What I Do

  • Internship with The Educational Technology Research Group @Brandeis University
    Continuing with PhD work in Computer-Supported Agile Teaching (CSAT) with emphasis on:
    • Exploring the commercial possibilities of the Discovery Teaching (formerly TeachBack) classroom system through improvements, funding, marketing, and user growth campaigns.
    • Adding new pedagogical features to Discovery Teaching.
    • Improving the UI/UX, performance and scalability of Discovery Teaching.
    • Conducting pedagogy experiments, tests on new features, and publish research papers.
    PhD Summary
    Researched and developed applications and appropriate pedagogies for more effective learning and teaching in the classroom. My research is aimed at helping teachers and their students engage in more fruitful classrooms, by facilitating pedagogically effective classroom interactions.
PhD Work

Post PhD

Writing and submitting papers to conferences, as well as conference attendance. Maintanance and continued design and development of new Discovery Teaching features.

Stock Market Trading

Stock Market Trading

A new post-PhD hobby. I'm learning on 'my feet' about stock trading. I also manage a portifolio on Robinhood. So far great, I'm enjoying it.

Web Development


I like startups, because I'm passionate about innovation and problem-solving using technology.

Web & App Design

Software Engineering

Recent projects include creating web applications for my research and personal projects.

Featured Project: TeachBack

Effective & Efficient Pedagogy Interactive & Active Classrooms Learning & Teaching Analytics

Discovery Teaching an online platform designed to engage university classrooms with interactive teaching and learning, while simultaneously providing professors with real-time student feedback and teaching insights based on analytics.Get Started With Discovery Teaching

About Me

I am William T. Tarimo, an August 2016 PhD graduate in Computer Science at Brandeis University.
My doctoral research focuses on Education Innovation - researching and designing technologies and computer-supported pedagogies for optimal learning and teaching in the college classroom.
My dissertation is titled: Computer-Supported Agile Teaching (CSAT).

I received my Master of Arts degree in Computer Science from Brandeis University, and Bachelor of Arts degree in Computer Science and Mathematics from Connecticut College. At undergraduate I conducted various research works on using (Cyclic) Genetic Algorithms to learn optimal and adaptive walking gaits for 4 and 6 legged servo-robots. I published 4 conference works with Prof. Gary Parker, my undergraduate teacher, mentor and faculty-advisor.

Keep a smile on your face and let your personality be your autograph.

Leo Daniel (?)

I Got the Skills!


Scientific Research


Teaching, Tutoring & Mentoring


Scientific Analytics & Writing


Full-Stack Web Development


Software Engineering


Basic Applied Robotics


IT & Customer Service


Agile Methodologies








Khmer (Cambodian)

Ruby On Rails Java Javascript jQuery Matlab Python SQL HTML5 CSS3 PHP Scheme NodeJS Git Github Heroku Parse iOS Android CodeShip Hadoop MapReduce Linux OS X Windows Cordova Phonegap Pivotal Tracker MarkLogic: Enterprise NoSQL Database AngularJS
PROGRAMMING SKILLS: Ruby On Rails Ruby Python Java Matlab SQL NoSQL HTML5 CSS3 JavaScript JQuery Scheme PHP XML NodeJS XQuery

FRAMEWORKS SKILLS: Windows MacOS Linux iOS Android GitHub Bitbucket CodeShip Heroku Git Meteor Express AngularJS MarkLogic Bootstrap Android Development Roxy

OTHER SKILLS: Robotics Web Development Customer Service IT Service Agile Development IT Entrepreneurship Education Education Technology Research Teaching Analytical Organizational Quantitative Initiative

LANGUAGE SKILLS: English - Fluent Swahili - Native Spanish - Conversational Khmer - Beginner

Some Of My Works

Research Projects Class Projects Personal Projects
Discovery Teaching

Discovery Teaching

Disrupting Learning & Teaching

Discovery Teaching is a lightweight clicker-based audience response system with additional features for supporting back channel communication and feedback from the students. It also provides statistics on student performance in formative assessments and participation. Discovery Teaching allows students and instructors to engage in online dialogs and discussions about class material. These discussions can be driven by the instructor or left to the back channel among students alone. We provide functionality for students to give instantaneous personal cognitive and affective feedback to the instructor on how the students are feeling about the class. Students can give feedback at various points during the lecture of their own choice. Alternatively, an instructor can ask all students for feedback at any particular point. Discovery Teaching allows for a highly interactive pedagogy that involves all of the students, TAs and the instructor.

