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Designers, makers, and others often use 3D printing to rapidly prototype a range of functional objects, from movie props to medical devices. Accurate print previews are essential so users know a fabricated object will perform as expected.

But previews generated by most 3D-printing software focus on function rather than aesthetics. A printed object may end up with a different color, texture, or shading than the user expected, resulting in multiple reprints that waste time, effort, and material.

To help users envision how a fabricated object will look, researchers from MIT and elsewhere developed an easy-to-use preview tool that puts appearance first.

Users upload a screenshot of the object from their 3D-printing software, along with a single image of the print material. From these inputs, the system automatically generates a rendering of how the fabricated object is likely to look.

The artificial intelligence-powered system, called VisiPrint, is designed to work with a range of 3D-printing software and can handle any material example. It considers not only the color of the material, but also gloss, translucency, and how nuances of the fabrication process affect the object’s appearance.

Such aesthetics-focused previews could be especially useful in areas like dentistry, by helping clinicians ensure temporary crowns and bridges match the appearance of a patient’s teeth, or in architecture, to aid designers in assessing the visual impact of models.

“3D printing can be a very wasteful process. Some studies estimate that as much as a third of the material used goes straight to the landfill, often from prototypes the user ends of discarding. To make 3D printing more sustainable, we want to reduce the number of tries it takes to get the prototype you want. The user shouldn’t have to try out every printing material they have before they settle on a design,” says Maxine Perroni-Scharf, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on VisiPrint.

She is joined on the paper by Faraz Faruqi, a fellow EECS graduate student; Raul Hernandez, an MIT undergraduate; SooYeon Ahn, a graduate student at the Gwangju Institute of Science and Technology; Szymon Rusinkiewicz, a professor of computer science at Princeton University; William Freeman, the Thomas and Gerd Perkins Professor of EECS at MIT and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and senior author Stefanie Mueller, an associate professor of EECS and Mechanical Engineering at MIT, and a member of CSAIL. The research will be presented at the ACM CHI Conference on Human Factors in Computing Systems.

Accurate aesthetics

The researchers focused on fused deposition modeling (FDM), the most common type of 3D printing. In FDM, print material filament is melted and then squirted through a nozzle to fabricate an object one layer at a time.

Generating accurate aesthetic previews is challenging because the melting and extrusion process can change the appearance of a material, as can the height of each deposited layer and the path the nozzle follows during fabrication.

VisiPrint uses two AI models that work together to overcome those challenges.

The VisiPrint preview is based on two inputs: a screenshot of the digital design from a user’s 3D-printing software (called “slicer” software), and an image of the print material, which can be taken from an online source or captured from a printed sample.

From these inputs, a computer vision model extracts features from the material sample that are important for the object’s appearance.

It feeds those features to a generative AI model that computes the geometry and structure of the object, while incorporating the so-called “slicing” pattern the nozzle will follow as it extrudes each layer.

The key to the researchers’ approach is a special conditioning method. This involves carefully adjusting the inner workings of the model to guide it, so it follows the slicing pattern and obeys the constraints of the 3D-printing process.

Their conditioning method utilizes a depth map that preserves the shape and shading of the object, along with a map of the edges that reflects the internal contours and structural boundaries.

“If you don’t have the right balance of these two things, you could use up with bad geometry or an incorrect slicing pattern. We had to be careful to combine them in the right way,” Perroni-Scharf says.

A user-focused system

The team also produced an easy-to-use interface where one can upload the required images and evaluate the preview.

The VisiPrint interface enables more advanced makers to adjust multiple settings, such as the influence of certain colors on the final appearance.

In the end, the aesthetic preview is intended to complement the functional preview generated by slicer software, since VisiPrint does not estimate printability, mechanical feasibility, or likelihood of failure.

To evaluate VisiPrint, the researchers conducted a user study that asked participants to compare the system to other approaches. Nearly all participants said it provided better overall appearance as well as more textural similarity with printed objects.

In addition, the VisiPrint preview process took about a minute on average, which was more than twice as fast as any competing method.

“VisiPrint really shined when compared to other AI interfaces. If you give a more general AI model the same screenshots, it might randomly change the shape or use the wrong slicing pattern because it had no direct conditioning,” she says.

In the future, the researchers want to address artifacts that can occur when model previews have extremely fine details. They also want to add features that allow users to optimize parts of the printing process beyond color of the material.

“It is important to think about the way that we fabricate objects. We need to continue striving to develop methods that reduce waste. To that end, this marriage of AI with the physical making process is an exciting area of future work,” Perroni-Scharf says.

“‘What you see is what you get’ has been the main thing that made desktop publishing ‘happen’ in the 1980s, as it allowed users to get what they wanted at first try. It is time to get WYSIWYG for 3D printing as well. VisiPrint is a great step in this direction,” says Patrick Baudisch, a professor of computer science at the Hasso Plattner Institute, who was not involved with this work.

This research was funded, in part, by an MIT Morningside Academy for Design Fellowship and an MIT MathWorks Fellowship.

In biology, defects are generally bad. But in materials science, defects can be intentionally tuned to give materials useful new properties. Today, atomic-scale defects are carefully introduced during the manufacturing process of products like steel, semiconductors, and solar cells to help improve strength, control electrical conductivity, optimize performance, and more.

But even as defects have become a powerful tool, accurately measuring different types of defects and their concentrations in finished products has been challenging, especially without cutting open or damaging the final material. Without knowing what defects are in their materials, engineers risk making products that perform poorly or have unintended properties.

Now, MIT researchers have built an AI model capable of classifying and quantifying certain defects using data from a noninvasive neutron-scattering technique. The model, which was trained on 2,000 different semiconductor materials, can detect up to six kinds of point defects in a material simultaneously, something that would be impossible using conventional techniques alone.

“Existing techniques can’t accurately characterize defects in a universal and quantitative way without destroying the material,” says lead author Mouyang Cheng, a PhD candidate in the Department of Materials Science and Engineering. “For conventional techniques without machine learning, detecting six different defects is unthinkable. It’s something you can’t do any other way.”

The researchers say the model is a step toward harnessing defects more precisely in products like semiconductors, microelectronics, solar cells, and battery materials.

“Right now, detecting defects is like the saying about seeing an elephant: Each technique can only see part of it,” says senior author and associate professor of nuclear science and engineering Mingda Li. “Some see the nose, others the trunk or ears. But it is extremely hard to see the full elephant. We need better ways of getting the full picture of defects, because we have to understand them to make materials more useful.”

Joining Cheng and Li on the paper are postdoc Chu-Liang Fu, undergraduate researcher Bowen Yu, master’s student Eunbi Rha, PhD student Abhijatmedhi Chotrattanapituk ’21, and Oak Ridge National Laboratory staff members Douglas L Abernathy PhD ’93 and Yongqiang Cheng. The paper appears today in the journal Matter.

