MIT computer scientist Aleksander Madry has dedicated his career to a transformative vision: creating robust machine learning systems that society can trust and rely on.
Madry's groundbreaking research focuses on enhancing artificial intelligence by making it more accurate, efficient, and resilient against errors. Beyond technical advancements, he emphasizes the critical importance of ethical computing as AI becomes increasingly influential across various sectors of our society.
"For society to fully embrace machine learning, we must develop models that people can use safely, reliably, and transparently," explains Madry, a recently tenured professor in MIT's Department of Electrical Engineering and Computer Science.
Interestingly, Madry's journey into machine learning began only recently, after joining MIT in 2015. Despite this relatively short timeframe, his research group has already published several influential papers demonstrating vulnerabilities in existing models and proposing innovative solutions to make them more robust against adversarial attacks.
His ultimate goal extends beyond technical fixes: Madry aims to make AI decision-making processes interpretable to humans, allowing researchers to identify and correct issues. Simultaneously, he strives to enable non-experts to deploy these improved models in real-world applications, from medical diagnostics to autonomous vehicle control.
"It's not just about cracking open the machine-learning black box—I want to understand its inner workings thoroughly and then package it in a way that makes it accessible and usable for everyone," Madry affirms.
The Algorithm Enthusiast's Journey
Born in Wroclaw, Poland, Madry attended the University of Wroclaw in the mid-2000s. Initially interested in computer science and physics, he never envisioned becoming a scientist. His passion for video games led him to enroll in computer science with dreams of programming his own games.
However, everything changed when he joined friends in theoretical computer science classes, particularly algorithm theory. "I discovered my love for deep thinking and problem-solving," recalls Madry, who ultimately pursued dual majors in physics and computer science.
For graduate studies in algorithms, MIT was his first choice. Under the mentorship of Michel X. Goemans and Jonathan A. Kelner, Madry developed algorithms solving numerous longstanding problems in graph theory. His exceptional PhD dissertation earned him the 2011 George M. Sprowls Doctoral Dissertation Award for best MIT doctoral thesis in computer science.
Following his doctorate, Madry spent a year as a postdoc at Microsoft Research New England, then taught for three years at the Swiss Federal Institute of Technology Lausanne. Yet MIT's unique energy drew him back: "The thrilling atmosphere at MIT is part of who I am—it's in my DNA."
Tackling Adversarial Challenges
Shortly after returning to MIT, Madry found himself captivated by the emerging field of machine learning, particularly deep learning—a multi-layered approach to extracting high-level features from raw data. At the time, MIT's campus was alive with groundbreaking innovations in this domain.
This raised a crucial question: Was machine learning merely hype or substantial science? "These systems seemed to work, but nobody truly understood the mechanisms behind their success," Madry notes.
Answering this question launched his group on an extensive experimental journey to understand deep learning's fundamental principles. A significant breakthrough came in 2018 with their influential paper introducing a methodology for making machine-learning models resistant to "adversarial examples"—subtle input modifications imperceptible to humans that cause models to make erroneous predictions.
Madry's research revealed that these adversarial vulnerabilities expose how machine-learning models make decisions based on features misaligned with human classification methods. By altering these features, models can consistently misclassify images without changing elements humans consider meaningful.
These findings have profound implications for critical applications like medical imaging analysis and autonomous vehicle object recognition. "Many assume these models possess superhuman capabilities, but they haven't actually solved the classification problems we intend them to address," Madry explains. "Their vulnerability to adversarial examples clearly demonstrates this limitation—a truly eye-opening discovery."
This insight drives Madry's mission to create more interpretable machine-learning models. His innovative approaches highlight which pixels most influence predictions, enabling researchers to refine models to focus on features more closely aligned with human-identifiable characteristics. Ultimately, this work aims to make AI decisions more human-like—or even "superhuman-like"—in their reliability and accuracy.
To advance this mission, Madry and colleagues established the MIT Center for Deployable Machine Learning, a collaborative initiative within the MIT Quest for Intelligence focused on developing machine-learning tools ready for real-world implementation.
"We need machine learning to evolve beyond a fascinating technology into something dependable enough for critical applications like autonomous vehicles or healthcare systems," Madry emphasizes. "Currently, our understanding remains insufficient for the level of confidence these domains require."
Shaping Education and Policy
Madry views artificial intelligence and decision-making—now one of three new academic units in MIT's Department of Electrical Engineering and Computer Science—as "the computing interface with the greatest potential societal impact."
Consequently, he ensures his students consider computing's human dimensions, including the potential consequences of their innovations. "Students often focus on creating impressive technologies without fully considering their societal implications," Madry observes. "Building something cool isn't sufficient justification—we must ask not whether we can build something, but whether we should."
Madry also actively participates in discussions about laws and policies governing machine learning. These conversations aim to balance the benefits and potential costs of deploying AI technologies throughout society.
"We tend to oscillate between overestimating machine learning's potential as a solution to all problems and underestimating its societal costs," Madry concludes. "To develop machine learning responsibly, we still have much to discover and implement."
