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We build computer systems to train and deploy state-of-the-art machine learning (ML) models. We focus on improving the scalability, efficiency, and security of systems across the end-to-end ML pipeline. Our interests span the entire systems stack — including distributed/operating systems, databases, computer architecture, and security — and their intersection with machine learning.

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.

• :��Tues.,��Oct.��8th, 2024, 10 a.m. (MST)
• Wed, Oct. 2nd, 2024, 2:30 p.m. (MST)

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.��

We are always looking forward to working with motivated and dedicated students. We look for:��
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1. A clear alignment of interests
2. Prior research experience in any area (ideally with publications)
3. Excellent collaboration and communication skills
4. A “can do” attitude that exhibits confidence, enthusiasm (for research), and perseverance in the face of challenges.
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If you would like to inquire about joining us, please send us a note with your CV/resume, brief statement of prior work, and how you see yourself contributing to our work. We do receive a lot of requests and only reply if the mutual interest seems to be sufficiently strong.

• We are a team of spatial data scientists at the GeoHAI lab directed by Dr. Morteza Karimzadeh at the ɫ��Ƶ����. Our research is use-inspired and interdisciplinary, where we tackle scientific challenges in the context of operational needs and urgent societal problems.
• We believe in open science, and publish about our innovations in disciplinary literature of Computer Science and Geography, where we are homed, as well as interdisciplinary journals and domains to which our work extends.
• Beyond publishing on our work in scientific venues, our products have direct users in other disciplines or outside academia, or have immediate impact for policy making. Browse through our many projects on our lab website and team members to learn more!

��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.

In this NSF-funded project, we’re working to develop curriculum modules to support youth in��exploring critical ethical issues related to emerging AI technologies. Through these modules, which can be delivered on their own or integrated in other STEM, computer science/robotics or English Language Arts classes, middle and high school age youth can��explore what��AI��is, how machines learn from data, and how��AI��systems may carry the biases of their programmers. Using a combination of��online activities, short stories, comics and��design/Making projects, these curriulum modules��dive into the technology that powers��AI��and explore the potential societal impacts of this rapidly expanding technology.

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.

My work seeks to integrate deep learning and symbolic AI to achieve the best of both worlds. Examples of symbolic AI include knowledge graphs, logics, and automata. We are also researching application domains for this novel integration, including the discovery of new molecules for drug design and depolymerization and the optimization of organ exchanges.

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.

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.

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.

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I work on topics in algorithms, computational complexity, and cryptography. My main focus is on lattices, error-correcting codes, algebraic and geometric problems, and fine-grained complexity.

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).

Spectrum availability for wireless internet access (cellular, Wi-Fi, etc.), for weather observing via Earth Observing Satellites (EOS), for Radio Astronomy scientific observations investigating the formation of the Universe, and for aircraft and military operations truly pervade every aspect of daily life. Our research focus is on the intersection of wireless communications, passive and active spectrum sharing, spectrum policy, and hardware security in support of secure spectrum sharing for communications and scientific purposes. Several of the lab’s ongoing projects are supported by the National Science Foundation and by NASA.