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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteMIT Technology Review announced its 2024 class of 35 Innovators Under 35 on September 10, 2024. The global editorial program recognized early-career researchers, founders, and technology builders working in artificial intelligence, biotechnology, climate and energy, materials science, and robotics. Shawn Shan, then a 26-year-old computer-science PhD student at the University of Chicago, was named Innovator of the Year.
The official package is available at MIT Technology Review’s 2024 Innovators Under 35 page, although access to some profiles may require a subscription or account. Because the accessible source material does not reliably expose every name and profile, this overview does not invent or claim an independently verified complete roster.
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What is the 2024 Innovators Under 35 list?
Innovators Under 35 is MIT Technology Review’s annual recognition program for people whose research, companies, or technical work could influence technology and society. The program began in 1999, making the 2024 edition its 25th year according to institutional coverage of the announcement.
It is a people-focused program, not a ranking of products or technologies. MIT Technology Review’s separate 10 Breakthrough Technologies package focuses primarily on important technologies; Innovators Under 35 focuses on the people developing them.
#1 Best Overall
The 2024 selection should also not be confused with regional editions such as Innovators Under 35 China or Asia Pacific. Those programs can use different categories and contain different honorees.
At a glance
- Announcement: September 10, 2024
- Class size: 35 innovators
- Global categories: artificial intelligence, biotechnology, climate and energy, materials science, and robotics
- Innovator of the Year: Shawn Shan
- Official package: technologyreview.com/innovators-under-35/2024
Shawn Shan was Innovator of the Year
Shawn Shan was recognized for developing tools intended to help artists prevent generative-AI companies from using their online artwork to train models without permission. At the time of the recognition, Shan was a computer-science PhD student at the University of Chicago.
Shan had previously worked on Fawkes, a tool designed to help protect facial images from facial-recognition systems. With his adviser Ben Zhao, he later turned to the problem artists described: artwork can be collected and incorporated into training datasets without a creator’s consent or compensation.
According to the magazine’s September–October 2024 preview coverage, Shan’s work was informed by conversations with and feedback from more than 1,000 artists. The broader significance is not that a technical tool settles copyright law. It does not. Rather, the work represents a defensive response to a fast-moving conflict over consent, artistic labor, data collection, and generative-AI development.
What the tools can—and cannot—do
Anti-scraping or anti-training tools may make artwork harder to collect or less useful for particular machine-learning systems. They should not be treated as an absolute shield. Technical defenses can be bypassed, may not work equally well across models, and do not replace legal remedies, licensing agreements, platform policies, or enforcement.
That distinction matters. Shan’s recognition reflects the importance of creator-control technology in 2024, but it does not establish that every artwork is protected, that unauthorized training has ended, or that the underlying legal questions have been resolved.
Rank #2
The five categories in the 2024 class
Artificial intelligence
The AI category sits at the center of several 2024 technology debates: how models are trained, how systems can become safer and more accountable, and how computing can become more efficient. Shan’s work shows one side of that landscape—tools that give people a technical defense against unwanted use of their data.
More broadly, the category should be read as covering both AI research and practical systems. Selection does not by itself prove that a model is safe, commercially successful, or superior to competing approaches.
Biotechnology
The biotechnology honorees worked on problems including infectious-disease diagnostics, drug development, cellular engineering, and biological tools. Two confirmed examples illustrate the range.
Mireille Kamariza, a UCLA assistant professor of bioengineering, was recognized for diagnostic technologies for infectious pathogens, including tuberculosis and Ebola. UCLA describes her work as emphasizing simpler diagnostic tools for underserved populations and says her research includes tests for nearly two dozen bloodborne pathogens. That figure should be understood as UCLA’s description of the research, not as an independently audited performance claim.
Nabiha Saklayen, CEO and cofounder of Cellino, was recognized as a Pioneer for patented inventions involving cellular laser editing. The technology concerns precise manipulation of cells and could support future work in cell therapy and regenerative medicine. “Could” is important: recognition does not mean that a cellular technology is clinically approved or broadly available.
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This category connects emerging technology with industrial emissions, energy storage, agriculture, and environmental stress. The central question is not simply whether an invention has a climate-related use case, but whether it can scale, compete economically, and produce measured environmental benefits.
Rank #3
A technology designed to reduce emissions is not automatically a verified emissions-reduction solution. Its eventual impact depends on materials, manufacturing, energy sources, deployment conditions, durability, and adoption.
Materials science
Inho Kim, a Caltech postdoctoral researcher and KAIST materials-science graduate, was recognized for inexpensive artificial-muscle fibers intended for wearable assistive devices and future wearable robotics.