Visit Discovery Teaching

The Affective Tutor

The Affective Tutor

Before There Was TeachBack

The Affective Tutor, envisioned to help instructors keep all students in a large lecture class engaged and actively learning material related to the course. We have found, by asking for feedback after every class, that in a large class of over 50 students, a significant number of students will feel the class went too slow and another group will feel the class went too fast. One of the most challenging aspects of teaching a large class is to keep the entire class engaged in the course material. The Affective Tutor helps instructors to determine in real-time how students feel about the pace of the class.
The tool also helps instructors to pin-point confusing topics, and to tackle the problem of keeping students engaged by providing a monitored back-channel that allows the bored students to help the confused students get back on track and into an engaged mode.

Visit The Affective Tutor

Image Evolution

Evolving images using transparent overlapping polygons

In this project we used genetic algorithm and hill-climbing theories to implement a system to machine-learn optimal attributes and arrangements of polygons that recreates a target image. The polygons can be of any one chosen shape, the arrangements are (x,y) coordinates on the canvas & overlapping order of each polygon, and attributes are color value(s), size, transparency and so on, for each polygon. The idea is to start with a randomly generated population of polygons and use a fitness function to evolve the population towards a set of arrangements and attributes of the polygons that are a close approximation of the features of a target image.

Our project attempted to use machine learning to recreate or redefine features of an image using an arrangement of trans- parent overlapping polygons. From Genetic Evolution and Hill Climbing, we came up an implementation that starts by generating a random sequence of polygons then iteratively mutating the sequence (slightly modifying a random attribute of a random polygon), incrementally building on mutations that yield results that are closer to a target image[3]. In the context of evolving images using polygons, we learned and explored the balance between visual appeal of the generated images and the efficiency of the implementation.

View Project Report

PAL With Fitness Biasing

Punctuated Anytime Learning in Evolutionary Robotics

This work was based on an internship with Prof. Gary Parker at Connecticut College. The internship was based on a research project to develop a machine learning system that utilizes machine learning using a computer simulation of a robot, wireless communication and an over-head camera vision to learn control programs for on-ground mobile robots. The internship involved intense computer programming, robotics design and construction, systems design, assembly and construction, and utilization of theories of artificial intelligence and evolutionary computation. I spent about 10 fun and busy weeks in a Computer Science lab where I mostly worked alone under Prof Parker’s supervision and help whenever necessary.

This research uses periodic tests on the actual robot to test the control programs learned on the robot model by evolutionary computation and improve the learning process by altering the learning algorithm (Fitness Biasing) or changing the model (Co-Evolving Model Parameters).

Cyclic Genetic Algorithms

Cyclic Genetic Algorithms

Using CGAs to Generate Gaits for Legged Robots

In project we use a Cyclic Genetic Algorithm (CGA) to learn optimal gaits for a quadruped servo-robot with three degrees of movement per leg. An actual robot was used to generate a simulation model of the movement and states of the robot. The CGA used the robot’s unique features and capabilities to develop gaits specific for that particular robot. Tests done in simulation show the success of the CGA in evolving a reasonable control program and preliminary tests on the robot show that the resultant control program produces a suitable gait.

Greedy Genetic Evolution

The Effects of Using a Greedy Factor in Hexapod Gait Learning

This project investigates a new selection scheme that is tested using a Cyclic Genetic Algorithm (CGA) for learning gaits for a hexapod servo-robot. The effectiveness of CGA in learning optimal gaits with roulette wheel selection and selection with greedy factors is compared. The results were analyzed based on fitness of the individual generated gaits, convergence time of the evolution process, and the fitness of the entire populations evolved. In this work, we use a previously developed CGA to learn gaits using a simulation model created from an actual hexapod robot. Results demonstrate that greedy factors tested tend to prematurely converge with sub-optimal fitness, whereas training with roulette wheel selection evolves more diverse populations with individuals that result in the desired optimal gaits.


