Jingjing Liang


Associate Professor, Purdue University

Founder & CSO, Science-i


FNR57400 Big Data, AI, And Forests

FNR35500 Quantitative Methods for Natural Resource Managers

FNR57400 Big Data, AI, And Forests

                                         FNR57400— Big Data, AI, and forests

                                                                         (3 Credits)

                                                                        Spring 2023



Dr. Jingjing Liang <jjliang at purdue.edu>

Office: PFEN G021B

Office hours: Fridays 11am-12pm (please email me first) or by appointment


Teaching Assistant




  • Course Webpage (check for announcements, updated schedule, course materials, etc.)




Time: Tuesdays and Thursday            12:00pm – 1:15pm                 

Location:  TBD 




Data science-focused courses are core to the Ag Research and Graduate Education’s initiative on Preparing Graduate Students for Our Data-Rich Future. However, forestry courses in which data sciences are central are limited, especially at the graduate level. FNR59800 Big Data, AI, and forests is developed as a data science-central course to prepare graduate students for the upcoming challenges and opportunities in data-driven research.


This course is focused on introductory big data analysis, artificial intelligence, and associated applications in large-scale forest research. The lecture will cover the challenges we encounter in big data ecological research, and the approaches to overcome these challenges. Real-time forest inventory and wildlife survey data at national and continental levels will be utilized in this course, and actual high-impact research projects will be introduced as case studies to inform students of the state-of-the-art in this subject area. High-performance computing clusters will be utilized for big data analysis.


This course is also open to non-forestry majors. We will introduce basic machine learning techniques that are applicable to other subject areas. Guest lectures may cover big data analyses in different fields, internet-of-things, and/or data management and optimization/decimation for collaborative Virtual Reality experiences.


The class will be evaluated through a final project, for which students will work independently or in a group setting to develop a ‘mini’ research manuscript with a title of their own selection. All students are encouraged to submit their manuscript for publication at peer-reviewed journals, and those whose manuscripts have been submitted to a peer-reviewed journal by the end of the semester will get 50 bonus points (see §Grading and Evaluation for details).   


The major topics to be covered in this course, including big data compilation, application of machine learning models, and using high-performance computer clusters in data analyses, are designed for graduate students. That being said, interested undergraduate students are encouraged to contact the professor, and approval to take this course for undergraduate students can be granted on an individual basis.



No textbook is required for this course.


The following textbooks are recommended for further learning (with free pdf online):

The following papers and are required for reading assignments (with free pdf online):

  • Liang, J., et al. 2016. Positive biodiversity-productivity relationship predominant in global forests. Science 354.
  • Steidinger, B., T. Crowther, J. Liang, M. Van Nuland, G. Werner, P. Reich, M. Zhou, and GFBI. 2019. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 569, 404–408.

This course is intended to provide students with exposure to and understanding of ecological research in the big data era. The students who have completed this course are expected to be equipped with i) critical thinking skills to evaluate big-data research topics and their potential alignment with top-tier journals such as Science, Nature, and PNAS; ii) general problem-solving skills to overcome practical big-data challenges; and iii) a synthetic understanding of the strength and weakness of various big data tools and machine learning algorithms.


The students who have completed this course are expected to have the following specific skills:

  1. Define big data,
  2. Think critically about unique challenges involved with big data,
  3. Compile and error-check big data,
  4. Use statistics and machine learning tools to analyze big data,
  5. Acquire basic skills to publish big data studies,
  6. Use high-performance computing (HPC) for big data analysis.



Lectures will be face-to-face, except that during the pandemics, lectures can be delivered online in a synchronous setting (via Zoom meetings). Attendance policy is listed below. Lectures will cover the important concepts of big data and big data methods. Students are encouraged to ask questions in class and Dr. Liang is available at other times to answer questions.  Attendance and active engagement in the class will be highly encouraged. All students are expected to inform the professor ahead of time about anticipated, unavoidable absences (see the attendance policy below).


  • Attendance Policy

Students are expected to attend all classes in-person unless they are ill or otherwise unable to attend class. If they feel ill, have any symptoms associated with COVID-19, or suspect they have been exposed to the virus, students should stay home and contact the Protect Purdue Health Center (496-INFO).


In the context of pandemics, in-person attendance cannot be a factor in the final grades. However, timely completion of alternative assessments can certainly be part of the final grade. Students need to inform the instructor of any conflict that can be anticipated and will affect the timely submission of an assignment or the ability to take an exam.


Classroom engagement is extremely important and associated with your overall success in the course. The importance and value of course engagement and ways in which you can engage with the course content even if you are in quarantine or isolation, will be discussed at the beginning of the semester. Student survey data from Fall 2020 emphasized students’ views of in-person course opportunities as critical to their learning, engagement with faculty/TAs, and ability to interact with peers.