Detecting defects

Manufacturers have gotten good at tuning defects in their materials, but measuring precise quantities of defects in finished products is still largely a guessing game.

“Engineers have many ways to introduce defects, like through doping, but they still struggle with basic questions like what kind of defect they’ve created and in what concentration,” Fu says. “Sometimes they also have unwanted defects, like oxidation. They don’t always know if they introduced some unwanted defects or impurity during synthesis. It’s a longstanding challenge.”

The result is that there are often multiple defects in each material. Unfortunately, each method for understanding defects has its limits. Techniques like X-ray diffraction and positron annihilation characterize only some types of defects. Raman spectroscopy can discern the type of defect but can’t directly infer the concentration. Another technique known as transmission electron microscope requires people to cut thin slices of samples for scanning.

In a few previous papers, Li and collaborators applied machine learning to experimental spectroscopy data to characterize crystalline materials. For the new paper, they wanted to apply that technique to defects.

For their experiment, the researchers built a computational database of 2,000 semiconductor materials. They made sample pairs of each material, with one doped for defects and one left without defects, then used a neutron-scattering technique that measures the different vibrational frequencies of atoms in solid materials. They trained a machine-learning model on the results.

“That built a foundational model that covers 56 elements in the periodic table,” Cheng says. “The model leverages the multihead attention mechanism, just like what ChatGPT is using. It similarly extracts the difference in the data between materials with and without defects and outputs a prediction of what dopants were used and in what concentrations.”

The researchers fine-tuned their model, verified it on experimental data, and showed it could measure defect concentrations in an alloy commonly used in electronics and in a separate superconductor material.

The researchers also doped the materials multiple times to introduce multiple point defects and test the limits of the model, ultimately finding it can make predictions about up to six defects in materials simultaneously, with defect concentrations as low as 0.2 percent.

“We were really surprised it worked that well,” Cheng says. “It’s very challenging to decode the mixed signals from two different types of defects — let alone six.”

A model approach

Typically, manufacturers of things like semiconductors run invasive tests on a small percentage of products as they come off the manufacturing line, a slow process that limits their ability to detect every defect.

“Right now, people largely estimate the quantities of defects in their materials,” Yu says. “It is a painstaking experience to check the estimates by using each individual technique, which only offers local information in a single grain anyway. It creates misunderstandings about what defects people think they have in their material.”

The results were exciting for the researchers, but they note their technique measuring the vibrational frequencies with neutrons would be difficult for companies to quickly deploy in their own quality-control processes.

“This method is very powerful, but its availability is limited,” Rha says. “Vibrational spectra is a simple idea, but in certain setups it’s very complicated. There are some simpler experimental setups based on other approaches, like Raman spectroscopy, that could be more quickly adopted.”

Li says companies have already expressed interest in the approach and asked when it will work with Raman spectroscopy, a widely used technique that measures the scattering of light. Li says the researchers’ next step is training a similar model based on Raman spectroscopy data. They also plan to expand their approach to detect features that are larger than point defects, like grains and dislocations.

For now, though, the researchers believe their study demonstrates the inherent advantage of AI techniques for interpreting defect data.

“To the human eye, these defect signals would look essentially the same,” Li says. “But the pattern recognition of AI is good enough to discern different signals and get to the ground truth. Defects are this double-edged sword. There are many good defects, but if there are too many, performance can degrade. This opens up a new paradigm in defect science.”

The work was supported, in part, by the Department of Energy and the National Science Foundation.

As one of the first students in MIT’s new Music Technology and Computation Graduate Program, Mariano Salcedo ’25 is researching the intersection between artificial intelligence and music visuals.

Specifically, his graduate research focuses on neural cellular automata (NCA), which merges classical cellular automata with machine learning techniques to grow images that can regenerate.

When paired with a stimulus like music, these images can “show” sounds in action.

“This approach enables anyone to create music-driven visuals while leveraging the expressive and sometimes unpredictable dynamics of self-organized systems,” Salcedo says. Through the web interface Salcedo has designed, users can adjust the relationship between the music’s energy and the NCA system to create unique visual performances using any music audio stream.

“I want the visuals to complement and elevate the listening experience,” he says.

Last year Salcedo, the Alex Rigopulos (1992) Fellow in Music Technology and Computation, earned a BS in artificial intelligence and decision making from MIT, where he explored signal processing in machine learning and how a classical understanding of signals can inform how we understand AI. Now he’s one of five master’s students in the Music Technology and Computation Graduate Program’s inaugural cohort.

The program, directed by professor of the practice in music technology Eran Egozy ’93, MNG ’95, is a collaboration between MIT Music and Theater Arts in the School of Humanities, Arts, and Social Sciences, and the School of Engineering. It invites practitioners to study, discover, and develop new computational approaches to music. It also includes a speaker series that exposes students and the broader MIT community to music industry professionals, artists, technologists, and other researchers.

Rigopulos ’92, SM ’94, is a video game designer, musician, and former CEO of Harmonix Music Systems, a company he co-founded with Egozy in 1995. Harmonix is now a part of Epic Games, where Rigopulos is the director of game development for music.

“MIT is where I was first able to pursue my passion for music technology decades ago, and that experience was the springboard for a long and fulfilling career,” says Rigopulos. “So, when MIT launched an advanced degree program in music technology, I was thrilled to fund a fellowship to help propel this exciting new program.”

Egozy is enthusiastic about Salcedo’s work and his commitment to further exploring its possibilities. “He is a beautiful example of a multidisciplinary researcher who thinks deeply about how to best use technology to enhance and expand human creativity,” he says.

Salcedo has been selected to deliver the student address at the 2026 Advanced Degree Ceremony for the School of Humanities, Arts, and Social Sciences. “It’s an honor and it’s daunting,” he says. “It feels like a huge responsibility,” though one he’s eager to embrace. His selection also pleases Egozy. “I am super excited that Mariano was chosen to deliver this year’s keynote,” he enthuses.

Changing gears

Growing up in Mexico and Texas, Mariano Salcedo couldn’t readily indulge his passion for creating music. “There are no bands in Mexican public schools,” he says. While some families could pay for instruments and lessons, others like Salcedo’s were less fortunate.

“I’ve always loved music,” he continues. “I was a listener.”

Salcedo began his MIT journey as a mechanical engineering student, applying to MIT through the Questbridge program. “I heard if you like engineering and science that attending MIT would be a great choice,” he recalls. “Nerds are welcomed and embraced.” While he dutifully worked toward completing his MechE curriculum, music and technology came calling after a chance encounter with an LLM.

“I was introduced to an LLM chatbot and was blown away,” he recalls. “This was something that was speaking to me. I was both awed and frightened.” After his encounter with the chatbot, Salcedo switched his major from mechanical engineering to artificial intelligence and decision making.

“I basically started over after being two thirds of the way through the MechE curriculum,” he says. He learned about the possibilities available with AI but also confronted some of the challenges bedeviling researchers and developers including its potential power, ensuring its responsible use, human bias, limited access for people from underrepresented groups, and a lack of diversity among developers. He decided he might be able to change that picture.

