Wednesday, August 20, 2025

Global Virus Network launches first-ever “Global Guardians” youth camp to prepare the next generation of virus hunters

Three-day program in partnership with USF engages Tampa high school students in virology, pandemic response, and public health careers

Global Virus Network

Tampa, FL, August 20, 2025 – The Global Virus Network (GVN), in partnership with the University of South Florida (USF) Youth Experiences and Hillsborough County Public Schools STEM Department, recently concluded its inaugural “Global Guardians: Youth for Pandemic Preparedness” summer camp, an immersive, hands-on experience that brought together some of Tampa’s brightest high school students with internationally renowned virologists, public health experts, and scientists.

“This camp shows our students that science is a living, evolving field with real-world impact,” said Catherine White, EdD, supervisor of K–12 STEM in the Academic Services Division of Hillsborough County Public Schools. “By giving them access to leading scientists, advanced laboratories, and hands-on investigations, we’re not just teaching content, we’re igniting curiosity and empowering them to see themselves as the problem-solvers and innovators our future will depend on.”

Held July 23–25, 2025, the three-day program welcomed 14 high school students in grades 9–12 to explore the science of pandemics through a series of interactive sessions, laboratory and hospital tours, demonstrations, and collaborative group activities. Participants examined topics such as virus transmission, outbreak detection, media misinformation, and career paths in virology, guided by experts from the GVN, USF, Tampa General Hospital, and various units of the Florida Department of Health.

“It was inspiring to see such curiosity and focus from these students,” said Robert C. Gallo, MD, international scientific director and co-founder of the GVN. “This generation will inherit the responsibility of pandemic prevention. We must prepare them early, honestly, and with scientific integrity.” Dr. Gallo, who is most widely known for his co-discovery of HIV as the cause of AIDS, is also the James P. Cullison Professor of Medicine, director of the Institute of Translational Virology and Innovation at the USF Health Morsani College of Medicine, and director of the Microbial Oncology Program at Tampa General Hospital Cancer Institute.

Hosted in partnership with USF at GVN’s international headquarters, the program emphasized both education and empowerment. Students engaged directly with virus hunters, epidemiologists, and lab scientists, participated in simulated outbreak investigations, and practiced putting on complete personal protective equipment in a mock BSL-3 lab.

“I am so grateful to the Global Virus Network for giving this talented group of students an opportunity to imagine their futures as the next generation of science explorers and learners,” said Charles J. Lockwood, MD, MHCM, executive vice president of USF Health and dean of the Morsani College of Medicine. “I expect that this experience will put some of these students on a path to become leaders in public health and virology. Kudos to the Hillsborough County Public Schools STEM Department, our USF Youth Experiences team, and USF Health faculty, including GVN’s Dr. Bob Gallo, for making time to prepare our future ‘global guardians.’“ Dr. Lockwood is also executive vice president and chief academic officer at Tampa General Hospital.

Highlights of the camp included:

A live discussion with Dr. Gallo, who shared insights from his early experiences as a young scientist, reflections on novel virus discoveries, and addressed the impact of scientific misinformation. He encouraged students to “follow your passion and pursue truth.”
An immersive tour of Tampa General’s CareComm, a state-of-the-art command center for patient safety and hospital efficiency.
Hands-on demonstrations of viral transmission, diagnostic testing, and vector surveillance, led by experts in virology, entomology, and public health.
Sessions on media literacy and misinformation, challenging students to think critically in the age of digital noise.
A closing ceremony where each student received a certificate of completion recognizing their participation in the inaugural Global Guardians program.

Students described the program as “life-changing,” “eye-opening,” and “the most engaging experience I’ve ever had in science.” One participant reflected, “I really loved all of the tours and the lectures. Everything felt very nicely organized, and it was extremely fun,” referring to the mix of hospital tours, advanced lab visits, and hands-on experiments. Another student shared, “I liked when we had the disease detectives lesson,” recalling the simulated outbreak investigation that encouraged teamwork, problem-solving, and data analysis.

For some, the camp solidified career ambitions. “The camp has strengthened my interest in pursuing a career in the medical field,” said one student, while another added, “It changed my opinion about medical school; now I’m interested in going.” Even those still deciding felt a renewed sense of direction, with one participant noting, “Nothing has changed, but it has definitely pushed me more toward the education I want.”

