Junshu bao

Junshu Bao in Fall semester of

Worked with fellow peers at University of Pittsburgh under supervision of Dr. The purpose of this project was to do a Differential Gene Expression Analysis on a Dexamethasone Treatment data set and incorporate Machine Learning to predict which genes could be differentially expressed. We specifically used files GSM Dexamethasone specifically affects cell growth and can cause apoptosis cell death as specified here. Final PDF file outputted from knitting the source code.

Junshu bao

Distortion product otoacoustic emissions DPOAE testing is a promising alternative to behavioral hearing tests and auditory brainstem response testing of pediatric cancer patients. This is accomplished through the construction of normal reference charts, or credible regions, that DP-gram differences lie in, as well as contour probabilities that measure how abnormal or in a certain sense rare a test-retest difference is. A challenge is that the data were collected over varying frequencies, at different time points from baseline, and on possibly one or both ears. A hierarchical structural equation Gaussian process model is proposed to handle the different sources of correlation in the emissions measurements, wherein both subject-specific random effects and variance components governing the smoothness and variability of each child's Gaussian process are coupled together. Keywords: Contour probability; Functional analysis; Growth curves; Serial monitoring; Structural equation modeling. Abstract Distortion product otoacoustic emissions DPOAE testing is a promising alternative to behavioral hearing tests and auditory brainstem response testing of pediatric cancer patients.

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Overall Quality Based on 50 ratings. Junshu Bao. Professor in the Statistics department at University of Pittsburgh. Rate Compare. I'm Professor Bao. Professor Bao's Top Tags.

Department of Statistics SC. Recent Ph. Challenge the conventional. Create the exceptional. No Limits. Statistical methods for analyzing dependence structures with applications in single-cell experiments. Statistical methods for analyzing multi-omics data: Dependence structure and missing values. Deep learning, clustering, and decision process approaches for modeling time series data. Using concurrent functional regression to reconstruct river stage data during flood events and identify influential functional measurements.

Junshu bao

Overall Quality Based on 50 ratings. Junshu Bao. Professor in the Statistics department at University of Pittsburgh. Rate Compare. I'm Professor Bao. Professor Bao's Top Tags. Check out Similar Professors in the Statistics Department 5. Dec 6th, For Credit: Yes.

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Files inside this folder relate to creation of many types of stastical models to leverage and validate meaningful data from messy data sets. Lots of homework Group projects Lecture heavy. I've had her for two classes and would take more with her in a heartbeat. Dec 4th, May 2nd, She assigns homework almost every week. The only thing is that she assigns weekly homework that is long, so make sure to start on it as early as possible. May 9th, Gives good feedback Respected Accessible outside class. She's not the greatest lecturer, but there's not much else bad about the class. Dec 6th, Her class is a bit too ez imo because assignments are ez if you have any prior experience with R or programming and you even get extra credit for attendance.

Statistics knowledge at the level of STAT or above. No prior knowledge of programming required. Any student suspected of violating this obligation for any reason during the semester will be required to participate in the procedural process, initiated at the instructor level, as outlined in the University Guidelines on Academic Integrity.

I'm Professor Bao. Bao is one of my favorite professors in the stats department. Notifications Fork 2 Star 2. If you want to learn the course material, I suggest steering clear. Latest commit. Aug 3rd, You will definitely pass the class, but you may fall asleep. It was a super super easy class. Last commit date. Gives good feedback Respected Accessible outside class. Take another class. Amazing lectures Clear grading criteria Caring. Branches Tags. Clear grading criteria Lots of homework Lecture heavy.