Only the instructor can excuse a student from a course requirement or responsibility. When conflicts can be anticipated, such as for many University-sponsored activities and religious observations, the student should inform the instructor of the situation as far in advance as possible. For unanticipated or emergency conflicts, when advance notification to an instructor is not possible, the student should contact the instructor/instructional team as soon as possible by email, through Brightspace, or by phone. In cases of bereavement, quarantine, or isolation, the student or the student’s representative should contact the Office of the Dean of Students via email or phone at 765-494-1747. Our course Brightspace includes a link to the Dean of Students under ‘Campus Resources.’



If you must quarantine or isolate at any point in time during the semester, please reach out to me via email so that we can communicate about how you can continue to learn remotely. Work with the Protect Purdue Health Center (PPHC) to get documentation and support, including access to an Academic Case Manager who can provide you with general guidelines/resources around communicating with your instructors, be available for academic support, and offer suggestions for how to be successful when learning remotely. Your Academic Case Manager can be reached at acmg@purdue.edu. Importantly, if you find yourself too sick to progress in the course, notify your academic case manager and notify me via email or Brightspace. We will make arrangements based on your particular situation.


The Protect Purdue Plan, which includes the Protect Purdue Pledge, is campus policy and as such all members of the Purdue community must comply with the required health and safety guidelines. Required behaviors in this class include: staying home and contacting the Protect Purdue Health Center (496-INFO) if you feel ill or know you have been exposed to the virus, properly wearing a mask in classrooms and campus building, at all times (e.g., mask covers nose and mouth, no eating/drinking in the classroom), disinfecting desk/workspace before and after use, maintaining appropriate social distancing with peers and instructors (including when entering/exiting classrooms), refraining from moving furniture, avoiding shared use of personal items, maintaining robust hygiene (e.g., handwashing, disposal of tissues) prior to, during and after class, and following all safety directions from the instructor.


Students who are not engaging in these behaviors (e.g., wearing a mask) will be offered the opportunity to comply. If non-compliance continues, possible results include instructors asking the student to leave class and instructors dismissing the whole class. Students who do not comply with the required health behaviors are violating the University Code of Conduct and will be reported to the Dean of Students Office with sanctions ranging from educational requirements to dismissal from the university.


Any student who has substantial reason to believe that another person in a campus room (e.g., classroom) is threatening the safety of others by not complying (e.g., not properly wearing a mask) may leave the room without consequence. The student is encouraged to report the behavior to and discuss the next steps with their instructor. Students also have the option of reporting the behavior to the Office of the Student Rights and Responsibilities. See also Purdue University Bill of Student Rights.



—A point grading system will be used in this course in which a student’s final course grade will be based on total cumulative points. Final course grades of A, B, C, and D with “+/-” will be assigned from a scale of 90%, 80%, 70%, and 60% of the total cumulative points respectively. Any student having less than 60% at the end of semester will receive a final course grade of F, if this student has missed more than one third (>1/3) of lecture sessions.


Students may easily calculate their current course grade at any point during the semester by simply dividing the total points they earned by the total amount of points possible to earn at that time in the semester. Below is a breakdown of class assignments by points:


Homework assignments                 100 points

Term paper                                     200 points 

Final presentation                          100 points

Attendance                                   100 points

Total                                             500 points

Term paper bonus*                         50 points


— Term paper guideline

  • The paper must be an original contribution and is not published or currently under evaluation at any other journal or publishing outlet. See Nature’s guideline on plagiarism and fabrication:



  • The students are encouraged to closely communicate with their major advisor(s) and include them in the decision process with regard to paper development, submission, and authorship.
  • The students are recommended to follow Nature’s authorship guideline (see the link below) in deciding the authorship of all the papers generated from this class. The inclusion of the instructor, TA, or any other participant(s) as coauthors should be decided based on whether one has contributed substantially to the development and writing of a particular paper. See Nature’s authorship guideline for details: https://www.nature.com/authors/policies/authorship.html  
  • The students can choose to submit their term papers to any journal of their own choice, as long as the journal of their choice is indexed by SCI (http://mjl.clarivate.com/#journal_lists). Note that the students and their faculty advisors will be responsible to cover their own publication fee if applicable.

— Final presentation

Students are expected to summarize and showcase their term papers in a final presentation that will be open to the entire class as well as outside audience (announcement of the final presentation will be circulated across the Ag College). The presentation sessions will be scheduled in the last week of class. Each student will have 20 minutes to present, plus 5 minutes for questions and answers. It is recommended that the students deliver their presentations with visual aids, such as Powerpoint or Prezi. The final presentation will be evaluated through the attached rubric sheet.   


— Deadlines

  • The deadline for homework assignments is usually one-week after the assignment date, unless otherwise announced;
  • To get the term paper bonus, a student must submit the manuscript to a journal by Monday of the final exam week.




