senior systems engineer

Jianhua Ge, U.S. Citizen

Irvine, CA 818-***-****

*********@***.***

Senior Systems Engineer

Design & Implement Technical Solutions that Optimize Performance & Efficiency

Summary

Systems Engineer with extensive aerospace experience spanning model-based systems engineering (MBSE), autonomy, flight controls, environmental control systems, and prognostics across NASA, Air Force, Navy, and Army programs.

Experienced in end-to-end requirements engineering, including decomposition of customer needs into system, subsystem, and interface requirements using Cameo and System Composer.

Strong background in MBSE-driven architecture design with bidirectional traceability (deriveReqt, satisfy) and verification planning for safety-critical systems.

Led complex system trade studies evaluating competing architectures using metrics such as performance, robustness, interpretability, and computational efficiency to drive engineering decisions.

Proven track record in developing digital twin and prognostic health management (PHM) systems, integrating physics-based modeling with data-driven analytics for aerospace applications.

Experienced in leading technical design reviews with cross-functional and customer stakeholders, resolving engineering conflicts, and aligning system solutions with mission and certification requirements.

Skills

Model Based System Design

Autonomous Controls

Verification & Validation

Requirements Management

System Modeling, Design, & Integration

Automated Contingency Management

Concise Communication of Methodology & Conclusions

MATLAB/SIMULINK, Simulink Test, C/C++, Python. System Composer, Cameo, DOORS

PROFESSIONAL EXPERIENCE

PHOTO-SONICS, INC, Los Angeles, CA

Senior Systems Engineer, May 2023 – September 2025

Generated prediction model and predicted the stability and performance of optical tracking systems. Hardware-in-the-loop testing.

Developed auto-tuning servo control tool for optical tracking systems. Tuning servo controllers for several optical tracking systems.

Verified Controllers via Monte Carlo simulation by modeling the optical tracking system, introducing random variations into sensitive parameters, and running simulations to observe how the controller behaves under a wide range of conditions.

Model based design using MATLAB/Simulink.

Real time implementation of servo controller in C++.

Performed system-level requirement decomposition and traceability mapping from customer performance needs to subsystem control and estimation requirements using MBSE principles.

Conducted trade studies comparing alternative servo tuning strategies (adaptive vs fixed-gain vs robust control) using stability margins, response time, and tracking error metrics.

Led internal technical design reviews with cross-functional stakeholders to align modeling assumptions, simulation results, and hardware implementation constraints.

LOCKHEED MARTIN CORPORATION, Los Angeles, CA

Data Scientist Analyst Staff, Sikorsky Aircraft Corporation, November 2011 – March 2023

Developed flight critical systems modeling and simulation to support formal verification methods (NASA). Support software certification efforts for flight-critical control systems developed under DO-178C and DO-331 standards (MATLAB V&V Tools, System Composer, Simulink, Simulink Test, C++).

Built Prognostic Digital Twin (PDT) in 1LMX IRAD project for Aircraft Environmental Control System (ECS) and related health management applications.

Generated predictive maintenance models using Python with co-variates from S-92 maintenance data to estimate risk of unscheduled maintenance.

Developed CAMEO models to document PDT architecture, interfaces, and data flow using Cameo Systems Modeler.

Performed system-level requirement decomposition and MBSE-based derivation of system and subsystem requirements for PHM Digital Twin using Cameo Systems Modeler.

Ensured bidirectional traceability between stakeholder needs, system requirements, and subsystem design using <<deriveReqt>> and <<satisfy>> relationships.

Built traceability matrix to support impact analysis and requirement change management across ECS PHM architecture.

Modeled ECS digital twin behavior using state machine diagrams (Healthy Degrading Failure Imminent).

Developed simulation scenarios for PHM validation and verification using MATLAB/Simulink and system-level modeling.

Integrated physics-based ECS (Environmental Control System) thermal behavior models with data-driven prognostic models to support hybrid digital twin architecture.

Led system requirements decomposition and MBSE execution (Cameo) for PHM Digital Twin (ECS Program):

Translated high-level customer requirements into system, subsystem, and interface requirements.

Example requirement decomposition included RUL prediction accuracy, sensor sampling rate, and real-time execution constraints.

Allocated subsystem requirements to Prognostics, Data Management, and Sensor Processing components.

Created validation rules in Cameo to detect orphan requirements and ensure completeness of requirement coverage.

