Hardware · Embedded · Signal Processing · Applied ML

Mahdi Jazini

Sensor systems engineer, from circuit to validated data.

Bioengineering PhD with industry experience building mixed-signal hardware, embedded acquisition, DSP/ML pipelines, and validation systems. My current domain is cardiovascular monitoring; the engineering transfers to any product where noisy sensors must become trustworthy decisions. Available for full-time roles in early 2027.

View selected work Download full CV See role fit
Mixed-Signal14-layer PCB · sensor AFE
EmbeddedC/C++ · FPGA · DAQ
DSP & MLPython · MATLAB · time series
Validationbench · clinical · automation
Where I fit

Strongest where hardware, sensing, and data meet.

My domain depth is health sensing. My engineering toolkit is broader: I can own the signal chain from sensor interface and acquisition through algorithms, test, and validation.

Hardware & Embedded R&DMixed-signal PCB, MCU/FPGA, control, bring-up
Sensor Systems & InstrumentationAFE, DAQ, calibration, characterization
DSP / Algorithm EngineeringFiltering, spectral methods, feature extraction
Applied ML for Time SeriesQuality scoring, estimation, interpretable models
Test & Validation AutomationPython/C#, instruments, repeatable evidence
Medical Device & Wearable R&DClinical studies, safety mindset, physiology

Relevant to medical devices, wearables, instrumentation, industrial sensing, robotics/automation, and data-rich hardware products.

Recruiter details & logistics →
End-to-end product R&D Smart Cuff Arm cuff → hospital-grade hemodynamic monitor. CO r=0.60 vs thermodilution, 83% trending concordance in 34 surgical patients. Case study → DSP · Applied ML Time-Series Signal Toolkit MATLAB/Python pipelines for noisy sensor data — quality gating, event detection, spectral analysis, and feature extraction. See the work → Embedded · DAQ software Acquisition & Control Software PyQt acquisition on Raspberry Pi 5 + Windows — configurable sensor arrays, DAQ/PWM, calibration, and synchronized imaging. See the work → Hardware · FPGA · Industry Mixed-Signal & FPGA Systems 14-layer DSP/IF PCB design (EMI/EMC, PDN, thermal) and Artix-7/Kintex-7 signal chains for a control unit. See the work →
University of Pittsburgh· Cardiovascular Health Tech Lab· Carnegie Mellon University· IEEE-EMBS · Body Sensor Networks· MACSOS Outstanding Abstract 2025· Safar Symposium · Top Poster 2025· Safar Symposium · Top Prize 2026· Professional Development Award 2026· Scientific Reports· medRxiv· University of Pittsburgh· Cardiovascular Health Tech Lab· Carnegie Mellon University· IEEE-EMBS · Body Sensor Networks· MACSOS Outstanding Abstract 2025· Safar Symposium · Top Poster 2025· Safar Symposium · Top Prize 2026· Professional Development Award 2026· Scientific Reports· medRxiv·
PPG
photoplethysmogram
ECG
electrocardiogram

About

Mahdi Jazini portrait
MJ
Pittsburgh, PA

I build real-world sensor systems end to end — from analog front ends and embedded acquisition to signal processing, machine learning, and validation.

I'm a PhD candidate in Bioengineering at the University of Pittsburgh (Biosignals track), advised by Prof. Ramakrishna Mukkamala. Cardiovascular monitoring is my proving ground: custom hardware and algorithms validated against invasive references in multi-site surgical studies.

I came to Pitt after a Master's in Integrated Circuits from University of Tehran and a Bachelor's in Digital Electronics from Amirkabir University of Technology. Between degrees I spent three years at Tosan Co. leading 14-layer mixed-signal PCB design and microprocessor signal-processing chains — I came to research with a production mindset.

That combination makes me useful beyond one application area. I work best on products where sensors are noisy, hardware and software interact, calibration matters, and the team needs evidence — not just a convincing demo.

125+
Surgical patients
total data collected; 34 in CO validation study
4
Awards & Prizes
MACSOS 2025 · Safar 2025 · Safar 2026 · PDA 2026
5
Peer-reviewed
+ 1 preprint under review (2 first-author at Pitt)

Featured Work

Custom cuff electronics board and packaged device enclosure
Scatter plot of generated power versus step rate
Clinical validation workflow and perioperative stages
Cuff-compliance validation setup with simulator and monitor
Custom cuff electronics and packaged device enclosure
From my work · 2024–2025
Flagship project · NIH-funded ($2.7M)

Smart Cuff

An end-to-end non-invasive platform that turns an automatic arm cuff into a hospital-grade hemodynamic monitor — pneumatics, ECG, respiration, and pressure on one system. Intended use: intraoperative and ICU hemodynamic monitoring; an investigational device today, no FDA submission yet.

