The Hertz Foundation announced that it awarded 2026 fellowships to three current MIT students as well as an incoming graduate student. They are: Annika Marschner, Alvin Q. Meng, Zachary S. Siegel, and Matthew Wanta.

The prestigious science and technology award provides each recipient with five years of financial support — a stipend and full tuition equivalent — which gives them an unusual measure of autonomy to pursue ground-breaking research in their graduate work.

“What particularly impresses me about this cohort is their fearlessness in taking on new challenges and advancing the frontiers of science,” says Philip Welkhoff, a Hertz Fellow and director of the malaria program at the Gates Foundation, who co-led the selection process. “Each has exhibited tremendous creativity, grit, and vision, and I cannot wait to see what each accomplishes with the freedom to innovate provided by the Hertz Fellowship.”

In addition to funding, fellows receive lifelong access to Hertz Foundation programs including events, mentoring, and networking opportunities, with the over 1,300 fellows named since the fellowship was established in 1963. The connections forged among these individuals have sparked collaborative startups, research, and commercialization in a range of technology, science, and engineering fields. Hertz Fellows have contributed to breakthroughs in such areas as advanced medical therapies, global defense networks, and the James Webb Space Telescope.

This year’s MIT-affiliated recipients are among a total of 19 Hertz Foundation Fellows scholars selected from across the United States. 

Annika Marschner ’26 majored in mechanical engineering and will begin her PhD at MIT in the fall. Her undergraduate research centered on the development of novel technologies for both biointerfacing and bio-inspired systems, including a custom benchtop stereoscope-compatible incubator and extrusion-based desktop bioprinter for MIT’s Raman Lab, a light-based filamented bioprinting system for ETH Zürich’s Tissue Engineering and Biofabrication Lab, and large-scale hardware designs for robotic systems in MIT’s Biomimetic Robotics Lab. Marschner’s undergraduate thesis focused on improving the speed and dexterity of dynamic motions in bio-inspired robotic limbs. As a graduate student, she plans to continue her work on both hardware and control system design in biologically relevant settings, especially in the areas of assistive medical technology and surgical robotics. 

Alvin Q. Meng is doctoral student in inorganic chemistry focusing on understanding the fundamental interactions underlying chemical structure and reactivity. He is currently studying iron-sulfur clusters under the guidance of Professor Daniel L.M. Suess. Born in Tianjin, China, Meng immigrated to the United States at the age of 10. He received undergraduate degrees in chemistry and mathematics from the University of Virginia, where he worked in the research group of Professor W. Dean Harman. His research involved the synthesis and characterization of dihapto-coordinated tungsten complexes of cyclopentadiene, focusing on a class of unusual binuclear species containing a carbon–carbon bond linking two metal-bound five-membered rings.

Zachary S. Siegel is an electrical engineering and computer science graduate student pursuing a PhD in the Computer Science and Artificial Intelligence Laboratory, where he works at the intersection of robotics, cognitive science, and artificial intelligence. He graduated summa cum laude from Princeton University with a BSE in computer science and a minor in philosophy, receiving honors including Tau Beta Pi, Sigma Xi and the Outstanding Computer Science Independent Work Prize. His senior thesis, advised by Tom Griffiths and Jacob Andreas, investigated how humans infer the goals of others in open-ended, real-world environments. Siegel demonstrated how Bayesian inference serves as an accurate model of people’s goal predictions by comparing partial observations to a learned library of possible plans weighted by their prior likelihood. His doctoral research goal is to build machines that learn and reason more like people — systems that can learn from limited data and generalize to new situations by combining robot planning and Bayesian inference. Siegel is particularly interested in combinatorial generalization: the human capacity to compose known skills in novel ways to solve previously unseen problems without additional demonstrations. At MIT, he is advised by Leslie P. Kaelbling, Tomás Lozano-Pérez, and Joshua B. Tenenbaum.

Matthew Wanta is an incoming doctoral student who will begin operations research at MIT in the fall. He is a class of 2026 graduate of the United States Military Academy at West Point with a bachelor’s degree in computer science and mathematical sciences, both with honors. His work centered on machine learning for autonomous systems, integrating probabilistic modeling and computer vision into cooperative drone search and swarm control frameworks. In collaboration with DEVCOM Armaments Center, Wanta developed computer vision models for detecting energetic defects in artillery munitions, enabling rapid, nonintrusive quality control in defense manufacturing. His work with U.S. Special Operations Command and Army C5ISR organizations focused on autonomous aerial search and sensing, where he built simulation architectures for probabilistic target localization and multi-agent coordination. Wanta served as company commander for Bravo Company, 2nd Regiment; president of Upsilon Pi Epsilon; and vice president of Phi Kappa Phi. He is an Astronaut Scholar and Sapper School graduate, and commissioned as an Army officer in the Cyber Corps.

