See who is recruiting for fall 2025!

My research group is investigating:

1. System support for building IoT and AI applications at the edge including generative AI models and digital twins at the edge.
2. Socio-technical system to empower environmental justice communities.
3. Cybersafety issues in the Internet including toxicity detection, classification and mitigation in online social media platforms.

The team applies insights gleaned from this basic research program to develop intelligent technologies that help people achieve to their fullest potential by coordinating what they think and feel with what they know and do. This research uses a range of techniques such as eye tracking, speech and language processing, physiological sensing, computer vision, time series analyses, discourse modeling, and machine learning. The interaction contexts include educational games, collaborative problem solving, classroom discourse, computerized reading, and workplace activities. Data is collected in the lab, online, in schools, and the workplace.

D'Mello also directs the NSF National Institute for Student-Agent Teaming (iSAT) and a Learning Engineering Virtual Institute (LEVI) called Hybrid Human-Agent Tutoring (HAT) Platform to accelerate middle school math achievement for low income students.

 The goal of the group is developing robot technologies that enable close, natural, and extended cooperation with humans. As Director of the HIRO Group, Assistant Professor Alessandro Roncone seeks to lower the barriers to entry for humans to naturally interact with robots, and in doing so he designs robot systems that are able to work with and around people. With a large lab of undergraduate, graduate, and PhD researchers, HIRO Group alumni have gone on to work in industry at companies such as Microsoft, Facebook, Lumen, and Amazon.

We are looking for students who have backgrounds in computer graphics, simulation/optimization, robotics, electrical engineering, electromechanical design, and digital fabrication (such as 3D printing).

• Opportunities
• Recent Press About the Lab

We use various computational techniques, including deep learning, natural language processing, graph analytics, image processing, and causal inference. See a list of our projects and publications. The ideal student should have a good grasp of quantitative methods and be a good programmer. In addition, the ideal candidate should have an undergraduate or master’s degree in Computer Science, Engineering, Applied Statistics, Mathematics, or a similar quantitative field.

Such "robotic materials" tightly integrate sensing, actuation, computation, and communication to allow materials to sense their environment, and change their shape and appearance. Leading to novel tactile sensors, skins, and high-speed actuators that can augment existing robotic systems, such materials are also posing new challenges in manufacturing, in particular robotic assembly. 

Current directions include developing algorithms and infrastructure for reliable environmental monitoring using machine learning, and understanding the multifaceted nature of representation in data and how that affects our ability to train fair and effective machine learning systems. I'm looking for students interested in statistical or geospatial machine learning methodology and tailoring those methods to the context of real world problems. Prospective students should be excited about working in and actively fostering a collaborative, supportive, and communicative research community in our lab doing exciting, innovative, and sometimes interdisciplinary research. Ideal background includes skills in statistics, optimization, linear algebra and python.

This is rooted in the fact that a significant portion of security and reliability issues are often a result of limitations in the management of networked systems. My research has been enabling and capitalizing on a more dynamic and programmable computing and network infrastructure, via such technologies as virtualization/containerization, software-defined networking, and the movement toward cloud based services.

In general, my students are interested in systems programming (writing software, using and modifying open source code, etc.). Entrepreneurial interest is a bonus -- I believe systems research and entrepreneurship go hand-in-hand, and find it a great avenue for students to pursue.

The focus is on adaptive, efficient, and high-order accurate methods for the sake of user-friendliness and robustness even for badly conditioned problems. We emphasize building open-source software implementations of these methods.

The group is looking for students with a strong mathematical background (whether that is from engineering, physics, or applied math courses; particular emphasis on calculus, real and complex analysis, linear algebra, numerical ODEs/PDEs), some coding experience (for scientific computing), and an open mind to acquire new skills/learn new methods.

I intend to work on morphing the system and architecture of various types of quantum computers, encompassing both near-term devices and fault-tolerant systems, as well as emerging accelerators. My goal is to improve their reliability and applicability, addressing the full-stack optimization problem, including error correction/mitigation, compilers, programming languages, and pre/post-processing policies. Additionally, I am committed to ensuring the security and privacy of quantum users. Moreover, I plan to explore synergistic opportunities between quantum computing, Artificial Intelligence (AI), and Machine Learning (ML), encompassing AI/ML applications for quantum computing and vice versa. Furthermore, I aim to employ quantum computers and quantum AI/ML models to address computationally challenging problems, including optimization, natural language processing, drug discovery, and climate studies. I am always looking for highly motivated students to join my research group. While a background in quantum computing is not necessary, students should possess a strong desire to explore new ideas and a passion for continuous learning.

Karimzadeh conducts integrative research that brings together data science with social/environmental science to inform best practices for a more sustainable and equitable society. His research primarily focuses on method development, spanning various domains including social media analytics, energy (resilience and production), crisis management, situational awareness, precision agriculture, and digital humanities and beyond.