“I thought one more person in the field could make a difference,” he says.

While completing his undergraduate studies, Salcedo’s love of music resurfaced. “I began DJ’ing at MIT and was hooked,” he says. While he hadn’t learned to play a traditional instrument, he discovered he could create engaging soundscapes with technology. “I bought a digital audio work station to help me make music,” he continues.

Egozy and Salcedo met in 2024 while Salcedo completed an Undergraduate Research Opportunities Program rotation as a game developer in Egozy’s lab. “He was incredibly curious and has grown tremendously over a very short time period,” Egozy says. Egozy became an informal, though important, mentor to Salcedo. “He brings great energy and thoughtfulness to his work, and to supporting others in the [music technology and computation graduate] program,” Egozy notes.

Salcedo also took a class with Egozy, 21M.385/21M.585/6.4450 (Interactive Music Systems), which further fed his appetite for the creativity he craved while also allowing him to indulge his fascination with music’s possibilities. By taking advantage of courses in the HASS curriculum, he further developed his understanding of music theory and related technologies.

“I took a class with professor Leslie Tilley, 21M.240 (Critically Thinking in Music), which helped establish a valuable framework for understanding music making,” he says, “while a class like 6.3000 (Signal Processing) helped me connect intuition with science.”

Working across disciplines

While Salcedo is passionate about his music and his research, he’s also invested in building relationships with his fellow students. He’s a member of the fraternity Sigma Nu, where he says he “found a home and community.” He also took a MISTI trip to Chile in summer 2023, where he conducted music technology research. Salcedo praises the culture of camaraderie at MIT and is grateful for its influence on his work as a scholar. “MIT has taught me how to learn,” he says.

Professors encouraged him to present his research and findings. He presented his work — Artificial Dancing Intelligence: Neural Cellular Automata for Visual Performance of Music — at the Association for the Advancement of Artificial Intelligence conference in Singapore in January 2026.

Salcedo believes his research can potentially move beyond music visualization. “What if we could improve the ways we model self-organized systems?” he asks. “That is, systems like multicellular organisms, flocks of birds, or societies that interact locally but exhibit interesting behaviors.” Any system, Salcedo says, where the whole is more than the sum of its parts.

Developing the technology used to design his application can potentially help answer important ethical questions regarding AI’s continued expansion and growth. The path to his work’s development is both daunting and lonely, but those challenges feed his work ethic.

“It’s intimidating to pursue this path when the academy is currently focused on LLMs,” he says. “But it’s also important to explain and explore the base technology before digging into more nuanced work, which can help audiences understand it better.” Knowing that he has the support of his professors helps Salcedo maintain excitement for his ideas. “They only ask that we ground our interests in research,” he says.

His investigations are impacting his work as a musician. “My music has gotten more interesting because of the classes I’m taking,” he says. He’s also interested in understanding whose music the academy and the world hears, exploring biases toward Western music in the canon and exploring how to reduce biases related to which kinds of music are valued.

“The work we do as technologists is far less subjective than we’re led to believe,” he believes.

Salcedo is especially grateful for the support he’s received during his time at MIT. “Program faculty encourage a variety of pursuits,” he says, “and ask us to advance our individual aims rather than focusing on theirs.” During his time in the graduate program, he notes with enthusiasm how often he’s been challenged to pursue his ideas.

Ultimately, Salcedo wants people to experience the joy he feels working at the intersection of the humanities and the sciences. Music and technology impact nearly everyone. Inviting audiences into his laboratory as participants in the creative and research processes offers the same kind of satisfaction he gets from crafting a great beat or solving for a thorny technical challenge. Helping audiences understand his work’s value fuels his drive to succeed.

“I want users to feel movement and explore sounds and their impact more fully,” he says.

Proteins are far more than nutrients we track on a food label. Present in every cell of our bodies, they work like nature’s molecular machines. They walk, stretch, bend, and flex to do their jobs, pumping blood, fighting disease, building tissue, and many other jobs too small for the eye to see. Their power doesn’t come from shape alone, but from how they move. 

In recent years, artificial intelligence has allowed scientists to design entirely new protein structures not found in nature tailored for specific functions, such as binding to viruses, or mimicking the mechanical properties of silk for sustainable materials. But designing for structure alone is like building a car body without any control over how the engine performs. The subtle vibrations, shifts, and mechanical dynamics of a protein are just as critical to its functions as its form.

Now, MIT engineers have taken a major step toward closing the gap with the development of an AI model known as VibeGen. If vibe coding lets programmers describe what they want and then AI generates the software, VibeGen does the same for living molecules: specify the vibe — the pattern of motion you want — and the model writes the protein. 

The new model allows scientists to target how a protein flexes, vibrates, and shifts between shapes in response to its environment, opening a new frontier in the design of molecular mechanics. VibeGen builds on a series of advances from the Buehler lab in agentic AI for science — systems in which multiple AI models collaborate autonomously to solve problems too complex for any single model.

“The essence of life at fundamental molecular levels lies not just in structure, but in movement,” says Markus Buehler, the Jerry McAfee Professor of Engineering in the departments of Civil and Environmental Engineering and Mechanical Engineering. “Everything from protein folding to the deformation of materials under stress follows the fundamental laws of physics.”

Buehler and his former postdoc, Bo Ni, identified a critical need for what they call physics-aware AI: systems capable of reasoning about motion, not just snapshots of molecular structure. “AI must go beyond analyzing static forms to understanding how structure and motion are fundamentally intertwined,” Buehler adds.

The new approach, described in a paper March 24 in the journal Matter, uses generative AI to create proteins with tailor-made dynamics.

Training AI to think about motion 

The revolution in AI-driven protein science has been, overwhelmingly, a revolution in structure. Tools like AlphaFold solved the decades-old problem of predicting a protein’s three-dimensional shape. Existing generative models learned to design new shapes from scratch. But in focusing on the folded snapshot — the protein frozen in place — the field largely set aside the property that makes proteins work: their motion. “Structure prediction was such a grand challenge that it absorbed the field’s attention,” Buehler says. “But a protein’s shape is just one frame of a much longer film, and the design space extends through space and time, where structure sits on a much broader manifold.” Scientists could design a protein with a particular architecture. They couldn’t yet specify how that protein would move, flex, or vibrate once it was built.

VibeGen does something no protein design tool has done before. It inverts the traditional problem. Rather than asking, “What shape will this sequence produce?” it asks, “What sequence will make a protein move in exactly this way?”

To build VibeGen, Buehler and Ni turned to a class of AI diffusion models, the same underlying technology that powers AI image generators capable of creating realistic pictures from pure noise. In VibeGen’s case, the model starts with a random sequence of amino acids and refines it, step by step, until it converges on a sequence predicted to vibrate and flex in a targeted way.

