Danielle Picarello
Danielle Picarello is an Associate Computational Scientist at the Icahn School of Medicine at Mount Sinai, where she works at the forefront of computational biology and neurodegenerative disease research. With a strong foundation in bioengineering and bioinformatics, Danielle focuses on analyzing large-scale genomic data to better understand the genetic and regulatory factors influencing Alzheimer’s disease and related disorders. Her work integrates whole genome and exome sequencing, genome-wide association studies, and advanced epigenomic analyses to identify candidate risk genes and uncover mechanisms underlying disease onset and progression.
Driven by a passion for innovation, Danielle is particularly interested in applying emerging machine learning approaches to protein modeling, including structure prediction, enzyme design, and data-driven drug discovery. She thrives at the intersection of biology, computation, and medicine, combining rigorous problem-solving with creative scientific thinking. Her research experience spans molecular dynamics simulations, protein structure modeling, and collaborative, cross-institutional projects that aim to shift how neurodegenerative diseases are studied—from tracking progression to understanding initiation.
Danielle holds both a bachelor’s and master’s degree from Lehigh University, where she pursued an integrated path in biocomputational engineering and bioengineering. In addition to her research, she has demonstrated a strong commitment to mentorship, teaching, and community building through roles as a resident assistant, teaching assistant, and trained mentor. Known for her curiosity, discipline, and collaborative spirit, Danielle is motivated by the potential of computational science to drive meaningful advances in human health.
• Trained Mentor
• Lehigh University- B.S.
• Lehigh University- M.S.
• LU Philharmonic Orchestra
• Woodwind Ensemble
• Clarinet Choir
• Gryphon Society
• Society of Women Engineers
What do you attribute your success to?
I attribute my success to curiosity, persistence, and being open to unexpected intersections—especially discovering how biology, data, and computation could come together through hands-on research experiences. Seeking mentorship, choosing growth over a prescribed path, and continuously building skills through real-world work have shaped both my confidence and career direction.
What’s the best career advice you’ve ever received?
The best career advice I’ve received is that there is no single “right” path in science—impact and leadership come from experience, curiosity, and collaboration, not titles alone. Guidance from mentors, peers, and colleagues reinforced that growth is communal and that choosing a path aligned with my interests can still lead to senior, meaningful roles.
What are the biggest challenges or opportunities in your field right now?
One of the biggest challenges in computational biology is navigating an interdisciplinary field without a clearly defined path, where professionals must actively advocate for the value of blended skills in biology, data, and computation. At the same time, it presents a major opportunity for those who are adaptable and proactive, as the demand for cross-disciplinary expertise continues to grow—especially for early-career scientists willing to build projects and experience beyond traditional roles.
What values are most important to you in your work and personal life?
Curiosity, adaptability, and impact guide both my work and personal life, especially as my journey continues to evolve at the intersection of computation and biology. I value embracing change, trusting my instincts, and staying open to new possibilities while pursuing meaningful work that makes a real difference.