Lorenzo Sanfilippo, PhD

• Develop C++ software used to configure, control, and monitor a high-rate data acquisition system coordinating multiple hardware subsystems in continuous operation.
• Analyze system behavior using monitoring data to investigate anomalies and performance issues.
• Diagnose and resolve failures during live system operation by identifying root causes across interacting components, cross-checking independent signals, and applying corrective actions to restore normal behavior.

• Developed statistical models to characterize time-dependent instabilities in a high-precision timing distribution system, assessing the impact of environmental and system-level factors.
• Validated models against observed timing data and applied results to design mitigation strategies.
• Supervised three students, reviewing code and integrating their work into production systems.

• Developed C++ APIs interfacing with FPGA-based hardware for register access, configuration, and control within a distributed data acquisition system.
• Designed and implemented monitoring tools to collect, process, and analyze system-level data from data acquisition workflows.
• Built web-based interfaces for real-time visualization and interaction with system state and performance metrics.

• Developed and implemented thermodynamic modeling for multi-component combustion systems, including numerical solutions of nonlinear heat-balance equations.
• Built data-driven models for early-stage plant configuration to estimate equipment size and cost drivers.
• Analyzed project data to extract engineering and cost insights supporting commercial decisions.

• Performed technical bid evaluations and compliance assessments for pressure equipment in EPC projects.
• Reviewed vendor documentation to ensure conformity with specifications and standards, including ASME and PED.
• Coordinated interfaces across process, layout, and procurement teams in schedule-driven projects.

• Worked on technical design and drafting support for heavy-industry equipment fabrication.

• Developed a time-series forecasting pipeline for short-term volatility prediction using lagged returns and rolling statistical features.
• Benchmarked a persistence baseline, Ridge regression, and Random Forest models using chronological holdout and walk-forward evaluation.
• Persistence-based forecasts remain hard to beat, as strong volatility persistence limits gains from return-based features and reduces improvements from machine learning models.

• Built a real-time market-making system ingesting multi-exchange order book data from Binance and Coinbase.
• Reconstructed local order books and estimated fair value from cross-exchange data.
• Implemented bid-ask quoting with inventory-aware pricing and dynamic sizing.
• Designed safeguards for quoting under stale or inconsistent data conditions.
• Sub-second quote updates, approximately 250 ms, in a live data environment.

Statistical modeling and control of timing instabilities (C++, Python)

• Modeled timing instabilities under controlled perturbations using regression and time-series analysis with parameter estimation and uncertainty quantification.
• Identified dominant drivers of instability and quantified their contribution through variance decomposition.
• Designed and validated a feedback stabilization algorithm, PID-based, reducing timing variation from 30 ps to less than or equal to 6 ps peak-to-peak.

Parametric capacity scaling and cost estimation model (Python, PyQt)

• Derived empirical scaling relationships between plant capacity and equipment size using historical data.
• Evaluated variance and residuals of scaling relationships to assess extrapolation limits.
• Implemented an algorithm generating preliminary equipment lists, sizes, and layout drafts from a single capacity input.

Testing the high-precision timing distribution for the ATLAS experiment at the HL-LHC

Development of a monitoring system for the muon trigger of the ATLAS detector

Implementation of quantum logic gates and quantum computation with trapped-ion systems