Rumelhart DE, Hinton GE, Williams RJ (1986). **Learning representations by back-propagating errors**. Nature 323: 533–536. [paper][notes]
+- LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989). **Backpropagation applied to handwritten zip code recognition**. Neural Comput 1: 541–551. [paper][notes]
+- Krizhevsky A, Sutskever I, Hinton GE (2012). **ImageNet classification with deep convolutional neural networks**. Adv. Neural Inf. Process. Syst. 25:1097–105. [paper][notes]
+
+### Task-optimized deep CNNs predict aspects of neural responses in brains 2014
+
+- Yamins DLK, Hong H, Cadieu CF, Solomon EA, Seibert D, DiCarlo JJ (2014). **Performance-optimized hierarchical models predict neural responses in higher visual cortex**. Proc Natl Acad Sci USA 111: 8619–8624. [paper][notes]
+- Khaligh-Razavi SM, Kriegeskorte N (2014). **Deep supervised, but not unsupervised, models may explain IT cortical representation**. PLoS Comput Biol 10: e1003915. [paper][notes]
+
+### Visualizing unit activity in deep CNNs and brains
+
+- Zeiler MD, Fergus R (2014). **Visualizing and Understanding Convolutional Networks**. arxiv.org/abs/1311.2901. [paper][notes]
+- Bashivan P, Kar K, DiCarlo JJ (2019). **Neural population control via deep image synthesis**. Science 364, eaav9436. [paper][notes]
+
+### Inferring mechanisms of neural circuit computation from deep CNNs
+
+- McIntosh L, Maheswaranathan N, Nayebi A, Ganguli S, Baccus S (2016). **Deep Learning Models of the Retinal Response to Natural Scenes**. Advances in Neural Inf Processing Systems 29:1369–1377. [paper][notes]
+- Lindsey J, Ocko SA, Ganguli S, Deny S (2019). **A Unified Theory Of Early Visual Representations From Retina To Cortex Through Anatomically Constrained Deep CNNs**. https://doi.org/10.1101/511535. [paper][notes]
+
+### Recurrent networks are dynamic
+
+- Kar K, Kubilius J, Schmidt K, Issa EB, DiCarlo JJ (2019). **Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior**. Nature neuroscience, 22(6), 974. [paper][notes]
+- Kietzmann TC, Spoerer CJ, Sörensen LKA, Cichy RM, Hauk O, Kriegeskorte N (2019). **Recurrence is required to capture the representational dynamics of the human visual system**. PNAS 116: 21854-21863. [paper][notes]
+
+### Reinforcement learning explores 2016
+
+- Cross L, Cockburn J, Yue Y, O’Doherty JP (2021). **Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations in high-dimensional environments**. Neuron 109: 724-738. [paper][notes]
+- Wang JX, Kurth-Nelson Z, Kumaran D, Tirumala D, Soyer H, Leibo JZ, et al (2018). **Prefrontal cortex as a meta-reinforcement learning system**. Nat. Neurosci. 21: 860–868. [paper][notes]
+
+### Unsupervised learning is biologically plausible
+
+- Lotter W, Kreiman G, Cox D (2017). **Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning**. https://arxiv.org/abs/1605.08104. [paper][notes]
+- Zhuang C, Yan S, Nayebi A, Schrimpf M, Frank MC, DiCarlo JJ, Yamins DLK (2021). **Unsupervised neural network models of the ventral visual stream**. Proc of the National Academy of Sciences 118: e2014196118. [paper][notes]

_i18n/en/courses.md

@@ -1,4 +1,4 @@
-## Washington University in St. Louis
+## Graduate: Washington University in St. Louis
 
 ### Physics
 
@@ -11,7 +11,7 @@
 ### Biophysics and Cell Biology
 
 * PHY554 - Physics of Living Cells
-* PHY563 - Topics in Theoretical Biophysics
+* PHY563 - Topics in Theoretical Biophysics [[2024]({{ site.baseurl }}{% post_url 2024-02-12-biophysics-journal-club-on-neural-networks-and-brain %})]
 * CSE587A - Algorithms in Computational Biology
 
 ### Workshops and Summer Schools
@@ -21,7 +21,7 @@
 * QC-Hack, a hackathon of quantum computation
 
 
-## Hongyi Honorable Class, Wuhan University
+## Undergraduate: Hongyi Honorable Class, Wuhan University
 
