Recent recognition of the 黑料老司机 includes a Berkeley prize recognizing research on ancient galaxies, a 2026 Marconi Society Paul Baran Young Scholar Award for a doctoral student, and an NSF CAREER Award supporting research on the brain circuits that drive social behavior.

UW astronomer is part of team that receives Berkeley prize for study of ancient galaxies

, UW postdoctoral scholar in astronomy, is part of a team of astronomers who received the 2027 Lancelot M. Berkeley鈥揘ew York Community Trust Prize for Meritorious Work in Astronomy.听

Khullar is one of 60 astronomers who make up the UNCOVER team, an international collaboration built to study tens of thousands of galaxies, black holes and star clusters. The Berkeley Prize recognized the UNCOVER team for its work using the to study galaxies behind 鈥淧andora鈥檚 Cluster,鈥� a massive galaxy cluster. The large mass of this cluster causes it to act as a powerful magnifying glass, bending the light from faraway background galaxies.听

Deep imaging and spectroscopy of this gravitational lens have enabled the UNCOVER project team to identify and characterize distant galaxies from some of the earliest stages of our universe.听

鈥淭his team award is a genuine recognition of the incredible work that this new collaboration has conducted, spearheaded by early career astronomers,鈥� Khullar said. 鈥淚 am fortunate to have played a small part in contributing to studying the first galaxies in the universe which have paused star formation 鈥� systems called quiescent galaxies.鈥�

Awarded annually since 2011 by the American Astronomical Society (AAS), the Berkeley prize recognizes research that advances the field of astronomy. The prize includes a monetary award and an invitation to give the closing plenary lecture at the AAS winter meeting in January.

This story was adapted from a press release by AAS.

UW doctoral student receives 2026 Marconi Society Paul Baran Young Scholar Award听

, a doctoral student in the UW Department of Electrical & Computer Engineering, was , which recognizes exceptional early-career researchers advancing the future of information and communications technology.听

A researcher in the Paul G. Allen School of Computer Science & Engineering鈥檚 Mobile Intelligence Lab, Itani works with , a UW professor of computer science and engineering, to develop AI-powered听 devices that give users greater control over what they hear. The technology could allow someone wearing earbuds or hearing aids to amplify specific voices or sounds they want to focus on while reducing unwanted background noise.听

That ability could听make听 listening听more customizable, from following a conversation in a crowded restaurant to better distinguishing important sounds in noisy environments. Itani and his collaborators are currently advancing systems designed to enable this kind of real-time 鈥渟uperhuman鈥� hearing on earbuds and hearing aids.听

鈥淚 feel like I鈥檝e hit a niche that鈥檚 going to be so transformational,鈥� Itani said, noting that the technology could eventually reach billions of devices. 鈥淚t鈥檚 going to change the way we hear the world.鈥�听

Because of that potential reach, Itani said he feels a responsibility to continue advancing the technology and its ability to听impact听people鈥檚听 lives.听

Itani and his collaborators have also launched听Hearvana听AI to commercialize their research. He and his fellow honorees will be formally recognized at the Marconi Awards Gala & Institute Forums, held Nov. 4鈥�6 in San Francisco.听听

UW assistant professor of psychology and neuroscience receives NSF CAREER Award to study brain circuits driving social behaviors听

Sama Ahmed, UW assistant professor of psychology and neuroscience, received a 2026 National Science Foundation CAREER Award of more than $1.4 million.听

The NSF CAREER Award is the agency鈥檚 most prestigious honor for early-career faculty, recognizing those with the potential to become academic leaders in both research and education.听

With the award, Ahmed and his team will study the brain circuits behind “proceptivity” 鈥� the active behaviors animals use to pursue and engage potential mates. Using fruit flies, the most complex organism with a fully mapped brain and a species with well-studied social behaviors, the researchers will use advanced genetic and imaging tools in the Ahmed Lab to watch how different neural networks in the brain talk to each other in real time.听

Instead of looking at how the brain does one thing at a time, Ahmed鈥檚 team tracks how the nervous system switches between responding to external social cues and actively generating social behaviors. Understanding how the brain juggles these competing choices helps researchers understand how more complex brains 鈥� including our own 鈥� prioritize information and decide when to act.听

