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This page will be updated irregularly. Hope these might be helpful to you as well. For entries below without links, please search the key words in Google. The author/platform requires not to link to it directly, or simply because there are too many versions of it :-)

My writings in spare time

• Introduction to Photonic Design Automation

• Experiences in applying to EECS Ph.D. programs

• Experiences in taking the course 6.437 Inference and Information at MIT

• Original solution manual to Pattern Recognition and Machine Learning

• EECS Phd Internships Unveiled: Experiences and Suggestions

Past presentations potentially useful

• Tutorial: A Journey to Optical Computing (at DAC, 2023)

• A 20-min introduction to analog circuit simulation (in a private meeting, 2023)

• Bayesian Elegance in Resolving Semiconductor Manufacturing Challenges (at Lam Research Webminar, 2024)

• The 1-min pitch winning the Best Pitch Award at an internal MIT student conference (at MARC, 2024)

Some references for EECS students

Linear algebra

• Prof. Gilbert Strang's course ‘Linear Algebra’.

• Gilbert Strang, 'Introduction to Linear Algebra,’ Wellesley-Cambridge Press.

Algorithm

• Prof. Tom Leighton's course ‘Mathematics for Computer Science’.

Numerical optimization

• Jorge Nocedal, Stephen J. Wright, 'Numerical Optimization,’ Springer.

• Prof. Stephen Boyd's course ‘Convex Optimization’.

Machine learning

• Christopher M. Bishop, 'Pattern Recognition and Machine Learning,’ Springer.

• Prof. Andrew Ng's lecture on Cousera.

Apply to top EECS Ph.D. programs

• Prof. Philip Guo's article ‘A Five-Minute Guide to Ph.D. Program Applications.’

• Prof. Scott E. Fahlman's answer on Quora about how admission committes at top schools select students.

Why and how to find a fauclty position

• MIT EECS communicaiton lab seminar series

How to do great research

• You and Your Research by Richard Hamming