Led complex system trade study for ECS PHM Digital Twin architecture:

Evaluated three alternatives:

Pure data-driven ML model (Neural Networks / Random Forests)

Pure physics-based thermal and thermodynamic ECS model

Hybrid digital twin architecture (physics + data-driven residual learning / PINNs)

Metrics used:

Prediction accuracy (RUL estimation)

Interpretability for safety-critical certification

Data availability and quality

Computational efficiency for real-time monitoring

Robustness under sensor noise and operational uncertainty

Result:

Selected hybrid digital twin architecture as best balance between accuracy and explainability. Improved predictive performance and enabled earlier detection of ECS degradation trends while maintaining engineering interpretability for certification and maintenance teams.

Led technical design reviews with US Air Force and internal stakeholders:

Presented ECS digital twin architecture, simulation results, and PHM performance analysis.

Used system-level visualizations and health-state dashboards instead of raw data outputs for clarity.

Managed disagreements between data science teams (favoring ML-only approaches) and systems engineers (favoring physics-based models).

Resolved conflicts by proposing hybrid architecture combining physics-based ECS modeling with machine learning residual correction.

Addressed concerns regarding deployability by implementing layered architecture (cloud-based model with simplified outputs integrated into legacy maintenance systems).

Built consensus across stakeholders and secured approval for hybrid PHM implementation roadmap.

Created statistical and machine learning algorithms for Predictive AI for Supply and Sustainment (PASS) for F-35 aircraft (Python).

Developed data-driven prognosis for engine bleed air system (EBAS) for C-5M aircraft and ensured integrated mast schedule reflects all critical project milestones (Air Force CBM+) (Python).

Crafted reasoner using diagnosis matrix for avionics in Combat Rescue Helicopter (Air Force).

Senior Engineering Specialist, Sikorsky Aircraft Corporation, May 2002 – November 2011

Designed integrated sensor registration and data association algorithm (Air Force).

Developed integrated health monitoring and reconfigurable control algorithms for genetic transport (NASA).

Built robust analysis tool for integrated flight and propulsion control system (Navy).

Generated advanced pattern factor control systems for engines (Air Force).

Created advanced multi-pursuer multi-evader differential game techniques (Army).

Developed software agents for airspace management and deconfliction for networked UAVs. Responsible for real time cooperative trajectory generation using swarm intelligence and model predictive control algorithms, UAV simulation visualization (Army).

Built transient-detection algorithms using wavelet packets and higher-order-statistics for accelerometer data from structural composite subjected to dynamic impact loading (Air Force).

Developed automated contingency management for UAVs. Responsible for rotorcraft modeling, integrated reconfigurable flight control and health management, hierarchical contingency management algorithm development, coding, and software-in-the-loop simulation (DARPA).

Generated data-driven prognostics system. Responsible for Support Vector Machines (SVM) based classification and dynamic wavelet neural network-based prognosis for helicopter gear vibration data (Missile Defense Agency).

Model based design using MATLAB/Simulink. C++ programming.

AMERICAN GNC CORPORATION, Simi Valley, CA

Senior Engineering Specialist, 1995 – 2001

Created a multiagent decision and control system. Responsible for battlefield scenario design, algorithm coding including tracking management, fuzzy logic, genetic algorithm-based assignment algorithm, and simulation evaluation (Army, Missile Defense Agency).

Built an integrated intelligent control and health monitoring system for propulsion. Responsible for propulsion modeling, fuzzy logic and neural network-based algorithm design, coding, and simulation evaluation (NASA, Navy).

Developed an intelligent collision avoidance system for Unmanned Air Vehicles (UAVs). Responsible for real-time signal processing, guidance, navigation, and control algorithms (NASA).

Developed an integrated robust and intelligent control technique for a remotely piloted sailplane using robust control, fuzzy logic control, and neural adaptive control algorithms (NASA).

Designed a nonlinear H-infinity flight control system for aircraft. Responsible for MATLAB coding for a procedure to find a Taylor series approximation to the solution of the Hamilton-Jacobi-Isaacs (HJI) equation, and simulation evaluation (Air Force).

Model based design using MATLAB/Simulink. C++ programming.

PROFESSIONAL RECOGNITION & AFFILIATIONS

2013 Innovator Prize (iPrize) finalist in Sikorsky Aircraft Company

Senior member of IEEE and AIAA

EDUCATION

Doctor of Philosophy (PhD), Industrial Automation, Zhejiang University, Zhejiang, China

Master of Science (MS), Mechatronics Engineering, Zhejiang University, Zhejiang, China

Bachelor of Science (BS), Mechatronics Engineering, Zhejiang University, Zhejiang, China

RESUME ADDENDUM

AWARDS OF US GOVERNMENT FUNDED PROGRAMS

1.Intelligent Sensor Registration and Data Association (Phase I, Air Force SBIR AF103-050, 2010).

