As the field of bioengineering continues to evolve, researchers require innovative tools to optimize experimental design, analyze large datasets, characterize complex biological systems, and identify knowledge gaps. Over the past few years, AI and machine learning have emerged as transformative tools poised to revolutionize bioengineering research and these models are driving advancements in drug discovery and design, nanoparticle formulation optimization, biomaterials and device development, medical imaging analysis, diagnostic strategies, tissue engineering, and more.

On Monday, May 5, the Johns Hopkins Institute for NanoBioTechnology  hosted its 18^th Nano-Bio Symposium, with this year’s topic being “Transforming Bioengineering Research with AI and Machine Learning.” The symposium explored the immense potential of integrating AI/ML with scientific expertise and human creativity to help propel bioengineering breakthroughs.

“From powering research breakthroughs to transforming industries, AI is now embedded in the fabric of discovery and innovation. We are witnessing the evolution of scientific inquiry—where data, algorithms, and human creativity are accelerating the pace of discovery, engineering design, and impact,” said T.E. Schlesinger, Benjamin T. Rome Dean at Johns Hopkins University’s Whiting School of Engineering.

The event featured two keynote speakers, two invited talks, a panel discussion featuring eight speakers from across Johns Hopkins University and various industries, and student presentations that went along with a poster session in the afternoon.

The first keynote speaker, Dong Shen, received his PhD from the Johns Hopkins School of Medicine in 2010 in human genetics molecular biology. He is the CEO of RNAImmune, Inc., and talked about how AI can help drive development and production of the next generation of mRNA vaccines. Alexandra Sneider, who received her PhD from the Whiting School of Engineering in 2021 in chemical and biomolecular engineering, is now co-founder of Lila Sciences and gave the second keynote talk of the day. Sneider discussed how AI and machine learning are pioneering new technologies and scientific developments quicker than ever before.

The afternoon panel session focused on how AI and machine learning are empowering and working together with bioengineering research, while answering  questions from attendees on the effects of AI in research moving forward.

A networking reception and poster competition followed, which featured research by undergraduate students, graduate students, and postdoctoral fellows across the INBT and Johns Hopkins. With the help of volunteer judges, over 65 people competed for five cash prizes, which were sponsored by the INBT and Tom and Lois Fekete. Tom Fekete, former INBT director of corporate partnerships, and his wife Lois have been generously sponsoring the undergraduate awards since 2019. Fekete was also in attendance for the symposium and awarded the prizes to the winners of the poster competition.

”AI and machine learning are also revolutionizing the way we approach bioengineering research—enabling faster hypothesis generation, smarter experimental design, and deeper insights from complex data,” Schlesinger said. “Generative AI offers researchers tools to model biological systems, simulate molecular behavior, and even design novel biomaterials. These technologies are not replacing researchers they are amplifying human expertise and imagination.”