鈥淚’ve always wanted to understand how our brains generate and respond to social signals,鈥� Ahmed said, adding that the CAREER Award opens a new way for his lab to investigate that question.听

The project will also provide research training opportunities for high school, undergraduate and graduate students and support the development of open-source computational tools and STEM modules focused on coding and automated behavioral tracking. The award will also allow Ahmed to create a new Course-based Undergraduate Research Experience (CURE), where students will use technologies such as听connectomics听and optogenetics to investigate questions that, as Ahmed put it, 鈥渘obody has answered yet.鈥�听

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Noise-canceling headphones have gotten very good at creating an auditory blank slate. But allowing certain sounds from a wearer鈥檚 environment through the erasure still challenges researchers. The latest edition of Apple鈥檚 AirPods Pro, for instance, for wearers 鈥� sensing when they鈥檙e in conversation, for instance 鈥� but the user has little control over whom to listen to or when this happens.

A 黑料老司机 team has developed an artificial intelligence system that lets a user wearing headphones look at a person speaking for three to five seconds to 鈥渆nroll鈥� them. The system, called 鈥淭arget Speech Hearing,鈥� then cancels all other sounds in the environment and plays just the enrolled speaker鈥檚 voice in real time even as the listener moves around in noisy places and no longer faces the speaker.

The team presented May 14 in Honolulu at the ACM CHI Conference on Human Factors in Computing Systems. The is available for others to build on. The system is not commercially available.

鈥淲e tend to think of AI now as web-based chatbots that answer questions,鈥� said senior author , a UW professor in the Paul G. Allen School of Computer Science & Engineering. 鈥淏ut in this project, we develop AI to modify the auditory perception of anyone wearing headphones, given their preferences. With our devices you can now hear a single speaker clearly even if you are in a noisy environment with lots of other people talking.鈥�

To use the system, a person wearing off-the-shelf headphones fitted with microphones taps a button while directing their head at someone talking. The sound waves from that speaker鈥檚 voice then should reach the microphones on both sides of the headset simultaneously; there鈥檚 a 16-degree margin of error. The headphones send that signal to an , where the team鈥檚 machine learning software learns the desired speaker鈥檚 vocal patterns. The system latches onto that speaker鈥檚 voice and continues to play it back to the listener, even as the pair moves around. The system鈥檚 ability to focus on the enrolled voice improves as the speaker keeps talking, giving the system more training data.

The team tested its system on 21 subjects, who rated the clarity of the enrolled speaker鈥檚 voice nearly twice as high as the unfiltered audio on average.

This work builds on the team鈥檚 previous 鈥渟emantic hearing鈥� research, which allowed users to select specific sound classes 鈥� such as birds or voices 鈥� that they wanted to hear and canceled other sounds in the environment.

Currently the TSH system can enroll only one speaker at a time, and it鈥檚 only able to enroll a speaker when there is not another loud voice coming from the same direction as the target speaker鈥檚 voice. If a user isn鈥檛 happy with the sound quality, they can run another enrollment on the speaker to improve the clarity.

The team is working to expand the system to earbuds and hearing aids in the future.

Additional co-authors on the paper were , and , UW doctoral students in the Allen School, and , director of research at AssemblyAI. This research was funded by a Moore Inventor Fellow award, a and a .

For more information, contact tsh@cs.washington.edu.

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In virtual meetings, it鈥檚 easy to keep people from talking over each other. Someone just hits mute. But for the most part, this ability doesn鈥檛 translate easily to recording in-person gatherings. In a bustling cafe, there are no buttons to silence the table beside you.

The ability to locate and control sound 鈥� isolating one person talking from a specific location in a crowded room, for instance 鈥� has , especially without visual cues from cameras.

A team led by researchers at the 黑料老司机 has developed a shape-changing smart speaker, which uses self-deploying microphones to divide rooms into speech zones and track the positions of individual speakers. With the help of the team鈥檚 deep-learning algorithms, the system lets users mute certain areas or separate simultaneous conversations, even if two adjacent people have similar voices. Like a fleet of Roombas, each about an inch in diameter, the microphones automatically deploy from, and then return to, a charging station. This allows the system to be moved between environments and set up automatically. In a conference room meeting, for instance, such a system might be deployed instead of a central microphone, allowing better control of in-room audio.

