Resources

• How to read a paper? (Important!)
• What is a PhD ? see Illustration
• Basics for Bioinformatics
• Research Notes
• Useful Datasets
• eBooks: Electronic library, Springer Link

Important Tips

• Two kinds of problems
  • Technical problem
  • Scientific problem (Think bigger but verified with cautious)
  • A technique is important, if and only if it solves a important scientific problem.
• Perspective on developing algorithms
  • Methodolgy
  • Settings on datasets (data type, scale, privacy etc.)
  • aim from science
• Evaluation on Algorithms (Techniques)
  • General concept advance (Many people care about the problem)
  • Accuracy and Robust: Good performance on difference cases (or solid theoretical support)
  • Fast and resources-efficient

Related Top Journals (Read papers as many as you can)

• Bioinformatics: Nature, Cell, Science, Nature biotech, Communications Biology, Genome Biology, Nature Method, Nature Communications, NAR, Nature Review, Bioinformatics, Briefings
• Statistical Methodology: JASA, Annals of Statistics, Biometrika, JRSSB, Statistica Sinica
• Machine Learning: NIPS, JMLR, Foundations and Trends
• Biostatistics: Biostatistics, AoAS, PNAS, Biometrics, Statistics in Medicine
• Preprints: BioRxiv, arXiv: Machine Learning, Statistical Theory and Satistical Methodology
• Latest research and news on Nature

Related Research Groups

• Bioinformatics: Wong Lab at Stanford, Liu lab at Harvard, Lin lab at Harvard, Hongkai at Johns Hopkins
• Statisticians: Bradly Efron, Tibshirani, Larry Wasserman, Michael I. Jordan, David Dunson, Samuel Kou, Victor Chernozhukov
• Opitmization: Stephen P. Boyd, Michael I. Jordan
• Probability: Sourav Chatterjee (also a statistician)