The program’s hands-on approach resonated deeply with the students, who investigated real-world public health data, dissected misinformation, and practiced outbreak modeling using mapping tools and case data. They also toured advanced research labs, including USF’s Center for Global Health and Inter-Disciplinary Research, the Florida Department of Health Bureau of Public Health Laboratories, and the Hillsborough County Health Department.

“One of the reasons we founded the Global Virus Network was to address the shrinking pipeline of students entering the field of virology,” added Dr. Gallo. “Programs like this are essential to spark early interest and build the next generation of scientists who can help the world prepare for future pandemics.”

Building on the success of the pilot, GVN plans to expand the Global Guardians initiative to include students from across the state of Florida next year and internationally the year after, broadening access to virology education and inspiring a more diverse pipeline of future scientists and public health professionals. Similar to other comparative, merit-based programs, GVN intends to provide scholarships to participants for competitive applications.

For more information on GVN’s education programs and the Global Guardians initiative, visit here.

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About the Global Virus Network (GVN)
The Global Virus Network (GVN) is a worldwide coalition comprising 80+ Virology Centers of Excellence and Affiliates across 40+ countries, whose mission is to facilitate pandemic preparedness against viral pathogens and diseases that threaten public health globally. GVN advances knowledge of viruses through (i) data-driven research and solutions, (ii) fostering the next generation of virology leaders, and (iii) enhancing global resources for readiness and response to emerging viral threats. GVN provides the essential expertise required to discover and diagnose viruses that threaten public health, understand how such viruses spread illnesses, and facilitate the development of diagnostics, therapies, and treatments to combat them. GVN coordinates and collaborates with local, national, and international scientific institutions and government agencies to provide real-time virus informatics, surveillance, and response resources and strategies. GVN's pandemic preparedness mission is achieved by focusing on Education & Training, Qualitative & Quantitative Research, and Global Health Strategies & Solutions. The GVN is a non-profit 501(c)(3) organization. For more information, please visit www.gvn.org.

With human feedback, AI-driven robots learn tasks better and faster

A new AI training method created at UC Berkeley teaches robots how to perform complicated tasks, like assembling a motherboard or an IKEA drawer, with a 100% success rate

University of California - Berkeley

Timing belt assembly — image:
By studying demonstrations and learning from both human feedback and its own real-world attempts, a new AI-powered training protocol developed at UC Berkeley teaches robots how to perform complicated tasks like assembling a timing belt with a 100% success rate.
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Credit: Courtesy of the Robotics AI and Learning Lab

At UC Berkeley, researchers in Sergey Levine’s Robotic AI and Learning Lab eyed a table where a tower of 39 Jenga blocks stood perfectly stacked. Then a white-and-black robot, its single limb doubled over like a hunched-over giraffe, zoomed toward the tower, brandishing a black leather whip. Through what might have seemed to a casual viewer like a miracle of physics, the whip struck in precisely the right spot to send a single block flying out from the stack while the rest of the tower remained structurally sound.

This task, known as “Jenga whipping,” is a hobby pursued by people with the dexterity and reflexes to pull it off. Now, it’s been mastered by robots, thanks to a novel, AI-powered training method created by Levine and other members of the team. The new system, called Human-in-the-Loop Sample Efficient Robotic Reinforcement Learning (HiL-SERL), is described in a study appearing Aug. 20 in the journal Science Robotics.

By studying demonstrations and learning from both human feedback and its own real-world attempts, this training protocol teaches robots how to perform complicated tasks like Jenga whipping with a 100% success rate. What’s more, the robots are taught at an impressive speed, enabling them to learn within one to two hours how to perfectly assemble a computer motherboard, build a shelf and more.

The first time the robot conquered the Jenga whipping challenge, “that really shocked me,” said study first author Jianlan Luo, a postdoctoral researcher at UC Berkeley. “The Jenga task is very difficult for most humans. I tried it with a whip in my hand; I had a 0% success rate.”

In recent years, the robot learning field has sought to crack the challenge of how to teach machines activities that are unpredictable or complicated, as opposed to a single action, like repeatedly picking up an object from a particular place on a conveyor belt. To solve this quandary, Levine’s lab has zeroed in on what’s called “reinforcement learning.” In reinforcement learning, a robot attempts a task in the real world and, using feedback from cameras, learns from its mistakes to eventually master that skill.

The new study added human intervention to speed up this process. With a special mouse that controls the robot, a human can correct the robot’s course, and those corrections can be incorporated into the robot’s proverbial memory bank. Using reinforcement learning, the robot analyzes the sum of all its attempts — assisted and unassisted, successful and unsuccessful — to better perform its task. Luo said a human needed to intervene less and less as the robot learned from experience. “I needed to babysit the robot for maybe the first 30% or something, and then gradually I could actually pay less attention,” he said.

