![\"illustration](\"https:\/\/engineering.jhu.edu\/magazine\/wp-content\/uploads\/2026\/06\/Dredze-255x300.jpg\")
<\/figure>\n<\/div>\n\n\nUniversities are facing a critical question: How can they harness AI responsibly to advance knowledge across every field? Doing so will demand more than technical know-how. It will require leaders who can transcend disciplinary boundaries\u2014researchers who can connect computer scientists with clinicians, engineers with humanists, policymakers with data experts\u2014turning technological advances into collaborative research programs that solve real-world problems.<\/p>\n\n\n\n
At Johns Hopkins, that\u2019s a responsibility that\u2019s fallen to Dredze.<\/p>\n\n\n\n
Last October, after an extensive international search, Dredze was appointed director of the Data Science and AI Institute<\/a>, a university-wide initiative housed in the School of Engineering. The institute, which was launched in 2023 and brings together more than 150 faculty members across disciplines, aims to coordinate AI research across Johns Hopkins divisions and to accelerate interdisciplinary partnerships at a moment when the technology is advancing at breathtaking pace.<\/p>\n\n\n\n \u201cI don\u2019t say this lightly,\u201d says Dredze, who is a specialist in natural language processing and public-interest AI. \u201cThese tools have reached a point where they can fundamentally change the way research is done.\u201d<\/p>\n\n\n\n Dredze\u2019s new role is to guide the university\u2019s efforts at the foreground of the AI revolution. But long before AI dominated the headlines, Dredze was helping lay the foundations for that revolution, integrating computational methods with public health and medicine, showing how big data can shape critical decisions.<\/p>\n\n\n\n He\u2019s harnessed social media platforms to track disease and developed tools to analyze online misinformation. He\u2019s pioneered techniques for identifying racial bias in the way doctors interpret what their patients tell them and used AI to track online tobacco discourse, arming public health leaders with real-time intelligence. He\u2019s also advised congressional staff on AI. And although Dredze is a computer scientist by training, his breakthroughs are only possible by way of an unusually deep engagement with a multitude of domains.<\/p>\n\n\n\n What sets Mark apart,\u201d says Nanyun (Violet) Peng, Engr \u201917 (PhD), Dredze\u2019s former graduate student and now associate professor of computer science at the University of California, Los Angeles, \u201cis his curiosity about the human processes that create data: how a journalist plans a story or how a patient describes a symptom. Because Mark combines a domain curiosity with deep technical insight into natural language processing and machine learning, he can ask questions that others miss.\u201d <\/p>\n\n\n It\u2019s an instinct that took root early. <\/p>\n\n\n\n Between earning dual bachelor\u2019s degrees in computer science and engineering at Northwestern University and completing his PhD in computer science at the University of Pennsylvania, Dredze took an unlikely sidetrack: a master\u2019s degree in Jewish studies, where he focused on textual analysis and historical interpretation\u2014an early glimpse of the boundary-defying scholar he would later become. <\/p>\n\n\n\n \u201cIt was always meant to be a detour,\u201d Dredze says. And while he remained set on computer science, immersing himself in a humanistic discipline sharpened his interest in language and the ways meaning is constructed\u2014questions that would later animate his work in natural language processing. <\/p>\n\n\n\n During his doctoral studies, Dredze was torn between going into industry or academia. He took roles with IBM, Microsoft, and Google. But then he realized a university career would allow him more freedom to follow his diverse interests.<\/p>\n\n\n\n \u201cOne of the things I really love about an academic career is that there\u2019s space to get interested in different things and see where it takes you, without being too worried about the destination,\u201d he says. \u201cYou can look at a problem and think, \u2018This is interesting,\u2019 and maybe it takes you somewhere completely different than you expected. Maybe it\u2019s a dead end. Maybe it circles back. But the university gives you room to explore.\u201d<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n \u201cI don\u2019t say this lightly. These tools have reached a point where they can fundamentally change the way research is done.