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Achievement: StarstruckAchievement: Arctic Code Vault ContributorAchievement: Pull Shark

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nmningmei/README.md

Hi there, this is a CV of Ning Mei 👋

Hiring postdoctoral researchers all year round

  • Supervisor: Prof. Qi Chen and/or Dr. Ning Mei
  • Topic:
    \t flexibility of higher-order cognition function, OCD behavior and neural mechanism
    \t neual mechanism of attention and the computational modeling of attentions
  • Contact: [email protected]
  • Desired experience: experience with MRI, MEG and TMS; coding in Python and R is a plus
  • Annual salary: ¥500,000 - 80,000 ($69,000 - 110,000 or EUR64,000 - 103,000)
  • Location: Shenzhen, China (South of China, one of metropolitan cities of China, 45 minutes train from Center Hong Kong, 1 hour ship from Macau)

EDUCATION

  1. 2023
    Basque Center for Brain, Cognitive, and Language (BCBL), Donostia/San Sebastian, Basque, Spain
    Postdoctorate, Generative models of consciousness and metacognition of the human brain
  2. 2022
    Basque Center for Brain, Cognitive, and Language (BCBL), Donostia/San Sebastian, Basque, Spain
    Ph.D in Cognitive Neuroscience
  3. 2016
    New York University (NYU), New York, NY, US
    M.A in Psychology (General)
  4. 2014
    Arizona State University (ASU), Tempe, AZ, US
    B.A. in Psychology (minor in Statistics)
  5. 2012
    Guangzhou University of Traditional Chinese Medicine (UTCM), Guangzhou,Guangdong, China
    B.S. in Applied Psychology

Publication

@article{mei2023using,
  title={Using serial dependence to predict confidence across observers and cognitive domains},
  author={Mei, Ning and Rahnev, Dobromir and Soto, David},
  journal={Psychonomic Bulletin \& Review},
  pages={1--13},
  year={2023},
  publisher={Springer}
}


@article{mei2022informative,
  title={Informative neural representations of unseen contents during higher-order processing in human brains and deep artificial networks},
  author={Mei, Ning and Santana, Roberto and Soto, David},
  journal={Nature Human Behaviour},
  volume={6},
  number={5},
  pages={720--731},
  year={2022},
  publisher={Nature Publishing Group}
}

@article{soto2020decoding,
  title={Decoding and encoding models reveal the role of mental simulation in the brain representation of meaning},
  author={Soto, David and Sheikh, Usman Ayub and Mei, Ning and Santana, Roberto},
  journal={Royal Society open science},
  volume={7},
  number={5},
  pages={192043},
  year={2020},
  publisher={The Royal Society}
}

@article{mei2020similar,
  title={Similar history biases for distinct prospective decisions of self-performance},
  author={Mei, Ning and Rankine, Sean and Olafsson, Einar and Soto, David},
  journal={Scientific reports},
  volume={10},
  number={1},
  pages={1--13},
  year={2020},
  publisher={Nature Publishing Group}
}


@article{mei2020lateralization,
  title={Lateralization in the dichotic listening of tones is influenced by the content of speech},
  author={Mei, Ning and Flinker, Adeen and Zhu, Miaomiao and Cai, Qing and Tian, Xing},
  journal={Neuropsychologia},
  volume={140},
  pages={107389},
  year={2020},
  publisher={Elsevier}
}


@article{mei2017identifying,
  title={Identifying sleep spindles with multichannel EEG and classification optimization},
  author={Mei, Ning and Grossberg, Michael D and Ng, Kenneth and Navarro, Karen T and Ellmore, Timothy M},
  journal={Computers in biology and medicine},
  volume={89},
  pages={441--453},
  year={2017},
  publisher={Elsevier}
}

CONFERENCE POSTERS

  • Teng, X., Mei, N., Tian, X., & Poeppel, D. (2016). Auditory temporal windows revealed by locally reversing Mandarin speech. Society for Neurobiology of Language, Poster (co-first-author), Cognitive Neuroscience Society, 2016

