{
  "$schema": "https://jsonresume.org/schema",
  "basics": {
    "name": "Shahriar Austin Beigi",
    "label": "Civil Engineer | Traffic Safety and Machine Learning Researcher",
    "image": "https://austinbeigi.com/assets/biography/austin-beigi-professional-portrait.jpg",
    "email": "grp7dh@virginia.edu",
    "phone": "+1-202-437-1353",
    "url": "https://austinbeigi.com/",
    "summary": "Civil engineer and University of Virginia Ph.D. candidate focused on traffic safety, rear-end crash mitigation, uncertainty-calibrated machine learning, mixed-connectivity control, and transportation planning.",
    "location": {"city": "Reston", "region": "Virginia", "countryCode": "US"},
    "profiles": [{"network": "LinkedIn", "username": "sbeigi", "url": "https://www.linkedin.com/in/sbeigi"}]
  },
  "work": [
    {
      "name": "Federal Highway Administration, Eastern Federal Lands Highway Division",
      "position": "Civil Engineer, GS-11",
      "startDate": "2024-05",
      "endDate": "2025-09",
      "summary": "Technical Services assignment supporting traffic operations and highway safety across a multiple-project federal portfolio in Virginia.",
      "highlights": [
        "Promoted from Pathways Intern to full-time direct hire",
        "Conducted roadway safety audits and guardrail warrant evaluations and prepared plans, specifications, and estimates for federal projects",
        "Analyzed National Park Service crash data for multiple park-road sites in ArcGIS Pro to support guardrail warrants and roadside safety-barrier decisions",
        "Applied MUTCD standards for signs, pavement markings, and signals across five federal projects to improve safety for all road-user groups"
      ]
    },
    {
      "name": "HNTB",
      "position": "Civil Engineer II",
      "startDate": "2021-04",
      "endDate": "2023-06",
      "summary": "Bridge and tunnel inspection and inspection planning in Massachusetts.",
      "highlights": [
        "Inspected 20+ structures for MassDOT, MBTA, and SFMTA, including the I-93 and Ted Williams tunnels and the Zakim Bridge",
        "Authored inspection plans for 20+ structures and presented findings and specifications to officials across several states"
      ]
    },
    {
      "name": "AECOM",
      "position": "Civil Engineer I",
      "startDate": "2018-05",
      "endDate": "2021-03",
      "summary": "Structural inspection, load rating, and design in Massachusetts.",
      "highlights": [
        "Performed inspections and load ratings for 20+ MassDOT and RIDOT structures, including the I-93 tunnels, against federal and state live-load standards",
        "Designed and inspected structures for four owners, including MassDOT, Massport, and Logan Airport"
      ]
    },
    {
      "name": "New Hampshire Department of Transportation",
      "position": "Civil Engineer",
      "startDate": "2017-06",
      "endDate": "2018-05",
      "summary": "Structural plan review and construction monitoring in New Hampshire.",
      "highlights": [
        "Reviewed structural design plans for state-code compliance and tracked ground-improvement placement on the Spaulding Turnpike widening, including a new Route 4 prestressed-concrete beam bridge"
      ]
    }
  ],
  "projects": [
    {
      "name": "Traffic Data Analysis and Mixed-Traffic Safety Research",
      "entity": "University of Virginia",
      "type": "Research",
      "startDate": "2023-08",
      "endDate": "Present",
      "description": "Uncertainty-calibrated residual learning and buffered-risk-aware longitudinal control for mitigating rear-end collision risk in mixed-connectivity traffic.",
      "highlights": [
        "Developed a residual-learning framework that corrects acceleration-prediction errors for an unconnected preceding vehicle and quantifies predictive uncertainty for longitudinal control",
        "Screened and cleaned 100 NGSIM scenarios through data-quality checks, trajectory smoothing, and recalculation of acceleration before model development",
        "Designed separate training, validation, calibration, and test datasets to prevent temporal leakage; measured RMSE, MAE, and R-squared and assessed uncertainty using interval coverage and width",
        "Evaluated control strategies for three-vehicle mixed-connectivity platoons using stability and crash-risk measures",
        "A lower-bound acceleration policy produced a statistically significant 59.6% reduction in RCRI unsafe time, from 2.77 to 1.12 seconds",
        "Reduced TET below a 2-second time-to-collision threshold from 0.07 to 0 seconds and mean DRAC from 0.007 to 0.006 m/s²; these changes were not statistically significant",
        "Proposed a buffered-risk-aware governor that monitors time to collision, DRAC, and stopping-distance margins and shifts between safety-oriented residual feedforward input and baseline control"
      ]
    },
    {