The lab put its robotic system through a gauntlet of complicated tasks beyond Jenga whipping. The robot flipped an egg in a pan; passed an object from one arm to another; and assembled a motherboard, car dashboard and timing belt. The researchers selected these challenges because they were varied and, in Luo’s words, represented “all sorts of uncertainty when performing robotic tasks in the complex real world.”

The researchers also tested the robots’ adaptability by staging mishaps. They’d force a gripper to open so it dropped an object or move a motherboard as the robot tried to install a microchip, training it to react to a shifting situation it might encounter outside a lab environment.

By the end of training, the robot could execute these tasks correctly 100% of the time. The researchers compared their results to a common “copy my behavior” method known as behavioral cloning that was trained on the same amount of demonstration data; their new system made the robots faster and more accurate. These metrics are crucial, Luo said, because the bar for robot competency is very high. Regular consumers and industrialists alike don’t want to buy an inconsistent robot. Luo emphasized that, in particular, “made-to-order” manufacturing processes like those often used for electronics, automobiles and aerospace parts could benefit from robots that can reliably and adaptably learn a range of tasks.

A next step, Luo said, would be to pre-train the system with basic object manipulation capabilities, eliminating the need to learn those from scratch and instead progressing straight to acquiring more complex skills. The lab also chose to make its research open source so that other researchers could use and build on it.

“A key goal of this project is to make the technology as accessible and user-friendly as an iPhone,” Luo said. “I firmly believe that the more people who can use it, the greater impact we can make.”

Additional authors of the study include Charles Xu and Jeffrey Wu of UC Berkeley.

By studying demonstrations and learning from both human feedback and its own real-world attempts, a new AI-powered training protocol developed at UC Berkeley teaches robots how to perform complicated tasks like Jenga whipping with a 100% success rate.
Credit
Courtesy of the Robotics AI and Learning La

Journal

Science Robotics

DOI

10.1126/scirobotics.ads5033

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning

Article Publication Date

20-Aug-2025

A wearable robot that learns

New control algorithm personalizes user experience for stroke, ALS patients

Harvard John A. Paulson School of Engineering and Applied Sciences

wearable_1 — image:
Prabhat Pathak and James Arnold demonstrate the wearable robot in the lab.
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Credit: Eliza Grinnell / Harvard SEAS Communications

Key Takeaways

Harvard researchers have created a soft, wearable robotic device that provides personalized movement assistance for individuals with upper-limb impairment, such as stroke and ALS patients.
The latest version of the robot combines machine learning and a physics-based model to learn each user’s unique movements and provide support for daily activities like eating and drinking.
The device was tested with stroke and ALS patients and could someday offer both assistive and rehabilitative benefits.

Having lived with an ALS diagnosis since 2018, Kate Nycz can tell you firsthand what it’s like to slowly lose motor function for basic tasks. “My arm can get to maybe 90 degrees, but then it fatigues and falls,” the 39-year-old said. “To eat or do a repetitive motion with my right hand, which was my dominant hand, is difficult. I’ve mainly become left-handed.”

People like Nycz who live with a neurodegenerative disease like ALS or who have had a stroke often suffer from impaired movement of the shoulder, arm or hands, preventing them from daily tasks like tooth-brushing, hair-combing or eating.

For the last several years, Harvard bioengineers have been developing a soft, wearable robot that not only provides movement assistance for such individuals but could even augment therapies to help them regain mobility.

But no two people move exactly the same way. Physical motions are highly individualized, especially for the mobility-impaired, making it difficult to design a device that works for many different people.

It turns out advances in machine learning can create a more personal touch. Researchers in the John A. Paulson School of Engineering and Applied Sciences (SEAS), together with physician-scientists at Massachusetts General Hospital and Harvard Medical School, have upgraded their wearable robot to be responsive to an individual user’s exact movements, endowing the device with more personalized assistance that could give users better, more controlled support for daily tasks.

The research published in Nature Communications was led by Conor Walsh, the Paul A. Maeder Professor of Engineering and Applied Sciences, whose lab develops human-centered assistive robotic devices for those with movement impairments. For more than six years, Walsh’s lab has collaborated with stroke and neurorehabilitation specialist Dr. David Lin, director of the Massachusetts General Hospital Neurorecovery Clinic; and ALS specialist Dr. Sabrina Paganoni, co-director of the Massachusetts General Hospital Neurological Clinical Research Institute — both paper co-authors — to develop clinically relevant devices for patients.