Purdue has a strong policy against cheating, and we work to reduce the likelihood that you can successfully copy off of each other and otherwise try to avoid doing your own work during tests. See the following website for the Purdue policy:


http://www.purdue.edu/studentregulations/student_conduct/index.html . Policies on how exams are administered will be explained in class before the first one. One point: you will need a picture ID for all exams. Students who cheat on tests will receive an F on that test and will most likely be expelled from the course with an F grade. All instances of cheating will be reported to the Dean of Students, and further disciplinary action at the university level (e.g., expulsion) is possible. Do not speak to other students during exams, even to translate words into another language for them. Notes and books are not allowed for any tests.


Academic integrity is one of the highest values that Purdue University holds. Individuals are encouraged to alert university officials to potential breaches of this value by either emailing integrity@purdue.edu or by calling 765-494-8778. While information may be submitted anonymously, the more information is submitted the greater the opportunity for the university to investigate the concern. More details are available on our course Brightspace table of contents, under University Policies.




In this course, each voice in the classroom has something of value to contribute. Please take care to respect the different experiences, beliefs and values expressed by students and staff involved in this course. We support Purdue’s commitment to diversity, and welcome individuals of all ages, backgrounds, citizenships, disability, sex, education, ethnicities, family statuses, genders, gender identities, geographical locations, languages, military experience, political views, races, religions, sexual orientations, socioeconomic statuses, and work experiences. A hyperlink to Purdue’s full Nondiscrimination Policy Statement is included in our course Brightspace under University Policies.



If you have a disability that requires some special accommodation, please talk to the instructors during the first three weeks of the semester to discuss the instruction techniques in this class, tests or any other academic adjustments that you may need.


Purdue University strives to make learning experiences as accessible as possible. If you anticipate or experience physical or academic barriers based on disability, you are welcome to let me know so that we can discuss options. You are also encouraged to contact the Disability Resource Center at: drc@purdue.edu or by phone: 765-494-1247.




We will learn to use the high-performance computer clusters in Scholar. Scholar is part of the Research Computing environment, and as such is completely separate, both logically and physically, from the normal classroom resources at Purdue. In particular, Scholar has no direct connection to students’ ITaP home directory, or to Teaching Laboratory software packages, or to Blackboard.

Scholar provides:

A Research Home directory for each student, with a quota of 25 GB

A Scratch directory, specific to Scholar, for transient work

The ability to submit batch jobs to a resource scheduler

Access to a standalone Jupyter notebook server

Access to a standalone R Studio server

Access to the Linux command line through either SSH (text-oriented) or Thinlinc (Graphical Remote Desktop)

Access to a (limited) Hadoop environment


CLASS CONTENTS (tentative)



Introduction to Big Data


What is big data? How is it different from conventional problems? Advantages and challenges?

Practice: Introduction to R programming

Big Data in Forestry and Ecology

Examples of big data in forestry and ecology, and types of data

Introduction to forest inventory data and database structure

Practice: FIA data acquisition and compilation

Big Data Research I

Examples of forest/ecological research using big data

Practice: Map and analyze Indiana’s forest stocking using Forest Inventory and Analysis (FIA) data

Big Data Computing

Data formatting and storage; Statistics and regression

Practice: Assessing biodiversity-forest productivity relationship in the Unites States, using regression analyses

Machine Learning

1. Introduction to machine learning; what is it? How is it different from AI and deep learning? When to choose machine learning over conventional statistics?


2. Introduction to machine learning techniques in ecological settings: random forests, xgboost.


Practice: Assessing biodiversity-forest productivity relationship in the Unites States, using random forests

High Performance Computing (HPC)

Introduction to HPC

Tips for big data processes

Guest lecture by ITAP

Practice: how to access and use HPC

Big Data Research II

Big data and related studies in other subject areas

Guest Lecture:

·         Big data analytics in engineering

·         Big data and the Internet of Things

Publishing a paper

How to publish high-impact papers using big data

Data Management

Guest Lecture:

Using cyberinfrastructure to manage and analyze big data

Term paper

Some lectures will be allocated to work on the final project;

Final presentation will be scheduled for the last week of the class



If you have a disability that requires some special accommodation, please talk to the instructors during the first three weeks of the semester to discuss the instruction techniques in this class, tests or any other academic adjustments that you may need.


