Julia Rosander

• Drives commercialization efforts for 20+ Yale academic teams in digital health and AI, conducting in-depth market research, business modeling, and go-to-market (GTM) strategy development.
• Fosters innovation by identifying and securing strategic partnerships to advance project goals and collaboration opportunities.
• Served as operator roles for pre-seed stage digital health startups to enhance fundraising capabilities.
• Streamlines operations and ensures successful project execution, aligning deliverables with commercialization objectives.
• Evaluated AI and Machine Learning investments for the Colton Autoimmunity Fund

• Led market research and investment analysis for a biotechnology client in medical diagnostics, creating client deliverables, and identifying future diagnostic avenues for innovation.
• Created client deliverables to showcase optimal market entrance strategies.
• Identified current medical advancements to pinpoint future diagnostic avenues.

• Applied video-based segmentation methods to evaluate ophthalmological surgeon trainee performance and determine regions of surgery that could be improved based on instrument localization.
• Utilized methods such as YOLOv4 and Darknet53 to classify surgical instruments across 1,000,000 frames, to determine localization over time.
• Improved the mean average precision of the model significantly by increasing the dataset, using computational power, and efficiently parsing frames.
• Created and deployed Beta-VAE for neurological tissue classification and discovery, while maintaining high performance.
  
• Utilized bioinformatic tools, such as single cell sequencing analysis, for the analysis of genetic data relating to the resistance of Alzheimer's and Parkinson's diseases. Aided the development of a multi-modal VAE model for disease classification across collected mouse and human data.
• Aided the development of a multi-modal VAE model for disease classification across collected mouse and human data.

Complex Systems 251: Computational Science and Analysis Applied to Society and Science

• Developed lab material through JupyterLab to reinforce lecture information, and to provide hands-on programming and statistical analysis experience.
• Led weekly lab sections where lecture content was reviewed, and data analysis skills were taught.
• Scheduled and maintained regular office hours to meet with students.
• Collected 4.7/5.0 teaching evaluation ratings in all topics, including the ability to help and maintain an effective learning environment.

• Developed and coded a pipeline to concatenate two years of protein sequence reads to analyze drug effectiveness.
• Automated pipeline streamlined analysis and increased efficiency by 20%.
• Utilized bioinformatics tools to run statistical analysis for small-molecule drug development.
• Developed and deployed structured testing and research processes among molecule creation and drug research groups.
• Presented a poster of analysis and pipeline in the Genentech Intern Showcase to multiple colleagues while fielding questions and received priority standing.

• Used R programming and Imaris Image Analysis software to quantify specific neuronal expression in Drosophila melanogaster.
• Utilized 3D image modeling to better understand neuronal structure.
• Conducted fluorescent microscopy imaging after conducting neural dissection and fixation.
• Specifically studied the transcription factor Dawdle and its differing effects on lipogenesis and glycolysis.
• Ran Triglyceride assays and analyzed data correlating to differences in feeding behavior
• Supported principal investigators during research into the physiological and anatomical changes of the brain on differing diets.