\u201d\u2014 Mark Dredze<\/p>\n<\/blockquote>\n\n\n\n Arriving at the Whiting School of Engineering in 2009 as an assistant research professor, Dredze had never worked on anything health-related. His training was in speech and language processing, building systems to analyze text and model how people write and speak. But within two years, he\u2019d pioneered an entirely new subfield of public health: social media health informatics. <\/p>\n\n\n\n So how does a scholar trained to parse language start tracking disease? <\/p>\n\n\n While Dredze continued his work teaching AI systems to interpret spoken language when he began at Johns Hopkins, he was also scanning the horizon for larger questions. Rather than rushing into a new specialty, he chose to spend his first years simply listening. <\/p>\n\n\n\n \u201cI said, \u2018Okay, well, this is great\u2014but where are the other interesting, smart people at Hopkins? And everyone said, \u2018Well, you really should go to East Baltimore and talk to people in public health and medicine,\u2019\u201d Dredze recalls. \u201cAnd so that\u2019s what I did.\u201d <\/p>\n\n\n\n He discovered that epidemiologists and clinicians at the university\u2019s medical campus and the Bloomberg School of Public Health<\/a> were grappling with a persistent challenge: how to track infectious diseases such as influenza quickly and accurately. Traditional surveillance systems depended on hospital reports and laboratory testing. They were rigorous\u2014but they were also slow, with outbreaks often outpacing the official data. <\/p>\n\n\n\n At the same time, Twitter was taking off, with millions suddenly narrating their daily routines in real time. Alongside people\u2019s posts about their breakfast or their travel plans were updates about their fevers, sore throats, and aches and pains. Dredze, who had already been studying the social media platform as a source of large-scale language data, wondered whether that volume of data could be made useful for public health officials. <\/p>\n\n\n\n It looked promising at first sight. But turning those posts\u2014like \u201cI gots da flu\u201d or \u201csick with this this flu it\u2019s taking over my body ughhhhh\u201d\u2014into public health insights required more than keyword searches. In a project that came to be known as You Are What You Tweet<\/a><\/em>, Dredze and his students developed models capable of distinguishing genuine reports of infection from expressions of worry or general discussion. <\/p>\n\n\n\n A sudden rise in tweets mentioning \u201cflu\u201d could signal an uptick in cases, Dredze explains, or just heightened anxiety following a news story. \u201cSpotting that distinction came from Mark\u2019s ability to think like an epidemiologist while solving problems like a natural language-processing researcher,\u201d says Peng. \u201cHe realized that to track the disease, we first had to decode the way people talk about the disease.\u201d<\/p>\n\n\n\n The work began modestly. But it quickly gathered momentum. NPR and The Washington Post <\/em>covered the research, with subsequent research teams citing it heavily. The Centers for Disease Control and Prevention began taking notice. Colleagues in medicine and public health started reaching out about using social media platforms to study vaccine hesitancy, mental health, and emerging outbreaks. Within a few years, a new field had firmly taken root, with Dredze having helped establish what is now known as social media health informatics. But he would soon confront a more sinister dimension of the data he\u2019d learned to decode. <\/p>\n\n\n\n By 2018, a different kind of outbreak was commanding attention. <\/p>\n\n\n\n Intelligence agencies had concluded that foreign actors had infiltrated American social media, with Twitter disclosing 10 million tweets that had been posted by Russia\u2019s Internet Research Agency. <\/p>\n\n\n\n Dredze, who at that point had been collecting and archiving Twitter data to study public health for years, quickly understood the stakes. He was in an ideal position to ask what role health might be playing in these ominous campaigns. <\/p>\n\n\n\n He and his collaborators sifted through the posts and began to decode the agenda. Their expectation was that Russian-linked accounts would be pushing anti-vaccine propaganda\u2014but they soon realized that the data was telling a more complicated story. Some accounts did criticize vaccination. Unexpectedly, though, some accounts seemed to promote it. Instead of pushing a single agenda, the bots seemed to be amplifying the most inflammatory voices on both sides. <\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n <\/p>\n\n\n\n \u201cThe trolls seemed to be using vaccination as a wedge issue, promoting discord in American society,\u201d Dredze says. <\/p>\n\n\n\n Thanks to his previous work, Dredze was able to move quickly. \u201cWhen someone identified this information need, we were in a position to fulfill it,\u201d Dredze says, about a project that ended up informing journalists, lawmakers, and public health officials about the nature of an unfolding threat. <\/p>\n\n\n\n \u201cMark possesses a unique radar for emerging data sources,\u201d says David Broniatowski, a professor of engineering management and systems engineering at The George Washington University, who collaborated on the project. \u201cBy the time the rest of the field caught up to the problem, Mark had already built the infrastructure to answer it.