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  • Empowering Entrepreneurs

    Empowering Entrepreneurs
    Feb 10 2026
    A newly unified support system and online community for WPI entrepreneurs is aiming to connect student and faculty inventors to outside advisors who can help turn discoveries into commercial success stories. ACIS—which stands for Advising, Connecting, Innovating, and Supporting—brings together WPI offices that support entrepreneurism among students and faculty members. The initiative also revives the university’s use of StartupTree, an online platform that makes it possible for WPI inventors to connect with alumni, mentors, and potential industry partners. “WPI has a history of supporting entrepreneurs and innovators through multiple offices and programs, and ACIS brings all of that support together under one banner,” says Terry Adams, director of the Office of Technology Innovation and Entrepreneurship. “ACIS is not a new program,” says Ardian Preci, director of innovation and entrepreneurship programs for The Business School. “It’s marshalling WPI’s existing resources to help entrepreneurs, whether they are faculty members or students, launch their business ideas.” Innovation and entrepreneurship at WPI flows from the university’s standing as an R1 institution and a leading science, technology, engineering, and mathematics (STEM) university with an emphasis on solving real-world problems. Undergraduates conduct independent research, often in interdisciplinary teams, as a requirement for graduation. Students also have access to entrepreneurial education through The Business School, which bridges business and technology to prepare future leaders of the tech economy. Faculty-led startups emerge from multiyear research projects supported by corporate, state, federal, and philanthropic funders. During the academic year that ended June 30, 2025, WPI expenditures on research totaled $79.2 million, including university seed grants awarded to faculty members. Student and faculty ideas have led to diverse startups focused on everything from recycling to artificial intelligence (AI). Ascend Elements, a lithium-ion battery materials company, traces its roots to a startup co-founded in 2015 by Yan Wang, the William B. Smith Professor of Mechanical Engineering. Cyvl, a data company powered by artificial intelligence, was founded in 2021 to map public infrastructure after Daniel Pelaez ’20 discovered during a summer job that towns had no good digital technology to map problems such as potholes and broken signs. “Startups launched with technology that traces back to WPI currently employ more than 500 employees and have raised more than $1.7 billion in capital,” Adams says. During an event to launch ACIS in November at WPI’s Innovation Studio, representatives from the newest wave of student and faculty startups briefly pitched their ideas to WPI faculty, students, staff, alumni, and outside advisers. Student startup ideas ranged from Braille language instruction technology to online business tools for contractors. Faculty members pitched startups focused on advanced materials and energy, sustainable construction, and precision manufacturing. The next ACIS event will be held from 5 to 8 p.m. Feb. 11, 2026, at the Innovation Studio. To build the ACIS network of advisors and mentors, organizers are seeking individuals with industry or startup experience, alumni interested in mentoring, experts in STEM fields, and people from the investment industry who are interested in getting an early look at startup opportunities. Those who volunteer will have a chance to give back to WPI, network, and shape new ventures. Preci says the next step for ACIS, as it builds out its network, will be to bring startup advisors and business mentors together with students and faculty members on StartupTree. “The important thing is to connect our student and faculty entrepreneurs with people outside of WPI who can provide expert feedback on ideas and connect our community to the resources that will launch innovations into the marketplace,” Preci says.
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  • The Privacy Risk on Your Wrist

    The Privacy Risk on Your Wrist
    Feb 12 2026
    Sometimes, cybersecurity isn’t about passwords or computer chips or networks. Instead, it may be about what’s on your wrist. New research led by WPI faculty members and students shows that electromagnetic signals from smartwatches that connect to cellular networks can be collected and used to make inferences about a wearer’s behavior, activities, and even health. The research is in its early stages, and the team noted that a system they developed to collect signals worked accurately only when smartwatches were within about 5 inches of collection devices. Yet the demonstration revealed a potential security vulnerability that has received little attention, says Xiaoyan (Sherry) Sun, associate professor in the Department of Computer Science and an author of the research. “People use smartwatches to monitor their heart rates, send text messages and emails, stream music, and so much more,” Sun says. “It is possible to track that activity to build a profile of a wearer, perhaps for targeted advertising or even criminal activities.” The researchers reported that they developed a system, called MagWatch, to probe smartwatches for “side-channel” weaknesses. Side-channel analysis involves collecting information, such as power use, that is inadvertently leaked by a computer system to gain access to secrets. MagWatch includes a small sensor device to capture and process electromagnetic signals from smartwatch hardware, an algorithm to enhance the signals, and artificial intelligence tools to analyze the data. The research did not include Bluetooth-only smartwatches, which emit less electromagnetic information than smartwatches that are continuously connected to cellular networks. The team experimented by placing a collection device under a desk and capturing signals from nearby Android and Apple smartwatches. Tests showed that the system could match signals emitted by smartwatches to apps for music, video, social media, navigation, health, and banking services. In addition, the signals could be matched to activities taking place within apps, such as recording a voice message or texting, says Jun Dai, associate professor in the Department of Computer Science. “In cybersecurity, we talk about social engineering, which involves learning about a person and gaining their trust so they give up sensitive information,” says Dai. “A side-channel attack on a smartwatch could enable a bad actor to collect a lot of behavioral information that could be used to profile and target that person.” In addition to Sun and Dai, the research team consisted of Haowen Xu, a PhD student in Sun’s lab, and PhD student Tianya Zhao and Assistant Professor Xuyu Wang, both of Florida International University. Sun and Dai are cybersecurity researchers whose work has been supported by the National Science Foundation. At WPI, they lead the DRiving Automotive Industry WorkForce Transformation (DRIFT) program, a $2.5 million five-institution initiative funded by the National Centers of Academic Excellence in Cybersecurity, led jointly with Oakland University, and focused on developing workforce training to strengthen cybersecurity in the auto industry. Sun says the next step in smartwatch research may involve examining how a wearer’s movement or environmental electromagnetic signals impact the effectiveness of side-channel attacks. Protective measures might include new regulations on data collection, adding shielding to smartwatches, or deploying technologies that jam signal collectors. “As researchers, we know that cyber threats exist, and that convenient technologies can also be vulnerable to hackers,” Sun says. “We want to raise awareness about how much information is available on people’s everyday devices and that there are ways that cyberattacks can gather information about us. That’s why we study this field and focus our research on cybersecurity.”
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  • Identifying Skin Disease with AI

