School of Engineering
Showing 3,851-3,900 of 5,933 Results
-
Ekin Gunes Ozaktas
Ph.D. Student in Electrical Engineering, admitted Autumn 2024
Ph.D. Minor, PhysicsBioI am a PhD candidate and Stanford Graduate Fellow at Stanford University working with Prof. Shanhui Fan.
Contact: eozaktas [at] stanford [dot] edu -
Ayfer Ozgur
Professor of Electrical Engineering
BioOzgur's research focuses on information theory, wireless communication and networks, distributed estimation and learning
-
Punnag Padhy
Postdoctoral Scholar, Materials Science and Engineering
Current Research and Scholarly InterestsCurrently, I am working on an on-chip platform to simultaneously trap and manipulate micron scale beads and droplets with an intention to implement chemical reactions on a chip at ultrasmall volumes.
-
Marshall Scott Padilla
Affiliate, Materials Science and Engineering
BioMarshall Scott Padilla will begin as an Assistant Professor of Materials Science and Engineering and a Sarafan ChEM-H Institute Scholar at Stanford in September 2026. His research takes a rational-design approach to RNA medicine, engineering lipids and lipid nanoparticles (LNPs) that deliver RNA and proteins to specific cells. Rather than relying on empirical, large-scale screening, he couples the synthesis of structurally defined lipid libraries with multimodal biophysical characterization and in vivo screening to extract the structure–activity relationships that govern delivery.
His research group aims to move beyond the field's default of hepatic delivery toward LNPs that direct RNA and protein cargoes to defined cell types, enabling durable and precise therapies. Group interests span ionizable lipid synthesis, gene editing, cancer immunotherapy, ionic liquids, mapping endosomal escape, and the analytical and biophysical methods needed to relate nanoparticle structure to function. He is broadly interested in establishing generalizable chemical and structural principles for the next generation of delivery vehicles.
Prior to joining Stanford, Marshall was an NIH postdoctoral fellow in the Department of Bioengineering at the University of Pennsylvania with Prof. Michael J. Mitchell, where he developed the Branched ENdosomal Disruptor (BEND) lipid architecture for mRNA and CRISPR-Cas9 delivery (Nature Communications, 2025), advanced solution-based biophysical methods for characterizing LNP structure (Nature Biotechnology, 2025), and engineered LNPs co-delivering mRNA and small-molecule drugs for oral cancer chemoimmunotherapy (Advanced Materials, accepted). He completed his PhD in Chemistry (Chemical Biology) at the University of Wisconsin–Madison and his B.S. in Chemistry at the College of William & Mary. His work has been recognized by the Society for Biomaterials (Burroughs Wellcome Fund BioInterfaces Rising Star Award), the American Association for Dental, Oral, and Craniofacial Research (Hatton Award, postdoctoral first place), and an NIH/NIDCR T90 fellowship. -
Julia Palacios
Associate Professor of Statistics and of Biomedical Data Science
BioDr. Palacios’s research spans Bayesian nonparametrics, probabilistic AI, stochastic processes, and computational statistics. Her group develops stochastic models and efficient inference algorithms for understanding evolutionary dynamics in population genetics, infectious diseases and cancer.
-
Daniel Palanker, PhD
Professor of Ophthalmology and, by courtesy, of Electrical Engineering
Current Research and Scholarly InterestsInteractions of electric field and light with biological cells and tissues and their applications to imaging, diagnostics, therapeutics and prosthetics, primarily in ophthalmology.
Specific fields of interest:
Electronic retinal prosthesis;
Electronic enhancement of tear secretion;
Electronic control of blood vessels;
Interferometric imaging of neural signals;
Interferometric imaging of cellular physiology -
Feng Pan
Postdoctoral Scholar, Materials Science and Engineering
BioFeng Pan is a postdoctoral scholar with Prof. Jennifer A. Dionne in the Department of Materials Science and Engineering at Stanford. He received his Ph.D. degree at the University of Wisconsin Madison, advised by Prof. Randall H. Goldsmith. His research expertise spans several aspects, including quantum optics, nanophotonics, metasurfaces, chiral metamaterials, plasmonics, and single-particle microscopy and spectroscopy. He is interested in harnessing photonics to address critical challenges in energy, quantum information science, and sustainability.
-
William Pan
Masters Student in Electrical Engineering, admitted Autumn 2022
BioI am a junior at Stanford University studying Mechanical Engineering and Electrical Engineering. My goal in life is to build the future through translational medical technologies and purposeful ventures.
Things I have built: health{hacks}, bicompatible hydrogel ostomy adhesive, kinesthetic latticed programmable tape -
Yuandong PAN
Postdoctoral Scholar, Civil and Environmental Engineering
BioYuandong Pan is a Postdoctoral Researcher in the School of Engineering at Stanford University. His research focuses on developing digital and smart approaches to support more sustainable buildings, infrastructure, and cities. Before joining Stanford, he was a Marie Skłodowska-Curie Actions Future Road Research Fellow at the University of Cambridge. His work aims to improve how the built environment is designed, managed, and maintained, contributing to smarter, more resilient, and more sustainable urban systems.
-
Antonello Paolino
Affiliate, Program-Iaccarino, G.
BioI am a Visiting Student Researcher at Stanford Mechanical Engineering, working on the PSAAP project under Prof. Gianluca Iaccarino's supervision.
I received my BSc (2018) and MSc (2021) in Aerospace Engineering from the University of Naples Federico II.
I am currently a PhD Student at the Italian Institute of Technology (IIT) in Genoa and the University of Naples Federico II under the supervision of Dr. Daniele Pucci and Prof. Renato Tognaccini. My PhD research focuses on the modeling and control of the aerodynamic forces acting on the jet-powered humanoid robot iRonCub using both classical and machine learning approaches.