Caltech describes the fibers as strong, light, and flexible, with possible uses in wearable assistance. Those descriptions should be attributed to the institution and should not be converted into an unconditional claim that the fibers are the strongest, most efficient, or most humanlike available. The key remaining questions for any such material include durability, manufacturing scale, power requirements, control systems, safety, and cost.
Robotics
The robotics category reflects a shift from machines operating separately from people toward adaptive, wearable, human-centered, and biologically inspired systems. That creates opportunities in rehabilitation, industrial assistance, and mobility, but also introduces practical questions about comfort, reliability, maintenance, safety, and who can afford the technology.
Artificial-muscle research such as Kim’s sits naturally at the intersection of materials science and robotics: the material is only one part of a useful robotic system. A deployable product also needs sensing, control, power, software, and an appropriate human-factors design.
Confirmed examples from the 2024 class
| Honoree | Area | Work recognized |
|---|---|---|
| Shawn Shan | Innovator of the Year; AI-related creator protection | Tools intended to help artists make online artwork less useful for unauthorized generative-AI training and scraping. |
| Inho Kim | Materials science | Artificial-muscle fibers intended for wearable assistive devices and future wearable robotics. |
| Mireille Kamariza | Biotechnology | Diagnostic tools for infectious pathogens, including tuberculosis and Ebola, with an emphasis on accessibility. |
| Nabiha Saklayen | Biotechnology and cellular engineering | Cellino’s patented cellular laser-editing technology for precise cell manipulation. |
The table is a set of verified examples, not a substitute for the official 35-person directory. The accessible material supporting this article does not provide a dependable complete roster with every name, category, affiliation, project description, and individual profile link. It would be misleading to fill those gaps with unsourced listicles or memory.
How MIT Technology Review selects the class
Institutional reporting about the program describes a multi-stage process:
- More than 500 people are nominated in a typical year.
- Editors select approximately 100 semifinalists.
- Judges with expertise in fields such as AI, biotechnology, software, energy, and materials evaluate the work.
- MIT Technology Review’s editors select the final 35.
This is a description of the process reported by Caltech; it should not be mistaken for a published, fully transparent scoring rubric. The program is an editorial recognition, not a peer-reviewed citation ranking, venture-capital performance table, government award, or market-share ranking.
How to evaluate the honorees’ work
The most useful way to read the list is to ask the same questions of every selected project:
- Problem: What specific health, climate, industrial, social, or technical problem is being addressed?
- Novelty: What is technically different about the approach?
- Evidence: Is the claim supported by a research result, prototype, field test, clinical study, or commercial deployment?
- Maturity: Is the work in a laboratory, pilot, trial, open-source release, startup, or established product?
- Beneficiaries: Who gains access, and who might be excluded?
- Remaining barriers: What must improve before the work can scale safely and affordably?
This framework prevents a common mistake: treating 35 promising innovators as if they all have equally mature technologies. Some selected projects may be deployed products or companies; others may remain research prototypes or defensive tools.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Global list versus regional editions
The 2024 global list is distinct from regional programs. For example, the China edition uses categories including entrepreneurs, humanitarians, pioneers, visionaries, and inventors. An Asia-Pacific announcement can likewise refer to a regional recognition rather than the global MIT Technology Review class.
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When checking a name, verify the edition, year, geography, category, and official profile. Similar branding does not mean the lists are interchangeable.
Best Value
What selection does—and does not—prove
Selection means MIT Technology Review judged the person’s work notable and promising. It does not independently establish clinical efficacy, commercial viability, safety, scalability, patent enforceability, measured environmental benefit, or superiority over competing technologies.
It also does not guarantee that an honoree’s work will succeed commercially. The practical value of the recognition is better understood as visibility and editorial endorsement at an early or important stage—not as a prediction that every project will transform its field.
Where to read more
- Official 2024 Innovators Under 35 package
- MIT Technology Review announcement and Shawn Shan recognition
- Caltech coverage of Inho Kim and the selection process
- UCLA coverage of Mireille Kamariza and the five categories
- MIT Technology Review event profile for Nabiha Saklayen
Frequently Asked Questions
When was the 2024 Innovators Under 35 class announced?
MIT Technology Review announced the global 2024 class on September 10, 2024.
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How many people were selected?
The class contains 35 innovators.
What were the 2024 categories?
Artificial intelligence, biotechnology, climate and energy, materials science, and robotics.
Was this a global or regional list?
The article covers MIT Technology Review’s global 2024 program, not the separate China or Asia-Pacific regional editions.
Does selection guarantee commercial success?
No. It is an editorial recognition of promising work, not proof of commercial viability, safety, clinical effectiveness, or future market success.
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