The system works through two cooperating agents that design and challenge each other. A “designer” proposes candidate sequences aimed at a target motion profile. A “predictor” evaluates those candidates, asking whether they’ll actually move the way the designer intended. The two models iterate back and forth like an internal dialogue, until the design stabilizes into something that meets the goal. By specifying this vibrational fingerprint as the design input, VibeGen inverts the usual logic: dynamics becomes the blueprint, and structure follows.

“It’s a collaborative system,” Ni says. “The designer proposes, the predictor critiques, and the design improves through that tension.”

Most sequences VibeGen produces are entirely de novo, not borrowed from nature, not a variation on something evolution already made. To confirm the designs actually work, the team ran detailed physics-based molecular simulations, and the proteins behaved exactly as intended, flexing and vibrating in the patterns VibeGen had targeted.

One of the study’s most striking findings is that many different protein sequences and folds can satisfy the same vibrational target — a property the researchers call functional degeneracy. Where evolution converged on one solution, VibeGen reveals an entire family of alternatives: proteins with different structures and sequences that nonetheless move in the same way. “It suggests that nature explored only a fraction of what’s possible,” Buehler says. “For any given dynamic behavior, there may be a large, untapped space of viable designs.”

A new frontier in molecular engineering

Controlling protein dynamics could have wide-ranging applications. In medicine, proteins that can change shape on cue hold enormous potential. Many therapeutic proteins work by binding to a target molecule — a virus, a cancer cell, a misfiring receptor. How well they bind often depends not just on their shape, but on how flexibly they can adapt to their target. A protein that is engineered with motion could grip more precisely, reduce unintended interactions, and ultimately become a safer, more effective drug.

In materials science, which is an area of Buehler’s research, mechanical properties at the molecular scale affect their performance. Biological materials like silk and collagen get their strength and resilience from the coordinated motion of their molecular building blocks. Designing proteins that are stiffer, flexible, or vibrate in a certain way could lead to new sustainable fibers, impact-resistant materials, or biodegradable alternatives to petroleum-based plastics.

Buehler envisions further possibilities: structural materials for buildings or vehicles incorporating protein-based components that heal themselves after mechanical stress, or that adjust in response to heavy load.

By enabling researchers to specify motion as a direct design parameter, VibeGen treats proteins less like static shapes and more like programmable mechanical devices. The advance bridges artificial intelligence, medicine, synthetic biology, and materials engineering — toward a future in which molecular machines can be designed with the same precision and intentionality as bridges, engines, or microchips.

“VibeGen can venture into uncharted territory, proposing protein designs beyond the repertoire of evolution, tailored purely to our specifications. It’s as if we’ve invented a new creative engine that designs molecular machines on demand,” Buehler adds.

The researchers plan to refine the model further and validate their designs in the lab. They also hope to integrate motion-aware design with other AI tools, building toward systems that can design proteins to be not just dynamic, but multifunctional; machines that sense their environment, respond to signals, and adapt in real-time.

The word “vibe” comes from vibration, and Buehler sees the connection as more than wordplay. “We’ve turned ‘vibe’ into a metaphor, a feeling, something subjective,” he says. “But for a protein, the vibe is the physics. It is the actual pattern of motion that determines what the molecule can do, the very machinery of life.”

The research was supported by the U.S. Department of Agriculture, the MIT-IBM Watson AI Lab, and MIT’s Generative AI Initiative. 

Inside a giant autonomous warehouse, hundreds of robots dart down aisles as they collect and distribute items to fulfill a steady stream of customer orders. In this busy environment, even small traffic jams or minor collisions can snowball into massive slowdowns.

To avoid such an avalanche of inefficiencies, researchers from MIT and the tech firm Symbotic developed a new method that automatically keeps a fleet of robots moving smoothly. Their method learns which robots should go first at each moment, based on how congestion is forming, and adapts to prioritize robots that are about to get stuck. In this way, the system can reroute robots in advance to avoid bottlenecks.

The hybrid system utilizes deep reinforcement learning, a powerful artificial intelligence method for solving complex problems, to figure out which robots should be prioritized. Then, a fast and reliable planning algorithm feeds instructions to the robots, enabling them to respond rapidly in constantly changing conditions.

In simulations inspired by actual e-commerce warehouse layouts, this new approach achieved about a 25 percent gain in throughput over other methods. Importantly, the system can quickly adapt to new environments with different quantities of robots or varied warehouse layouts.

“There are a lot of decision-making problems in manufacturing and logistics where companies rely on algorithms designed by human experts. But we have shown that, with the power of deep reinforcement learning, we can achieve super-human performance. This is a very promising approach, because in these giant warehouses even a 2 or 3 percent increase in throughput can have a huge impact,” says Han Zheng, a graduate student in the Laboratory for Information and Decision Systems (LIDS) at MIT and lead author of a paper on this new approach.

Zheng is joined on the paper by Yining Ma, a LIDS postdoc; Brandon Araki and Jingkai Chen of Symbotic; and senior author Cathy Wu, the Class of 1954 Career Development Associate Professor in Civil and Environmental Engineering (CEE) and the Institute for Data, Systems, and Society (IDSS) at MIT, and a member of LIDS. The research appears today in the Journal of Artificial Intelligence Research.

Rerouting robots

Coordinating hundreds of robots in an e-commerce warehouse simultaneously is no easy task.

The problem is especially complicated because the warehouse is a dynamic environment, and robots continually receive new tasks after reaching their goals. They need to be rapidly redirected as they leave and enter the warehouse floor.

Companies often leverage algorithms written by human experts to determine where and when robots should move to maximize the number of packages they can handle.

But if there is congestion or a collision, a firm may have no choice but to shut down the entire warehouse for hours to manually sort the problem out.

“In this setting, we don’t have an exact prediction of the future. We only know what the future might hold, in terms of the packages that come in or the distribution of future orders. The planning system needs to be adaptive to these changes as the warehouse operations go on,” Zheng says.

The MIT researchers achieved this adaptability using machine learning. They began by designing a neural network model to take observations of the warehouse environment and decide how to prioritize the robots. They train this model using deep reinforcement learning, a trial-and-error method in which the model learns to control robots in simulations that mimic actual warehouses. The model is rewarded for making decisions that increase overall throughput while avoiding conflicts.

Over time, the neural network learns to coordinate many robots efficiently.

“By interacting with simulations inspired by real warehouse layouts, our system receives feedback that we use to make its decision-making more intelligent. The trained neural network can then adapt to warehouses with different layouts,” Zheng explains.

It is designed to capture the long-term constraints and obstacles in each robot’s path, while also considering dynamic interactions between robots as they move through the warehouse.

By predicting current and future robot interactions, the model plans to avoid congestion before it happens.

After the neural network decides which robots should receive priority, the system employs a tried-and-true planning algorithm to tell each robot how to move from one point to another. This efficient algorithm helps the robots react quickly in the changing warehouse environment.

