About Me

I am an engineer comfortably at the intersection of computational and protein sciences, and am motivated to scale up access to healthcare technologies. I have been focusing on how to increase sensitivity and multuplexing capabilities for quantifying small molecules and proteins. Through developing these systems in my PhD, I got to explore and play with statistical modeling, image processing, and optimization techniques, while manipulating and designing molecular systems. As a student, I have gotten to take classes ranging from many project based AI/ML computation courses to mathematics in linear dynamical systems, to deep dives into chemistry of therapeutic drug development.

I look forward to bringing my wide base of experiences in both computational and biological realms towards designing bigger jumps in how precision health, therapeutics, and medical diagnostics is approached -- particularly for lower resource settings.

For a traditional resume, please download the file below.

Download Resume

Experience

Postdoc and PhD Student, Advisor: H. Tom Soh, Stanford University

Bioengineering PhD• 2023

Electrical Engineering MS• 2021

My thesis highlights and begins addressing some fundamental problems in scaling up the quantification of molecular biomarkers for medical diagnostics and health monitoring [1]. Particularly, many current solutions assume that the intrinsic affinity between the affinity reagent and the target are the limiting factor, or view background signal and noise as an annoying feature. Works between my collaborators and I take advantage of these aspects to provide more robust and scalable molecular quantification techniques including:

  • • Maturing a tuning mechanism for molecular assays that enables multiplexed measurement of proteins across 7 orders of dynamic range in 100% serum [2,3] .
  • • Developing a mathematical framework to enable accurate and quantitative use of cross-reactive affinity reagents [4] .
  • • Implementing image segmentation and spatial autocorrelation algorithms for spatiotemporal monitoring of cellular molecular signaling [5].
  • • Creating efficient code pipeline for image registration and analysis of 100+ GBs of TIRF images of single molecule experiments [6].
  • • Designing a multiplexed molecular assay that can quantify small molecules using DNA as a signal readout.
  • • Wrote API to automate OT-2 wet-lab robot for in-house SELEX protocols.

Data Science Intern

BigHat Biosciences • 07/22-09/22

Developed statistical and computational methods that highlighted importance of un-monitored data signal for antibody biophysics. Efficiently integrated signal into company-wide data science pipeline and machine learning infrastructure. Proposed in-silico antibody designs with expected improvement in specified antibody characteristic.

Research Scientist, University of Washington

Molecular Information Systems Lab (MISL), School of Computer Science and Engineering • 08/16 - 08/17

Spearheaded wet lab automation device (PurpleDrop) for DNA data storage. More general information on PurpleDrop and its spinout projects since its inception can be explored on the MISL webpage . News piece on work in Nature Communications 2019.

Fulbright Scholar and Whitaker Fellow, Albert Ludwigs Univeristat

IMTEK, Freiburg, Germany • 2015-2016

Stress mitigation in thin film electrodes for neuroprosthetics. News article on proposed work and follow up describing Fulbright experience.

Undegraduate Research Assistant, University of Washington

Bioengineering BS, BioRobotics Lab • 2015

Analyzing Targeted Muscle Reinnervation for real-time myoelectric control of prosthetics and creating algorithm for robotic sensory feedback mapping (ROS, Linux). Developed 3D tissue models for auricular reconstruction surgical practice. Publication and News article .

Teaching

Course Instructor, Curriculum Developer, Intro to ML and AI in healthcare

Inspirit AI, Stanford and Mumbai, India • 05/2019-Present

Teaching and helping develop project based curriculum to introduce modern artificial intelligence to high school students in multi-week intensive course. Basic curriculum develops from linear and logistic regression, building up to KNNs, NLP, and CNNs via socially impactful projects. Advanced curriculum delves into Healthcare focused AI with more advanced algorithms such as GNNs and saliency maps. Also lead discussions on ethical and societal implications of AI, with focuses on India--our pilot location.

Graduate Teaching Assistant, Biological Macromolecules

Stanford University • 09/2020-12/2020

Reshaped graduate BIOC 241 course w/ Rhiju Das for Online Learning. Flipped classroom, developed, and led synchronous section. Topics covered include: thermodynamics and statistical mechanics of macromolecular folding, kinetics of enzymatic processes, and bistable systems using fixed point analysis.

Graduate Teaching Assistant, Systems Biology

Stanford University • 09/2019-12/2019

Taught BIOE101/201 Introduction to Systems Biology. Led recitations, wrote and graded homeworks and exams, held office hours.