  • Kim, T., Mei, N., Poeppel, D., & Flinker, A. (2015). A new acoustic space for hemispheric asymmetries. Society for Neurobiology of Language, Poster (co-first-author), Society for Neuroscience, 2015

  • Mei, N., Sheikh, U., Santana, R., & Soto, D. (2019, September). How the brain encodes meaning: Comparing word embedding and computer vision models to predict fMRI data during visual word recognition. Cognitive Computational Neuroscience Conference, Berline, Germany.

  • Mei, N., & Soto, D. (2019, September). Predicting human prospective beliefs and decisions to engage using multivariate classification analyses of behavioural data. Cognitive Computational Neuroscience Conference, Berline, Germany.

  • Mei, N., Santana, R., & Soto, D. (2021, December). Informative neural representations of unseen contents during higher-order processing in human brains and deep artificial networks. Flash talk: NeuroMatch conference 2021.

  • Mei, N., Santana, R., & Soto, D. (June, 2021). Informative neural representations of unseen objects during higher-order processing in human brains and deep artificial networks. Oral presentation at Association for the Scientific Study of Consciousness, Israel, Virtual Conference.

  • Mei, N., Santana, R., & Soto, D. (October, 2022). Informative neural representations of unseen contents during higher-order processing in human brains and deep artificial networks. Oral presentation at Scientific Conference about Attention RECA XIII, Granada, Spain.

Tutorials

AWARDS

  • Arizona State University, Dean’s list 2013, 2014
  • Data Science RoAD-Trip Award 2016 - 2017

Research and Internships

  1. David Soto Group Doctoral researcher

    • Running psychophysics experiments, fMRI experiments, data analysis (M/EEG, fMRI, behavioral, etc)

    • Ongoing project:

      a. [Benchmarking decoding models of Spanish/Basque conscious/unconscious noun words in various conditions (i.e. shallow/deep process, social conditions)](https://github.com/nmningmei/METASEMA_encoding_model)

b. [How computer vision and semantic representation models provide insights of unconscious processing of object images and their semantic categories](https://github.com/nmningmei/unconfeats)

c. How the history of behavioral pattern could predict the future confidence rating, [a](https://github.com/nmningmei/decoding_confidence_dataset), [b](https://github.com/nmningmei/Decode_confidence_dataset)

d. [Ecoding-based representational similarity analysis](https://github.com/nmningmei/metasema_encoding_based_RSA)

  2. Fall 2014 – Fall 2017 David Poeppel Lab (NYU)
    MA research assistant

    • Running psychophysics experiments, MEG experiments, data analysis
    • Ongoing project: Investigating hemispheric asymmetry in perceiving Mandarin Tones, in conditions of hums or lexical tones.

  3. Spring 2015 – Fall 2016 Catherine Good Lab (CUNY-Baruch)
    MA research assistant

    • Experimental subject testing, data collection, data analysis
    • Data analysis on how sense of belonging in math moderating self-estimation in different confidence levels

  4. Spring 2016 – Spring 2018 Timothy Ellmore Lab (CUNY-North)
    MA research assistant

    • Develop python/Matlab Input/Output interacting scripts/protocol for EEG data processing
    • Selecting features to detect target brain wave patterns (i.e. spindles, k-complex, sleeping stages) in the signal
    • Automatic pipeline of non-supervised models to detect spindles (https://osf.io/fc3u5/ and get the data here)

  5. Fall 2016 – Spring 2017 Data Science RoAD-Trip (Fund awarded - $4000) The RoAD-Trip Joint Data Science Plan (Mentor: Gaurav Pandey)

