Roberts Innovation Fund to Support Inventions in AI, Quantum, Water
New methods to purify our water, advance quantum computing, democratize artificial intelligence, and deliver life-saving drugs are just some of the projects funded by the 2024 Roberts Innovation Fund Awards, providing $1 million in accelerator funding to support 10 new inventions led by faculty from Yale’s School of Engineering & Applied Science.
The Roberts Innovation Fund, an accelerator fund created in 2022 and managed by Yale Ventures, focuses on supporting technologies with significant potential to benefit a wide-ranging number of fields. In addition to funding, awardees receive world-class support and mentorship, access to industry experts and other perks as well as an opportunity to present at the upcoming Yale Innovation Summit.
In line with Yale Engineering’s Strategic Vision, the Roberts Innovation Fund bolsters the School’s culture of innovation and entrepreneurship by advancing an ecosystem that brings to market discoveries that will benefit humanity.
Yale Engineering Dean Jeffrey Brock said the large number of excellent proposals this year demonstrates the continued need for the kind of opportunities that the Roberts Fund provides.
“Our faculty have long been producing groundbreaking research, and once again, the Roberts Innovation Fund is helping to get these inspired ideas out of the laboratories and to the market, and ultimately to the people and industries that will benefit the most from them,” Brock said. “The areas of research in this year’s selected projects emphasize Yale's priorities in quantum, artificial intelligence, and planetary solutions, demonstrating how Yale Engineering’s goals and our Strategic Vision are advancing the goals of the University.”
In its second year, the Roberts Innovation Fund has significantly expanded its reach and resources, setting the stage for a transformative impact on how Yale Engineering’s innovations make the leap from research to real-world applications.
"This year has been about expansion for the Roberts Innovation Fund,” said the Fund’s director, Claudia Reuter. “As we considered more ways to support the innovative work happening within Yale Engineering advance from ‘lab to market,’ we broadened the aperture of potential domains for consideration, added new members to our external advisory board with additional industry experience, and thanks to the leadership of Yale Engineering we were able to allocate more capital. We're now in the process of refining the types of support we provide to awardees and look forward to helping each project advance to their greatest potential impact."
The Roberts Innovation Fund is open to Yale faculty with a primary or secondary appointment in the School of Engineering & Applied Science with a novel innovation that solves a significant problem with the potential for scale. Applicants must demonstrate a proof of concept and a clear articulation for the use of the awarded funds.
One of the awardees, Jaehong Kim, said he’s looking forward to working with the Roberts Fund on his project to develop a more efficient catalytic material and wastewater treatment method.
“Properly treating toxic wastewater and potentially recycling it are becoming more important for industries to remain cost-competitive and compliant to increasingly stringent environmental regulations,” said Kim, the Henry P. Becton Sr. Professor of Chemical & Environmental Engineering. “The Roberts Innovation Fund brings our research one step toward commercializing our technology and one step closer to making a major impact on this pivotal matter.”
In addition to providing $1 million in accelerator funding, the Roberts Innovation Fund also provided Phase II follow-on awards of $50,000 to two projects from last year. As the fund team works to support projects on their path from lab to market, they recognize that some projects may require additional runway to advance, and plan to provide a pathway for some Phase II follow on funding each year. This year's Phase II projects are "Foundational AI in Radiology: Transformative text-based tools for radiology,” led by Arman Cohan (Computer Science) and Sophie Chheang (Radiology and Biomedical Imaging) and “Combining DeFi and AI for intelligent credit scoring, led by Electrical Engineering faculty member Leandros Tassiulas, with Georgios Palaiokrassas, Jason Ofeidis, Aosong Feng, and Farooq Anjum.
This year’s awardees are:
Advancing Novel PFAS Treatment Technologies from Lab to Commercialization
Prof. John Fortner with Lydia Watt, Susanna Maisto, Seung Soo Lee, and Öze Durak
Chemical & Environmental Engineering
PFAS pollution - the harmful substances also known as “forever chemicals” - is now a globally ubiquitous and a serious public health threat. Potentially, affecting millions of people through tap water alone, PFAS is one of the emerging water contaminants targeted by the U.S. government’s recent $10 billion management effort. The team led by John Fortner has developed technologies to separate and destroy PFAS at point-of-use water treatment technology companies, municipal water treatment facilities, and other locations. His technologies have significant potential to outperform conventional PFAS treatment approaches.
Scaling Catalytic Membranes for Oil & Gas Produced Water Treatment - Industrial wastewater treatment and reuse
Prof. Jaehong Kim with James Licato and Claire Chouinard
Chemical & Environmental Engineering
As the cost of water rises due to global challenges, reusing and recycling water has become inevitable for industries in the U.S. and around the world for their sustainable growth. Technologies that can actually destroy contaminants are ideal, but they require major breakthroughs to become viable and cost-effective.
Commonly employed in many industries, advanced oxidation processes (AOPs) are designed to destroy water pollutants, but current AOPs can’t achieve complete destruction of contaminants. A team led by Jaehong Kim has developed a new catalytic material and treatment scheme that demonstrates near-complete destruction of organic pollutants with the highest-ever recorded efficiency at a low operating cost.
Data-Centric Quantum Computing
Prof. Yongshan Ding with Shifan Xu
Computer Science
While quantum processing units (QPUs) have advanced significantly, the challenge of efficiently translating classical data into a format compatible with QPUs hinders the deployment of quantum applications in data-driven fields like machine learning, optimization, and scientific computing. Yongshan Ding has created a solution that provides user-friendly access to cutting-edge quantum random access memory (QRAM) - a technology that’s pivotal for converting classical data into quantum data, a crucial but challenging step for practical quantum computing. This unique platform serves as a valuable testing ground to explore the commercial potential of various quantum applications.