Smart Cuff integrates custom mixed-signal PCBs (pump/valve PID control, pressure, and ECG front-ends) with a PyQt6 clinical GUI for real-time acquisition, automated calibration, and data collection in multi-site surgical studies. My first-author work on cardiac output estimation from Smart Cuff data earned the Outstanding Abstract Award at MACSOS 2025 and is under review (medRxiv 2025).

For clinicians: A continuous, catheter-free trend signal you could trust to direct a fluid bolus or vasoactive titration is the goal — comparable in trending performance to invasive pulse-contour today (concordance 83% vs. its 81%), but without the arterial line. Not yet a replacement for thermodilution at the population level; an addition to the toolkit when an arterial catheter isn't appropriate.

Where it sits: noninvasive cardiac-output landscape includes NICOM (bioreactance), ClearSight / CNAP (continuous finger cuff), and intermittent oscillometric devices. Smart Cuff aims to combine the form-factor of a familiar arm cuff with the trending capability typically reserved for finger cuffs — a single device for both BP and CO, with no consumables.

34surgical patients
24 liver transplant + 10 cardiac
r=0.60vs thermodilution
the invasive gold standard
83%trending concordance
above the 80% clinical-acceptability threshold; invasive pulse contour: 81%
  • Custom mixed-signal PCB
  • Pneumatic PID control
  • ECG · pressure AFE
  • PyQt6 clinical GUI
  • Piecewise-linear valve cal
  • Multi-site surgical studies
Read the Smart Cuff case study →
DSP · Applied ML

Time-Series Signal Toolkit

MATLAB/Python pipelines for noisy sensor data: filtering, harmonic-SNR quality gating, event detection, envelope fitting, spectral analysis, and interpretable feature extraction.

Embedded · DAQ software

Acquisition & Control Software

PyQt GUI running on Raspberry Pi 5 and Windows, integrating configurable sensor arrays, MCC128 and NI-DAQ acquisition, PWM control, synchronized imaging, calibration, and repeatable deployment.

Industry · Tosan

Mixed-Signal Hardware & Signal Chains (2019 – 2022)

Led 14-layer DSP/IF PCB design (EMI/EMC, PDN, thermal budgeting) for an electrical-motorcycle control unit. Built automated characterization workflows and directed microprocessor signal-processing chains on Artix-7 and Kintex-7.

Engineering Range

Sensor Hardware
& Instrumentation

Mixed-signal PCBs up to 14 layers, sensor analog front ends, ADC/DAC selection, power integrity, EMI/EMC, calibration, bench characterization, and failure analysis.

Embedded Acquisition
& Control

MCU and FPGA signal chains, C/C++, Raspberry Pi, DAQ integration, serial protocols, real-time acquisition, pneumatic PID control, and cross-platform PyQt tools.

Signal Processing
& Applied ML

Time-series filtering, FFT and spectral methods, event detection, physics-informed feature extraction, quality scoring, estimation models, and interpretable validation.

Now

  • medRxiv revision — addressing reviewer comments on the cardiac-output paper, then submitting to a journal.
  • Sub-diastolic hold prototype — closed-loop pressure tracking under DBP, with PDA + recalibration. Safety layers come first.
  • Real-time quality scorer — a single number per measurement, derived from harmonic SNR, motion, and arrhythmia detection. So we never silently report a bad reading.
  • Toolbox — adding a Bland–Altman builder and a PPG quality scorer. Public, free, opinionated.
Research snapshot · Jun 2026
Try the research

The physics behind the cuff: arterial blood volume.

An arm cuff doesn't measure pressure directly — it reads volume oscillations from the artery underneath, mapped through a sigmoidal volume–transmural-pressure curve. Drag the cuff pressure: invasive arterial pressure rolls in along the X-axis, gets mapped through the curve, and rolls out as volume on the Y-axis. The output is loudest when the cuff sits exactly at MAP.

Cuff pressure 150 mmHg
Transmural P (mean) −57 mmHg
Volume DC 0 % Vmax
Pulse AC (pk-pk) 0 % Vmax
State artery collapsed
0 80 (DBP) 100 (MAP) 120 (SBP) 200
● SBP 120 ● MAP 100 ● DBP 80
For hiring managers the 10-second version
  • Who — Sensor-systems engineer and Bioengineering PhD (Dec 2026). I own the full stack: hardware → embedded acquisition → signal processing / ML → test and validation.
  • Proof — Shipped 14-layer industry hardware, built cross-platform acquisition software, published signal-processing methods, and validated a complete sensing system in 34 surgical patients.
  • Want — Hardware/embedded R&D, sensor systems, DSP/algorithms, applied time-series ML, test/validation automation, or medical-device/wearable roles.
  • Logistics — Available early 2027 · Pittsburgh, open to relocation / hybrid / remote · F-1 OPT.
Email me about a role Download CV