 ### Math & General physics
 

_i18n/en/home.md

@@ -1,7 +1,20 @@
-I am Shixing (Simon) Wang, a Ph.D in physics. I work with Prof. Shankar Muherji in the physics department at Washington University in St. Louis. Despite my major, the main research topic in our lab is actually cell biology. 
+## Prior Research Experience:
 
-My Ph.D project is a system-level study of sizes and counting numbers of multiple organelles in individual yeast cells. In order to do this, my colleagues and I constructed a strain of "rainbow yeast", in which 6 different organelles were tagged with fluorescent proteins of distinct colors.
+My name is Shixing Wang. I am currently a postdoctoral researcher in the physics department at Washington University in St. Louis, working at Shankar Mukherji's lab. I obtained my PhD degree in July 2023 from the same lab, where my research focused on the response and impact of organelles to cellular growth.
 
-The big question that I want to tackle throughout my career is: *which level of complexity marks the definitional difference between living and non-living systems*. If one reviews their middle school biology textbook, they would be surprised to find that, although we can say a lot about what living systems can do, there is not much about what qualifies a system to be a living system, i.e. the definition of living systems is incomplete. The ultimate ambition of a physicist who studies biology is, of course, to make biology rigorously a subset of physics.
+My doctoral research involved hyperspectral imaging, with the main project centered around using hyperspectral microscopy to examine six fluorescent protein-tagged organelles within single cells. I currently have a paper under review at Cell Systems, and we are in the process of preparing another manuscript to elucidate our data with a physical model.
 
-Besides biophysics, I am interested in various intellectual topics. I keep following the advances in artificial intelligence, and am trying to apply some of their techniques to the imaging system in our lab. I have also published a few papers in astrophysics. I also attended the quantum computing hackathon and got a small prize from Microsoft. 
+Right now, I am cooperating with colleagues in the Biomedical Engineering Department to develop a machine learning model to learn from hyperspectral images and distinguish organelles in bright-field fluorescent microscopy images.
+
+## Expertise:
+
+- **Hyperspectral Imaging**: In-depth experience in utilizing hyperspectral microscopy for cellular studies.
+- **Deep Learning in Biological Microscopy**: Attended the Deep Learning in Biological Microscopy summer school at Marine Biological Lab and actively involved in a project to apply deep learning to fluorescent microscopy images for organelle identification.
+- **Algorithm in Computational Biology**: Proficient in algorithms such as posterior expectation maximization, hidden Markov model, etc., acquired through a computational biology course.
+- **Data Processing and Analysis**: Handled datasets in astrophysics and planetary science during side projects in my PhD.
+
+## Current interests: 
+
+My overarching goal in biology is to identify the defining differences between living and non-living systems, exploring the role of physical laws in selecting length scales across various biological levels. Ultimately, I aspire to contribute to making biology a rigorous subset of physics.
+
+Although my previous experience and interests are more upon experimental cell biology, I also hope to accumulate experience in theory and simulation, and also on the other levels of living systems. The current cell biology is starting to consider organelles as physical objects in real space and paying more attention to spatial and temporal data. With the advances in imaging, fluorescence, and machine learning, I believe the perspectives and methodology in inter-cellular and tissue level research can offer inspiration to lower-level systems.

_i18n/en/research.md

@@ -0,0 +1,23 @@
+
+## Uncovering the principles coordinating systems-level organelle biogenesis with cellular growth
+
+> Shixing Wang, Deepthi Kailash, Shankar Mukherji
+
+A complete framework of eukaryotic cellular growth control must include the growth of
+its defining hallmarks, spatial compartments known as organelles. Here we map out
+the correlation structure of systems-level organelle biogenesis with cellular growth
+using “rainbow yeast”, allowing simultaneous visualization of 6 major metabolically
+active organelles. Hyperspectral imaging of thousands of single rainbow yeast cells
+revealed that systems-level organelle biogenesis is organized into collective organelle
+modes activated by changes in nutrient availability. Chemical biological dissection
+suggests that the sensed growth rate and size of the cell specifically activate these
+distinct organelle modes. Mathematical modeling and synthetic biological control of
+cytoplasmic availability suggests that the organelle mode structure allows the cell to
+maintain growth homeostasis in constant environments while remaining responsive to
+environmental change. This regulatory architecture may underlie how
+compartmentalization allows eukaryotes to flexibly tune cell sizes and growth rates to
+satisfy otherwise incompatible environmental and developmental constraints.
+
+## Solar radio-frequency reflectivity and localization of FRB from solar reflection
+
+## Arrokoth’s necklace