“This has been a wonderful collaboration as Dr. Walsh’s team prioritized including both the clinician and patient perspectives from Day one,” Paganoni said. “This collaborative approach allowed us to work together on the very initial prototypes and study design.”

Nycz was referred to the SEAS study team by Paganoni in 2018, not long after she was diagnosed with ALS a week shy of her 33rd birthday. Nycz has provided data and user testing for several iterations of the device, including the latest that includes a personalized motor feedback component. “I’m big on technology and devices to help improve quality of life for people living with ALS … I feel like this robot could help with that goal,” she said.

Software update with machine learning model

The paper describes a major update to the software powering the device, which consists of a sensor-loaded vest with a balloon attached underneath the arm that inflates and deflates to apply mechanical assistance to a weak or impaired limb.

The researchers used a machine learning model that personalizes assistance levels to the individual user by learning which movements the user is trying to do, via sensors that track both motion and pressure.

In previous versions of the device, which only tracked motion, the researchers found that users had had trouble pushing their arm back down once the robot had helped lift it up. “Some people didn’t have enough residual strength to overcome any kind of mistake the robot was making,” explained co- first author and graduate student James Arnold.

In the new version, in addition to the machine learning model, they incorporated a physics-based model they had previously developed that estimates the minimum pressure needed to support the arm during movement. This makes the robot’s assistance feel more natural to the user, offering more nuanced help on basic tasks like eating and drinking. Combining the models allowed the robot to quickly dial up or down how much assistance it is giving at any time, based on what it has learned about how that user normally moves.

User testing

In collaboration with the clinical researchers at MGH, the engineers tested their device with nine volunteers, including Nycz – five who had experienced a stroke and four living with ALS.

“For people living with ALS, the most important considerations include comfort, ease of use, and the ability of the device to adapt to their specific needs and movement patterns,” Paganoni said. “Personalization is crucial to enhance their functional independence and quality of life … This device holds the potential to significantly improve upper limb function, enhance daily living activities, and reduce compensatory movements.”

Results showed that a robot trained on an individual user’s movement data could distinguish the user’s shoulder movements with 94% accuracy. The amount of force a person needed to lower their arm was reduced by about a third, compared to previous versions. The users also showed larger ranges of motion in their shoulders, elbows, and wrists, reducing the need to compensate with body leaning or twisting, and making their movements overall more exact and efficient.

Past studies with the wearable robot had focused on a single joint or a single clinical score for evaluating patient movement, explained co-first author and postdoctoral fellow Prabhat Pathak. “What we did here was look at simulated activities of daily living, using a highly accurate motion capture system — similar to systems used in movies. We looked at how each and every joint movement changed, and if they were able to do the tasks more efficiently.”

Nycz said seeing the different versions of the device over the years has been gratifying, and she’s noticed some of her feedback has been reflected in newer versions.

“They’ve done a great job incorporating and including the person,” she said. “They’re not sitting in the lab just playing with the robot. I felt like they were really engaged with me. I didn’t feel like a lab rat or a cog in a wheel.”

Generalizable to many populations

The researchers noted that their device could be generalizable to many populations of people with upper limb impairments. While stroke patients are usually focused on rehabilitation through gradual regaining of strength and movement, ALS is degenerative which means the device might be more valuable for movement assistance only. Through continued support from the National Science Foundation’s Convergence Accelerator, under the Directorate for Technology, Innovation and Partnerships, the team is continuing to refine the technology to someday enable users to independently use it in the home.

The paper was co-authored by Yichu Jin, David Pont-Esteban, Connor M. McCann, Carolin Lehmacher, John P. Bonadonna, Tanguy Lewko, Katherine M. Burke, Sarah Cavanagh, Lynn Blaney, Kelly Rishe, and Tazzy Cole.

The research had federal support from the National Science Foundation under grant No. 2236157 and 2345107, and the NSF Graduate Research Fellowship under grant No. DGE 2140743.

Watch: https://www.youtube.com/watch?v=ZhHnEOf7eeY

wearable_robot_social_media_video [VIDEO] |

The robot could help individuals with movement impairments perform daily activities like eating and drinking.

A close-up of the wearable robot in the lab.

Credit

Eliza Grinnell / Harvard SEAS Communications