Graduate Teaching Assistant, Analytical Methods in Biotechnology

Stanford University • 11/2018-03/2019

Taught and revamped curriculum for EE 235, Analytical Methods in Biotechnology. Led lab modules with two other TAs. Topics include restriction enzymes, immunoassays, bacterial transformation, and sequencing. Instigated and taught mini-lectures during down time ranging from DNA/protein structure and dynamics to details on Nanopore sequencing.

Honors and Awards

Stanford Graduate Fellowship (SGF) • 2017-Present

Graduate Research Fellowship, NSF • 2019-Present

Whitaker International Fellow, Whitaker International • 2015-2016

U.S. Student Fulbright Scholar, Fulbright • 2015-2016

Publications

For the most up-to-date catalog of publications, please visit my ORCiD account

Park, C.H, Thompson A.P, Newman, S.S. et al. Real‐Time Spatiotemporal Measurement of Extracellular Signaling Molecules Using an Aptamer Switch‐Conjugated Hydrogel Matrix Advanced Materials 36 (4), 2306704 (2024).

Newman, S.S.* , Hein, L.* et al. Theoretical framework and experimental validation of multiplexed analyte quantification using cross-reactive affinity reagents. bioarxiv pre-print (2023).

Newman, S.S.* , Wilson, B*, Mamerow, D* et al. A TUNABLE PROXIMITY ASSAY THAT CAN OVERCOME DILUTIONAL NON-LINEARITY WO 2023/154909 A1

Newman, S.S.* , Wilson, B*, Mamerow, D* et al. Extending the dynamic range of biomarker quantification through molecular equalization Nature Communications. 14 (1), 4192 (2023).

Hariri, A.A.*, Newman, S.S.* , Tan, S.* et al. Improved immunoassay sensitivity and specificity using single-molecule colocalization. Nat Comm 13, 5359 (2022).

Newman S. , Stephenson A., Willsey M., Nguyen B., Takahashi C., Strauss K., Ceze L., ‘Hierarchical DNA Storage Library with Digital Microfluidics Retrieval’. Nature Communications 2019, 10 1706.

Ceze L., Strauss K., Stephenson A., Newman S., Willsey M., Ngyuen B., Takahashi C. ‘Hierarchical DNA Storage Library with Digital Microfluidics Retrieval’. Filed 03/01/2019. Patent Pending.

Willsey M., Stephenson A., Takahashi C., Vaid P., Nguyen B., Piszczek M., Betts C., Newman S., Joshi S., Strauss K., Ceze L., Puddle: A Dynamic, Error-Correcting, Full-Stack Microfluidics Platform. ASPLOS ’19, April 13–17, 2019

Čvančara P., Newman S., Stieglitz T., ‘3D Patterned Thin-Film Electrodes for Neural Prosthetics—Proof of Concept’ IEEE NER ‘19"

Organick L., Ang S., Chen Y., Lopez R., Yekhanin S., Makarychev K., Racz M., Kamath G., Gopalan P., Nguyen B., Takahashi C., Newman S., Parker H., Rashtchian C., Stewart K., Gupta G., Carlson R., Mulligan J., Carmean D., Seelig G., Ceze L., Strauss K., ‘Random access in large-scale DNA data storage’ Nature Biotechnology 2017. 26, 242-248.

Berens, A., Newman, S., Bhrany A., Murakami C., Sie K., Zopf D., 'Computer-Aided Design and 3D Printing to Produce a Costal Cartilage Model for Simulation of Auricular Reconstruction’, Otolaryngology Head Neck Surgery , 2016, Vol.155(2), pp.356-359

Selected Presentations

Newman S.S., Hein L. et al. 'Multiplexed Analyte Quantification with Cross-Reactive Affinity Reagents. DNA 29 (2023)

Newman S.S., et al. 'Tuning Mechanisms for Simultaneous Quantification of fM and high nM Proteins' Gordon Research Conference, Bioanalytical sensors. Best Poster Prize. (2022)

Newman S., , Chen, R., Noyola, T., ‘Prediction of partially structured DNA aptamer libraries’, Stanford CS221 Project. (2019).

Newman S., Persson, T., ‘White Blood Cell Differential Counting in Blood Smears via Tiny YOLO’, Stanford CS230 Project. (2018).

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*Jokes are not mine. Source here