Philadelphia, Pennsylvania, United States
I am a technically fluent product and engineering leader who enjoys turning ambiguous problems into structured, measurable outcomes. I’ve worked across product management, UX research, and hands-on software development, often sitting at the intersection of technical teams and business goals. My strength is not just building systems, but designing the processes, metrics, and collaboration that allow teams to execute effectively. In recent roles, I've led cross-functional teams, trained early-stage founders, and implemented data-driven evaluation frameworks to standardize decision-making. I’ve also contributed directly to software projects and research efforts involving full-stack development, low-level systems, and cloud infrastructure—enough technical depth to ask good questions, spot risks early, and make informed tradeoffs. Alongside my professional experience, I'm completing a computer science degree with a focus on artificial intelligence and machine learning, which informs my work on data-driven decision-making, systems design, and research-oriented projects. I'm especially interested in roles where I can combine technical literacy with leadership: technical product management, engineering or program management, and early-stage startup operations. I enjoy environments where clarity, execution, and learning matter more than titles. If you're working on complex problems and value people who can bridge strategy, technology, and execution, I’m always open to a conversation.
- Deployed a state-of-the-art computer vision and biomechanics pipeline on a multi-GPU Linux cluster, resolving deep learning dependency conflicts across PyTorch, CUDA, and OpenMMLab frameworks. - Extended a human motion analysis ML pipeline to real-world fitness video data, engineering preprocessing and fallback logic to enable end-to-end inference on out-of-distribution inputs. - Debugged a physics-based musculoskeletal dynamics model, resolving numerical instability in the optimization loop and patching pretrained weight incompatibilities to achieve stable end-to-end execution.
- Review internship candidate resumes and conduct interviews with potential interns - Conduct research on business problems through survey data gathering and customer interviews - Produced data-backed user personas on a variety of potential customers
- Managed the product team to produce 70+ validated startup problems in fintech, healthcare, and consumer Saas - Trained 6 pre-seed startup teams from ideation to investor pitching using a self-built 10-week curriculum - Implemented intern performance tracking across 10+ metrics in Google Sheets, standardizing hiring evaluation
- Conducted user interviews with over 20 potential customers to collect data on business problems - Ideated and validated 200+ B2B and B2C startup problems through interviews, surveys, and rapid iteration cycles - Produced analysis reports on workflow and methodologies, improving performance metrics by 25%
- Lead other teaching assistants in conducting interactive labs for first year undergraduates - Assisted students during office hours by hosting make up quizzes and answering questions - Graded students fairly according to the rubric in a timely fashion using Blackboard Learn
- Conducted novel research applying machine learning techniques to cloud cybersecurity systems - Built a low-level eBPF data filter capturing 1000+ system and network calls per second, supporting telemetry analysis - Analyzed Kubernetes architecture to evaluate deployment strategies for ML-based cybersecurity anomaly detection