Rosalyn Abbott(opens in new window), assistant professor of biomedical engineering(opens in new window), received an NSF CAREER grant for her work in developing cultured meat as a way to combat the environmental, public health and food security issues that accompany regular protein consumption. This research looks at the 3D structures of the animal meat tissues and aims to recreate an artificial meat product that mimics these properties. Abbott also seeks to increase consumer awareness of the current state of the animal meat supply chain through educational outreach.

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Amanda Krause(opens in new window), assistant professor of materials science and engineering(opens in new window), has been awarded a five-year NSF CAREER grant for her research in ceramic materials. Her work investigates the process in which grains, microscopic crystals that compose most ceramics, grow in high temperature conditions. Grain size is important for controlling material properties such as crack resistance, which is needed to improve the performance and lifetime of high-tech devices like airplane engines and microprocessors. A better understanding of grain growth processes when heated will help develop newer and more effective processing methods for ceramics and hopefully yield tougher and more reliable products.

Andrew Li(opens in new window), assistant professor of operations research at Carnegie Mellon University’s Tepper School of Business(opens in new window), has earned an NSF CAREER award for his work on developing blood tests to detect early-stage cancer. The award will support the development of precise algorithms to optimize the design of liquid biopsies while carefully balancing accuracy and cost. It will also facilitate interdisciplinary collaborations with medical researchers and practitioners to apply these algorithms effectively. The award supports a plan to disseminate this work to students, the academic medical community, and private companies.

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Theresa Anderson(opens in new window), an assistant professor of mathematical sciences, received a five-year NSF CAREER grant to build bridges between number theory and harmonic analysis, two areas of mathematics often viewed as separate. Number theory, which deals with properties of whole numbers, such as quickly factoring large numbers into primes, underpins computer security and analysis of the behavior of black holes. Harmonic analysis, which takes a complicated function and breaks it up into simple pieces, is central to medical imaging and quantum states.

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Sauvik Das(opens in new window), an assistant professor in the Human-Computer Interaction Institute(opens in new window) (HCII), earned an NSF CAREER award to design and evaluate adversarial machine learning antisurveillance technologies to combat automated identity detection online. Das will develop a human-centered application that allows users to touch up images they choose to share online in a manner that helps evade facial recognition and other forms of automated surveillance.

Zhihao Jia(opens in new window), an assistant professor in the Computer Science Department(opens in new window) (CSD), received an NSF CAREER award to explore an automated, end-to-end approach to building efficient, scalable and sustainable machine learning (ML) systems for diverse applications and hardware platforms. Deploying ML models requires significant manual effort to ensure optimal performance. Jia will study methods for replacing manually designed performance optimizations with automated generation, verification and application processes. Jia's work could reduce the energy consumption and financial cost of modern machine learning techniques. 

Matthew O'Toole(opens in new window), an assistant professor in CSD and the Robotics Institute(opens in new window) (RI), received an NSF CAREER award for studying a mixed-state computational imaging theory and developing corresponding techniques. The theoretical underpinning of any computational imaging technique is its model for light propagation. Many existing models depend on incident light — or light that falls onto a subject — being coherent, which means it comes from a source with consistent wavelengths. They do not always account for incident light having varying wavelengths or polarization states, making it incoherent. O'Toole's work will develop a comprehensive theory that connects techniques used in both the incoherent and coherent states to establish a mixed-state approach to sense, illuminate and analyze scenes, and to develop new computational imaging tools, including microscopes, 3D sensors and optical vibration sensors.

Andrej Risteski(opens in new window), an assistant professor in the Machine Learning Department(opens in new window), received an NSF CAREER award to build scientific and mathematical foundations of modern generative models. Generative models like ChatGPT exhibit remarkable potential for emerging technologies, but training them often involves extensive trial and error, and considerable amounts of human hours and computational resources. Furthermore, the models cannot easily transfer and apply learned skills in new settings, which makes the possibility of using them in safety-critical scenarios unlikely. Risteski will develop new algorithmic tools to better understand and enhance these models, and to formalize settings where they can successfully operate across different data.

Hirokazu Shirado(opens in new window), an assistant professor in the HCII, earned an NSF CAREER award to explore how technology can improve intergroup communications and cooperation online, enabling diverse societies to operate more effectively. Shirado's work will develop models of intergroup communication and patterns of cooperation to then inform interventions. The research will incorporate natural language processing, conversational AI techniques, network science and user-modeling methods to design algorithms that can suggest conversation partners, topics and styles that will help people across different groups communicate more effectively.

Dimitrios Skarlatos(opens in new window), an assistant professor in CSD, received an NSF CAREER award to design and build a virtual memory abstraction that is scalable, heterogenous and secure to meet the current breadth of data center computing. Virtual memory is a cornerstone abstraction of modern computing systems that enables virtualization, programmability and isolation of memory resources. However, existing virtual memory mechanisms were not designed for the current era of data center computing and its ample memory capacity, plethora of heterogeneous hardware resources, and abstraction-breaking security vulnerabilities. Skarlatos' research will address these challenges to create more efficient, sustainable and secure data centers.

Min Xu(opens in new window), an assistant professor in the Computational Biology Department(opens in new window), received an NSF CAREER award to develop computational methods for integrating two imaging techniques currently used to visualize microscopic objects inside cells. Cryo-electron tomography uses electrons to develop three-dimensional images of small complex objects, while fluorescent microscopy uses light to do similarly. Integrating the two techniques will allow researchers to inspect the structures, molecular identities, and spatial patterns of objects inside single cells.

Wenting Zheng(opens in new window), an assistant professor in CSD, earned an NSF CAREER award to build a framework for automating multiparty computation (MPC), a cryptographic technique that allows organizations to run complex computations on joint data sets without revealing sensitive inputs to other parties. Zheng's work will accelerate and democratize the adoption of MPC by designing and building an end-to-end, integrated compiler-runtime framework that automatically generates and executes optimized, workload-specific MPC protocols.

Jun-Yan Zhu(opens in new window), an assistant professor in RI, earned an NSF CAREER award to develop visual-recognition algorithms that can distinguish rare and unseen objects by using deep generative models. Modern visual recognition systems rely on human input to capture and annotate large amounts of real data, which can be costly for common objects and impractical for rare objects. Zhu's project will explore the use of large-scale generative models to learn and analyze visual recognition, automatically creating and labeling data that can fully depict rare objects and corner cases.

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