_i18n/en/resume.md

@@ -0,0 +1,45 @@
+## Shixing Wang
+
+1 Brookings Drive, MSC 1105-109-02, St. Louis, MO 63130, United States
+
+`Mobile`: +1-(314)-four-four-one-0384 
+
+`Email`: wangshixing [AT] wustl [DOT] edu
+
+## EDUCATION
+
+- `2017-2023` Ph.D. in Physics, Washington University in St. Louis, MO
+- `2017-2019` A.M. in Physics (without thesis), Washington University in St. Louis, MO
+- `2015.8–2015.12`: Visiting student, Duke University, Durham, NC
+- `2013-2017` B.Sc. in Physics, Honorable Graduation from Hongyi, Wuhan University, China
+
+## PUBLICATIONS
+
+- Wang, S., S. Mukherji. *Uncovering the principles coordinating systems-level organelle biogenesis with cellular growth*. bioRxiv preprint. https://doi.org/10.1101/2022.11.01.514705
+- Wang, S., J. I. Katz. *Solar radio-frequency reflectivity and localization of FRB from solar reflection*, Monthly Notices of the Royal Astronomical Society, Volume 518, Issue 2, January 2023, Pages 2119–2122, https://doi.org/10.1093/mnras/stac3291.
+- J I Katz, S Wang, *Arrokoth’s necklace*, Monthly Notices of the Royal Astronomical Society, Volume 504, Issue 1, June 2021, Pages 601–609, https://doi.org/10.1093/mnras/stab718
+
+## TEACHING EXPERIENCES
+
+- `2018 fall` Assistant to Instructor, PHY 217, Electrodynamics
+- `2018 spring` Assistant to Instructor, PHY 318, Introduction to Quantum Theory
+
+## COMMUNITY SERVICE
+
+- `2019-2023` Peer Mentor Committee, Physics Department, Washington University in St. Louis
+- `2022-2023` Outreach Committee, Physics Department, Washington University in St. Louis
+
+## SKILLS
+
+- `2022` Using GPUs with Python and accelerated data analysis, Nvidia Deep Learning Institute
+- `2021` Winning Team, Microsoft challenge at QCHack (Quantum Computation Hackathon)
+- `2021` Deep Learning for Microscopy Image Analysis, Marine Biological Lab
+- `2017-present` Cell biology lab, fluorescent confocal microscopy imaging, image processing
+- `2017` Award II, International Theoretical Physics Olympiad for Undergraduates, Team 104
+- `2016` Honorable Mention, Mathematical Contest in Modeling, COMAP
+
+## SCHOLARSHIPS & AWARDS
+
+- `2016` Lei Jun Scholarship, Wuhan University.
+- `2015` International Interaction Program of Excellent Undergraduates, China Scholarship Council
+- `2014` National Scholarship, Ministry of Education, People’s Republic of China

_i18n/zh-CN.yml

@@ -1,6 +1,7 @@
 global:
   title:       "王世兴"
-  description: "王世兴的个人网站"
+  description: "博士后研究员<br>圣路易斯华盛顿大学"
+  copyright:   "本站保留版权"
 titles:
   index:    王世兴
   home:     主页

_i18n/zh-CN/_posts/2024-02-12-biophysics-journal-club-on-neural-networks-and-brain.md

