Tan Zhou
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I am a Senior Data Scientist with 8+ years of experience(both industry and academic) in developing innovative solutions to business problems using quantitative and machine learning models.
I obtained my Ph.D. from Texas A&M University with the major of Applied Statistics and Geo-computation focusing on signal processing, Bayesian and Machine learning.
I specialized in writing for publication, algorithm development, and applying machine learning models to develop analystical solutions using various kinds of data such as time series data, text, geospatial and remote sensing data.
Passionate about applying advanced statistical methods such as machine learning and deep learning-based approaches, NLP and time series analysis to perform deep-dive analysis to identify emerging trends, pain points and opportunity areas from data, and to turn data into actionable insights and provide support for decision making and business optimization.
Technical expertise in statistcs, machine learning, NLP, time series analysis,big data processing, remote sensing, computer vision, and geospatial data engineering based on years of experience in university research, non-government, start-up, and private sector.
Skills
- Python
- R
- git
- linux
- bash
- Bayesian
- random forest
- Neural network & deep learning (CNN, Resent, Unet, LSTM, transfer learning)
- decision tree
- nearest neighbor
- support vector machine
- recommender system
- Nature Language Processing
- Partial least square regression
- univariate and multivariate regression
- linear discriminant analysis
- logistic regression
- time series analysis
- factor analysis
- mixed effect modeling
- MySQL
- Domino Data lab
Sentimental analysis of reviewers' feedback using BERT vs. Machine learning
To predict sentiment (postive, neutral, negeative) of customer feedback using tweet texts of differnt airline companies and compare different models'performace on text classification.
Enhanced fraud detection using ML and PySpark framework with feature selection
To develop a generalized model to deal with big and imblance data prediction that suitable for real-time fraud detection at the PySpark framework
Fraud detection using ML and PySpark framework
To develop a generalized model to deal with big and imblance data prediction that suitable for real-time fraud detection at the PySpark framework
Ensemble models in PySpark
To examplify the uses of ensemble models in PySpark as the ensemble models in [previous project using sklearn and keras](https://github.com/tankwin08/ensemble-models-ML-DL-) and predict if the client will subscribe (yes/no) a term deposit (variable y) using market campaign data.
Bayesian Uncertainty for time series data (EVI) prediction using LSTM and autoencoder
To investigate the trend and pattern of time seriese data (MODIS data) using the Long Short Term Memory (LSTM) networks and quantify the uncertianty of the time series prediction of target variables.
Time series analysis using ARIMA & LSTM - MODIS
To investigate the trend and pattern of time seriese data (MODIS data) using the Autoregressive Integrated Moving Averages (ARIMA) and Long Short Term Memory (LSTM) networks and further to check if we can use the current model to predict further values of target variables.
Sentiment analysis for review classification using SWIVEL and a small datasets
To retrain the pretrained model (Submatrix-wise Vector Embedding Learner (SWIVEL) using using a small collected review datasets and classify the reviews of customer feedback as either positive or negative.
Ensemble models for classification (combine deep learning with machine learning)
To develop a robust approach to conduct classification on data (a person is wearing glasses or not) using a ensemble of models, which include machine learning models (random forest,Gradient Boosting and Extra Trees) and deep learning model (optimized NN using Bayesian optimization).
Bayesian optimization deep learning
To construct the architecture of Nentural Network (NN) and conduct paramter optimization of the NN.
waveform decomposition vs. deconvolution
Bayesian decompostion of waveform lidar and uncertaitny analysis
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R package - waveformlidar data processing and analysis
Developed an open source R package to the community for processing waveform lidar data and exemplify their uses.
Tree segmentation using free available tools
Publications
A brief introduction of articles, presentations or talks.
