The American Institute for Medical and Biological Engineering (AIMBE) has inducted Ioannis (Yannis) Paschalidis into its 2026 College of Fellows . This peer-elected membership is among the highest professional distinctions in the field, honoring the top two percent of medical and biological engineers whose contributions have had a transformative impact on healthcare and medicine.
Paschalidis is a distinguished professor of engineering (electrical & computer engineering, systems engineering, and biomedical engineering), a founding professor of the Faculty of Computing & Data Sciences, and a professor of biostatistics in the School of Public Health at Boston University. He is also director of the Rafik B. Hariri Institute for Computing and Computational Science & Engineering , the university’s largest research hub, which fosters multidisciplinary computing and AI research across all BU colleges and schools.
Paschalidis was recognized for his "outstanding contributions to artificial intelligence and machine learning methods in computational biology and medicine."
At the core of Paschalidis’s work is a fundamental challenge: designing intelligent systems that remain reliable under uncertainty. His research integrates optimization, control, and stochastic modeling with machine learning to develop methods that remain robust to the uncertainty, incompleteness, and noise inherent in medical data—while maintaining interpretability for real-world use. This mission is powered by convergent research, a strategic approach that merges disparate fields—from data science to clinical practice—into a single, unified framework to solve complex societal problems.
“Yannis’s work reflects the strength of convergent research at Boston University, synthesizing electrical and biomedical engineering, medicine, computer science, and artificial intelligence approaches to create transformational impact on medicine, says Kenneth Lutchen , vice president and associate provost for research at Boston University . “His work demonstrates how interdisciplinary and convergent collaboration can translate discovery into meaningful advances in healthcare."
This sentiment is echoed by leadership within the engineering community, who see Paschalidis as a bridge between technical innovation and human health.
"Yannis Paschalidis’s induction into the AIMBE College of Fellows is a powerful recognition of the kind of bold, convergent research that is most needed today,” says Elise Morgan , Dean of the College of Engineering and Maysarah K. Sukkar Professor of Engineering Design and Innovation (mechanical engineering, materials science & engineering, biomedical engineering). “His ability to bring together artificial intelligence, systems thinking, and biomedical insight is not only advancing engineering research, but reshaping how we approach some of the most complex challenges in healthcare."
A Systems Approach to AI in Medicine
Across domains, Paschalidis’s work is unified by a systems-level perspective. In computational biology, his work on protein–protein docking applied optimization techniques to predict molecular interactions, contributing to the mathematical foundations of drug discovery. Optimization was also at play in designing microbial communities with engineered metabolic division of labor . In clinical settings, his research using electronic health records (EHRs) has shown that longitudinal patient data contains early warning signals for disease , enabling prediction of major health events well before they occur.
At a time when privacy regulations limited data sharing, he introduced a framework for federated learning across distributed EHR systems. This approach demonstrated that collaborative machine learning was possible without moving sensitive data—establishing a new paradigm for secure, large-scale healthcare analytics.
His work in cardiovascular medicine further illustrates this shift. By applying supervised learning to longitudinal EHR data, his models were able to predict heart-related hospitalizations nearly a year in advance . These insights allow for earlier intervention, supporting a broader transition from reactive treatment to proactive care.
More recently, Paschalidis and his group developed a new robust machine learning framework , motivated primarily by biomedical applications. He has extended these approaches to neurodegenerative disease. His research developed an AI-driven approach to analyze speech patterns and identify early markers of cognitive decline. These models demonstrated high efficacy in predicting progression from mild cognitive impairment to Alzheimer’s disease years before clinical diagnosis . By relying on accessible, non-invasive data, this work points toward scalable and cost-effective screening approaches for Alzheimer’s disease and related dementias.
A defining feature across these efforts is the integration of diverse data sources. By combining clinical, demographic, and behavioral signals—including digital biomarkers such as speech—his work captures dimensions of disease that single-modality approaches may miss.
From Models to Infrastructure: The BEACON Platform
These methodological advances—robust optimization, multimodal learning, and interpretable AI—are now being operationalized at scale through BEACON , an AI-driven platform for global infectious disease surveillance operated in partnership with BU’s Center on Emerging Infectious Diseases (CEID) and Boston Children’s Hospital. The BEACON platform continuously ingests and analyzes heterogeneous data streams, leveraging AI to filter, evaluate, and prioritize signals of emerging health threats. Its architecture reflects a core principle of integrating automated intelligence with expert validation and decision making.
“The goal is not to replace human expertise, but to extend it,” Paschalidis says. “BEACON is designed to bring together diverse data and rigorous models to support faster, more informed decisions.”
In doing so, BEACON represents a shift from standalone predictive models to integrated, open-access real-time systems for population-level decision-making—addressing a longstanding need for shared, transparent public health infrastructure.
Leadership and Impact
Beyond his scientific achievements, Paschalidis has helped shape Boston University’s broader research ecosystem at the intersection of engineering, computing, and health. In addition to his leadership of the Hariri Institute, he co-led Boston University’s task force on AI in Research and Education, was a member of the Task Force on Convergent Research and Education, is a Director of Academics for the AI Development Accelerator (AIDA) and serves on the advisory board of the Center for Health Data Science . He previously served as director of the Center for Information and Systems Engineering (CISE) and remains a faculty affiliate.
With over 10,000 citations and an h-index of 56, Paschalidis’s induction into the AIMBE College of Fellows highlights a career dedicated to creating trustworthy AI systems capable of addressing the world's most complex health challenges. He was formally inducted on April 13, 2026, during the AIMBE Annual Event in Arlington, Virginia, joining a distinguished body of AIMBE peers that includes four Nobel Prize laureates and 27 Presidential Medal of Science and Technology and Innovation awardees, as well as more than 400 fellows previously inducted into the National Academies of Engineering, Medicine, and Science.