Teaching

Teaching

Click here to see the webpages of many of the courses that I am teaching/have taught.

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Research

Click here to see the projects I'm working on, and see if there's anything you may like to work on...

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Publications

Click here to browse my publications

Teaching

Here are some of the courses I teach/have taught. The courses I am teaching are the first ones. Most of the content is online as well as on my Youtube playlists. If you are taking one of my classes, you can get to the course website from here. Swipe sideways to show more

Mobile Apps(old course). The goal of this course is to cover basic iOS and Android development and object oriented techniques. The content is outdated as it uses Objective C (Mac) and Java (Android).
Programming I (Java) This course is ideal to learn how to program in Java. It is the first of two courses. You can actually follow it to learn at home.
Programming II (Java) This playlist corresponds to an introductory object oriented programming course.
Web application development (old).(old) Web application development
Basic programming on python A basic course on python and its elementary object orientation capabilities.
Intro to AI Is a basic introduction to search and probabilistic algorithms for AI.
NLP Where students learn fundamental natural language processing techniques and challenges.
Machine Learning Introductory course on Machine Learning and analytics.
Programming Fundamentals II This course reviews the basics and delves into more advanced concepts around programming. It is intended for school teachers seeking a CS Endorsement in Illinois.
Databases This course introduces relational databases, NoSQL and a small intro to big data.
Intro to HCI In this course students think about design, cognition and technology.
Server Side Web Dev. In this course students learn to create web applications with a server on the back-end. (.net)
OOP in Java An advanced course on Object Oriented Design
Data Analysis A course that blends basic research methods with data science. We use KNIME for many units.
Intro to AI Is a basic introduction to search and probabilistic algorithms for AI.
NLP Where students learn fundamental natural language processing techniques and challenges.
Machine Learning Introductory course on Machine Learning and analytics.
Programming Fundamentals II This course reviews the basics and delves into more advanced concepts around programming. It is intended for school teachers seeking a CS Endorsement in Illinois.
Databases This course introduces relational databases, NoSQL and a small intro to big data.
Mobile Apps(old course). The goal of this course is to cover basic iOS and Android development and object oriented techniques. The content is outdated as it uses Objective C (Mac) and Java (Android).
Programming I (Java) This course is ideal to learn how to program in Java. It is the first of two courses. You can actually follow it to learn at home.
Programming II (Java) This playlist corresponds to an introductory object oriented programming course.
Web application development (old).(old) Web application development
Basic programming on python A basic course on python and its elementary object orientation capabilities.
AI and Robotics This is a basic course on probabilistic robotics.
Mobile Apps(old course). The goal of this course is to cover basic iOS and Android development and object oriented techniques. The content is outdated as it uses Objective C (Mac) and Java (Android).
Programming I (Java) This course is ideal to learn how to program in Java. It is the first of two courses. You can actually follow it to learn at home.
Programming II (Java) This playlist corresponds to an introductory object oriented programming course.
Web application development (old).(old) Web application development
Basic programming on python A basic course on python and its elementary object orientation capabilities.
Intro to AI Is a basic introduction to search and probabilistic algorithms for AI.
NLP Where students learn fundamental natural language processing techniques and challenges.
Machine Learning Introductory course on Machine Learning and analytics.
Programming Fundamentals II This course reviews the basics and delves into more advanced concepts around programming. It is intended for school teachers seeking a CS Endorsement in Illinois.
Databases This course introduces relational databases, NoSQL and a small intro to big data.

Research

Health disparities are, in some dimensions, a problem of information access. My research sits at the intersection of artificial intelligence, human computer interaction and education to advance cancer health equity through intelligent user interfaces purposefully designed to interact with marginalized, low-literacy communities. Check the following projects:

Intelligent Tutoring Systems for Low Literacy Minority Populations

Intelligent Tutoring Systems for Minorities

Trying to understand and develop an intelligent tutoring system (ITS) that are culturally and linguistically appropriate for low literacy Latinas

Social Determinants of Health

Using Machine Learning for Social Determinants of Health

This project aims to collect data and devise methods to predict social determinants of health. It also aims to produce interfaces that can help new patient navigators be alert to potential social determinants of health in patients.

Previous projects:

  • MiGuia:An application to help breast cancer survivors improve their health and quality of life.
  • Automatic Personality Detection:Understanding how personality is expressed and perceived through textual data
  • Social Tools to Learn Computer Programming: We are exploring educational tools to help computer science students overcome the learning curve of programming.
  • Tell Me More: Purposeful News Filtering System and Virtual Peers

Some of these projects have been on the national and international press: Check my interview with Le Monde,L'Atelier and this article in the New Scientist.

Publications

Here's a list of peer reviewed publications

About

I am an associate professor of health informatics and data science, and of surgey at Loyola University Chicago. I usually am at CTRE 425. My email is fiacobelli@luc.edu
I am affiliated with the Center for Advancing Safety and Machine Intelligence -CASMI-. I previously taught in the Computer Science Department at Northeastern Illinois University. I got my undergraduate degree in Systems Engineer and Informatics at Universidad Diego Portales in Santiago, Chile. A masters degree in Computer Science at DePaul University with a concentration in AI; I got a Ph.D. in Computer Science at the Infolab, in the Computer Science department of Northwestern University. Other interests include: Computational linguistics and enhancing social interactions with technology that can help make a difference, such as intelligent tutoring systems and other aspects of AI applied to the education of children. In the past I worked developing Virtual Peers for minority children with Justine Cassell at the ArticuLab.
Here's my CV

Software

(swipe to see more) (I'll put github links to these soon)

Other Simple Scripts

I have written a bunch of simpler scripts to emulate argparse in Java, small unit testing software for grading java assignments, models to convert simple beamer (latex presentations) into Reveal.js, a student appointment system, etc. these are my pet projects.
Check my "pets" repo. on Github

You are welcome to check other larger projects on my Github repos.

Latent Dirichlet Allocation (LDA)

If we assume documents are comprised of different topics, then we can assume that certain words belong to certain topics. Therefore, a document is a set of words that are more or less grouped according to their topic. For example: A sports story can have a few topics in it such as "the actual game" (score, plays, etc.), "the players" (injuries, drug testing, etc.) and "speculation" (what will happen against the next rival). For each of these topics there are words that are strongly associated with them. For example: "score" is strongly associated to "the actual game" topic, while "steroids" is probably associated to "the players" topic. However, "score" may also be associated with the "speculation" topic albeit less strongly.
LDA attempts to group these words automatically from large collections fo text. Github Repo.

Other Simple Scripts

I have written a bunch of simpler scripts to emulate argparse in Java, small unit testing software for grading java assignments, models to convert simple beamer (latex presentations) into Reveal.js, a student appointment system, etc. these are my pet projects.
Check my "pets" repo. on Github

You are welcome to check other larger projects on my Github repos.

Latent Dirichlet Allocation (LDA)

If we assume documents are comprised of different topics, then we can assume that certain words belong to certain topics. Therefore, a document is a set of words that are more or less grouped according to their topic. For example: A sports story can have a few topics in it such as "the actual game" (score, plays, etc.), "the players" (injuries, drug testing, etc.) and "speculation" (what will happen against the next rival). For each of these topics there are words that are strongly associated with them. For example: "score" is strongly associated to "the actual game" topic, while "steroids" is probably associated to "the players" topic. However, "score" may also be associated with the "speculation" topic albeit less strongly.
LDA attempts to group these words automatically from large collections fo text. Github Repo.
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