For remotely operated underwater vehicles, cloudy and turbulent waters are often a no-go. When vehicles settle on the seafloor or dig through a sandbed, they can kick up clouds of sediment that make it tough for onboard cameras to see through. Often, the only thing to do is to wait until the marine dust settles before a vehicle can safely proceed. 

But a new underwater mapping technique developed by engineers at MIT and the Woods Hole Oceanographic Institution (WHOI) may allow vehicles to see through murky, low-visibility waters. 

The method fuses visual images from optical cameras with acoustic data from sonar sensors. The combination enables a vehicle to quickly map the general shape of its surroundings using sonar, even in low-visibility waters. A vehicle can move toward certain shapes in the sonar-mapped environment, coming close enough for optical cameras to visually resolve specific objects in detail. 

The technique is akin to pairing a dolphin’s echolocation with a sea turtle’s close-range vision to see and navigate through murky water, in real-time. 

The researchers tested the method in tank experiments where they could control the water’s degree of visibility. Even in the cloudiest conditions, the system was able to see through the sediment to map the tank’s environment and visualize centimeter-scale details of objects in the tank. 

The team is further improving the technique, which they’ve named Sonar-MASt3R. They envision that the mapping method could safely guide underwater vehicles through murky environments for a range of applications, including scientific exploration, underwater construction and maintenance, and deep-sea recovery. 

“We hope that this work enables us to do more operations in those challenging, low-visibility environments, and helps provide more coverage in areas that are difficult to operate in today,” says Amy Phung, a graduate student in MIT’s Department of Aeronautics and Astronautics, who led the work. 

Phung presented a paper detailing Sonar-MASt3R this week at the IEEE International Conference on Robotics and Automation (ICRA). The paper’s co-author is Richard Camilli, senior scientist of applied ocean physics and engineering at WHOI. 

The best of both

To see underwater, scientists have generally taken an either/or approach, using either optical cameras or sonar sensors to guide the way. Optical cameras can provide detailed visual imagery of a scene, but only in waters that are relatively clear and well-lit. In contrast, sonar sensors perform just as well in clear and murky water; by emitting acoustic waves and measuring the time and angle at which they return, sonar sensors can determine the exact shape, distance, and depth of objects in the environment, though a sonar map lacks any visual detail. 

To get the best of both modes, scientists have looked to combine the two in a new approach known as “opti-acoustic fusion.” In a handful of prior works, research groups have merged sonar and optical data in mapping techniques that are mostly geared toward object recognition and reconstructing workplace environments. Most techniques require time to sync and process the data and therefore do not work in real-time, while only a few can map an environment in 3D. None have been applied to high-resolution mapping underwater in murky, turbid conditions. 

Phung, who is a student in the MIT-WHOI Joint Program, and Camilli, her advisor, aimed to develop an opti-acoustic fusion technique that would generate detailed 3D maps of underwater environments in real time and in low-visibility conditions. The team was motivated, in part, by challenges in safely recovering unexploded underwater mines.

“There can be old explosives in areas that make it unsafe for ships to be in, and the ability to get rid of those safely is best done by robotics,” Camilli says. “But a lot of these explosives are set in surf zone environments where visibility adds to the challenge of doing this safely. That’s one of many applications that our technique can be used for.”

Cloudy, with a chance of mapping

The new method, Sonar-MASt3R, builds on an existing technique, MASt3R, that was developed by researchers in France. MASt3R is an image matching algorithm that is trained to take in visual images of the same scene and quickly estimate the relative depth of each pixel in the scene. In this way, MASt3R can generate a 3D map of the environment in real-time, based on a camera’s 2D images. 

“The downside is that there is no sense of scale,” Phung says. “It will say ‘this pixel is five units closer than this pixel,’ but it can’t say whether that’s 5 meters or 5 feet.”

Luckily, sonar provides absolute measurements of scale. The timing of sonar reflections can be translated directly into a specific depth and distance of objects that the signals bounced off, as well as their shape and contour. 

In their new work, Phung and Camilli used sonar data to correct MASt3R’s scaling and generate precise 3D maps of underwater environments. Even in murky water, the method’s sonar-corrected map would enable a vehicle to know the precise location of objects, and therefore how far to safely move in for a closer inspection, which the vehicle could then do using conventional optical cameras.

