I’m a deeply curious person and genuinely enjoy learning about others’ work. If my skills might be useful to unblocking your project, I’d love to hear from you.
You’ll find brief outlines of my areas of expertise below. I’m happiest when working at the edge of my abilities and am adept at transferring my skills to challenges in new fields, so opportunities to explore frontiers will certainly get my attention.
To explore a potential collaboration, reach out to monicaspisar (Gmail); direct links to email/socials are on the landing page of this site.
Machine learning, data, and software engineering
AI systems, research, MCP/automation
My personal and professional interests in AI are strongly focused on responsible applications of machine learning, and I prioritize work related to technical AI safety and reliability.
Robust automation of tasks that require analysis of unstructured data or synthesis of data returned from external services’ APIs can boost capabilities and productivity significantly. MCP and individual LLM APIs enable full automation of some operations and research tasks previously requiring a human in the loop. My technical background is enriched by research and operations experience that provides significant leverage in building out such automation, and in anticipating automation pitfalls.
For domain-tuning objectives, I bring expertise in RAG and fine-tuning; together with extensive experience in dataset preparation/evaluation and ML ops, I can provide practical insights on where deploying AI may make sense. Likewise, I can help identify where AI is unlikely to be the right approach.
Interested in a compact overview of the foundations of artificial neural networks? My post on the design of neural networks has you covered.
Data science & signal processing
More traditional data science skills are also in my roster of tools, and I bring a graduate-level background in probability & statistics to that work.
I have significant experience with signal processing of raw data and with image reconstruction of medical imaging datasets, including statistical image reconstruction techniques.
Systems achitecture, software engineering, & data pipelines
My experience working on large-scale internet services informs my approach to software architecture. Designing Data Intensive Applications (Kleppmann) and Software Engineering at Google (Winters, Manshreck, Wright) are trusty references. My preference is to work on backend engineering and (cloud based) infrastructure projects, but I can find my way around networking and fullstack projects, as well.
Data engineering — instrumentation, pipeline architecture, storage, analytics — are all in my wheelhouse, and I’ve been the lead on a data warehouse migration of a live and mission-critical database for an internet and mobile application serving millions of clients. I’ve coordinated bespoke and off-the-shelf tools, and also integrated third party services for custom data pipeline builds to meet efficiency, privacy, and cost objectives for transactional, client usage, and observability data.
Hardware & electronics
Optical and acoustic imaging systems
I have extensive experience with acoustic signal generation, capture, and pre-processing for medical ultrasound, primarily in the context of medical ultrasound synthetic arrays with both novel optical and traditional piezoelectric transducers.
As a medical imaging systems researcher developing optical ultrasound technologies, I gained hard-won intuition and practical experience working with optical systems over many years in the laser lab. Among other things, that work involved building custom electronics for laser tuning and output stabilization, designing active detector control systems, and handling signal capture from detector to database.
Domain-specific expertise
High resolution medical ultrasound imaging: optical (laser based) generation/detection
I worked at the bleeding edge of medical ultrasound imaging research for 7+ years. Specifically, optoacoustic array detection of ultrasound at frequencies and device scale suitable for high resolution medical imaging. These techniques fall under the umbrella of laser based ultrasound, where the piezoelectric elements typically used for ultrasound transduction are replaced by components responsive to both acoustic and optical signals. I also led research developing microfluidics vascular models for ultrasound imaging of angiogenesis, which can be a tumor growth indicator.
A brief overview of my PhD research is included on the Publications page.
Drug discovery for longevity interventions
From early 2019 to mid-2021, I built and managed a longevity bioscience research portfolio at the University of Oxford. A high level overview “Hedging bets on healthier aging” touches on longevity research, drug discovery, and academic project/portfolio management. Beyond what’s covered in that post, I gained significant knowledge and expertise specific to clinical trial design for longevity interventions.
Medical imaging + AI in radiology
I keep up with advances in AI in medical imaging/radiology, and have extensive expertise to draw on for signal generation and image capture (hardware) as well as image processing/reconstruction (software).
In 2017, I wrote Picture Perfect: AI + Medical Imaging for a general audience. Despite the date, a decent proportion of the piece remains relevant.
Jeff Fessler (one of my professors in grad school) works on the cutting edge of ML in medical imaging. Jeff’s numerous papers on arXiv and talks are excellent resources. An Introduction to Score Based Generative Models is a great place to start exploring techniques for applying GenAI in medical imaging.
Bridging industry & academia
I spent a number of years working in roles that involved crafting project proposals and agreements between academic research groups and industry. Tremendous value can be found in such collaborations, but they can be tricky to set up for success. I’ve resolved dozens of stalemates to unblock the path - I speak the language on either side, and understand both institutional and commercial/corporate constraints.
Operations
My operations experience is broad, spanning organizational strategy to financial management to stakeholder engagement. I have extensive experience designing and implementing robust operational infrastructure, from process optimization to implementing requisite technologies. I’ve tackled operational challenges in a variety of environments, meeting organizational needs while optimizing team ‘UX’ on processes and tech interfaces.
Entrepreneurship and business operations
During graduate school, I was on a startup team at the very earliest stages of commercializing a research prototype low-dose radiation, small-footprint CAT scanner. Our team participated in Joshua Coval’s course “Idea to IPO in 14 weeks” at the University of Michigan business school, building a business plan and pitching VCs. Ultimately, the startup team received an SBIR grant for close to $2M in two phases and evolved into a thriving venture. Xoran
I’ve also acquired and founded non-technical brick & mortar businesses, generally leading those teams alongside my primary career in technology and science. Over the course of a decade as CEO, I gained significant operations and strategy experience — including successfully leading a growing retail and wholesale operation through the challenges of the 2008 financial crisis.