    Identifying Skin Disease with AI
    Feb 3 2026
    WPI Professor Dmitry Korkin and researchers in Senegal are using a unique type of artificial intelligence to develop a tool that could not only help pathologists in tropical regions diagnose skin diseases, but also show those pathologists how AI makes its decisions. The research involves explainable artificial intelligence, an approach that draws back the curtain on AI to reveal the processes of machine-learning algorithms. The researchers say their tool can analyze skin specimen images to identify pathogens that cause mycetoma, a disease often found in rural parts of Asia, Africa, and Latin America where medical and technical resources may be limited. “AI can feel like a black box holding something that is very difficult to comprehend,” says Korkin, the Harold L. Jurist ’61 and Heather E. Jurist Dean’s Professor of Computer Science. “With explainable AI, we can build a tool that will help diagnose skin diseases and provide down-to-earth explanations about the entire decision-making process.” Known as SINDI, for Skin INfectious Diseases Intelligent framework, the tool evolved from the work of Kpetchehoue Merveille Santi Zinsou, a PhD student who arrived at WPI in 2024 for a year in Korkin’s lab under the Partnership for Skills in Applied Sciences, Engineering and Technology. Since leaving WPI, Zinsou has continued to work on SINDI within the Institute of Research for Development at UMMISCO, a research organization in Dakar, Senegal. Mycetoma causes tumor-like lesions, often on the feet, where breaks in the skin and exposure to contaminated soil or water can provide a pathway for invading pathogens. Farmers, laborers, and people who walk barefoot are especially prone to mycetoma. If not treated, mycetoma can invade deep tissues, cause deformities, and impair the body’s ability to function. Antibiotics or antifungal medications can be used to treat mycetoma, depending on the cause of the infection, but determining the cause is not always easy. Pathologists typically examine tissues and cells under a microscope to identify abnormal structures called “grains” that aid in diagnosis. Sometimes, however, grains are not visible in specimens and additional costly, time-consuming tests are needed “For patients, any delay in diagnosing the cause of mycetoma can delay proper treatment,” Zinsou says. “Tools that speed up diagnosis can help patients get the help they need so they can recover quickly.” To develop SINDI, the researchers started with a dataset of 7,000 healthy tissue images and 1,324 labeled images of tissue infected by fungal and bacterial pathogens known to often cause mycetoma. Then the researchers developed mathematical algorithms to examine the dataset images. The researchers found that the tools learned to successfully identify infected tissues and pinpoint pathogens, even when no grains were visible. “We think that the tool can find complex patterns and details, even beyond the lesion areas, that are too tiny for a human expert to detect,” Korkin says. The next step was to configure SINDI to show clinicians multiple images that would explain how the tool had identified the disease and the pathogen responsible for a patient’s lesions. The research team published their SINDI research on the biology preprint server BioRXiv. In addition to Zinsou and Korkin, authors were Habone Ahmed Mahamoud, Abdou Magib Gaye, and Maodo Ndiaye, all of Cheikh Anta Diop University and National University Hospital of Fann in Senegal; Idy Diop of Cheikh Anta Diop University in Senegal; and Doudou Sow and Cheikh Talibouya Diop, both of the University of Gaston Berger in Senegal. Zinsou says researchers in Senegal are working with pathologists to begin testing SINDI. After collecting feedback from users, the researchers plan to refine the tool, seek approval from Senegal’s Ministry of Health and Social Action, and deploy SINDI in hospitals. “We want to ensure that the tool, which users can access through a computer interface or through a mobile app in the near future, is streamlined as much as possible so it can be easily used by doctors in rural clinics to help patients who need treatment,” Zinsou says.
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