Historical instructor

PENG SHI

Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 4.33/5 from 3 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

RMP profile ↗ · All captured review dates; profile matched by name.

What students say

Original student reviews · all captured dates

GENBUS 656
Quality 5/5Difficulty 3/5
He is a great lecturer, one of the best I have ever had for a coding/stats class. He is able to explain difficult material very easily. If you go to lecture and pay attention you cover all the questions that will be on the exams. He asks them throughout the lectures. Very friendly, approachable, and down to earth. Highly recommend.
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GENBUS 656
Quality 4/5Difficulty 2/5
Professor Shi is very helpful and easy to talk to if you don't understand something. Sometimes the lectures can be a bit boring, but he has R exercises mixed in that are pretty useful to understand the material. Overall, a great guy and a good professor.
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ACTSCI 654
Quality 4/5Difficulty 2/5
Prof Shi is a very likable, humorous, and down-to-earth professor. The material was actually quite difficult and theoretical (a lot of it just went right over my head), but he makes the exams manageable. I highly recommend him.
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Personal experiences, not a representative survey. Profile matching and captured coverage are shown in the source details.

Classes with PENG SHI

ACTSCI 657

Risk Analytics

Credits unavailable

RISK ANALYTICS teaches modeling and managing business risk using probabilistic forecasting and machine learning-integrated regression.

No offering record · Spring 2026

with this instructor · course overall, matching historical terms

Peng Shi

historical GPA · grades

Spring 2023–Spring 2026 · limited sample
GENBUS 656

Foundations of Statistical Learning for Business Analytics

2–3 credits

An introduction to predictive modeling for business applications, focusing on linear regression, classification, and model selection techniques.

No offering record · Spring 2026

with this instructor · course overall, matching historical terms

Peng Shi

historical GPA · grades

Fall 2021–Spring 2026
RMI 999

Reading and Research-risk Management and Insurance Phd

1–6 credits

RMI 999 is an independent reading and research course for PhD students in Risk Management and Insurance.

No offering record · Spring 2026

with this instructor · course overall, matching historical terms

Anita MukherjeePeng Shi

historical GPA · grades

Spring 2022–Spring 2022 · limited sample

Recorded teaching history

Distinct courses recorded per term

Explore courses taught by this instructor →
Browse recorded courses
TermCourseTitle
Spring 2026ACTSCI 657Risk Analytics
Spring 2026GENBUS 656Foundations of Statistical Learning for Business Analytics
Spring 2026RMI 990Risk & Insurance Independent Research Phd Thesis
Spring 2026RMI 999Reading and Research-risk Management and Insurance Phd
Fall 2025ACTSCI 640Actuarial Statistics for Risk Modeling
Fall 2025RMI 990Risk & Insurance Independent Research Phd Thesis
Spring 2025ACTSCI 657Risk Analytics
Spring 2025GENBUS 656Foundations of Statistical Learning for Business Analytics
Spring 2025RMI 990Risk & Insurance Independent Research Phd Thesis
Fall 2024ACTSCI 640Actuarial Statistics for Risk Modeling
Fall 2024RMI 990Risk & Insurance Independent Research Phd Thesis
Spring 2024ACTSCI 657Risk Analytics
Spring 2024GENBUS 656Foundations of Statistical Learning for Business Analytics
Spring 2024RMI 990Risk & Insurance Independent Research Phd Thesis
Fall 2023ACTSCI 653Advanced Short-term Actuarial Modeling
Fall 2023RMI 990Risk & Insurance Independent Research Phd Thesis
Spring 2023ACTSCI 657Risk Analytics
Spring 2023GENBUS 656Foundations of Statistical Learning for Business Analytics
Spring 2023RMI 990Risk & Insurance Independent Research Phd Thesis
Fall 2022ACTSCI 399Reading and Research-actuarial Science
Fall 2022ACTSCI 653Advanced Short-term Actuarial Modeling
Fall 2022RMI 990Risk & Insurance Independent Research Phd Thesis
Spring 2022GENBUS 656Foundations of Statistical Learning for Business Analytics
Spring 2022RMI 990Risk & Insurance Independent Research Phd Thesis
Spring 2022RMI 999Reading and Research-risk Management and Insurance Phd
Fall 2021ACTSCI 653Advanced Short-term Actuarial Modeling
Fall 2021RMI 990Risk & Insurance Independent Research Phd Thesis
Fall 2020GENBUS 656Foundations of Statistical Learning for Business Analytics
Fall 2020RMI 990Risk & Insurance Independent Research Phd Thesis
Fall 2020RMI 999Reading and Research-risk Management and Insurance Phd
Spring 2020RMI 990Risk & Insurance Independent Research Phd Thesis
Fall 2019RMI 990Risk & Insurance Independent Research Phd Thesis
Spring 2019ACTSCI 653Advanced Short-term Actuarial Modeling
Spring 2019RMI 990Risk & Insurance Independent Research Phd Thesis
Fall 2018ACTSCI 653Advanced Short-term Actuarial Modeling
Fall 2018RMI 990Risk & Insurance Independent Research Phd Thesis
Spring 2018ACTSCI 654Regression and Time Series for Actuaries
Spring 2018RMI 399Reading and Research-risk Management
Spring 2018RMI 990Risk & Insurance Independent Research Phd Thesis
Fall 2017ACTSCI 654Regression and Time Series for Actuaries
Fall 2017RMI 990Risk & Insurance Independent Research Phd Thesis
Spring 2017ACTSCI 653Advanced Short-term Actuarial Modeling
Fall 2015ACTSCI 652Fundamentals of Short-term Actuarial Modeling
Fall 2015ACTSCI 654Regression and Time Series for Actuaries
Fall 2014ACTSCI 652Fundamentals of Short-term Actuarial Modeling
Fall 2014ACTSCI 654Regression and Time Series for Actuaries
Spring 2014ACTSCI 652Fundamentals of Short-term Actuarial Modeling
Spring 2014ACTSCI 654Regression and Time Series for Actuaries
Fall 2013ACTSCI 654Regression and Time Series for Actuaries
Fall 2007ACTSCI 300Probability for Actuaries
Spring 2007ACTSCI 300Probability for Actuaries

Teaching history may be incomplete. Course pages contain course-specific feedback and citations.

Instructor identity & provenance 
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