Open to Work

Actively interviewing for early-2027 start dates
Graduating December 2026
Based in Pittsburgh, PA
Open to Relocation · Hybrid · Remote
Work auth F-1 OPT

Target roles

  • Hardware / Embedded R&D Engineermixed-signal PCB, MCU/FPGA, control, bring-up
  • Sensor Systems / Instrumentation Engineersensor interfaces, DAQ, calibration, characterization
  • DSP / Algorithm Engineertime-series, spectral analysis, feature extraction
  • Applied ML Engineersensor data, quality scoring, interpretable estimation
  • Test / Validation Automation EngineerPython/C#, instruments, repeatable evidence
  • Medical Device / Wearable R&Dphysiology, clinical studies, safety-minded systems

What I bring

  • System ownershipsensor board → acquisition → algorithms → user-facing tool
  • Hardware depthup to 14-layer PCBs, FPGA pipelines, AFE, EMI/EMC, PDN
  • Physics-grounded algorithmsmodels tied to how the sensor and system actually behave
  • Validation disciplinebench tests, automated characterization, clinical references
  • Cross-functional communicationhardware, software, data, clinicians, papers, and demos

Best-fit problems

I do my best work on real-world sensing products where signal quality matters — medical devices, wearables, instrumentation, industrial sensing, robotics/automation, or semiconductor/test systems.

Start a conversation → Download full CV (PDF)
Same hobby, different decade

It's all just signals.

Chased a decaying sine wave off a SAW sensor in 2018. Chasing an oscillometric envelope off an arm cuff now. The physics is different — the tricks are the same. Watch one morph into the other:

2018 Damped sine wave SAW resonator · IEEE
2025 Oscillometric envelope Arm cuff · Sci Reports · medRxiv

CV & Background

Download PDF
  1. Apr 2026
    ★ Professional Development Award
    19th Research Symposium
    University of Pittsburgh
  2. May 2026
    ★ Top Prize
    Safar Symposium 2026
    Dept. of Anesthesiology · University of Pittsburgh
  3. Sept 2025
    ★ Outstanding Abstract Award
    MACSOS Conference
    University of Pittsburgh
    "Cardiac Output Monitoring via an Automatic Arm Cuff Device: Potential in Surgical Patients."
  4. May 2025
    ★ Top Poster Prize
    Safar Symposium
    Dept. of Anesthesiology · University of Pittsburgh
    "Smart Cuff for Multi-Parameter Hemodynamic Monitoring."
  5. Aug 2022 – Dec 2026
    Education · PhD Candidate
    PhD, Bioengineering (Biosignals)
    University of Pittsburgh
    Joint coursework at Carnegie Mellon: Intro to ML (10-601), ML in Healthcare (10-742), DSP (18-691), Biostatistics (42-685).
  6. 2022 – Present
    Teaching & Mentorship
    TA / Instructor · Undergraduate & Graduate courses
    University of Pittsburgh · Prior: University of Tehran
    Lab assistant and instructor for undergraduate and graduate-level courses; co-mentoring junior researchers on signal-processing pipelines and hardware bring-up.
  7. 2019 – 2022
    Experience · Industry
    Digital Electronics Engineer
    Tosan Co.
    Electrical-motorcycle control unit: 14-layer DSP/IF PCB design; automated characterization workflows; microprocessor signal-processing chains on Artix-7 and Kintex-7.
  8. 2016 – 2018
    Education
    MS, Integrated Circuits
    University of Tehran
  9. 2012 – 2016
    Education
    BS, Digital Electronics
    Amirkabir University of Technology

Technical Skills

Hardware
AltiumPCB (14-layer)Sensor AFEADC/DACPID controlFailure analysisLab bench (scope, SA, VNA)
Software & ML
PythonPyQtPandasSciPyNumPyPyTorchscikit-learnMATLABC/C++LTspiceCadence
Embedded & Systems
I²CSPIUARTFPGA (Artix-7, Kintex-7)MCU firmwareRaspberry PiNI-DAQTest automation & cal
Dev & Process
Git / GitHubVersion controlLinuxAgile sprintsTechnical writingPeer review
Medical-device literacy
IEC 62304 (aware)ISO 13485 (aware)IEC 62366 usability (aware)Design controls (21 CFR 820.30)Risk mgmt (ISO 14971)IRB / human-subjects research

Not a formal QMS role — but I've worked adjacent to these standards on clinical studies and can speak the language from day one.