The team tested Sonar-MASt3R in experiments with a tank that they filled with water, sediment, and a variety of objects such as a small boulder, a coffee mug, and a packing crate. Inside the tank, they also set up a robotic arm, onto which they mounted an underwater camera, and a sonar sensor. 

For each experimental run, they first carried out a sweep trajectory, in which the robotic arm slowly swept from one side of the tank to the other to capture sonar and visual data. With this first sweep, Sonar-MASt3R quickly creates a coarse sonar-based map of the shapes and contours of the tank and its objects. The coarse map is then used to record close-up camera images of the objects, which are used to improve the map resolution. A “keyframe” approach quickly compares each new image frame to the last keyframe. If a frame provides new information not contained in the last keyframe, the image is added as a new keyframe to the map. If it is similar, it is immediately discarded. In this way, the approach can quickly fill in the map with relevant visual detail, in real-time. 

The researchers tested their new approach underwater, testing eight different levels of turbidity, which they created by stirring up the tank’s sediment. Compared with other opti-acoustic fusion approaches, Sonar-MASt3R generated more accurate 3D maps and resolved smaller, centimeter-scale details, and in cloudier conditions. In the cloudiest condition, which the robotic arm’s cameras could not see through, its sonar sensors were able to generate a rough map of the tank’s hidden objects. This initial map enabled the arm to move safely through the murk and closer to specific objects, which its underwater camera could then visualize in more detail. 

“An analogy would be if you were to go into a china shop in the dark, and try to pick your way around to find a specific coffee mug without knocking things over,” Camilli offers. “This would allow you to do that.”

The team plans to test the approach in natural underwater conditions, where they suspect that the mapping task should be more straightforward. 

“In a tank, it’s like an echo chamber,” Camilli says. “It’s like trying to do this in a funhouse mirror setting where you get all these distortions and reverberations and ghost images that really complicates the processing. If you put it in the real world, it should be easier.”

Then, they say, Sonar-MASt3R could help scientists safely explore in cloudy, turbid, and murky underwater regions.

“The real value in this effort is so we can use this technology in mission scenarios that are untractable right now,” Phung says. “And there are plenty of untractable missions because we don’t have the observational or perception capabilities.”

This research was supported, in part, by NASA, and the National Science Foundation.

When you picture a worker at a retail store, you probably think of someone at a cash register or helping a customer. But employees also spend a lot of their time combing through stockrooms and shop floors, fulfilling requests or online orders and generally trying to keep track of all their inventory.

Keeping track of inventory takes so much time, in part, because retailers don’t always know where everything is located. That’s why when you ask a store associate to check if they have a shirt in your size, it may take them 20 minutes to get back to you.

Cartesian is helping retailers keep track of inventory with a technology invented at MIT. The system uses wireless signals from radio frequency identification (RFID) tags attached to items to find their precise location in a store, from the stockroom to the shop floor.

Last year, Cartesian did a study with a retailer and found its platform delivered meaningful annual savings at the store level by streamlining inventory tracking, optimizing workflows, and improving customer experiences.

“The big problem we’re solving is that about 50 percent of working hours in retail stores go to managing inventory,” says co-founder Fadel Adib SM ’13, PhD ’17, an associate professor at MIT. “That is roughly a $15 billion problem in the U.S. alone. We use algorithms to decipher indoor locations using wireless signals. The core technology enables a new level of indoor localization.”

Cartesian is already deployed in more than 700 stores across 15 countries and is working with one of the world’s largest fashion groups, Inditex, which is the parent company to brands like ZARA, Pull&Bear, and Oysho.

Beyond retailers and warehouses, Cartesian’s platform could also improve indoor location tracking for manufacturers, logistics operators, and robotics companies.

“The broad vision for what we are doing is spatial AI,” says Adib. “Today, AI does extremely well in the digital world. Now it has to move into the physical world. That means allowing machines to perceive their environment in such a way that they can interact with it. That’s where spatial AI comes in and where Cartesian sits.”

From technology to product

Adib, who holds a joint appointment in MIT’s Media Lab and Department of Electrical Engineering and Computer Science, has been studying wireless signals at the Institute for more than 15 years, dating back to research during his master’s degree.

“My group today researches how to use wireless signals to sense the world in ways that were not possible before,” Adib says. “We develop the fundamental technology and then we build systems around them. Our goal is to see these systems deployed in the real world for impact.”

When Adib joined MIT’s faculty, the first project he worked on was indoor localization using RFID tags. Isaac Perper ’20, MEnG ’21 later joined his lab as a student, and together they developed machine-learning algorithms to process RFID data to translate them into location patterns, with an initial focus on helping robots locate RFIDs indoors.