  6. Spring 2017 – Fall 2017 Denis Pelli Lab
    Research assistant

    • Study of noise dynamic in visual grouping effect

  7. Spring 2014 American Cancer Society Cancer Prevention Study – 3
    Volunteer, Research assistant

    • Recruiting subjects, social media research

  8. Fall 2012-Summer 2014 ASU Changemaker center, Tempe, AZ
    Volunteer

    • Creating communities of support around new solutions/ideas

  9. Fall 2009, Spring 2010 Canton Life Hot Line, Guangzhou, China
    Intern

    • Consulting, recording consulting results

  10. Fall 2010, Spring 2011 Research team, prisoner emotional health, Guangzhou, China
    Intern

    • Collecting data about prisoners’ mental health assessments

Working experience

  1. Fall 2012 to Spring 2018 Varsity Tutor Tutor

    • Multivariate Calculus
    • Linear Algebra
    • Trigonometry
    • Statistics (i.e. research methods, analysis methods, simulation, signal detection theory)
    • Mandarin
    • Programming data analysis (Python, R, and Matlab)

  2. March 2013 to present
    Translator, MCC Translation, Phoenix, AZ

SKILLS and CERTIFICATIONS

Computer Skills:

  1. Excellent – Microsoft Office Word, Excel, Presentation, Poster Design

  2. Excellent – Matlab

    • Parametric tests
    • Nonparametric tests
    • Factorial analysis
    • Principle Component Analysis
    • Psychophysics Toolbox
    • Signal Processing Toolbox
    • Data Visualization
    • Scripts of Functions.

  3. Excellent – Python

    • Parametric tests
    • Nonparametric tests
    • Factorial analysis
    • Principle Component Analysis
    • Bayesian Model building (PYMC3)
    • Model Evaluation, Data Visualization, Lambda Functions,
    • Extensions of Python such as MNE-python (specialize in EEG, MEG data analysis), Nipype (specialize in fMRI)
    • Pandas
    • Deep learning (Tensorflow/Keras, pytorch, JAX)
    • Import and export excel, matlab, SPSS, and SAS files. Extract, transform, and load databases.

  4. Excellent – SPSS

    • Parametric tests
    • Nonparametric tests
    • Factorial Analysis
    • Principle Component Analysis
    • Independent Component Analysis

  5. Excellent – R

    • Parametric tests
    • Nonparametric tests
    • Factorial Analysis
    • Principle Component Analysis
    • probabilistic programming
    • Shiny – interactive graphs

  6. Good – Letax Editor

    • Equations and special effects in presentation slides, posters

  7. Beginner – Julia

    • Julia ikernel interacting with Jupyter projects
    • Deep learning (FLUX)

Samples of courses taken:

  • Calculus/Analytic Geometry I – III
  • Probability
  • Mathematical statistics
  • Simulation and Data Analysis
  • Mathematical Tools for Psychology and Neuroscience

Statistics Skills:

  • Parametric statistics
  • Non-parametric statistics
  • Factorial Analysis
  • Principle Component Analysis
  • Independent Component Analysis
  • Least square regression
  • Multivariate regression
  • Step-wise hierarchical regression
  • Bayesian Inference
  • Machine Learning: general to deep learning

Pinned Loading

  1. intuitive_probability_games intuitive_probability_games Public

    Jupyter Notebook

  2. parametric_nonparametric_statistics_comparison parametric_nonparametric_statistics_comparison Public

    A comparion between parametric and nonparametric statistics using Python

    Jupyter Notebook 2

  3. METASEMA_encoding_model METASEMA_encoding_model Public

    Decoding and encoding models reveal the role of mental simulation in the brain representation of meaning

    Python 1

  4. metasema_encoding_based_RSA metasema_encoding_based_RSA Public template

    Python 1

  5. preprocessing_pipelines preprocessing_pipelines Public

    Preprocessing Pipelines for EEG (MNE-python), fMRI (nipype), MEG (MNE-python/autoreject) data

    Jupyter Notebook 22 4

  6. unconfeats unconfeats Public

    Python 1 4