UC Merced graduate students Anuvetha Govindarajan and Jocelyn Ornelas-Munoz have each received a $25,000 fellowship from SandboxAQ, a company that creates solutions at the intersection of artificial intelligence and quantum technology. Govindarajan, a physics Ph.D. student, studies quantum optics and quantum computing, while Ornelas-Munoz, an applied mathematics Ph.D. student, focuses on implementing constrained optimization techniques to regularize deep learning models. The fellowships will support their research and future careers in quantum information science. SandboxAQ has been partnering with universities and corporations to meet the rising demand for quantum- and AI-trained researchers and engineers.
SandboxAQ Fellowship Supports Quantum and AI Research
UC Merced graduate students Anuvetha Govindarajan and Jocelyn Ornelas-Munoz have been awarded a $25,000 fellowship each from SandboxAQ, a company that develops solutions at the intersection of artificial intelligence (AI) and quantum technology. The company aims to address some of the world’s most pressing challenges through these advanced technologies.
For the past six years, SandboxAQ has been fostering relationships between industry and academia to meet the growing need for researchers and engineers trained in quantum technology and AI. The company has collaborated with over 30 corporations, major universities, and other educational institutions to cultivate a pool of tech talent and expand training in the field of AI and quantum technologies.
Quantum Optics and Quantum Computing Research
Anuvetha Govindarajan, a physics Ph.D. student from India, is conducting research in quantum optics and quantum computing under the guidance of Professor Lin Tian. Govindarajan has developed a unique design to generate many-body entangled states in optomechanical systems, which are resilient to temperature and noise fluctuations.
She is also working on quantum optimal control to study state-preparation in a finite-sized Jaynes-Cummings model lattice. After graduation, Govindarajan plans to seek opportunities in the industry where she can apply the skills she has acquired at UC Merced as a physics researcher.
Deep Learning Research
Jocelyn Ornelas-Munoz, an applied mathematics Ph.D. student from México, is delving into the field of deep learning (DL) with a primary focus on implementing constrained optimization techniques to regularize DL models. She is co-advised by professors Erica Rutter and Roummel Marcia.
Ornelas-Munoz’s research aims to narrow down the space of potential mappings from which the DL model learns. This involves a comprehensive exploration of mathematical constrained optimization, enabling her to seamlessly integrate physical and biological constraints into deep-learning models. Her research has found practical applications in diverse fields such as genomics, coded aperture imaging, and medical imaging.
Future Plans and Career Goals
The fellowship will enable Ornelas-Munoz to expand her knowledge base and actively contribute to the academic discourse by submitting her work for publication. She also plans to participate in conferences later this year to share and discuss her research findings.
After graduation, Ornelas-Munoz plans to join a Department of Energy national laboratory or a similar federally funded research and development center. Her ultimate career goal is to engage in impactful research that not only expands the frontiers of knowledge in applied mathematics and deep learning but also translates into practical applications for the betterment of the broader population.
SandboxAQ’s Commitment to Education and Research
SandboxAQ values collaborations with universities like UC Merced, with whom they partner to create engaging educational opportunities, increase interest and diversity in STEM education and careers, and expand the global quantum workforce. The company congratulates Anuvetha Govindarajan and Jocelyn Ornelas-Munoz on being named SandboxAQ Fellowship recipients and looks forward to supporting their future careers in quantum information science.
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