Rochester Institute of Tech

NSF Awards · FY 2024 · 2024-09

The dust and gas between stars emits light with wavelengths much longer than we can see with our eyes. This light, called far-infrared (FIR) light, can teach us about the material in our galaxy when we divide the light into channels that correspond to the wavelengths where different molecules radiate. In this way, we can learn about how stars form, evolve, and die. The devices used to split FIR light into these channels, called spectrometers, are complicated and expensive. The next generation of telescopes will need smaller and less costly technologies to reach their science goals. This project could lead to a new type of spectrometer that could reduce their size and expense by a factor of 100 or more. In this program, the investigator will build and use a system to test prototype spectrometers, a crucial step in developing this technology. This investigation will also help train students, providing important opportunities for them to work hands-on with cutting-edge technology in the new field of Quantum Information Science. FIR observations probe both objects and processes that are often invisible at other wavelengths, as well as a rich set of atomic and molecular transition lines that are diagnostic of heating and cooling processes in interstellar material. Unfortunately, standard FIR spectral dispersion technologies are challenging and expensive to implement, particularly in the context of the large-format spectrophotometers that will be deployed on the next generation of telescopes. Recent advances in semiconductor fabrication offer the potential for new, integrated on-chip devices that use quasi-photonic methods to disperse and sense the light. This kind of technology offers large scalability, ease of manufacture, size, and performance advantages that would enable the instruments required to meet the demands of astrophysics over the next two decades. The investigator is involved in a multi-institution collaboration developing next-generation quasi-photonic FIR on-chip spectrometer technology, but a sub-Kelvin testbed to perform device characterization of these novel spectrometers is necessary to make progress. In this ATI program the testbed will be designed, fabricated, commissioned, and used to characterize prototype on-chip spectrometer devices. This award also supports a new institutional capability for students and faculty to perform sub-Kelvin cryogenic testing of photonic and quantum information technologies. These include augmenting the hands-on training in RIT's new Quantum Information Science and Technology degree track and providing a new experimental capability for students in RIT's nascent PhD in Physics program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Collaborative Research: Designing Intelligent Industrial Robots for STEM Inclusion by Leveraging Self-Determination Theory to Foster Autistic Talent in Manufacturing Work Workforce diversity is crucial in today's rapidly changing world. Autistic adults, with their unique perspectives and skills, can significantly contribute to workplace diversity. However, compared to similarly qualified peers, they often struggle to find and retain jobs, including in STEM fields where the U.S. faces an increasing skills gap. Autistic adults comprise at least 2% of the U.S. population, so increasing their employment rate could meaningfully expand and enhance the U.S. manufacturing and STEM workforce. This project aims to address this issue by developing smart industrial robots that provide personalized support for autistic employees in manufacturing and STEM work environments. By creating more supportive and inclusive workplaces, we seek to improve job retention, income, and independence for autistic employees. Furthermore, this initiative will help bridge the skills gap in manufacturing and boost economic growth. The advancements from this project will also enhance educational opportunities and improve employment prospects for autistic adults, fostering more neurodiverse and productive work environments that drive innovation in the U.S. manufacturing sector. This project focuses on developing smart industrial robots that offer personalized support for autistic employees in STEM and manufacturing jobs. Our approach combines the co-design framework of mutual shaping with the principles of Self-Determination Theory (SDT). We will engage key stakeholders, including autistic adults and industry experts, throughout all development cycles in an iterative design process to advance industrial robot intelligence. The primary objectives of this project are twofold: (1) to co-create support approaches based on SDT that address fundamental psychological needs (i.e., autonomy, competence, and relatedness) through interviews, focus groups, and human-in-the-loop simulations, and (2) to enhance robot intelligence for accurately identifying and meeting workers' psychological needs in manufacturing settings, resulting in adaptive and personalized support. By integrating SDT-based support into industrial robot design, we anticipate increased motivation, work quality, and job satisfaction for all employees. This neuro-affirming work environment will, in turn, promote inclusion, productivity, and innovation in the STEM workforce. This award has been made in response to the NSF solicitation “Workplace Equity for Persons with Disabilities in STEM and STEM Education” (NSF 23-593). This project is funded by the Advancing Informal STEM Learning (AISL) Program in the Division of Research on Learning in Formal and Informal Settings (DRL) in the Directorate for STEM Education. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Multimessenger astrophysics (MMA) combines different means of observation such as electromagnetic and gravitational waves (GWs), as seen in the 2017 LIGO-Virgo observation of a binary neutron star inspiral associated with a gamma-ray burst, followed by multiple observations of an electromagnetic counterpart. A promising MMA target is the low-mass X-ray binary (LMXB) Scorpius X-1, long studied in X-ray, radio, and optical. This binary star system consists of a neutron star, an extremely dense object more massive than the sun and the size of a city, together with a less massive star, the outer layers of which are being pulled onto the neutron star by its gravity. The neutron star is believed to be spinning tens to hundreds of times a second and is one of the most promising sources of long-lived gravitational waves. The goal of this project is to detect or further constrain those waves, which will broaden the field of Gravitational Wave Astronomy beyond the short-lived GW signals observed so far by LIGO and Virgo. This project will involve faculty, scientific staff, and students of the Rochester Institute of Technology, who will conduct the most sensitive search ever for GWs from Sco X-1 using data from the fourth and fifth observing runs of the Advanced LIGO, Advanced Virgo, and KAGRA detectors, probing the range of plausible signal strengths over a wide range of frequencies, and possibly leading to the first observation of continuous gravitational waves. The program will also be extended by expanding the search to other LMXBs, improving the validation of potential detections, and transitioning from a mode of searching all the data of a run at once to ongoing incremental observations. The search for GWs from Sco X-1 is considered a "highest priority" by the LIGO scientific collaboration, and this grant will be instrumental in maximizing its success. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Recently there has been a revitalization of the domestic semiconductor manufacturing industry that has catalyzed new opportunities for research in microelectronics-related fields. The continual growth and evolution of the US semiconductor sector and its competitiveness on the global stage, however, invoke a pressing need for a highly-skilled, adaptable, and diverse workforce. The cultivation of such a workforce requires innovative and multifaceted pedagogical methods that integrate theoretical knowledge with practical skills. To address this demand, Rochester Institute of Technology (RIT) will collaborate with Monroe Community College (MCC) and Finger Lakes Community College (FLCC), as well as corporate partners GlobalFoundries, Inc. (GF) and Micron Technology, Inc. (Micron), to develop an experiential learning-based pilot model toward the sustained growth of the US semiconductor workforce. RIT will serve as a central pipeline for two parallel cohorts of participants from diverse backgrounds, enabling career pathways from 2-year community colleges to leading chip manufacturers via comprehensive, cleanroom-based undergraduate studies in the emerging technology fields of semiconductors and microelectronics. The participants will earn stackable Associate in Science (A.S.) degrees in Engineering Science and Bachelor of Science (B.S.) degrees in Microelectronic Engineering, while developing practical skills through industry-guided research in cleanroom laboratories and on-the-site training during co-operative education work placements in microelectronics foundries. This program will ultimately cultivate future workforce generations by establishing cross-sectional partnerships between regional community colleges, research-intensive univerisities, and leading industry stakeholders, thereby safeguarding the long-term growth of the US microchip manufacturing industry toward domestic semiconductor independence. Two cohorts of participants from broadly representative and diverse populations with fundamental STEM proficiencies gained through their pursuit of A.S. degrees at MCC and FLCC will transfer to the third curriculum year of the Microelectronic Engineering B.S. degree program at RIT. Participants will gain research experiences in the following technical areas determined by industry partners GF and Micron: (a) advanced materials synthesis/characterization; (b) device-level fabrication, simulation, and testing; (c) integrated circuit/systems-level design; and (d) cleanroom instrument operation/troubleshooting. The participants will also receive practical training through up to three semesters of industrial co-operative education work placements. These opportunities will result in transferable technical proficiencies and will nurture a sense of belonging in the microelectronics field. Tailored mentorship and professional development activities will be implemented to guide participants towards their intended career paths, encourage reflection, foster group cohesion, and promote professional growth through seminars, site visits, conferences, and community-building events. The program will undergo external evaluation by the Center for Professional Development and Education Reform at the University of Rochester. The program’s successful interventions, determined through external evaluation, will be broadly disseminated toward large-scale implementation at the national-level beyond the project end date. This project aligns with the NSF ExLENT Program, funded by the NSF TIP and EDU Directorates, as it seeks to support experiential learning opportunities for individuals from diverse professional and educational backgrounds to increase their interest in, and their access to, career pathways in emerging technology fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