This combination of methods is key.

“This hybrid approach builds on my group’s work on how to achieve the best of both worlds between machine learning and classical optimization methods. Pure machine-learning methods still struggle to solve complex optimization problems, and yet it is extremely time- and labor-intensive for human experts to design effective methods. But together, using expert-designed methods the right way can tremendously simplify the machine learning task,” says Wu.

Overcoming complexity

Once the researchers trained the neural network, they tested the system in simulated warehouses that were different than those it had seen during training. Since industrial simulations were too inefficient for this complex problem, the researchers designed their own environments to mimic what happens in actual warehouses.

On average, their hybrid learning-based approach achieved 25 percent greater throughput than traditional algorithms as well as a random search method, in terms of number of packages delivered per robot. Their approach could also generate feasible robot path plans that overcame congestion caused by traditional methods.

“Especially when the density of robots in the warehouse goes up, the complexity scales exponentially, and these traditional methods quickly start to break down. In these environments, our method is much more efficient,” Zheng says.

While their system is still far away from real-world deployment, these demonstrations highlight the feasibility and benefits of using a machine learning-guided approach in warehouse automation.

In the future, the researchers want to include task assignments in the problem formulation, since determining which robot will complete each task impacts congestion. They also plan to scale up their system to larger warehouses with thousands of robots.

This research was funded by Symbotic.

Each spring, river herring populations migrate from Massachusetts coastal waters to begin their annual journey up rivers and streams to freshwater spawning habitat. River herring have faced severe population declines over the past several decades, and their migration is extensively monitored across the region, primarily through traditional visual counting and volunteer-based programs. 

Monitoring fish movement and understanding population dynamics are essential for informing conservation efforts and supporting fisheries management. With the annual herring run getting underway this month, researchers and resource managers once again take on the challenge of counting and estimating the migrating fish population as accurately as possible. 

A team of researchers from the Woodwell Climate Research Center, MIT Sea Grant, the MIT Computer Science and Artificial Intelligence Lab (CSAIL), MIT Lincoln Laboratory, and Intuit explored a new monitoring method using underwater video and computer vision to supplement citizen science efforts. The researchers — Zhongqi Chen and Linda Deegan from the Woodwell Climate Research Center, Robert Vincent and Kevin Bennett from MIT Sea Grant, Sara Beery and Timm Haucke from MIT CSAIL, Austin Powell from Intuit, and Lydia Zuehsow from MIT Lincoln Laboratory — published a paper describing this work in the journal Remote Sensing in Ecology and Conservation this February. 

The open-access paper, “From snapshots to continuous estimates: Augmenting citizen science with computer vision for fish monitoring,” outlines how recent advancements in computer vision and deep learning, from object detection and tracking to species classification, offer promising real-world solutions for automating fish counting with improved efficiency and data quality. 

Traditional monitoring methods are constrained by time, environmental conditions, and labor intensity. Volunteer visual counts are limited to brief daytime sampling windows, missing nighttime movement and short migration pulses, when hundreds of fish pass by within the span of a few minutes. While technologies like passive acoustic monitoring and imaging sonar have advanced continuous fish monitoring under certain conditions, the most promising and low-cost option — manual review of underwater video — is still labor-intensive and time-consuming. With the growing demand for automated video processing solutions, this study presents a scalable, cost-effective, and efficient deep learning-based system for reliable automated fish monitoring. 

The team built an end-to-end pipeline — from in-field underwater cameras to video labeling and model training — to achieve automated, computer vision-powered fish counting. Videos were collected from three rivers in Massachusetts: the Coonamessett River in Falmouth, the Ipswich River (Ipswich), and the Santuit River in Mashpee. 

To prepare the training dataset, the team selected video clips with variations in lighting, water clarity, fish species and density, time of day, and season to ensure that the computer vision model would work reliably across diverse real-world scenarios. They used an open-source web platform to manually label the videos frame-by-frame with bounding boxes to track fish movement. In total, they labeled 1,435 video clips and annotated 59,850 frames. 

The researchers compared and validated the computer vision counts with human video reviews, stream-side visual counts, and data from passive integrated transponder (PIT) tagging. They concluded that models trained on diverse multi-site and multi-year data performed best and produced season-long, high-resolution counts consistent with traditionally established estimates. Going one step further, the system provided insights into migration behavior, timing, and movement patterns linked to environmental factors. Using video from the 2024 Coonamesset River migration, the system counted 42,510 river herring and revealed that upstream migration peaked at dawn, while downstream migration was largely nocturnal, with fish utilizing darker, quieter periods to avoid predators.

With this real-world application, the researchers aim to advance computer vision in fisheries management and provide a framework and best practices for integrating the technology into conservation efforts for a wide range of aquatic species. “MIT Sea Grant has been funding work on this topic for some time now, and this excellent work by Zhongqi Chen and colleagues will advance fisheries monitoring capabilities and improve fish population assessments for fisheries managers and conservation groups,” Vincent says. “It will also provide education and training for students, the public, and citizen science groups in support of the ecologically and culturally important river herring populations along our coasts.”

Still, continued traditional monitoring is essential for maintaining consistency in long-term datasets until fisheries management agencies fully implement automated counting systems. Even then, computer vision and citizen science should be seen as complementary. Volunteers will be necessary for camera maintenance and for contributing directly to the computer vision workflow, from video annotation to model verification. The researchers envision that integrating citizen observations and computer vision-generated data will help create a more comprehensive and holistic approach to environmental monitoring.

This work was funded by MIT Sea Grant, with additional support provided by the Northeast Climate Adaptation Science Center, an MIT Abdul Latif Jameel Water and Food Systems seed grant, the AI and Biodiversity Change Global Center (supported by the National Science Foundation and the Natural Sciences and Engineering Research Council of Canada), and the MIT Undergraduate Research Opportunities Program.

The next time you’re scrolling your phone, take a moment to appreciate the feat: The seemingly mundane act is possible thanks to the coordination of 34 muscles, 27 joints, and over 100 tendons and ligaments in your hand. Indeed, our hands are the most nimble parts of our bodies. Mimicking their many nuanced gestures has been a longstanding challenge in robotics and virtual reality.

Now, MIT engineers have designed an ultrasound wristband that precisely tracks a wearer’s hand movements in real-time. The wristband produces ultrasound images of the wrist’s muscles, tendons, and ligaments as the hand moves, and is paired with an artificial intelligence algorithm that continuously translates the images into the corresponding positions of the five fingers and palm.

The researchers can train the wristband to learn a wearer’s hand motions, which the device can communicate in real-time to a robot or a virtual environment.

In demonstrations, the team has shown that a person wearing the wristband can wirelessly control a robotic hand. As the person gestures or points, the robot does the same. In a sort of wireless marionette interaction, the wearer can manipulate the robot to play a simple tune on the piano and shoot a small basketball into a desktop hoop. With the same wristband, a wearer can also manipulate objects on a computer screen, for instance pinching their fingers together to enlarge and minimize a virtual object.

