Not a Typical Biomedical Engineer
I am a rising senior at Duke University studying Biomedical Engineering and Electrical and Computer Engineering. My work spans wet lab research, computational biology, and AI and machine learning in the life sciences. From engineering iPSC derived cells to working with protein language models and neural signal decoding, I enjoy turning biological insight into real technologies. I care deeply about building products that matter and hope to eventually create tools that genuinely improve human health and help make the world better.
Education
Skill
Publications
Developed end-to-end MNE-Python pipelines for intracranial sEEG analysis, from Audacity response annotation and high-gamma extraction to permutation-based electrode visualization for speech perception decoding. Analyzed 26 patient datasets to identify phoneme-dependent uniqueness points and characterize inter-subject variability in high-gamma neural representations.
Engineered automated T7 phage-directed evolution workflows on Hamilton liquid handlers for 72-hour autonomous operation. Reduced PCR and Qubit reaction cost by over 50% on the Opentrons OT-2, contributed PyLabRobot visualizer modules, and documented automation protocols for future use.
bioRxiv Publication
Conducted Cell Painting Assay on induced iPSC-derived neurons and progenitor cells to study Down Syndrome morphology. Built an XGBoost pipeline to rank discriminative imaging features, achieving 83% accuracy in classifying Down Syndrome versus control cells.
HSURV Abstract Book pg. 44
Analyzed RNA-seq data from iPSC-derived podocytes under injury conditions to identify potential therapeutic targets for chronic kidney disease. Gained hands-on experience with stem cell culture, injury treatment, and organ-on-chip technology; second-author manuscript under review at Stem Cell Reports.
Stem Cell Reports Publication
Designed a primal-dual constraint learning framework for protein pathogenicity and fitness score prediction on ProteinNPT data. Fine-tuned ESM2 8M with LoRA on about 700k variants, improved Spearman correlation by 2% over baselines, and built a scalable 5-fold PyTorch validation pipeline.
Incoming Protein Engineering Intern on the biohybrid BCI team. I will work on automating and improving the throughput of rhodopsin discovery assays for next-generation neural interface systems.
Incoming SDE Intern on the Devices team. I will work on bridging Alexa capabilities with Fire TV to create more seamless voice-driven entertainment experiences.
Built a calorie detection app by integrating smart glasses image capture, cloud-based calorie estimation, and frontend UI. Tested computer vision models including YOLOv8, SAM, and Depth Anything, and worked with vendors on camera requirements, cost, and hardware constraints.
Helped build AI-scientist systems to automate life science research, including bacterial transformation, Nanopore sequencing, colony picking, and custom lab automation tools.
Lab Auto Wiki
Built Prophet-based forecasting for 15-week Mac sales, improved MAPE by 56%, and incorporated the final model into the data team's prediction workflow.
Wind - AI wallet for the next generation of consumer AI applications.
TryIt - prompt-based virtual try-on for fashion exploration.
CalendarOCR - poster-to-calendar event capture from camera.
Period Detector - private cycle tracking and health statistics.
Copyright Bowen Jiang