\u201d <\/p>\n\n\n\n The project was timely, but it was also prescient. Since 2018, the misinformation ecosystem has grown more complex, and distrust in science runs deeper. Dredze was one of the first researchers to show how digital language could shape public health in ways few had previously anticipated. \u201cMark can sense the \u2018pulse\u2019 of a field, identifying critical problems before they hit the mainstream radar,\u201d says Broniatowski. And then Dredze began considering other ways written language can shape human health. If digital language could influence our health through our smartphones, he wondered, what might it be doing inside the clinic? <\/p>\n\n\n\n By the late 2010s, electronic health records had become near universal across major American health systems. Inside all those histories, assessments, and discharge summaries lay patterns\u2014trends that had the potential to shed light on treatment decisions and even uncover unexpected therapeutic benefits of drugs. The challenge was in finding a way to sift through the overwhelming volume of data and sniff out what was useful. <\/p>\n\n\n\n \u201cMedical decisions today, big and small, are all driven by evidence,\u201d Dredze says. \u201cWe need data to make these kinds of decisions. We\u2019ve been collecting data in the medical system for a long time that may contain the answers to many questions. But it\u2019s really hard to analyze because the data is so large. It\u2019s at scale. It\u2019s diverse.\u201d<\/p>\n\n\n\n Mary Catherine Beach, BSPH \u201999 (MPH), professor of medicine at the Johns Hopkins University School of Medicine<\/a>, had been thinking about one potentially concerning facet of that vast archive: the way physicians write about their patients. Some details in a medical record are necessarily difficult\u2014a history of substance use, say, or missed appointments. But other phrases, she says, carry judgments that may not be clinically essential and could end up informing the care a patient receives. <\/p>\n\n\n\n \u201cWe can bring our perspective, he can bring his perspective, and together we can create something that neither one of us could have done without the other.\u201d \u2014 Mary Catherine Beach<\/p>\n<\/blockquote>\n\n\n\n \u201cWe were looking at what we now call stigmatizing language in patient records,\u201d explains Beach, who has been collaborating with Dredze and other researchers on a long-term project to use clinical records to study bias since around 2020. \u201cSometimes there might be details that are important to record. But then there\u2019s stuff that is somewhat gratuitous, that clinicians will write, maybe out of frustration. That comes across to the people reading it.\u201d <\/p>\n\n\n\n Much of that language operates indirectly. A physician may not write that they doubt a patient\u2019s pain. Instead, they might say the patient \u201cclaims\u201d to have \u201c10 out of 10 pain,\u201d or describe someone as a \u201cpoor historian.\u201d It\u2019s a subtle signal\u2014but to clinicians it\u2019s as clear as day. <\/p>\n\n\n\n Beach and Dredze began discussing whether natural language processing could detect these patterns\u2014which meant Dredze had to learn to speak doctor. <\/p>\n\n\n\n Computer scientists and physicians often don\u2019t speak the same language, and bridging the gap required practice. \u201cI\u2019m not a medical doctor,\u201d says Dredze, who compares the process\u2014which gave him the fluency to identify where his tools can meet clinical needs and when to defer to the domain experts\u2014 to learning French. \u201cBecoming bilingual in disciplines is the same as becoming bilingual in language\u2014you just have to practice,\u201d he says. \u201cIf you want to learn how to speak French, you need to take some French lessons, but also, you just need to talk to people in French a lot, all the time.\u201d <\/p>\n\n\n\n![\"illustration](\"https:\/\/engineering.jhu.edu\/magazine\/wp-content\/uploads\/2026\/06\/JHE_Spring26_Falls_turn-1-copy.jpg\")
<\/figure>\n<\/div><\/div>\n\n\n\nRoom To Explore <\/h3>\n\n\n\n
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![\"illustration](\"https:\/\/engineering.jhu.edu\/magazine\/wp-content\/uploads\/2026\/06\/Twitter-254x300.jpg\")
<\/figure>\n<\/div>\n\n\nDecoding Social Media<\/h3>\n\n\n\n
Sensing \u2018The Pulse\u2019 <\/h3>\n\n\n\n
![\"Illustration](\"https:\/\/engineering.jhu.edu\/magazine\/wp-content\/uploads\/2026\/06\/JHE_Spring26_Falls_turn-2-copy.jpg\")
<\/figure>\n<\/div><\/div>\n<\/div>\n\n\n\nBecoming Bilingual<\/h3>\n\n\n\n
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