@@ -0,0 +1,60 @@
+---
+layout: post
+category: courses 
+title:  脑科学相关神经网络论文集
+---
+
+> 本系每年开一门一学期的《理论生物物理专题》课程，今年的主讲人是脑科学方向的，所以选择的论文基本全是神经网络相关的。
+>
+> 从博二跟老板夸下海口略懂人工智能，到参加暑期学校，再到现在做博后了，原来的项目还是没有很大进展。之前和合作者普遍认为瓶颈在于训练数据的质和量，但从我自己的角度看，我在想是不是应该把步子放小一点，先在简单项目里做出点成品再说。这样再出问题的话，至少可以确认不是自己的编程技术方面的短板。
+> 
+
+### 多层级神经通路启发了深度卷积神经网络 - 2012年
+
+Hierarchical neural circuits motivate deep convolutional neural networks (CNNs) 2012
+
+- Rumelhart DE, Hinton GE, Williams RJ (1986). **Learning representations by back-propagating errors**. Nature 323: 533–536. [论文][笔记]
+- LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989). **Backpropagation applied to handwritten zip code recognition**. Neural Comput 1: 541–551. [论文][笔记]
+- Krizhevsky A, Sutskever I, Hinton GE (2012). **ImageNet classification with deep convolutional neural networks**. Adv. Neural Inf. Process. Syst. 25:1097–105. [论文][笔记]
+
+### 针对任务优化的深度卷积神经网络预言了大脑中神经反应的诸多方面 - 2016年
+
+Task-optimized deep CNNs predict aspects of neural responses in brains 2014
+
+- Yamins DLK, Hong H, Cadieu CF, Solomon EA, Seibert D, DiCarlo JJ (2014). **Performance-optimized hierarchical models predict neural responses in higher visual cortex**. Proc Natl Acad Sci USA 111: 8619–8624. [论文][笔记]
+- Khaligh-Razavi SM, Kriegeskorte N (2014). **Deep supervised, but not unsupervised, models may explain IT cortical representation**. PLoS Comput Biol 10: e1003915. [论文][笔记]
+
+### 深度神经网络和大脑的单元活动之可视化
+
+Visualizing unit activity in deep CNNs and brains
+
+- Zeiler MD, Fergus R (2014). **Visualizing and Understanding Convolutional Networks**. arxiv.org/abs/1311.2901. [论文][笔记]
+- Bashivan P, Kar K, DiCarlo JJ (2019). **Neural population control via deep image synthesis**. Science 364, eaav9436. [论文][笔记]
+
+### 根据深度卷积神经网络推测神经通路的工作机制
+
+Inferring mechanisms of neural circuit computation from deep CNNs
+
+- McIntosh L, Maheswaranathan N, Nayebi A, Ganguli S, Baccus S (2016). **Deep Learning Models of the Retinal Response to Natural Scenes**. Advances in Neural Inf Processing Systems 29:1369–1377. [论文][笔记]
+- Lindsey J, Ocko SA, Ganguli S, Deny S (2019). **A Unified Theory Of Early Visual Representations From Retina To Cortex Through Anatomically Constrained Deep CNNs**. https://doi.org/10.1101/511535. [论文][笔记]
+
+### **循环神经网络**是动力学性的
+
+Recurrent networks are dynamic
+
+- Kar K, Kubilius J, Schmidt K, Issa EB, DiCarlo JJ (2019). **Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior**. Nature neuroscience, 22(6), 974. [论文][笔记]
+- Kietzmann TC, Spoerer CJ, Sörensen LKA, Cichy RM, Hauk O, Kriegeskorte N (2019). **Recurrence is required to capture the representational dynamics of the human visual system**. PNAS 116: 21854-21863. [论文][笔记]
+
+### 强化学习之探索 - 2016年
+
+Reinforcement learning explores 2016
+
+- Cross L, Cockburn J, Yue Y, O’Doherty JP (2021). **Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations in high-dimensional environments**. Neuron 109: 724-738. [论文][笔记]
+- Wang JX, Kurth-Nelson Z, Kumaran D, Tirumala D, Soyer H, Leibo JZ, et al (2018). **Prefrontal cortex as a meta-reinforcement learning system**. Nat. Neurosci. 21: 860–868. [论文][笔记]
+
+### 生物系统有能力进行无监督学习
+
+Unsupervised learning is biologically plausible
+
+- Lotter W, Kreiman G, Cox D (2017). **Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning**. https://arxiv.org/abs/1605.08104. [论文][笔记]
+- Zhuang C, Yan S, Nayebi A, Schrimpf M, Frank MC, DiCarlo JJ, Yamins DLK (2021). **Unsupervised neural network models of the ventral visual stream**. Proc of the National Academy of Sciences 118: e2014196118. [论文][笔记]