The team published Sept. 21 in Nature Communications.

鈥淚f I close my eyes and there are 10 people talking in a room, I have no idea who鈥檚 saying what and where they are in the room exactly. That鈥檚 extremely hard for the human brain to process. Until now, it鈥檚 also been difficult for technology,鈥� said co-lead author , a UW doctoral student in the Paul G. Allen School of Computer Science & Engineering. 鈥淔or the first time, using what we鈥檙e calling a robotic 鈥榓coustic swarm,鈥� we鈥檙e able to track the positions of multiple people talking in a room and separate their speech.鈥�

Previous research on has required using overhead or on-device cameras, projectors or special surfaces. The UW team鈥檚 system is the first to accurately distribute a robot swarm using only sound.

The team鈥檚 prototype consists of seven small robots that spread themselves across tables of various sizes. As they move from their charger, each robot emits a high frequency sound, like a bat navigating, using this frequency and other sensors to avoid obstacles and move around without falling off the table. The automatic deployment allows the robots to place themselves for maximum accuracy, permitting greater sound control than if a person set them. The robots disperse as far from each other as possible since greater distances make differentiating and locating people speaking easier. Today鈥檚 consumer smart speakers have multiple microphones, but clustered on the same device, they鈥檙e too close to allow for this system鈥檚 mute and active zones.

鈥淚f I have one microphone a foot away from me, and another microphone two feet away, my voice will arrive at the microphone that鈥檚 a foot away first. If someone else is closer to the microphone that鈥檚 two feet away, their voice will arrive there first,鈥� said co-lead author , a UW doctoral student in the Allen School. 鈥淲e developed neural networks that use these time-delayed signals to separate what each person is saying and track their positions in a space. So you can have four people having two conversations and isolate any of the four voices and locate each of the voices in a room.鈥�

The team tested the robots in offices, living rooms and kitchens with groups of three to five people speaking. Across all these environments, the system could discern different voices within 1.6 feet (50 centimeters) of each other 90% of the time, without prior information about the number of speakers. The system was able to process three seconds of audio in 1.82 seconds on average 鈥� fast enough for live streaming, though a bit too long for real-time communications such as video calls.

As the technology progresses, researchers say, acoustic swarms might be deployed in smart homes to better differentiate people talking with smart speakers. That could potentially allow only people sitting on a couch, in an 鈥渁ctive zone,鈥� to vocally control a TV, for example.

Researchers plan to eventually make microphone robots that can move around rooms, instead of being limited to tables. The team is also investigating whether the speakers can emit sounds that allow for real-world mute and active zones, so people in different parts of a room can hear different audio. The current study is another step toward science fiction technologies, such as the 鈥渃one of silence鈥� in 鈥淕et Smart鈥� and 鈥淒une,鈥� the authors write.

Of course, any technology that evokes comparison to fictional spy tools will raise questions of privacy. Researchers acknowledge the potential for misuse, so they have included guards against this: The microphones navigate with sound, not an onboard camera like other similar systems. The robots are easily visible and their lights blink when they鈥檙e active. Instead of processing the audio in the cloud, as most smart speakers do, the acoustic swarms process all the audio locally, as a privacy constraint. And even though some people鈥檚 first thoughts may be about surveillance, the system can be used for the opposite, the team says.

鈥淚t has the potential to actually benefit privacy, beyond what current smart speakers allow,鈥� Itani said. 鈥淚 can say, 鈥楧on鈥檛 record anything around my desk,鈥� and our system will create a bubble 3 feet around me. Nothing in this bubble would be recorded. Or if two groups are speaking beside each other and one group is having a private conversation, while the other group is recording, one conversation can be in a mute zone, and it will remain private.鈥�

, formerly a principal research manager at Microsoft, is a co-author on this paper, and , a professor in the Allen School, is a senior author. The research was funded by a Moore Inventor Fellow award.

For more information, contact acousticswarm@cs.washington.edu.

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