Welcome to BEE 4750/5750!
Lecture 01
August 24, 2026
Course Overview
About Me
Prof. Vivek Srikrishnan, , 318 Riley-Robb
- From Wappingers Falls, NY (via Champaign-Urbana, IL and State College, PA);
- Non-academic highlight: Was on Jeopardy! in 2016;
- Researches climate risk management;
- Particular interest in unintended consequences which result from neglecting uncertainty or system dynamics.
Meet My Supervisors
About the TA
Samantha Lauren Davies, Ph.D. student,
- B.S. in Earth and Environmental Science from University of Michigan
- Interested in mercury biogeochemistry in aquatic ecosystems
What Are We Discussing This Semester?
What Is A System?
A system is:
“an interconnected set of elements that is coherently organized in a way that achieves something…
A system must consist of three kinds of things: elements, interconnections and a function or purpose.”
— Donella Meadows, Thinking in Systems: A Primer, 2008
Examples of Systems
Can we think of any examples of systems?
What about things that are not systems?
Why Are Systems Interesting?
In other words, a system involves an interconnected set of components.
Those interconnections can lead to very different dynamics and outcomes than if the component processes were studied in isolation.
Example Topics
Topics
- Define systems
- Simulate system dynamics
- Analyze and assess risk
- Make decisions with optimization
Example Systems
- Air pollution
- Wastewater management
- Electric power systems
- Solid waste management
What Do You Hope To Get Out Of This Course?
Text: VSRIKRISH to 22333
Course Organization
- Introduction to Systems Analysis
- Simulating Systems and Risk
- Systems Design and Management with Optimization
- Decision-Making Under Uncertainty
Course Policies
Disclaimer
Sitting in class/looking at these notes is not a substitute for reading the syllabus.
Attendance
Not required, but students tend to do better when they’re actively engaged in class.
Lack of attendance could contribute to a low participation grade.
Office Hours
- Prof. Srikrishnan: TBD in 318 Riley-Robb
- TA: MW 4:15-4:15 in 217 Riley-Robb.
- Will send out a poll, but almost impossible to find a time that works for all (or even most); please feel free to email to make appointments as/if needed.
- Can be busy, will triage based on urgency (e.g. if you want code help but have not done basic debugging steps, you will be asked to wait until others are helped).
Accomodations
If you have any access barriers in this class, please seek out any helpful accomodations.
- Get an SDS letter.
- If you need an accomodation before you have an official letter, please reach out to me ASAP!
Course Website
https://envsys.viveks.me/fall2026/
- Central hub for information, schedule, and policies
- Will add link and some information to Canvas (assignment due dates, etc)
Communications
Use Ed Discussion for questions and discussions about class, homework assignments, etc.
- Try to use public posts so others can benefit from questions and can weigh in.
- I will make announcements through Ed, so check regularly.
- Urgent announcements will also be emailed.
When urgency or privacy is required, email is ok.
Julia
In this course, we will use the Julia programming language.
All assignments and labs will be provided as Jupyter Notebooks.
You do not have to use the notebooks for your own work.
What Is Your Programming Experience?
Text: VSRIKRISH to 22333
GitHub Classroom
Homework assignments and labs will be distributed using GitHub Classroom.
- Every student/team will have a unique “repository.”
- When assignment is released, I will share the link for repository creation on Ed Discussion.
- Makes it easy to share code for assistance and debugging (share links to repositories, not out-of-context code and screenshots).
Debugging Code
- Look at the class FAQ!
- Search for the error message you’re seeing.
- Try to divide code into logical “chunks” and test each one to isolate the error where there’s a syntax or conceptual error.
- Post on Ed. Do not include a screenshot, link to GitHub or provide a small snippet showing the syntax you’re trying.
- Come to office hours (last recourse!).
Grades
Assessments
| Category | Weight |
|---|---|
| Labs | 10% |
| Homework | 15% |
| Mini-Projects | 20% |
| Quizzes | 30% |
| Term Project | 25% |
Overall Guidelines
- Collaboration highly encouraged, but all work must reflect your own understanding
- Submit PDFs on Gradescope
- “Standard” rubric available for HW/exams
- Always cite external references
- Curve unlikely (not worth asking about…)
Late Work Policy
- Most work can be submitted up to 24 hours late at a 50% penalty.
- If you have an approved reason (illness, injury, etc), let me know ahead of time and we will make accomodations.
What Are We Grading?
- Setup and Interpretation: Can you set up a model, interpret outputs, and explain your reasoning?
- The logic behind your model and approach should be written clearly and you should provide interpretations of the results and what they mean for the problem.
- Not code: Will never look at your code while grading unless there is a concern, you don’t have to include it but it should be accessible on GitHub.
Labs
- In-class guided activities, but may need some time after class to complete
- Focus on “how” to apply methods and concepts from class
- If you can’t bring a laptop to these classes, you can work with someone else
- Can work in groups, but must submit your own work.
- Graded on a scale of 0-3, largely based on effort.
Exercises
- Auto-graded “quizzes” on Gradescope.
- Focus on conceptual questions or quick setups/calculations (at most minor coding)
- Can submit as many times as you like.
- Due before Monday class the next week.
- Will drop one.
Homework Assignments
- Mix of conceptual/analytical questions and light programming exercises.
- Released after prior assignment is due (though some weeks may not have a homework assignment).
- Due by 9:00pm on the due date (usually Thursday).
- Can work in groups of up to 3, only one submission required per group.
- Late submissions within 24 hours accepted at a 50% penalty.
- Will drop the lowest score by default.
Quizzes
- 5-6 given in-class during the first 25 minutes.
- Focus on concepts, problem formulations, interpretation.
- Completely closed book and closed note (no calculators, phones, or other electronic devices).
- Quizzes will be scanned and put on Gradescope for grades/feedback.
- None dropped by default for students in 5750 but will drop lowest score for students in 4750.
Mini-Projects
- 3 throughout the semester, each with a different focus (simulation, optimization, and decision-making).
- Will involve a real world application with a problem posed and relevant data provided.
- Can work in groups of 2 (4750), 5750 students must work alone.
Term Project
- Analyze a system of interest, including the regulatory environment, going beyond class examples/methods.
- Work in groups of 3–4.
- Submit proposal on 10/23/26.
- Record presentation by end of semester for peer review by classmates.
- Submit report and group/self evaluations by end of finals week.
Academic Integrity
Hopefully Not a Concern…
- Collaboration is great and is encouraged!
- Knowing how to find, evaluate, and use helpful resources is a skill we want to develop.
- Don’t just copy…learn from others and give credit.
- Submit your own original work.
- Group solutions should reflect the understanding of all group members.
LLMs: The Stochastic Parrot
- Think of LLMs as a ranting drunk: It’s stringing together words or code it heard in a way that sounds reasonable, but there’s no sense of concept.
- LLM debugging can be useful: Think of it as a (error-prone) approximation of a Google search. But it can also lead to new errors with no clear way to fix them, since you don’t understand what you did.
- Hallucinations: LLMs often just makes stuff up. Do you want your grade to involve a ChatGPT hallucination?
AI Policy
Using AI tools is not prohibited. But:
- Use them thoughtfully;
- Carefully document your query, the output, and how you integrated into your solution, or we can’t distinguish LLM output from your own understanding.
- We will grade your submissions on their own basis, but historically LLM-heavy output has not scored well and using LLMs uncritically may make it difficult for you to ask for help.
Upcoming Schedule
Next Classes
Wednesday: Intro to Systems
Setup For Class
Follow the guides to install git, Julia, and VS Code:
Assessments
- HW1: Available, due 9/3.