[2025] ATR Lab Summer Research Internship 

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[2025] ATR Lab Summer Research Internship

July 7 – August 1 | 4 Weeks | Hybrid Format

Non-Paid Internship | Gain Hands-On Research Experience in AI & Robotics

Looking to break into the world of AI, robotics, or machine learning? The ATR Lab invites you to join our prestigious 2025 Summer Research Internship. This non-paid opportunity offers valuable research experience, hands-on collaboration with expert mentors, and exposure to real-world technical challenges.

What to Expect

  • 8 total sessions (2 per week: 1 in-person + 1 online)
  • Collaborative, research-driven environment
  • Mentorship from experienced project leaders

Explore Exciting Research Projects

  1. SURA – Smart User-Responsive Assistant | led by Shawn Lin
    • Telepresence robots today often rely on generic voice assistants like Siri, Alexa, or Google Assistant, without having a unified, branded AI system like J.A.R.V.I.S. Systems such as Beam, Double Robotics, Temi, and AVA use varying levels of AI for navigation, voice control, and remote interaction. To address this gap, the team will develop AURA (Autonomous User-Responsive Assistant), a custom AI assistant tailored specifically for telepresence robots. AURA will deliver intelligent voice interaction, real-time navigation, and personalized communication, offering a unified and advanced AI experience beyond existing solutions
  2. CryptoClash – AI BattleBots with Real-Time Staking | led by Irvin Cardenas
    • a blockchain-powered gaming platform where players deploy AI-driven BattleBots to compete in real-time combat arenas. Users can train, upgrade, and customize their bots using machine learning strategies, and stake cryptocurrency on live matches. Winners earn rewards based on match outcomes, while smart contracts ensure transparency and trustless staking. Combining AI, DeFi, and competitive gameplay, CryptoClash delivers an engaging, high-stakes experience in the next evolution of Web3 gaming
  3. Machine2Machine Learning | led by Shuvo Mohammad
    • Collaborative learning in search and rescue (SAR) robotics, focusing on both human-machine interaction and machine-to-machine knowledge transfer. We analyze real-time coordination strategies, adaptive learning mechanisms, and the challenges of deploying intelligent systems in unpredictable environments. Emphasis is placed on federated learning, continual learning, and hybrid knowledge transfer models that enable cooperative autonomy in multi-agent SAR systems.
  4. AI Teaching Assistant for K–12 via Visual Programming | led by Cecilia Trikey
    • This study presents an AI-based real-time student monitoring system for K-12 coding and robotics education. It tracks engagement, coding progress, and emotions to alert teachers when support is needed. Using LLMs, visual programming data, and a scalable backend, the system enables personalized assistance and fosters interactive, inclusive STEM learning.
  5. PlatoonSim – Autonomous Truck Platooning Simulation | led by Raiyan Ashraf & Nafiul Alam
    • Autonomous truck platooning (ATP) promises improved fuel efficiency and safety, but faces regulatory and data challenges. To address this, PlatoonSim simulates real-world ATP scenarios using American Truck Simulator, capturing data on system performance and human behavior. It models convoy interactions, disruptions, and driver conditions through integrated sensors and defined roles. PlatoonSim enables safe, controlled testing to support ATP development and inform policy and design improvements
  6. Machine Learning for Dental Clinic AI | led by Nafiul Alam
    • an advanced AI development project focused on creating a powerful diagnostic model using over 2 million dental images and associated medical data. The goal is to enhance dental care by enabling accurate, real-time detection of oral health issues. By leveraging deep learning and clinical metadata, the AI system will support dentists in diagnosis, treatment planning, and patient monitoring, setting a new standard for intelligent, data-driven dental care.
  7. Smart Pepper Robot – Social Robotics for Smart Interaction | led by Marcus Arnett
    • a cutting-edge project aimed at enhancing human-robot interaction through the development of intelligent behaviors in the Pepper robot. By integrating advanced AI, natural language processing, and contextual awareness, the robot will be capable of engaging in meaningful conversations, recognizing emotions, and adapting to social cues in real time. This project explores applications in education, healthcare, and customer service, positioning Pepper as a friendly, interactive assistant in smart environment
  8. Unbiased AI: Mitigating Political Bias in Large Language Models : led by Nafiul Alam
    • This project addresses bias in Large Language Models used for political analysis, aiming to detect and correct skewed outputs. It seeks to develop unbiased AI frameworks that ensure balanced, fair, and trustworthy interpretations of political content.
  9. Smart Service Robot in Restaurant | led by Jay Lee & Shawn Lin
    • Smart service robots automate tasks like food delivery, order taking, and cleaning in restaurants, improving efficiency and customer experience. Current systems use basic AI with limited adaptability. Emerging technologies like Large Language Models (LLMs) and General AI offer new opportunities for natural interaction, personalized service, and better handling of dynamic environments, paving the way for more intelligent and flexible restaurant automation.

Who Should Apply

  • Students with general programming experience (e.g., Python, C++)
  • Robotics experience is a plus, but not required
  • Passion for AI, machine learning, or education technology
  • Exceptional candidates may be considered even if all criteria aren’t met

📅 Application Details

Application Period: May 1 – May 15

Acceptance Notification: May 30

To Apply: Complete the Google Form and submit your resume

Here is the link for the Google Form  https://forms.gle/YNhacr9mQG85fDNn8

Why Apply?

  • Build your portfolio with real AI/robotics research
  • Gain experience working with cutting-edge technologies
  • Collaborate with a passionate, diverse team of mentors and peers
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