Publications

Preprints & under review

  1. 1
    medRxiv preprint · 2025 · First author · ★ Outstanding Abstract, MACSOS 2025
    Cardiac output monitoring via an automatic arm cuff device: Potential in surgical patients
    Jazini, M., Daher, H., Kumar, R., et al. · medRxiv 2025.10.09.25337689
    Key result: 34 surgical patients (24 liver transplant + 10 cardiac). Cuff-based cardiac output estimates hit r=0.60, 83% concordance vs thermodilution — comparable to invasive pulse-contour performance (r=0.62, 81%) with no catheter.
    Stepwise cuff-based cardiac output estimation framework
    Clinical validation workflow with custom cuff and perioperative timeline

Peer-reviewed

  1. 2
    Scientific Reports · 2025 · Second author
    A popular validated home monitor uses the maximum oscillogram amplitude to compute blood pressure
    Dhamotharan, V., Jazini, M., Kumar, R., et al. · Scientific Reports 15, 35095
    Key result: A variable-ratio method (driven by max oscillogram amplitude) reduced systolic/diastolic BP errors from 5.8 / 1.5 mmHg → 1.5 / 0.8 mmHg — a ~4× improvement in systolic accuracy, and a window into how real home monitors actually work.
    Cuff-compliance validation setup with NIBP simulator, monitor, and varying cuff sizes
  2. 3
    Analysis & Sensing · 2024
    Self-powered wearable pressure sensors for detection and separation of signals for various human movements
    Momin, Md. A., Jazini, M., et al. · Analysis & Sensing
    In-shoe pressure sensor placement, encapsulation, and wiring circuit
    Generated-power clusters versus step rate across locomotion tasks
  3. 4
    ICSPIS · 2019 · First author · ★ Best Paper Award
    A novel combination of neural networks and FFT for frequency estimation of SAW resonators' responses
    Jazini, M. M., Khoshakhlagh, M., & Masoumi, N. · 5th Iranian Conf. on Signal Processing and Intelligent Systems, IEEE
    SAWR interrogation architecture with transmit and receive chains
    FFT, peak detection, differentiation, and neural-network correction pipeline
  4. 5
    IRSS · 2019 · First author
    Limitations of Fourier transform analysis in the wireless SAW sensor systems
    Jazini, M. M. & Masoumi, N. · IRSS · IEEE
    FFT spectrum of damped SAW signal showing sinc envelope and discrete sampling
  5. 6
    ICEE · 2018 · First author
    A new frequency detection method based on FFT in the application of SAW resonator sensor
    Jazini, M. M., Khoshakhlagh, M., & Masoumi, N. · Iranian Conf. on Electrical Engineering, IEEE
    Three-dimensional search space across IF frequency, phase, and amplitude

Conference Presentations

Google Scholar → ORCID →

Talks & Media

Where I’ve shown the work, given a poster, or sat on a panel.

FAQ

The questions I get asked most often by recruiters and collaborators — answered up front, so we can use our first call for the interesting stuff.

When are you available to start?

I will defend in late 2026 and am targeting a January – March 2027 start date. For the right role I can consider a part-time or research-collaboration engagement earlier while I wrap up.

Remote, hybrid, or on-site?

All three work. I'm based in Pittsburgh but open to relocating anywhere in the U.S. For a strong team and problem fit, I'll also consider opportunities in Canada or Europe.

What kind of team fits you best?

I do my best work on cross-disciplinary R&D teams building real-world sensing products. The industry can vary; the common thread is hardware, embedded acquisition, algorithms, and rigorous validation working as one system.

Have you worked on regulated medical devices?

My clinical studies are IRB-approved and follow human-subjects research protocols. I haven't led a formal 510(k) or IDE submission, but I've worked adjacent to design controls, IEC 62304 software lifecycle, and ISO 14971 risk management — and I can speak the language from day one.

Can you code, or just design hardware?

Both — honestly, I'm an embedded systems engineer at heart, so I code and design electronics together; which side leads depends on the project. I ship the whole stack: custom PCB → firmware → signal processing → ML → clinical GUI. Python daily (Pandas/SciPy/PyTorch/PyQt), C/C++ for embedded, MATLAB for quick prototyping, Git for everything.

References?

Available on request — my PhD advisor and clinical collaborators (PIs and Co-PIs). Reach out by email and I'll share contact details.

How should I reach you?

Email is fastest: Mahdi.Jazini@pitt.edu. LinkedIn DMs work too. I reply within a day or two.

Let's talk

Hiring for hardware/embedded R&D, sensor systems, DSP/ML, or test and validation?

I'm open to roles across medical devices, wearables, instrumentation, industrial sensing, robotics/automation, and other data-rich hardware products. Based in Pittsburgh, PA — open to relocation, hybrid, or remote.

Email me about a role Download CV
Email LinkedIn Scholar ORCID X GitHub
Role fit