In 2021, Adib went through the National Science Foundation’s I-Corps program, which challenges researchers to interview potential customers to find the right problems to solve with their technologies. That’s when he realized how big of a problem inventory management is for retailers.

Cartesian was officially founded by Adib and Perper in the beginning of 2023, after they received a small business award from the National Science Foundation. The pair worked with MIT’s Technology Licensing Office to license patents from Adib’s lab. They also received support from MIT’s Venture Mentoring Service.

“Our goal was to reduce the cost of the technology to make it scalable,” Adib recalls. “Isaac focused on simplifying the product, leveraging progress in machine learning, and making it fast. It was a lot of iterating and testing early on.”

Retail workers spend much of their time locating items for a number of reasons. They might get an online order to fulfill, need to restock store shelves, or get a customer inquiry about items in the back.

Stores differ in how they organize their inventory. Most separate items by categories in specific shelves and bins then use barcodes or inventory systems that tend to get outdated fast.

“It’s a big problem for stores because customers may just leave before asking an employee to look for their size, or customers may get frustrated and leave if it takes too long,” Adib says. “The associate also wastes time looking for items they could spend doing higher-value work.”

Cartesian’s platform works with retailers’ existing handheld RFID readers, which store associates already use to manage inventory. Each store installs Cartesian’s software into their existing inventory apps or uses a custom app for employees to access directly.

“The RFID readers are how stores tell what’s in stock and what’s out of stock,” Perper says. “We figured out a way to leverage the same scans they’re already using with the reader, put the data they generate into our machine-learning algorithms, and generate maps of where all the items are.”

Customers can build analytics on top of Cartesian’s technology to keep track of inventory levels, show customers maps of where each item is located, and create other services.

“They use our location intelligence platform and build different products on top,” Adib says. “We can work with any device, any store, any type of RFID. It’s a simple interface. All the sophisticated location algorithms sit in the cloud.”

Beyond retail

Cartesian signed its first big contract in 2025 and soon expanded to several hundred stores. One of Cartesian’s advantages is its ability to quickly scale. Perper says they can add a store in about one minute. Cartesian’s team doesn’t even have to travel to a new store to turn on its system if it’s already working with the company.

“It’s as simple as flipping a switch, preparing the data, and sending it to our customers,” Perper says. “One of our first big bets was, ‘Can we build this entirely on existing hardware?’ That bet is starting to pay off.”

Cartesian’s models can also work with Wi-Fi and Bluetooth signals, which the company plans to use with customers in other verticals.

“Right now, we’re focused on applications in retail, but this technology has a lot of value in manufacturing, warehouses, and other locations,” Adib says.

Cartesian’s team aims to be deployed in tens of thousands of stores over the next year and then begin expanding beyond retail into industries like manufacturing and robotics.

“What’s most exciting about Cartesian to me is we’ve built a lot of the technology foundation, and now that we have the fundamentals in place, we hope to build specific application layers,” Perper says. “Then we can ask customers in different verticals about their problems and apply our technology in different ways to solve it.”

When a team of MIT students turned up at a national robotics tournament, their robot — aptly named Timbot — wouldn’t work. They’d been invited to demonstrate Timbot at the inaugural United States Governors Cup in Washington, D.C., a March Madness-like competition for high school robotics teams from all 50 states.

Troubleshooting on the fly is par for the course at robotics tournaments. Timbot had a few technical issues, mostly with Wi-Fi, so the team sat cross-legged on the floor and set to work. Meanwhile, high school students started gathering around and asking questions about wiring and subsystems. After about an hour, Timbot was up and running again, scooping up and throwing foam balls as it was designed to do.

“It actually turned into a great moment,” says first-year student Lily Sand. “We ended up tethering the robot with a long Ethernet cable, instead of using wireless, and a lot of students were like, ‘whoa, we do that too!’ It was a nice connection point.”

Leveraging a cultural touchstone for good

Connecting younger students to robotics is one of the MIT students’ goals as members of a new club, FIRSTxMIT, which launched at the beginning of the academic year. Members are all alumni of programs offered by FIRST Robotics (FIRST), a nonprofit that aims to inspire interest in STEM for K-12 students worldwide through team-based robotics programs and competitions.

FIRST has deep roots at MIT. Inventor Dean Kamen collaborated with the late MIT Professor Woodie Flowers, a pioneer in hands-on engineering design education, to establish the FIRST Robotics Competition in 1992. The competition was modeled after the novel robotics competition Flowers had developed for his iconic mechanical engineering class 2.70 (Introduction to Design), which is now 2.007 (Design and Manufacturing I).