  • STRESS AND MENTAL HEALTH: Being in a large-enrollment lecture class can be stressful for many students, especially if you are just starting your undergraduate career. Purdue University is committed to advancing the mental health and well-being of its students. If you or someone you know is feeling overwhelmed, depressed, and/or in need of support, services are available. For help, such individuals should contact Counseling and Psychological Services (CAPS) at 765-494-6995 and http://www.purdue.edu/caps/ during and after hours, on weekends and holidays or through its counselors physically located in the Purdue University Student Health Center (PUSH) during business hours.
  • DIVERSITY STATEMENT: In this course, each voice in the classroom has something of value to contribute. Please take care to respect the different experiences, beliefs and values expressed by students and staff involved in this course. We support Purdue’s commitment to diversity, and welcome individuals of all ages, backgrounds, citizenships, disability, sex, education, ethnicities, family statuses, genders, gender identities, geographical locations, languages, military experience, political views, races, religions, sexual orientations, socioeconomic statuses, and work experiences.


  • CLASS CITIZENSHIP: A potential challenge of learning in a class with other students is that there can be an enormous amount of distraction. In an undisciplined classroom, students talking, rustling through papers, surfing the web, or watching movies on their cell phones in your vicinity can make it nearly impossible to pay attention to the lecturer. The same goes for students whose cell phones ring in class. If the professor has to spend class time telling students to stop disrupting the class, it takes away from everyone’s time to learn. Please do your part and ask students around you who are causing disruptions to stop. If your friend is talking to you, tell them to wait until after class. If it gets to the point where we (or the TA) have to quiet you down or notice you watching something on your computer or phone, we may penalize you by subtracting points from your grade.

Another challenge of being part of a class is that many students can be hesitant to ask or answer questions in front of so many of their peers. We recognize that you may be reluctant to speak in class, but we encourage you to try to overcome this reluctance – your participation in class, and your willingness to ask what you may think are “stupid questions” will improve the experience for everyone – including us! At the end of the semester, we may award extra participation points to students who have consistently made useful contributions to the class discussion, or have otherwise taken initiative to improve the learning environment.



  • EMERGENCY PREPARENESS PROCEDURES: Purdue University is a very safe campus and there is a low probability that a serious incident will occur here at Purdue. However, just as we receive a “safety briefing” each time we get on an aircraft, we want to emphasize our emergency procedures for evacuation and shelter in place incidents. Our preparedness will be critical if an unexpected event occurs! Emergency preparedness is your personal responsibility. Purdue University is actively preparing for natural disasters or human-caused incidents with the ultimate goal of maintaining a safe and secure campus. Let’s review the following procedures:
    1. To report an emergency, call 911.
    2. To obtain updates regarding an ongoing emergency, and to sign up for Purdue Alert text messages, view www.purdue.edu/ea
    3. If we hear a fire alarm, we will immediately suspend class, evacuate the building, and proceed outdoors, and away from the building. Do not use the elevator.
    4. If we are notified of a Shelter in Place requirement for a tornado warning, we will suspend class and shelter in the lowest level of this building away from windows and doors.
    5. If we are notified of a Shelter in Place requirement for a hazardous materials release, or a civil disturbance, including a shooting or other use of weapons, we will suspend class and
    6. shelter in our classroom, shutting any open doors or windows, locking or securing the door, and turning off the lights.



In the event of a major campus emergency, course requirements, deadlines and grading percentages are subject to change that may be necessitated by a revised semester calendar or other circumstances. Here are ways to get information about changes in this course: the Blackboard online course, our department emails, and our office phones (see top of syllabus).

FNR57400— Big Data, AI, and forests

Score Sheet for Final Presentation

(Score each item zero to maximum)










________Alloted time                            3____


Introduction/justification (does speaker adequately introduce the proposed work, does presentation describe background for and importance of this proposed work?)    15_____


Opening (pertinent, interesting?)            5____


Objectives, scientific question, and/or hypothesis (are these clearly stated, are they appropriate for what proposal author is hoping to accomplish in this proposed work?  15_____


Diction (enunciation, volume, inflection) 5____


Methods (are the methods adequately described without going into excessive detail, will they accomplish the stated objectives, are statistical procedures adequate?         15_____


Stage manner and enthusiasm (appears relaxed, addresses audience rather than looking at paper or lectern, speaker’s manner keeps your interest, presentation is well paced



Overall (did presentation follow logical order, i.e. was problem well stated, methods appropriate, did presenter convince you that this proposal is worth funding?)  


Visual aids (score only if visual aids used)


      legibility and clarity (lettering and

      graphics appropriate size and easy to read,

      not overcrowded, photographs easy to see)



      coverage (are points adequately

      illustrated?                                       6____


      speaker’s use of visual aids (does

      speaker refer to and use all visual aids to

      appropriately illustrate a point in talk? 5___



No visual aids (high score if visual aids were not needed, low score if they would have significantly enhanced the talk              17____



Ending (main points summarized and stressed)                                                5____



Total for presentation                        40____

Total for content                               60_____

Address:         Department of Forestry and Natural Resources, 715 W. State Street, West Lafayette, IN 47907, United States of America

Email:             jjliang@purdue.edu; alpenbering@gmail.com; albeca.liang@gmail.com