Paper - A Deep Learning Semantic Segmentation-Based Approach for Field-Level Sorghum Panicle Counting
Paper - Bayesian and Classical Machine Learning Methods: A Comparison for Tree Species Classification with LiDAR Waveform Signatures
Paper - waveformlidar: An R Package for Waveform LiDAR Processing and Analysis
Paper - Estimating aboveground biomass and forest canopy cover with simulated ICESat-2 data
Paper - Mapping forest aboveground biomass with a simulated ICESat-2 vegetation canopy product and Landsat data
Paper - From LiDARWaveforms to Hyper Point Clouds: A Novel Data Product to Characterize Vegetation Structure
Paper - Photon counting LiDAR: An adaptive ground and canopy height retrieval algorithm for ICESat-2 data
Paper - Detecting and Quantifying Standing Dead Tree Structural Loss with Reconstructed Tree Models Using Voxelized Terrestrial Lidar Data
Paper - Bayesian decomposition of full waveform LiDAR data with uncertainty analysis
Paper - Gold-A novel deconvolution algorithm with optimization for waveform LiDAR processing
Experience
Senior Data Scientist
Make business count on data and statistcs.
- Built automatic pipeline to extract and update multiple level data with SQL (DbVisualizer and TOAD).
- Conducted data cleaning and wrangling to convert unstructured lab data to useful knowledge and optimized supply chain pipeline by using them to predicted bio-workflow status.
- Combined machine learning models with sentiment analysis (pretrained embedding model - SWIVEL) to predict and sync the predicted yield during growing season (Python).
- Developed automated framework to process big geospatial datasets such as Sentinel-2 (at Domino & AWS S3) using Machine learning (e.g. Random forest, deep learning) and Bayesian methods to estimate soil attributes with uncertainty over a large scale (R & Python).
- Developed ARIMA and LSTM models (autoencoders) with uncertainty to understand the drivers of the patterns of time series data (e.g. sale and price) and forecasted further values (Python).
- Predicted customer churn using history sale and meta data such as customer service and feedback, identified the features most impact customer leaving and gave engagement suggestions (Python).
- Conducted interaction analysis of genetic (G), environment (E) and treatment (T, e.g. seed rate) for hybrids, developed the framework to assess the benefits of different GET and provide support for outcome-based price model.
Postdoctoral Research Associate
- Developed validation plan for upcoming ICESat-2 data using advanced algorithms (Machine learning and deep learning).
- Developed algorithms for waveform LiDAR visualization and R package.
- Predicted the corn and sorghum yield with the UAV-acquired photos and 3D point cloud with Random Forest and deep learning algorithms such as CNN and SegNet
Research Assistant
- Developed deconvolution and decomposition algorithms for waveform LiDAR processing using National Ecological Observatory Network (NEON) data.
- Developed R package for waveform data processing (waveformlidar, https://github.com/tankwin08).
- Applied Bayesian concept to waveform LiDAR (big dataset) decomposition using Amazon AWS and super-computer of TAMU.
- Identified tree species with uncertainty using waveform LiDAR data and machine learning methods (e.g., random forest, Bayesian logistics, SVM).
- Develop multiple models (step-wise model, random forest model, and hierarchical Bayesian model) to estimate forest biomass changes and related uncertainty.
- Developed algorithms for processing upcoming ICESat-2 data (photon counting LiDAR)
Research Assistant
- Processed Landsat and MODIS images to extract parameters for the watershed models and developed models to simulate the eco-hydrological process for the Abujiao River.
- Analyzed the land use and land cover change for the program “Regional ecological environmental process and safety control under the intensive agricultural development in Sanjiang Plain, Northeast China” supported by the NSF of China.
- Collected archive data, developed methods to generate the spatial pattern map for the program “Heavy metal environmental health risk prevention and control key regional division and classification technology research” supported by Special Environmental Research Funds for Public Welfare.
- Conducted filed work and wrote report for the program Water volume and level monitoring of Baiyangdian demonstration project
Education
Texas A&M University
Award :
- TAMU Distinguished Graduate Student Award for Excellence in Research - Doctoral.
- NSF Doctoral Dissertation Improvement Grant (DDIG).
- Teaching Certificate, Center for Teaching Excellence, Texas A&M University
Beijing Normal University
Tianjin University of Science & Technology