I am a Ph.D. Candidate in Computer Science at the University of Minnesota where I am advised by Prof. Maria L. Gini. I received my bachelor’s degree in Electrical and Computer Engineering from Addis Ababa Institute of Technology. My interests include artificial intelligence, machine learning, robotics, computer vision, and natural language processing.

Next Gen AI Team

Our Multi-Agent Research Group

As the lead of the Multi-Agent Research Group at the University of Minnesota, I oversee a dedicated team specializing in multi-agent systems. Our research focuses on distributed decision-making for autonomous agents and robots, covering areas such as task allocation, exploration of unknown environments, teamwork for search and rescue, and navigation in dense crowds. We are committed to advancing artificial intelligence through innovative research and collaboration.

Multi-Agent Research Group

Research Publications

MASSpace'24 at AAMAS 2024
Understanding Drill Data for Autonomous Application

Presented at the MASSpace'24 at AAMAS 2024.

ARMS at AAMAS 2024
Multi-agent Path Finding Using Time-Extended Graphs with Auctions

Presented at the ARMS at AAMAS 2024.

IEEE IROS 2023
Ethical Robot Design Considerations for Individuals Suffering from a Neurodegenerative Disease

Presented at the IEEE International Conference on Intelligent Robots and Systems Workshop 2023.

IEEE IROS 2023
Autonomy and Dignity for Elderly Using Socially Assistive Technologies

Presented at the IEEE International Conference on Intelligent Robots and Systems Workshop 2023.

AAAI SIAIA 2023
What We Know So Far: Artificial Intelligence in African Healthcare

Presented at the AAAI International Workshop on the Social Impact of AI for Africa.

ACM IUI 2022
Emotion Recognition in Conversations Using Brain and Physiological Signals

Presented at the ACM International Conference on Intelligent User Interfaces 2022.

Professional and Academic Experiences

NASA L’Space Mission Concept Academy

2024 – Present
Data and Command Handling (CDH) for Lunar Exploration Mission, leading subsystem trade studies and Preliminary Design Review (PDR) for the lunar mission.

NASA Ames Research Collaboration

2023 – 2024
Partnered on drill data analysis for fault detection and autonomy using ML techniques.

Research Assistant, University of Minnesota

2022 – Present
Researching topics in Multi-Agent Reinforcement Learning (MARL) and Multi-Agent Systems (MAS). Worked on autonomous robots, including manipulation tasks with Kinova and navigation tasks with Boston Dynamics Spot.

Teaching Assistant, University of Minnesota

2022 – Present
I was a TA for Robotics (CSCI 5551), Operating Systems (CSCI 4061), Machine Architecture and Organization (CSCI 2021), Linear Algebra (2023), and Introduction to C/C++ Programming for Scientists and Engineers (CSCI 1113).

Lead QA, Safaricom

2022
Integrated enterprise systems with S&D. Trained over 100 users and optimized workflows for efficiency.

Research Intern, Emphatic Computing Lab

2021
Analyzed physiological signals (PPG, GSR) for emotion recognition using machine learning.

Research Intern, University of Michigan

2019 – 2020
Conducted bottleneck analysis of RL algorithms, optimizing CPU usage and execution time.

Projects & Activities

Here are some of the projects I've worked on, spanning robotics, artificial intelligence, and federated learning.

Visual Odometry with Spot Robot

Visual Odometry with Spot Robot

As part of my graduate study, I worked on visual odometry with Spot using ORB-SLAM 3.

Histotripsy Robotics Simulation

Histotripsy Robotics Simulation

A simulation setup for underwater robotic mechanisms using a UR5 robotic arm, focused on histotripsy treatment. This project involves modeling the interaction of the robot's end-effector with a water-like environment and tackling challenges like buoyancy forces and water resistance.

Emotion Recognition Using Brain Signals

Emotion Recognition Using Brain Signals

This project focuses on using brain activity and physiological signals to recognize emotions in conversations.

Federated Learning for Traffic Flow Prediction

Federated Learning for Traffic Flow Prediction

This project explores federated learning using the Flower framework for traffic flow prediction.

Path Planning with TurtleBot

Path Planning Using D* Lite Algorithm

This project focuses on utilizing the D* Lite algorithm for path planning in mobile robotics.

Pinball Game using Processing

Pinball Game using Processing

A classic pinball game with interactive flippers, bouncy balls, and engaging sound effects. Score points by hitting obstacles and keeping the ball in play.

Multi-UAVs for Precision Agriculture

Multi-UAVs for Precision Agriculture

This work explores the application of multiple UAVs in precision agriculture.

Options

Interactive Spider Animation

This project demonstrates an interactive spider animation with dynamic leg movements using inverse kinematics and canvas rendering.

Scorpion Simulation Project

Scorpion Simulation Project

A dynamic 3D simulation of a scorpion interacting with a customizable environment. The simulation includes animated legs, tail movements, and a responsive, interactive design built using Processing.

Federated Learning for Traffic Flow Prediction

Federated Learning for Traffic Flow Prediction

This project explores federated learning using the Flower framework for traffic flow prediction.

Cloth and Fluid Simulation Project

Cloth and Fluid Simulation Project

A dynamic 3D simulation showcasing cloth physics and fluid interactions, leveraging gravitational forces, spring-damping mechanics, and SPH fluid dynamics. Includes interactive features and responsive camera controls for a rich simulation experience.

My Research at Empathic Computing Lab

During my time at the Empathic Computing Lab, I explored how physiological signals, such as PPG and GSR, change in response to various emotional stimuli. Using both machine learning and deep learning models, I worked on recognizing emotions in human conversations.