A manufacturable process for a material to combine logic and memory in semiconductor devices
Prof. Charles Ahn with Fred Walker, Kidae Shin
Applied Physics and Mechanical Engineering & Materials Science
Materials technology for current microelectronics, based on conventional silicon and insulating oxides, is approaching its physical limits. At the same time, demands for electronic devices with superior processing capabilities and lower power consumption are ever increasing. Charles Ahn’s research group is developing materials technologies capable of being manufactured on a large-scale that would address current limitations of today's logic and memory devices. With such functionalities as non-volatility, extremely low energy consumption, and neuromorphic computing capabilities for AI applications, the approach has the potential to advance semiconductor technology, attracting the interest of semiconductor companies that are actively pursuing new materials systems.
AI in Your Pocket
Prof. Amin Karbasi with Amir Zandieh and Insu Ha
Electrical Engineering and Computer Science
Despite significant advancements, democratizing large AI models remains a challenge. The development of such models demands thousands of GPU training hours, incurring costs in the hundreds of thousands of dollars, limiting powerful AI models to organizations with substantial resources. Led by Amin Karbasi, this project aims to make AI cost-effective, enabling individuals to run them on their edge devices offline, fostering broader accessibility and unlocking their full potential. The solution enables the development of larger AI models without increasing the computational budget. This expansion of model sizes, as demonstrated in generative models like ChatGPT, unlocks new emergent properties of AI, like human-like reasoning abilities, and more.
Krania: Decentralized Machine Learning as a Service
Prof. Ben Fisch with Josh Beal
Computer Science
Due to the centralization risks of machine learning as a service (MLaaS) and the high cost of running large language models on consumer-grade hardware, there has been growing interest in decentralized inference networks, which crowdsource the computation over many participants. Two opposing challenges are incentivizing participation and trusting the results of computation. Participants may cut costs and claim the rewards by faking results rather than computing them accurately. Ben Fisch and his research team have created Krania – a system addressing these issues by employing verifiable distributed computation to validate results and streamline efficiency, demonstrating the potential for secure, decentralized MLaaS.
Towards manufacturable short-wave infrared (SWIR) vertical-cavity-surface-emitting lasers (VCSELs)
Prof. Jung Han with Jacob Tarn, Rebecca Levonian, Jin-ho Kang
Electrical Engineering
Vertical Cavity Surface Emitting Lasers (VCSELs) are critical in 3D optical sensing and data communications but are currently limited to an 800-1000 nm near infrared (NIR) range due to material constraints. Extending VCSELs into the 1300-2300 nm Short-Wave Infrared (SWIR) range could revolutionize this technology, enhancing data transmission speeds, fiber optic reach, and improve eye safety in free-space applications. Jung Han aims to commercialize SWIR VCSELs by advancing photonic technologies, bringing significant improvements to fields like 3D sensing, facial recognition, augmented and virtual reality, autonomous vehicles, biophotonics, and high-speed optical links.
Leibniz AI
Prof. Ruzica Piskac with Prof. Scott Shapiro
Computer Science and Yale Law School
As many have experienced, a paramount challenge with large language models is their propensity for "hallucinations," or generating plausible but false or misleading information. This is particularly problematic in the legal domain, where accuracy is critical. To address this, Piskac and collaborator Scott Shapiro from the Yale Law School are developing reliable and trustworthy legal chatbots (“lawbots”) to assist with legal inquiries by implementing formal verification methods to mathematically validate the responses. This verification aims to ensure that the lawbots are accurate and reliable, thereby establishing a new standard of dependability in AI assistance for legal experts and democratizing legal knowledge for ordinary citizens.
Geometric Deep Sensing Technology
Prof. Fengnian Xia with Ting Li
Electrical Engineering
The rapid growth of autonomous vehicles, biometric systems, and robotics has created an urgent need for advanced optical sensors that are compact, capable of handling large amounts of data, and AI-compatible. Traditional sensors, however, fall short in meeting these modern requirements. Fengnian Xia’s innovative and proprietary technology – geometric deep sensing – looks to fill this void by bringing this versatile and intelligent sensing solution to market. The design and manufacturing of these cutting-edge semiconductor devices will meet the critical need for smaller, smarter, and highly adaptable optical sensors, unlocking new commercial opportunities in a rapidly evolving landscape.
Evershield
Prof. Mark Saltzman with Fan Yang, Jennifer Ayres, Bruce Cao, and Gregory Kopf
Biomedical Engineering and Chemical & Environmental Engineering
There is a pressing need for more convenient and efficient treatments for individuals who currently rely on frequent medications and injections. This is especially relevant for HIV-positive individuals, and those at risk of HIV exposure. Additionally, improved drug delivery systems could greatly benefit conditions like endometriosis and chronic diseases requiring sustained medication.
Saltzman’s Evershield technology offers a long-acting, biodegradable polymer implant that ensures consistent release of therapeutic agents over extended periods. This innovative approach utilizes a safe, degradable polymer compatible with multiple active pharmaceutical ingredients and has proven effective for delivering contraceptive agents and anti-retrovirals. While addressing reproductive health needs, this technology also holds promise for treatments for glaucoma, macular degeneration, osteoarthritis, chronic pain management, and other applications.
For further project details and more information about the Fund, visit https://ventures.yale.edu/2023-2024-roberts-awards
Pictured L-R, Front row: Amin Karbasi, Ruzica Piskac. Middle row: Kidae Shin, Fengnian Xia Back row: Fred Walker, John Fortner, Yongshan Ding, Dean Jeffrey Brock, Jaehong Kim, Mark Saltzman, Fund Director Claudia Reuter, Jung Han. Not pictured: Charles Ahn, Ben Fisch.