A Phonegap based web app for suggesting and voting on discussion topics for the BrandITE club

A simple, mobile friendly Javascipt web app for managing discussion topics for a club.

Visit BrandITE

Train Departure Board

Train Departure Board

Managing Train Departures and Related Data.

A simple Rails application that shows the train departure times. Currently, the “departure board feed” is the contents of the North Station and South Station departure boards.

Visit The Departure Board

Research Publications



    William T. Tarimo
    Mentor: Prof. Timothy J. Hickey
    Published In: ProQuest and Brandeis Institutional Repository
    Read The Abstract | Read Full Dissertation | Find In ProQuest

  • Ph.D. Defense Slides: Computer-Supported Agile Teaching (CSAT)

    By William T. Tarimo
    Computer Science, Brandeis University - Jully 22nd, 2016
    PDF Slides | Google Slides

  • Fully Integrating Remote Students into a Traditional Classroom using Live-Streaming and TeachBack

    William T. Tarimo, Timothy J. Hickey
    To Appear In: Frontiers in Education (FIE) 2016
    October 2016, Erie, PA, USA
    FIE2016 Presentation | PDF | FIE2016

  • A Flipped Classroom With and Without Computers

    William T. Tarimo, Fatima Abu Deeb, Timothy J. Hickey
    Book: Computer Supported Education. Pg. 333-347.
    Springer International Publishing Switzerland, 2016
    PDF | Springer Link

  • Early Detection of At-Risk Students in CS1 Using TeachBack/Spinoza

    William T. Tarimo, Fatima Abu Deeb, Timothy J. Hickey
    Computer Supported Education
    April 2016, Clinton, NY, USA.
    PDF | CCSCNE 2016


  • Computers in the CS1 Classroom

    William T. Tarimo, Fatima Abu Deeb, Timothy J. Hickey
    The 7th International Conference on Computer Supported Education
    May 2015, Lisbon, Portugal.
    PDF | CSEDU 2015

  • Adopting a 'Flipped' Interactive Pedagogy Using TeachBack

    William T. Tarimo, Timothy J. Hickey
    Consortium for Computing Sciences in Colleges — Northeastern Region
    April 2015, Worcester, Massachusetts, USA.
    PDF | CCSCNE 2015


  • The Affective Tutor

    Timothy J. Hickey, William T. Tarimo
    Journal of Computing Sciences in Colleges, 29(6), pp. 50-56, 2014.
    April 2014, Providence, Rhode Island, USA.
    PDF | CCSCNE 2014


  • Evolving Images Using Transparent Overlapping Polygons

    William Tarimo, Chen Xing, Linyu Dong, Chao Li
    Introduction to Scientific Computing, Brandeis University
    Spring 2013
    PDF | GitHub Repo

  • Classifying Yelp Data

    Linyu Dong, William Tarimo, Christopher Parkin, Emmanuel Awa, Dimokritos Stamatakis
    Machine Learning, Brandeis University
    Spring 2013


  • Using Cyclic Genetic Algorithms to Learn Gaits for an Actual Quadruped Robot

    Gary B. Parker and William T. Tarimo
    Proceedings of the 2011 IEEE International Conference on Systems, Man, and Cybernetics
    October 2011, Anchorage, Alaska, USA.
    PDF | SMC 2011

  • Quadruped Gait Learning Using Cyclic Genetic Algorithms

    Gary B. Parker, William T. Tarimo, and Michael Cantor
    Proceedings of the 2011 IEEE Congress on Evolutionary Computation
    June 2011, New Orleans, Louisiana, USA.
    PDF | CEC 2011

  • The Effects of Using a Greedy Factor in Hexapod Gait Learning

    Gary B. Parker and William T. Tarimo
    Proceedings of the 2011 IEEE Congress on Evolutionary Computation
    June 2011, New Orleans, Louisiana, USA.
    PDF | SMC 2011



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