The team is using the wristband to gather hand motion data from many more users with different hand sizes, finger shapes, and gestures. They envision building a large dataset of hand motions that can be plumbed, for instance, to train humanoid robots in dexterity tasks, such as performing certain surgical procedures. The ultrasound band could also be used to grasp, manipulate, and interact with objects in video games, design applications, or other virtual settings.

“We think this work has immediate impact in potentially replacing hand tracking techniques with wearable ultrasound bands in virtual and augmented reality,” says Xuanhe Zhao, the Uncas and Helen Whitaker Professor of Mechanical Engineering at MIT. “It could also provide huge amounts of training data for dexterous humanoid robots.”

Zhao, Gengxi Lu, and their colleagues present the wristband’s new design in a paper appearing today in Nature Electronics. Their MIT co-authors are former postdocs Xiaoyu Chen, Shucong Li, and Bolei Deng; graduate students SeongHyeon Kim and Dian Li; postdocs Shu Wang and Runze Li; and Anantha Chandrakasan, MIT provost and the Vannevar Bush Professor of Electrical Engineering and Computer Science. Other co-authors are graduate students Yushun Zheng and Junhang Zhang, Baoqiang Liu, Chen Gong, and Professor Qifa Zhou from the University of Southern California.

Seeing strings

There are currently a number of approaches to capturing and mimicking human hand dexterity in robots. Some approaches use cameras to record a person’s hand movements as they manipulate objects or perform tasks. Others involve having a person wear a glove with sensors, which records the person’s hand movements and transmits the data to a receiving robot. But erecting a complex camera system for different applications is impractical and prone to visual obstacles. And sensor-laden gloves could limit a person’s natural hand motions and sensations.

A third approach uses the electrical signals from muscles in the wrist or forearm that scientists then correlate with specific hand movements. Researchers have made significant advances in this approach, however these signals are easily affected by noise in the environment. They are also not sensitive enough to distinguish subtle changes in movements. For instance, they may discern whether a thumb and index finger are pinched together or pulled apart, but not much of the in-between path.

Zhao’s team wondered whether ultrasound imaging might capture more dexterous and continuous hand movements. His group has been developing various forms of ultrasound stickers — miniaturized versions of the transducers used in doctor’s offices that are paired with hydrogel material that can safely stick to skin.

In their new study, the team incorporated the ultrasound sticker design into a wearable wristband to continuously image the muscles and tendons in the wrist.

“The tendons and muscles in your wrist are like strings pulling on puppets, which are your fingers,” Lu says. “So the idea is: Each time you take a picture of the state of the strings, you’ll know the state of the hand.”

Mapping manipulation

The team designed a wristband with an ultrasound sticker that is the size of a smartwatch, and added onboard electronics that are about as small as a cellphone. They attached the wristband to a volunteer’s wrist and confirmed that the device produced clear and continuous images of the wrist as the volunteer moved their fingers in various gestures.

The challenge then was to relate the black and white ultrasound images of the wrist to specific positions of the hand. As it turns out, the fingers and thumb are capable of 22 degrees of freedom, or different ways of extending or angling. The researchers found that they could identify specific regions in their ultrasound images of the wrist that correlate to each of these 22 degrees of freedom. For instance, changes in one region relate to thumb extension, while changes in another region correlate with movements of the index finger.

To establish these connections, a volunteer wearing the wristband would move their hand in various positions while the researchers recorded the gestures with multiple cameras surrounding the volunteer. By matching changes in certain regions of the ultrasound images with hand positions recorded by the cameras, the team could label wrist image regions with the corresponding degree of freedom in the hand. But to do this translation continuously, and in real-time, would be an impossible task for humans.

So, the team turned to artificial intelligence. They used an AI algorithm that can be trained to recognize image patterns and correlate them with specific labels and, in this case, the hand’s various degrees of freedom. The researchers trained the algorithm with ultrasound images that they meticulously labeled, annotating the image regions associated with a specific degree of freedom. They tested the algorithm on a new set of ultrasound images and found it correctly predicted the corresponding hand gestures.

Once the researchers successfully paired the AI algorithm with the wristband, they tested the device on more volunteers. For the new study, eight volunteers with different hand and wrist sizes wore the wristband while they formed various hand gestures and grasps, including making the signs for all 26 letters in American Sign Language. They also held objects such as a tennis ball, a plastic bottle, a pair of scissors, and a pencil. In each case, the wristband precisely tracked and predicted the position of the hand.

To demonstrate potential applications, the team developed a simple computer program that they wirelessly paired with the wristband. As a wearer went through the motions of pinching and grasping, the gestures corresponded to zooming in and out on an object on the computer screen, and virtually moving and manipulating it in a smooth and continuous fashion.

The researchers also tested the wristband as a wireless controller of a simple commercial robotic hand. While wearing the wristband, a volunteer went through the motions of playing a keyboard. The robot in turn mimicked the motions in real-time to play a simple tune on a piano. The same robot was also able to mimic a person’s finger taps to play a desktop basketball game.

Zhao is planning to further miniaturize the wristband’s hardware, as well as train the AI software on many more gestures and movements from volunteers with wider ranging hand sizes and shapes. Ultimately, the team is building toward a wearable hand tracker that can be worn by anyone, to wirelessly manipulate humanoid robots or virtual objects with high dexterity.

“We believe this is the most advanced way to track dexterous hand motion, through wearable imaging of the wrist,” Zhao says. “We think these wearable ultrasound bands can provide intuitive and versatile controls for virtual reality and robotic hands.”

This research was supported, in part, by MIT, the U.S. National Institutes of Health, the U.S. National Science Foundation, the U.S. Department of Defense, and Singapore National Research Foundation through the Singapore-MIT Alliance for Research and Technology.

Artificial intelligence holds promise for helping doctors diagnose patients and personalize treatment options. However, an international group of scientists led by MIT cautions that AI systems, as currently designed, carry the risk of steering doctors in the wrong direction because they may overconfidently make incorrect decisions.

One way to prevent these mistakes is to program AI systems to be more “humble,” according to the researchers. Such systems would reveal when they are not confident in their diagnoses or recommendations and would encourage users to gather additional information when the diagnosis is uncertain.

“We’re now using AI as an oracle, but we can use AI as a coach. We could use AI as a true co-pilot. That would not only increase our ability to retrieve information but increase our agency to be able to connect the dots,” says Leo Anthony Celi, a senior research scientist at MIT’s Institute for Medical Engineering and Science, a physician at Beth Israel Deaconess Medical Center, and an associate professor at Harvard Medical School.

Celi and his colleagues have created a framework that they say can guide AI developers in designing systems that display curiosity and humility. This new approach could allow doctors and AI systems to work as partners, the researchers say, and help prevent AI from exerting too much influence over doctors’ decisions.

