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Over the course of the 2024-25 academic year, five faculty members across the UMass Amherst campus were named the recipients of five-year U.S. National Science Foundation (NSF) CAREER awards.

The Faculty Early Career Development (CAREER) Program is a foundation-wide activity that offers NSF awards in support of early career faculty who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization.

Manning College of Information and Computer Sciences (CICS) faculty Ravi Karkar and Yair Zick were awarded CAREER grants for their work on integrating technology and personal healthcare decisions and large-scale resource allocation. The awards to Karkar and Zick bring the cumulative number of CAREER awards for CICS to 40.

The College of Engineering was awarded one CAREER grant this year, for Meghan Huber’s work on robotic exoskeletons, which brings the college’s six-year total to 25 awards.

The College of Natural Science (CNS) has been awarded two CAREER grants during this cycle, bringing its total to 65. This year’s recipients include Brian Cheng, who will be studying how rising ocean temperatures affect how marine species obtain food, and Yuan Li, who is seeking to understand the role that supermassive black holes play in early galaxy formation.
 

2024-25 NSF CAREER Grant Recipients
 

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Brian Cheng (environmental conservation) has been awarded $738,152 to help understand how marine life will respond to changing environmental conditions. This is critical for ocean management and the wellbeing of the people who rely upon fisheries and marine resources. A key problem in this area is understanding how environmental change affects the way that species obtain food. Rising ocean temperatures may increase the food requirements of predatory animals which can cause prey populations to decline. Alternatively, warming can cause multiple predators to compete and interfere with each other, reducing effects on prey. 

“The basic angle of my research,” says Cheng, “is that understanding how climate change affects species is hindered by the fact that species’ interactions (predation, competition, mutualisms, parasitism, etc.) are important but hard to study. Ecologists have long known that these interactions can ‘rewire’ food webs and cause effects that permeate throughout the ecosystem, but we don’t know exactly how.”

Cheng’s CAREER project addresses this problem by focusing on abundant native and invasive predatory crabs and their consumption of blue mussels in the Gulf of Maine, which is among the fastest warming habitats on the planet. This project measures how temperature affects the physiology of the focal species and how they interact with each other using a series of laboratory experiments, then uses mathematical models to calculate the impacts of temperature on the relative abundance of predators and prey. 

There’s an educational component, too, which Cheng says he’s “very excited about.” This includes the formation of two working groups that pair early career scientists with middle and late career mentors to train on team-based approaches to science.
 

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Meghan Huber (mechanical and industrial engineering) has been awarded $650,866 to study how people learn to walk using wearable robotic exoskeletons, ultimately as a means of improving mobility in aging populations or those with neurological injury. “There’s been this explosion of exoskeleton technology because we have better actuators now and our batteries are lighter. But now the big question is: How do you make them effective and useful for humans?’ This higher question of ‘how do humans learn to cooperate and work with physical systems that have artificial intelligence’ — it’s something that we don’t know.”

With this award, Huber will study the fundamental learning process of using new, robotic exoskeleton technology. “Armed with that information, then we can design robotic exoskeletons such that they adapt in such a way that they’re working with the human nervous system,” says Huber. She envisions future exoskeletons that can both adapt their mechanical assistance to the user while also providing feedback to the user on how best to work with the device.
 

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Ravi Karkar (CICS) was awarded $659,861 to spend the next five years improving our understanding of how to answer questions about one’s own health and wellbeing using self-tracked data, known as self-experimentation, and to provide better designs of technology to support the practice. 

Self-experimentation has been gaining traction due to the $54 billion health tracking industry. However, there is a mismatch between what technology currently provides (for example, step counts and sleep scores) and what people expect from it, which is personalized health insights and recommendations. 

“Currently, self-experimentation is largely limited to individuals who possess specialized knowledge and substantial resources. Even answering seemingly straightforward questions is not well-supported by mainstream health tracking technologies — Is coffee or milk the trigger for my irritable bowel syndrome symptoms? Does taking aspirin affect my menstrual flow? Will exercising in the evening improve my sleep quality?” explains Karkar. “My goal is to democratize this practice, enabling everyday users to conduct meaningful experiments, analyze their results effectively and make informed decisions about their health.”

The research will thus support current efforts to manage chronic diseases in the United States, for example, obesity, diabetes, and heart disease. In addition to engaging with different types of communities, this project will advance education by improving how students are taught to use the scientific approach to answer questions about their own health through self-experimentation.
 

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Yuan Li (astronomy) The past few decades have seen tremendous advances in our understanding of galaxy formation and evolution using both observations and detailed computer simulations. However, many key questions are still not fully understood. For example, most massive elliptical galaxies, among the oldest and most massive structures in the universe and hosts of supermassive black holes (SMBH), cease forming stars and transition into passively evolving systems. While the precise mechanisms underpinning such “quenching” are unclear, SMBHs appear to play a key role by suppressing the infall of fresh gas—the raw material of star formation—from the circumgalactic medium (CGM). 

Li was awarded $601,796 to conduct a computer simulation of a massive galaxy's formation and evolution with spatial resolution sufficient to resolve the CGM's complex structure and the small-scale physical processes occurring there. 

“One of the puzzles in astrophysics is that roughly half of today’s massive galaxies are ‘dead,’” says Li. “They have mysteriously ceased forming stars. Growing evidence suggests that their central supermassive black holes play a role. This project aims to study how supermassive black holes grow and how they shape the evolution of their host galaxies over cosmic history.”

This work will inform and complement large-scale cosmological simulations and will provide mentoring and training for three graduate students in cutting edge galaxy simulations. Undergraduate students will also be involved, and the investigator will engage in outreach to local high schools and public planetarium shows.
 

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Yair Zick (CICS) was awarded $600,000 to develop algorithms and theoretical analysis for large-scale resource allocation, which involves distributing resources among various users, applications, or tasks. This process is essential in many contexts, including scheduling work shifts, providing aid during disasters, assigning courses to students, matching research papers with reviewers and many other scenarios requiring distribution of goods, resources or services. A key example is optimizing worker shift assignments to maximize efficiency while ensuring a balanced distribution of workloads among staff. 

“We engage in preference elicitation problems all the time, whether it’s trying to figure out what movie to watch or choosing what’s for dinner. Doing this at scale is the real challenge,” says Zick. “My goal is to help design systems that help distribute limited resources for thousands of users, like allocating courses or deciding which parts of a road network to repair.”  

By improving resource allocation strategies, the project aims to enhance operational efficiency while achieving balanced distributions of resources to meet the needs of individuals and organizations across various fields. To achieve this, the project tackles three core challenges:

1. Effective Preference Elicitation – In many domains, users have complex preferences. For instance, a worker might be able to take either shift A or B but not both. This research direction focuses on developing methods to gather agent preferences effectively while minimizing cognitive burden.
2. Preference Uncertainty – Users may be uncertain about their preferences. For example, when assigning students to courses, match quality is inherently uncertain. The goal of this project is to develop allocation mechanisms that account for such inaccuracies in agent preferences.
3. Efficient Computation – Scaling algorithms to handle large problem instances is critical. For example, scheduling shifts in large organizations may involve hundreds of workers and thousands of shift slots. 

The project aims to design computational frameworks that produce balanced and efficient outcomes in such scenarios. The project will yield practical software tools that enhance AI-driven resource allocation.