This research culminated in a presentation at the International Conference on Intelligent User Interfaces (IUI) 2022, showcasing our findings on emotion recognition in conversations using brain and physiological signals. You can access the published paper here.

View Project on GitHub Google Scholar Access Paper
Empathic Computing Lab Research

Drill Data Analysis for Autonomy

In high-risk, high-cost environments like Mars, it is essential for robotic agents to anticipate and address potential issues before they escalate into mission-critical failures. The Regolith and Ice Drill for Exploring New Terrain (TRIDENT) is a rotary percussive 1-meter class drill developed by Honeybee Robotics for NASA. TRIDENT is slated for deployment in lunar missions such as the Polar Resources Ice Mining Experiment-1 (PRIME-1) and the Volatiles Investigating Polar Exploration Rover (VIPER), both scheduled for launch in 2024.

To enhance TRIDENT's operational reliability, we analyzed logged data from previous field tests to better understand potential drilling faults that the system may encounter. By applying time series analysis techniques, we identified trends in the data during fault occurrences. Additionally, we employed change point analysis and other machine learning methods to predict potential faults, enabling TRIDENT to respond proactively to drilling anomalies.

TRIDENT Drill Data Analysis
Drill Data Analysis
for Autonomy.
AAMAS 2024

This research contributes to the development of autonomous drilling systems capable of maintaining mission integrity in extraterrestrial environments. By equipping TRIDENT with advanced fault prediction and response capabilities, we aim to ensure its effectiveness in upcoming lunar missions and future applications on Mars.

Analyzed TRIDENT drill data with NASA Ames to identify fault patterns using time series and machine learning techniques, enhancing fault prediction for autonomous systems in high-risk environments.

You can find the paper published at the MASSpace’24 Workshop at AAMAS 2024. 

  @inproceedings{boelter2024understanding,
  title={Understanding Drill Data for Autonomous Application},
  author={Boelter, Sarah and Temesgen, Ebasa and Glass, Brian J and Gini, Maria},
  booktitle={Proceedings of the MASSpace'24 Workshop at AAMAS 2024},
  year={2024},
  location={Auckland, New Zealand}
  }

ORCA + Reinforcement Learning

Optimal Reciprocal Collision Avoidance (ORCA) is an algorithm in robotics for enabling collision-free navigation in multi-agent systems. By combining ORCA with Reinforcement Learning (RL), agents gain the ability to adapt to dynamic environments, making it particularly effective in dense crowds and unpredictable scenarios.

This integration has been pivotal in simulations of pedestrian dynamics, robot navigation, and cooperative multi-agent systems. Through advanced RL algorithms, agents learn to optimize their paths, maintain safety, and achieve goals while interacting with other entities in real-world-inspired environments.

Path Planning with Reinforcement Learning
Next Gen AI Team
ORCA + Reinforcement Learning for Crowd Simulation

We integrated ORCA with Reinforcement Learning to enable agents to navigate dense crowds autonomously. This involved designing and fine-tuning RL algorithms to enhance agents' adaptability to dynamic and unpredictable crowd behaviors.

To improve scalability, I developed and optimized the simulation environment using the Vectorized Multi-Agent Simulator (VMAS). This framework streamlined simulations and supported high-performance training of RL agents in multi-agent scenarios.

My work focused on ensuring efficient test environment development, parameter tuning for RL models, and enhancing scalability, providing a robust foundation for research in multi-agent reinforcement learning.

CLIP-NAV
CLIP-Based Navigation Project

Our team developed an advanced autonomous navigation system by leveraging OpenAI's CLIP model. This integration enhances the robot's ability to understand and execute natural language instructions, enabling seamless navigation in complex and dynamic environments.

The project focuses on empowering robots to interact more intuitively with human operators by interpreting instructions such as "Find the red chair" or "Navigate to the nearest exit," significantly improving versatility and user experience in various scenarios.

Community Outreach Activities

As a representative of the Minnesota Robotics Institute (MnRI), I have participated in various community outreach events, promoting robotics and STEM education.

Centennial School Summer Academy

Centennial School Summer Academy

Engaged students in robotics activities to broaden their perspectives on technology and innovation.

World Refugee Day Celebration

World Refugee Day Celebration

Represented MnRI at the event, collaborating with the Afghan Refugee community to promote STEM education among refugees.

Advanced Automation Conference at MN Convention Center

Advanced Automation Conference

Discussed the latest advancements in robotics and automation technologies while representing MnRI at the conference.

MnRI Tech Camp with MAWA

MnRI Tech Camp with MAWA

Collaborated with the Minnesota African Women's Association to introduce young women to technology and robotics through hands-on activities.

Day Camp Collaboration with SLHS Department

Day Camp Collaboration with SLHS Department

Organized a camp featuring Ozobots and Simple Circuits to provide engaging STEM learning experiences for children.

Professional Involvements

Explore my roles and contributions in various organizations and initiatives.

AI Research Lab

Member of the CS-IDEA Committee

(2024-2025) I am serving as part of the Computer Science Inclusiveness, Diversity, Equity, and Advocacy (CS-IDEA) Committee in computer science.

AI Research Lab

Treasurer of the Computer Science Graduate Students Association (CSGSA)

(2023-2024) Serving as Treasurer, I manage financial operations to support initiatives that enhance the graduate student experience in the Computer Science department.

AI Research Lab

Graduate Student Coordinator

(2024-2025) In this role, I facilitate communication and organize events to foster a supportive and inclusive environment for graduate students in the Computer Science department.

AI Research Lab

Mentor at IAAMCS

Mentor at the Institute for African-American Mentoring in Computing Sciences (IAAMCS), supporting African-American students in computing sciences through guidance and advocacy.