Celi is the senior author of the study, which appears today in BMJ Health and Care Informatics. The paper’s lead author is Sebastián Andrés Cajas Ordoñez, a researcher at MIT Critical Data, a global consortium led by the Laboratory for Computational Physiology within the MIT Institute for Medical Engineering and Science.

Instilling human values

Overconfident AI systems can lead to errors in medical settings, according to the MIT team. Previous studies have found that ICU physicians defer to AI systems that they perceive as reliable even when their own intuition goes against the AI suggestion. Physicians and patients alike are more likely to accept incorrect AI recommendations when they are perceived as authoritative.

In place of systems that offer overconfident but potentially incorrect advice, health care facilities should have access to AI systems that work more collaboratively with clinicians, the researchers say.

“We are trying to include humans in these human-AI systems, so that we are facilitating humans to collectively reflect and reimagine, instead of having isolated AI agents that do everything. We want humans to become more creative through the usage of AI,” Cajas Ordoñez says.

To create such a system, the consortium designed a framework that includes several computational modules that can be incorporated into existing AI systems. The first of these modules requires an AI model to evaluate its own certainty when making diagnostic predictions. Developed by consortium members Janan Arslan and Kurt Benke of the University of Melbourne, the Epistemic Virtue Score acts as a self-awareness check, ensuring the system’s confidence is appropriately tempered by the inherent uncertainty and complexity of each clinical scenario.

With that self-awareness in place, the model can tailor its response to the situation. If the system detects that its confidence exceeds what the available evidence supports, it can pause and flag the mismatch, requesting specific tests or history that would resolve the uncertainty, or recommending specialist consultation. The goal is an AI that not only provides answers but also signals when those answers should be treated with caution.

“It’s like having a co-pilot that would tell you that you need to seek a fresh pair of eyes to be able to understand this complex patient better,” Celi says.

Celi and his colleagues have previously developed large-scale databases that can be used to train AI systems, including the Medical Information Mart for Intensive Care (MIMIC) database from Beth Israel Deaconess Medical Center. His team is now working on implementing the new framework into AI systems based on MIMIC and introducing it to clinicians in the Beth Israel Lahey Health system.

This approach could also be implemented in AI systems that are used to analyze X-ray images or to determine the best treatment options for patients in the emergency room, among others, the researchers say.

Toward more inclusive AI

This study is part of a larger effort by Celi and his colleagues to create AI systems that are designed by and for the people who are ultimately going to be most impacted by these tools. Many AI models, such as MIMIC, are trained on publicly available data from the United States, which can lead to the introduction of biases toward a certain way of thinking about medical issues, and exclusion of others.

Bringing in more viewpoints is critical to overcoming these potential biases, says Celi, emphasizing that each member of the global consortium brings a distinct perspective to a broader, collective understanding.

Another problem with existing AI systems used for diagnostics is that they are usually trained on electronic health records, which weren’t originally intended for that purpose. This means that the data lack much of the context that would be useful in making diagnoses and treatment recommendations. Additionally, many patients never get included in those datasets because of lack of access, such as people who live in rural areas.

At data workshops hosted by MIT Critical Data, groups of data scientists, health care professionals, social scientists, patients, and others work together on designing new AI systems. Before beginning, everyone is prompted to think about whether the data they’re using captures all the drivers of whatever they aim to predict, ensuring they don’t inadvertently encode existing structural inequities into their models.

“We make them question the dataset. Are they confident about their training data and validation data? Do they think that there are patients that were excluded, unintentionally or intentionally, and how will that affect the model itself?” he says. “Of course, we cannot stop or even delay the development of AI, not just in health care, but in every sector. But, we must be more deliberate and thoughtful in how we do this.”

The research was funded by the Boston-Korea Innovative Research Project through the Korea Health Industry Development Institute.

The sense of support and community was palpable when Sojun Park, a postdoc at the MIT Center for International Studies (CIS), delivered a recent presentation on The Global Diffusion of AI Technologies and Its Political Drivers. The event, part of the CIS Global Research and Policy Seminar, filled the venue with audience members from across MIT. 

“My work is directly connected to what CIS faculty have previously done on international trade and security,” Park said afterwards. “If I hadn’t received a postdoctoral fellowship and come to MIT, I wouldn’t have been able to think through the security implications of my intellectual property research. I’ve been tremendously motivated by these scholars.”

Park’s time at CIS has been both grounding and transformative, offering him a scholarly home that has shaped his research and helped broaden his intellectual horizons.

Pursuing interdisciplinary research and connections 

Before pursuing a tenure-track position, Park set his sights on conducting research at MIT. When he came across a public posting about the CIS Postdoctoral Associate Program, he took a chance and applied.

“My own research is interdisciplinary, and I knew that I could really benefit from the interdisciplinary environment at MIT, and specifically at CIS, where faculty are coming not only from political science, but also affiliated with the Department of Economics and MIT Sloan [School of Management],” he says.

Park was thrilled to receive the paid fellowship, which offers an academic year at MIT and dedicated office space at CIS. At MIT, he is free to use his time toward his own research, and has found value in pursuing topics that are of interest to the CIS community — whether it’s AI or global governance. He’s published prolifically along the way, including two articles in the Review of International Organizations and the Review of International Political Economy.

He’s also continued to work on his forthcoming book, “From Privilege to Prosperity: Knowledge Diffusion and the Global Governance of Intellectual Property,” which examines how technologies can be transferred legitimately across borders. “By ‘legitimately,’ I am asking under what circumstances would firms volunteer to share their technologies? I’m interested in institutions and institutional environments that allow large businesses to share their technologies with smaller businesses based in the development world that may not possess the ability to come up with their own technologies,” he explains.

During the spring 2026 semester, he is collaborating with the center’s Undergraduate Fellows Program. This program enables postdocs to work on their research projects with MIT undergraduates. Park is working with two CIS undergraduate fellows to develop a new dataset examining international trade in green technologies. This opportunity reconnects Park to his early academic experiences in South Korea that set him on the path to MIT.

Path to MIT

“Students in South Korea are trained to be problem-solvers,” explains Park, who was born and raised in Seoul. The country’s rigorous college entrance exams reward those who can answer the most questions quickly and accurately in a limited amount of time.

While taking a test in high school, Park stumbled over a question that he couldn’t answer, regardless of how much time he spent concentrating on it. He handed in the exam, but took the problem home and spent hours puzzling over it — he just couldn’t let it go. “In hindsight, I see this as the moment I decided that I wanted to become a scholar,” Park says.

While majoring in international studies and economics (statistics) at Korea University, he had the opportunity to participate in a semester-long exchange program at the University of Texas at Austin. There, Park enrolled in a political science course on game theory that explored how individual state actors’ decisions influenced one another’s choices and outcomes in trade, conflict, and diplomacy. The instructor used the ongoing war between North and South Korea as a case study, demonstrating the unique circumstances for escalation or de-escalation depending upon how the key actors made choices along the way.