2.Adaptive Aeroservoelastic Suppression for Aircraft Upset and Damage Conditions (Phase I, NASA SBIR A1.07, 2010).

3.An Evolutionary Learning and Adaptive Underwater Object Recognition System (Phase I, Navy SBIR N91-066, 2009).

4.Adaptive Flight Envelope Estimation and Protection (Phase I, NASA SBIR A1.07, 2008).

5.Collaborative Engagement for Unmanned Systems (Co-author, Phase I, A06-052, 2006).

6.Application of Silicon Carbide Photodiode Flame Temperature Sensors in an Active Combustion Pattern Factor Control System (Co-author, Phase I, AF06-167, 2006).

7.Dynamic Decision Support (D2S) for Real-Time Assessment of System Health and Fault Contingency Planning (Co-author, Phase I, N05-104, 2005).

8.A Stochastic Pursuit-Evasion Differential Game for Autonomous Vehicles (Phase II, Principal Investigator, Army04-061, 2005).

9.Automated Contingency Management for Advanced Propulsion Systems (Phase II, NASA STTR 2003 T1.01).

10.Intelligent Damage Identification and Prognosis for Composite Structures (Phase II, Air Force04-141, 2004).

11.A Stochastic Pursuit-Evasion Differential Game for Autonomous Vehicles (Phase I, Principal Investigator, Army04-061, 2004).

12.Self-Diagnosis of Damage Criticality of Fibrous Composites Based on Multifunctional Characteristics (Phase II, Air Force STTR, AF03-T015, 2004).

13.Advanced Techniques for Verification and Validation of Prognostic and Health Maintenance (PHM) Capabilities (Phase I, Navy04-028, 2004).

14.Intelligent Damage Identification and Prognosis for Composite Structures (Phase I, Air Force04-141, 2004).

15.Advanced Control/Diagnostic/Maintenance System for Diesel Engines (Phase I, Navy04-079, 2004).

16.Automated Contingency Management for Advanced Propulsion Systems (Phase I, NASA STTR 2003 T1.01).

17.Software Agents for Airspace Management and Deconfliction of Networked UAVs (Phase I, Army03-066, 2003).

18.An Affordable Health and Usage Monitoring System (HUMS) for UAVs (Phase I, Army03-074, 2003).

19.Software Agents for Data Driven Prognosis (Phase I, MDA STTR 03-01, 2003).

20.An Integrated System Design and Maintenance Modeling Tool (Phase I, Navy02-123, 2002).

21.Intelligent Software Agents for Digital Battlefield (Phase II, Army00-007, 2001).

22.Distributed Battlefield Management Systems (Phase I, Army01-015, 2001).

23.Intelligent Cargo Handling Systems Using MEMS IMU/GPS and EO Sensor (Phase I, Army01-019, 2001, Co-author).

24.Adaptable Cognitive Decision-Making System (BMDO01-010, 2001).

25.Intelligent Software Agents for Battlefield (Phase I, Army00-007, 2000).

26.A Real-Time Information Fusion and Decision Aid System (Phase II, BMDO98-010, 1999).

27.Advanced Decision Aid Systems (Phase I, BMDO98-010, 1998).

28.Autonomous Multiagent Decision and Control (Phase II, Army97-127, 1998, Co-author).

29.Intelligent Distributed Multi-Agent Hybrid Control Systems (Phase I, Army97-127, 1997)

30.Autonomous Integrated Reconfigurable Collision Avoidance System (Phase I, NASA97-07-01, 1997).

31.Intelligent Multisensor Integration and Fusion (Phase I, Army97-120, 1997).

32.Nonlinear H-infinity Flight Control System (FCS) Design for Advanced Aircraft (Phase I, AF96-137, 1996).

33.Autonomous Spacecraft Guidance and Control (Phase I, NASA96-01, 1996, STTR).

PUBLICATION

1.J. Ge, B. Lefevre, M. Roemer and R. Martin, “Integrated health monitoring and fault adaptive control for an unmanned hexrotor helicopter,” SAE 2013 AeroTech Congress & Exhibition, Montreal, Quebec, Canada, SAE Technical Paper 201*-**-****, September 24-26, 2013.