      "name": "Virtual Reality Safety Evaluation of Connected Cruise Control",
      "entity": "University of Virginia",
      "type": "Research",
      "startDate": "2023-08",
      "endDate": "Present",
      "description": "Human-in-the-loop evaluation of connected-cruise-control behavior using BeamNG and virtual reality.",
      "highlights": [
        "Conducted a 20-participant BeamNG/VR study, screened trial validity, and retained 17 participants for paired analysis",
        "Built traffic data records for speed, acceleration, headway, throttle, and braking and evaluated time exposed time-to-collision, deceleration rate to avoid crash, and rear-end crash risk index",
        "Documented data elements, safety-performance measures, methods, and findings in three technical reports for research sponsors"
      ]
    },
    {
      "name": "Impact of Critical Situations on Autonomous Vehicles and Improvement Strategies",
      "entity": "University of Virginia",
      "type": "Research",
      "startDate": "2023-08",
      "endDate": "Present",
      "description": "Review of sensing and roadway-infrastructure limitations affecting automated vehicles.",
      "highlights": [
        "Reviewed 50+ studies on camera, LiDAR, radar, and GNSS limitations under fog, glare, contamination, faded lane markings, and construction zones",
        "Assessed roadway-infrastructure needs and published improvement strategies for lane markings, signs, maintenance, and work zones"
      ]
    },
    {
      "name": "Connected-Vehicle Field-Test and Adaptive Cruise Control Planning",
      "entity": "University of Virginia",
      "type": "Research",
      "startDate": "2023-08",
      "endDate": "Present",
      "description": "Field-test planning and risk-adaptive cruise-control concepts for connected vehicles.",
      "highlights": [
        "Prepared a Comma.ai openpilot field-test plan for synchronized vehicle, GPS, and driver-monitoring data collection, staged validation, and driver-override criteria",
        "Proposed a risk-adaptive strategy using time-to-collision, required deceleration, and stopping-distance margins and defined comparative safety and control tests"
      ]
    }
  ],
  "education": [
    {"institution": "University of Virginia", "area": "Civil Engineering", "studyType": "Ph.D.", "startDate": "2023-08", "endDate": "Present", "summary": "Autonomous and connected-vehicle decision-making, collision-risk evaluation, ADAS, simulation, and model validation."},
    {"institution": "University of New Hampshire", "area": "Civil Engineering", "studyType": "M.E.", "startDate": "2016-08", "endDate": "2017-12"},
    {"institution": "University of New Hampshire", "area": "Civil Engineering", "studyType": "B.S.", "startDate": "2012-01", "endDate": "2016-05"}
  ],
  "publications": [
    {"name": "Enhancing Cooperative Longitudinal Control in a Three-Vehicle Mixed-Connectivity Platoon Using an Uncertainty-Calibrated Deep Residual Learning Method", "publisher": "Transportation Research Record", "releaseDate": "2026-07", "summary": "Accepted."},
    {"name": "A Virtual Reality-Based Evaluation on Safety and Driving Performance of hC3 in Electric Vehicles", "publisher": "IEEE International Conference on Vehicular Electronics and Safety", "releaseDate": "2026-08", "summary": "Accepted."},
    {"name": "Impact of Critical Situations on Autonomous Vehicles and Strategies for Improvement", "publisher": "Future Transportation", "releaseDate": "2025-04-01", "url": "https://doi.org/10.3390/futuretransp5020039"}
  ],
  "skills": [
    {"name": "Traffic Operations and Safety", "keywords": ["traffic management", "transportation planning", "roadway safety audits", "crash-data analysis", "rear-end crash mitigation", "RCRI", "TET", "DRAC", "MUTCD", "traffic signs", "pavement markings", "traffic signals", "guardrail warrant evaluation", "plans, specifications, and estimates"]},
    {"name": "Machine Learning", "keywords": ["Python", "PyTorch", "PINNs", "LSTM", "CLSTM", "residual learning", "uncertainty-calibrated residual learning", "time-series prediction", "uncertainty calibration", "statistical analysis"]},
    {"name": "Autonomy and Simulation", "keywords": ["mixed-traffic control", "cooperative adaptive cruise control", "buffered-risk-aware control", "car-following models", "BeamNG.tech", "CARLA", "SUMO", "Simio", "virtual reality", "vehicle field-test planning"]},
    {"name": "Civil and Structural Engineering", "keywords": ["ArcGIS Pro", "MIDAS", "CSiBridge", "AASHTOWare BRR", "AutoCAD", "bridge inspection", "load rating", "highway safety"]},
    {"name": "Data and Evaluation", "keywords": ["trajectory preprocessing", "data-quality checks", "train-validation-test design", "RMSE", "MAE", "R-squared", "prediction validation", "technical reporting"]}
  ],
  "meta": {"canonical": "https://austinbeigi.com/", "aliases": ["S. Austin Beigi", "Shahriar Beigi", "Austin Beigi"], "lastModified": "2026-10-30"}
}