“I saw for the first time how quantitative methods could be applied to international relations and political economy,” Park says — and he knew that his next step was going to be graduate work in the United States. He began a joint MA and PhD program in political science at Princeton University the following year, supported by a Fulbright Fellowship.

Park’s 2025 dissertation examined the global governance of intellectual property rights — and it was timely. He began his PhD program in 2018, “the point at which the U.S. and China trade war had just begun.” During the pandemic, he was moved by the ongoing debates regarding vaccine inequality. “I realized then that intellectual property was at the center of these global economic challenges.” With little political science research on the topic, he “set out to create a systemic framework” to study it.

Simultaneously, he served as a teaching assistant in undergraduate courses in statistical analysis and realized that he deeply enjoyed the experience of teaching and interacting with students. It was a very different experience from his own college years. 

“In South Korea, it’s common for the learning environment to be one in which the professor just delivers lectures, but I found that in the United States’ higher education system, the classroom is truly interactive. I learned something from each of my students.” Soon, Park was certain that he not only wanted to build a career in academic research, but also a future that heavily incorporated teaching and mentoring students.

Before graduating, he spent a year at Georgetown University as a predoctoral fellow affiliated with the Mortara Center for International Studies. This experience enabled him to explore the policy implications of his research and engage with policymakers in Washington — skills he will draw on in his new position.

Lasting lessons from CIS

Park recently accepted a position as assistant professor at the National University of Singapore. Beginning fall 2026, he will be teaching graduate students affiliated with the school of public policy — most of whom will have career experience as practitioners in the public or private sectors. 

He’ll take many lessons from MIT to his new academic home, he says. “Based on what I learned in the United States, I’ll make the learning environment in the graduate courses I teach much more interactive and collaborative.”

At CIS, Mihaela Papa, director of research and principal research scientist, and Evan Lieberman, the center’s director and professor of political science, connected Park to associated faculty whose research interests were related with his own. “Meeting with all of these scholars whose research relates in some way to intellectual property rights made me think about how my own interests can expand to other topics,” Park explains. 

But the biggest takeaway of all is that he learned how to share his own research with scholars who study unfamiliar topics, to exchange ideas and discover commonality. “I’ll never stop using the communication skills that I got here at MIT,” Park says.

From enhancing international business logistics to freeing up more hospital beds to helping farmers, MIT Professor Dimitris Bertsimas SM ’87, PhD ’88 summarized how his work in operations research has helped drive real-world improvements, while delivering the 54th annual James R. Killian Faculty Achievement Award Lecture at MIT on Thursday, March 19.

Bertsimas also described how artificial intelligence is now being used in some of his scholarly projects and as a tool in MIT Open Learning efforts, which he currently directs — another facet of a highly productive and lauded career over four decades at the Institute. The Killian Award is the highest prize MIT gives its faculty.

“I have tried to improve the human condition,” Bertsimas said, summarizing the breadth of his work and the many applications to everyday living that he has found for it.

At MIT, Bertsimas is the vice provost for open learning, associate dean for online education and artificial intelligence, Boeing Leaders for Global Operations Professor of Management, and professor of operations research in the MIT Sloan School of Management. He also served as the inaugural faculty director of the master of business analytics program at MIT Sloan, and has held the position of associate dean of business analytics.

Bertsimas’ remarks encompassed both his past insights and his ongoing studies, as well as his current efforts to add AI to his research. Describing the concept of “robust optimization,” a highly influential approach that Bertsimas helped develop in the early 2000s, he explained how it has enabled, for instance, more reliable shipping through the Panama Canal. Other approaches to optimization aimed at getting more vessels through the canal every day — up to 48 — but would encounter significant problems at times. Bertsimas’ approach identified that 45 vessels a day was better — a slightly lower number, but one that “was always feasible,” he noted.

Over time, Bertsimas’ work has helped structure all kinds of solutions in business logistics; it has even been used for the allocation of school buses in Boston.

More recently, as Bertsimas explained in the lecture, he and his collaborators have been working with Hartford HealthCare in Connecticut on a wide range of issues, and are increasingly incorporating AI into the development of tools for diagnostics, among other things. On the optimization front, their research has suggested ways to reduce the average stay of a hospital patient, from 5.38 days to 4.93 days. In the main Hartford hospital they have studied, given the number of existing beds, that reduction has enabled more than 5,000 additional patient stays per year.

“It’s a very different ballgame,” Bertsimas said.

Bertsimas delivered his lecture, titled “Algorithms for Life: AI and Operations Research Transforming Healthcare, Education, and Agriculture,” to an audience of over 300 MIT community members in Huntington Hall (Room 10-250) on campus.

The award was established in 1971 to honor James Killian, whose distinguished career included serving as MIT’s 10th president, from 1948 to 1959, and subsequently as chair of the MIT Corporation, from 1959 to 1971.

“Professor Bertsimas’ scholarly contributions are both extensive and groundbreaking,” said Roger Levy, chair of the MIT faculty and a professor in the Department of Brain and Cognitive Sciences, while making introductory remarks. “He’s one of the rare individuals who has made significant contributions to both intellectual threads in the field of operations research: one, optimization — combinatorial, linear, and nonlinear — and number two, stochastic processes.”

Indeed, Bertsimas’ work has helped develop both better tools for studying and conducting operations, while also having a wide range of applications. As Bertsimas noted in his lecture, the deaths of both of his parents in 2009 helped propel him to start looking at extensively at ways operations research could help health care.

Bertsimas received his BS in electrical engineering and computer science from the National Technical University of Athens in Greece. Moving to MIT for his graduate work, he then earned his MS in operations research and his PhD in applied mathematics and operations research. Bertsimas joined the MIT faculty after receiving his doctorate, and has remained at the Institute ever since.

Bertsimas is also known as an energetic teacher who has been the principal advisor to a remarkable number of PhD students — 106 and counting, at this point.

“It is far and away my favorite activity, to supervise my doctoral students,” Bertsimas said. “It is a privilege, in my opinion, to work with exceptional young people like the ones we have at MIT, in ability and character and aspiration. They actually make me a better scientist, and a better person.”

“MIT is part of my identity,” Bertsimas quipped while noting that he is the only faculty member on campus who has those three letters, in order, in his first name.

In the latter part of the lecture, Bertsimas highlighted work he has been doing as vice provost of open learning at MIT. He has personally developed an large online course based on his own material, “The Analytics Edge.” In his current role, Bertsimas said, he now aspires for MIT to reach a billion learners with online courses, part of his effort to “democratize access to education.”

Bertsimas also demonstrated for the audience some AI tools he and his colleagues are working to bring to online education, including ways of condensing material, and the translation of online material into other languages.

It is just one more chapter in a long and broad-ranging career dedicated to grasping phenomena and developing tools to help us navigate it.

Or as Berstimas noted while summarizing his scholarship at one point in the lecture, “I try to increase the human understanding of how the world works.”