Name: Jun Yang
Email: junyang@temple.edu
Phone: (610) 996-0927
Skill
Software Development 90%About me
As technology is fast growing, I am motivated by the ever-evolving nature of technology and the endless possibilities it offers. I am passionate about software development and data science because they provide me with the opportunity to constantly learn, grow, and contribute to cutting-edge projects that can make a tangible impact on people's lives.
I would like to continue advancing my skills in software development and data science through continuous learning, practical application, and staying up to date with the latest advancements in the field. I am aspiring to work on challenging projects that push the boundaries of innovation and contribute to the advancement of technology in the field of software development and data science.
I am ready to take on new challenges, collaborate with fellow professionals, and contribute my skills to create innovative solutions that make a positive impact.
what I can do
Software Design and Development
I am proficient in several different programming languages such as Python,Java, C++ and so on, and have hands-on experience with Frameworks, such as Spring and React. I am well-versed in software development methodologies, including Agile, Scrum and I follow best practices in coding standards, code reviews, and version control using tools like Git or SVN. I have a deep understanding of software architecture, design patterns, and databases, and I am skilled in API development, testing, debugging, and performance optimization.
predictive modeling
I am proficient in using popular programming languages such as Python or R and have hands-on experience with machine learning libraries like scikit-learn, TensorFlow, or Keras. I have successfully applied predictive modeling in diverse industries, including healthcare and finance, where I have developed and deployed predictive models to solve complex business problems.
Data Visualization
I am proficient in using data visualization tools and technologies such as Tableau, Power BI, Spotfire and Matplotlib, and I am skilled in various data visualization techniques, like scatter plots, heatmaps, geographic maps, and more. I am experienced in data visualization best practices, including data storytelling, data-driven design, and effective visualization techniques. I have successfully designed and delivered data visualizations for diverse industries like healthcare and finance, helping stakeholders gain insights, make informed decisions, and drive actionable outcomes.
Database Design and Development
I am proficient in database management systems (DBMS) like Oracle and SQL Server, and I have hands-on experience in designing schemas, defining relationships, and optimizing performance. I am skilled in writing complex SQL queries, stored procedures, triggers, and functions to extract, manipulate, and analyze data. I have successfully designed and implemented databases for diverse applications such as web applications, enterprise solutions, data warehouses and business intelligence systems. I have a deep understanding of database design principles, normalization, indexing, and performance tuning, and I am experienced in database administration tasks.
Cloud Platforms
I am proficient in working with various cloud platforms, including [Cloud Platforms], such as Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and I have hands-on experience in building and deploying cloud-based applications, services, and infrastructure. I am skilled in cloud architecture, cloud computing concepts, cloud security, and cloud management tools. I am passionate about leveraging cloud technologies to drive business outcomes, and I continuously stay updated with the latest trends, best practices, and advancements in cloud computing.
Education & Experiance
Both college studying and real project help me grow up with academic knowledge and project experience especially in communication, system design, functionality define, optimized coding and deployment.
Master's in Computer Science,Temple University,PA
Programming Techniques, Operating Systems, Design and Analysis of Algorithms,Database Design & Programming, Networking & Operating Systems,Machine Learning, Distributed and Parallel Computer Systems, Computer Graphics and Image Processing
B.S. in Computer Science,Temple University,PA
Data Structures, Data Structures and Algorithms, Software Design, Computational Probability and Statistics, Introduction to Systems Programming and Operating Systems, Java, Computer Programming in C and so on.
Associate Director, Data Science & Technology, ClinChoice (Former FMD K&L)
Use different statistical models and machin learning methods to help analyze clinical trial data and run predictions and integrate predicted results into interactive dashboards. Help clients develop various web and desktop based applications
Android App Creator: Commuters Partner
Designed and developed an Android app using Java in Android studio integrating Google Map SDK (Software Development Kit) for real-time tracking and distance calculation.
Manager, Grant Thornton
Applied different predictive modeling like random forest tree classifier or regression, support vector machine and natural language processing (NLP)like TF-IDF and more to solve complex bussiness problems. Developed various web and desktop based applications and embedded well-trained models.
Senior Associate Developer, Grant Thornton
Helped gather and assess requirements and developed solution.
Mobile App Developer, Guiding Technologies
Developed and optimized mobile app for Autism Treatment .
Portfolio
Mixed Effect Regression Model(Python)
A helpful tutorial I wrote on how to use a public data to train mixed effect regression model, analyze the data, tuning the accuracy of model, make prediction based on data. It is useful as data is longitudial. Also, mixed effect regression is one of the most robust regression methods.
Multivariant Logistic Regression(Python)
This is an example of how to use a public data to train Multivariant Logistic Regression model, analyze the data, tuning the accuracy of model, make prediction based on data. It is helpful for using multiple variables to train a model and predict results. It is one of popular linear regression models.
Neural Network for Multi-class Classification(Python)
I will solve a complex problem using neural networks: artificial neural networks (ANNs) as it is a supervised algorithm which rely on training data to learn and improve their accuracy over time. Once these learning algorithms are fine-tuned for accuracy, they are powerful tools in classification and clustering data.
Random Forest(Python)
I will solve the problem using the algorithm: Random Forest as it is a supervised machine learning algorithm which analyzes data for classification and regression. I wrote on how to use a public data to train mixed effect regression model, analyze the data, tuning the accuracy of model, make prediction based on data. Also, Random Forest Classification is one of the most robust classification methods.
Support Vector Classifier(Python)
I will solve the problem using the algorithm: Support vector machine as it is a supervised machine learning algorithm which analyzes data for classification and regression. I wrote on how to use a public data to train SVC model, analyze the data, tuning the accuracy of model, make prediction based on data. Also,Support Vector Classification is one of the most robust classification methods.
Survival Analysis(Python)
Clinical trials in oncology largely include survival analysis, that is, the time to an event of interest as the main outcome under assessment. In this tutorial, I introduced the three types of models: Kaplan-Meier Estimate (non-parametric),COX Proportional Hazard Model (Semi- parametric), Accelerated Failure Time Model (Parametric) and how to analyze data and tune the accuracy of model.
Natural Language Process: TfidfVectorizer (Python)
As we may need to assess unstructured text, Natural language processing (NLP) can make text more accessible and can support information extraction and trending analysis. I wrote on how to use TfidfVectorizer to convert text to matrix and use different models to classify data and evaluate accuracy of each model. It is helpful to tackl with text problems.
Image Classification using CNN (Python)
In this keras deep learning Project, I talked about the image classification paradigm for digital image analysis. I discuss supervised image classifications and explain the CIFAR-10 dataset. Finally, you will see how to build a convolution neural network for image classification on the CIFAR-10 dataset and evaluate the model.