Creating robust and comprehensive cybersecurity solutions has become increasingly costly and time consuming due to the ever-expanding list of vulnerabilities to be considered and new attacks found continuously. On the other hand, security research quite often only focuses on a specific attack or even sub-components of it with a narrow scope. These constraints severely limit the opportunity in creating holistic, cross-disciplinary cybersecurity solutions. This project aims to develop a learner-centered co-pilot tool leveraging advances in artificial intelligence (AI) to produce attack scenarios and capture related data, which includes end-to-end attack interactions between the red team attacker and the cyber systems. The resulting high-quality and structured attack artifact repository will be a highly valuable resource to the cyber security research community, especially for the test and validation of security solutions. This project adopts large language model (LLM) to help cybersecurity research. Through an LLM adaptation approach, the red-team co-pilot will incorporate techniques such as prompt engineering, reasoning, parameter-efficient fine-tuning, and few-shot learning to guide users to emulate attack scenarios. The project will develop a curator-friendly methodology to enable the crowd-sourced aggregation of high-quality cyberattack artifacts associated with attack behaviors and system settings, when the tool is deployed in the research community. The captured dataset contains both functional and behavioral aspects of attacks such as tactics, techniques, and procedures. A successful research outcome, including the tools generated, can help facilitate security benchmarking, AI-based penetration testing, adversarial modeling, and research reproducibility. In addition, the red-team co-pilot brings a useful tool to cyber security education and workforce development since it offers an accessible, adaptive, reusable, and learner-centric platform for users to emulate attacks and develop cyber defense experiences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.