2.J. N. Juang, J. S. Lew, M. Roemer, and J. Ge, “Adaptive flutter suppression for aircraft upset and damage conditions,” AIAA Atmospheric Flight Mechanics Conference, Paper No. AIAA 2011-6369, Portland, Oregon, 8-11 August 2011.

3.L. Tang, J. Reimann, J. Ge, A. Crassidis, J. V. R. Prasad and C. Belcastro, “Methodologies for adaptive flight envelope estimation and protection,” 2009 AIAA Guidance, Navigation and Control Conference, Paper No. AIAA-2009-6260, Hyatt Regency McCormick Place, Chicago, Illinois, 10-13 August 2009.

4.L. Tang, M. Roemer, G. J. Kacprzynski and J. Ge, “Dynamic decision support and automated fault accommodation for jet engines,” 2007 IEEE Aerospace Conference, Big Sky, MT, March 2007.

5.J. Reimann, G. Vachtsevanos, J. Ge, L. Tang, and A. Liberson, “Collaborative unmanned vehicle engagement in adversarial missions,” Conference of SPIE on Defense Transformation and Net-Centric Systems, vol. 6578, Orlando, FL, 2007.

6.J. Ge, L. Tang, J. Reimann, and G. Vachtsevanos, “Suboptimal approaches to Multiplayer Pursuit-Evasion differential games,” AIAA Guidance, Navigation and Control Conference, Paper No. AIAA-2006-6786, Keystone, Colorado, 21-24 August 2006.

7.J. Reimann, G. Vachtsevanos, J. Ge and L. Tang, “An approach to controlling swarms of unmanned aerial vehicles in adversarial situations,” AIAA Guidance, Navigation and Control Conference, Paper No. AIAA-2006-6462, Keystone, Colorado, 21-24 August 2006.

8.J. Ge, L. Tang, J. Reimann, and G. Vachtsevanos, “Hierarchical decomposition approach for pursuit-evasion differential game with multiple players,” IEEE Aerospace Conference, Big Sky, MT, March 2006.

9.M. J. Roemer, L. Tang, G. Kacprzynski, J. Ge and G. Vachtsevanos, “Simulation-based health and contingency management,” IEEE Aerospace Conference, Big Sky, MT, March 2006.

10.G. P. Tandon, R. Y. Kim, M. J. Roemer, J. Ge, and A. Liberson, “Estimating location and severity of damage in composite panels,” Proceedings 2005 SEM Annual Conference & Exposition on Experimental and Applied Mechanics, Portland, Oregon, June 7-9, 2005.

11.M. J. Roemer, J. Ge, A. Liberson, G. P. Tandon and R. Y. Kim, “A feature and model-based approach to structural impact damage detection, isolation and severity prediction,” The 59th Meeting of the Society for Machinery Failure Prevention Technology, Virginia Beach, Virginia, April 2005.

12.M. J. Roemer, J. Ge, A. Liberson, G. P. Tandon and R. Y. Kim, “Autonomous impact damage detection and isolation prediction for aerospace structures,” The IEEE Aerospace Conference, Big Sky, MT, March 2005.

13.J. Ge, G. J. Kacprzynski, M. J. Roemer and G. Vachtsevanos, “Automated contingency management design for UAVs,” AIAA 1st Intelligent Systems Technical Conference, Chicago, Illinois, AIAA 2004-6464, September 2004

14.J. Ge, M. J. Roemer and G. Vachtsevanos, “An automated contingency management simulation environment for integrated health management and control,” The IEEE Aerospace Conference, Big Sky, MT, March 2004.

15.N. Coleman, C.F. Lin, Jianhua Ge and S. Braasch, “Intelligent multiagent modeling and decision system for battlefield,” AIAA Guidance, Navigation and Control Conference, Paper No. AIAA-99-3992, August 1999.

16.N. Coleman, G. Papanagapoulos, C.F. Lin, Jianhua Ge and X. Feng, “Advanced mine-to-target assignment algorithms and simulations,” AIAA Guidance, Navigation and Control Conference, Paper No. AIAA-99-3993, August 1999.

17.C.F. Lin, Jianhua Ge, and John Burken, “Robust lateral/directional control for high altitude aircraft,” AIAA Guidance, Navigation and Control Conference, Paper No. AIAA98-4301, August 1998.

18.C.F. Lin, T.J. Yu, Jianhua Ge, His-Han Yeh and Siva S. Banda, “Nonlinear H-infinity flight control system,” AIAA Guidance, Navigation, and Control Conference, Paper No. AIAA-97-3697, August 1997.

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