Through FIRST, students learn about more than designing, building, and programming robots. The program emphasizes the ethos of “gracious professionalism,” a term coined by Flowers for high-quality work, respect, and cooperation, even in the context of competition. Students also build self-confidence, gain leadership experience, and hone communication skills, as well as technical expertise. 

Many FIRST alumni feel deep gratitude for the program and a strong desire to stay involved. Debbie Ang, co-founder of FIRSTxMIT, still mentors her high school’s team in New Hampshire. Yet, there are few FIRST alumni clubs at universities. Ang and co-founder Perry Han, also a sophomore, met in high school through FIRST and reconnected at MIT. “We noticed that FIRST was founded here, and yet there wasn’t anything organized on campus, even though we kept running into people who had done FIRST and still cared about the community,” she explains.

In fact, participation in FIRST is somewhat of a cultural touchstone among MIT students. MIT associate director of admissions Trinidad Carney, a liaison to FIRST Robotics, estimates that 15-20 percent of undergraduates have participated in the program.

Han and Ang collaborated with Carney to launch FIRSTxMIT, under the auspices of the Edgerton Center, to foster connections among the MIT FIRST community and provide a way for members to channel their passion for FIRST into outreach and public service. Their hunch about the untapped potential an alumni club was spot-on: the kickoff event drew 185 students, and there are about 200 on their Discord channel.

Sharing the “power of FIRST”

Now the club is off and running. They have hosted a gathering for New England FIRST alumni; collaborated with the Josiah Quincy Elementary School in Boston to launch a LEGO Robotics league; volunteered as judges at local competitions; and helped the MIT Admissions Office with outreach. Carney, who advises the club, says, “We’ve actually had other universities reach out to us to say, ‘How did MIT manage to launch a club that’s so successful and compelling?’”

One of the club’s most ambitious undertakings to date was building Timbot, in three days, during Independent Activities Period in January. Robot in 3 Days (Ri3D) is a collegiate challenge in which students build a FIRST Robotics Competition-level robot in 72 hours, a feat that would take about six weeks for a high school team. Experiential Robotics, a consortium that leverages an experiential robotics platform to promote engineering and public service, provided support for MIT’s Ri3D challenge and invited the team to act as STEM ambassadors at the Governors Cup.

In addition to the robotics competition, the two-day event brought together governors and leaders from government, education, industry, and others to underscore the crucial role that states play in supporting STEM education.

To that end, the FIRSTxMIT team demonstrated Timbot, chatted with high schoolers, staffed the MIT Admissions booth, and mingled with VIPs, sharing the value of project-based STEM enrichment opportunities like FIRST. “Having MIT students tell the story of the power of FIRST is incredibly compelling,” says Carney. “They can say: I did this in high school, it shaped who I am, and now I’m at MIT continuing to build and give back.”

A number of governors stopped by the MIT Admissions booth to chat with the students, including Massachusetts Governor Maura Healey. “She talked about the importance of K-12 STEM education and was very supportive,” says Sand, FIRSTxMIT’s logistics coordinator. 

In addition to inspiring others, the MIT students drew inspiration themselves at the Governors Cup. Han recalls speaking to a state senator from Ohio, a former teacher and strong advocate for programs like FIRST. “It really showed me that, when you have people in government that are excited about STEM education, it can really go places.”

Building a better future

Looking forward, Han and Ang plan to take some time to further refine the club’s organization and future goals. Hands-on outreach figures prominently in their plans. “FIRST places a big focus on starting new teams, supporting underserved communities, and spreading awareness,” says Ang. “A lot of us feel that FIRST played a major role in shaping our academic and career paths, so we want to give that opportunity to others.”

“Part of our goal is, we want to put a robot in as many students’ hands as possible to kind of give them a sense that, STEM isn’t just reading the AP Physics C-Mechanics textbook,” Han adds. “It’s actually putting these ideas into practice and building something useful.”

They have no shortage of new ideas they are kicking around, as well. Han is particularly interested in advocating for students to earn Undergraduate Research Opportunities Program or class credit for projects like Ri3D, or for those in the Gordon Engineering Leadership Program to get leadership credit by mentoring a robotics team. He also wants to explore how to leverage FIRST alumni networks to help students with professional development.

Whatever path they take, Carney has no doubt they make an impact. She saw their potential on full display when they built Timbot.

“These students, many of whom hadn’t met before, came from all kinds of backgrounds: different schools, different regions, different life experiences,” she says. “But they worked together with respect, curiosity, and generosity. They’re collaborative, mission-driven, and passionate about making opportunities for others. They make MIT better, and they will make the future better.”