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Basics

Name Sadman Ahmed Shanto
Email shanto@usc.edu
Url https://sadmanahmedshanto.com
Summary Experimental physicist building superconducting quantum hardware and design automation tools. Physics PhD candidate at USC (Levenson-Falk Lab), creator of SQuADDS, and former Research Intern at Google Quantum AI.

Education

  • 2021.08 - 2026.05

    Los Angeles, CA

    Master of Arts (MA)
    University of Southern California (USC)
    Physics
  • 2021.08 - 2027.01

    Los Angeles, CA

    Doctor of Philosophy (PhD)
    University of Southern California (USC)
    Physics
    • Expected Graduation: January 2027
  • 2017.08 - 2021.05

    Lubbock, TX

    Bachelor of Science (BSc)
    Texas Tech University (TTU)
    Applied Physics
    • Minors: Computer Science & Mathematics

Work

  • 2025.08 - 2026.08

    Mountain View, CA, USA

    Research Intern
    Google
    • R&D for Quantum Processors @ Google Quantum AI
  • 2025.05 - 2025.08

    Thousand Oaks, CA, USA

    Summer Research Intern
    Quantum Elements Inc.
    • Built a production agentic AI framework that runs superconducting quantum experiments end to end, coordinating multiple specialized agents so they can calibrate, measure, diagnose, and analyze results without a physicist in the loop.
    • Architected the full-stack backend underneath it, with custom APIs, scalable databases, and containerized microservices on AWS, uniting the company's ML models, simulators, and instrument control into one system agents could act on through MCP servers.
    • Designed the evaluation and tracing layer that made agent behavior measurable, turning open-ended autonomous runs into tracked metrics and reproducible traces engineers could debug.
    • Contributed modular calibration nodes and DAG-based calibration graphs to QUAlibrate, Quantum Machines' open-source framework for large-scale QPUs, cutting processor tune-up from hours to minutes.
  • 2022.01 - Present

    Los Angeles, CA, USA

    Graduate Research Assistant
    Levenson-Falk Lab (LFL), University of Southern California
    • Creator and lead developer of SQuADDS, an open-source platform that takes superconducting device design from weeks to minutes; published in Quantum (2024) and adopted by academic and industrial groups worldwide for simulation, optimization, and fabrication-ready layout generation.
    • Leading the development of generalizable foundation ML models for quantum device simulation and design, in collaboration with NVIDIA, Northeastern University, and Fermilab.
    • Co-leading development of an agent harness with Lawrence Berkeley National Laboratory that carries a device from design through simulation to tape-out readiness end to end.
    • Developed and validated a high-yield nanofabrication process for nanobridge-SQUID resonators with 15 nm features, scaling functional device yield from under 2% to over 90%.
    • Designed, fabricated, and measured nanobridge resonators, offset-charge-sensitive transmons, “trimon” devices, “dissipator” devices and custom Josephson parametric amplifiers to probe quasiparticle dynamics and improve qubit readout, along with the PCBs, packages, and cryogenic microwave chain that house and measure them.
    • Built a Hidden Markov Model inference pipeline that extracts real-time quasiparticle occupation from I/Q trajectory data, enabling direct dynamic modeling of quasiparticle trapping and release.
    • Leading Andreev bound state spectroscopy and nanoSQUID quasiparticle trap experiments to characterize and mitigate quasiparticle-induced decoherence in superconducting qubits.
    • Led the creation of a fully automated design-to-simulation-to-GDS pipeline for the lab, with the Ansys and Palace simulation stack on USC's HPC cluster.
    • Leading the open-source integration of AWS Quantum's EM solver Palace with Quantum-Metal, giving the hardware community scalable cloud-based simulation pipelines.
    • Mentored five graduate and six undergraduate researchers through device design, fabrication, and measurement projects.
  • 2020.06 - 2020.08

    Nashville, TN, USA

    Summer Research Intern
    Institute for Software Integrated Systems (ISIS), Vanderbilt University
    • Built a full-stack calibration pipeline for microscopic traffic models, handling parameter identifiability and stochastic noise under multi-objective constraints, and parallelized the simulation-optimization sweeps with Ray for a 10x+ speedup.
    • Developed tooling to convert Intelligent Driver Model output into radar-style datasets for validation against real-world aggregate measurements, and contributed to Berkeley's Flow framework for closed-loop reinforcement learning in calibrated traffic environments.
  • 2019.01 - 2020.06

    Lubbock, TX, USA

    Undergraduate Research Assistant
    Texas Tech Multidisciplinary Research in Transportation (TechMRT)
    • Built an open-source simulator for mixed autonomous and human traffic on an extended Nagel-Schreckenberg cellular automaton, supporting both rule-based and learning agents.
    • Designed AV control strategies for shared-lane mobility and dynamic lane switching with RL agents that adapt to local density gradients, surfacing emergent behaviors such as intelligent herding and platoon formation and quantifying the system-wide flow effects of AV/HV composition.
  • 2018.11 - 2021.08

    Lubbock, TX, USA

    Undergraduate Research Assistant
    Advanced Particle Detector Laboratory (APDL), Texas Tech University
    • Led the end-to-end redesign of the optical system for muon telescopes, developing custom Winston cones that raised signal collection efficiency from 20% to 78%.
    • Co-designed and assembled both SiPM- and PMT-based muon telescopes, machining 50+ scintillator bars and calibrating 40 SiPMs and 44 PMTs.
    • Built DAQ systems on Arduino, CAMAC crates, and custom PCBs with wireless synchronization, using multithreaded sync and FPGA logic to cut channel deadtime by 300x, and wrote the real-time acquisition and analysis software that turns raw readout into muon flux maps on the university HPC.
    • Validated the instrument against a full Geant4 Monte Carlo of photon scattering and muon interactions, then built ML reconstruction methods, TDC photon time-of-propagation for depth inference and RNN/LSTM recovery of missing hits, that lift 2D detector data into 3D tomographic images.

Talks

Conferences

Awards

Skills

Layout & Verification
KLayout
gdsfactory
gdswell
Quantum-Metal
KQCircuits
Calibre nmDRC
PDKs
John O’Brien Nanofabrication Lab @ USC
Lincoln Lab SQUILL Foundry PDK
SkyWater Foundry PDKs
GlobalFoundry PDKs
Google Quantum AI Foundry PDKs
Quantum Measurements
QUA
OPX+
Labber
Zurich Instruments
AlazarTech PCIe Digitizer
PyVISA
RF & Microwave Simulation
Ansys HFSS Suite
Ansys Lumerical
AWS Palace
scikit-rf
COMSOL
Elmer
AWR Office
Keysight ADS
tidy3d
femwell
Fabrication & Cleanroom
EBL
Photolithography
Lift-off
RIE
ICP
ALD
Sputtering
CMP
FIB
Dicing
SEM
Metrology
Microwave & Packaging
Altium Designer
Ansys SIwave
RF Board Layout
PCB & Package Design
Wirebonding
Electroplating
Cryogenics
BlueFors
Oxford Instruments
RF Chain Setup
Cryo Filtering
Vacuum Pumps
Leak Detection
Programming Languages
Python
C++
Bash
Rust
Julia
HPC & Cloud
SLURM
OpenMPI
Docker
Kubernetes
AWS
GCP
Backend & Databases
FastAPI
REST
PostgreSQL
MongoDB
Apache Spark
Vercel
Version Control & DevOps
Git
Gerrit
GitHub
GitHub Actions
Argo
Bazel
ML
PyTorch
JAX
HuggingFace
Ray
Scikit-learn
pyKAN
HMMLearn
InterpretML
TensorFlow
LLMs
LangChain
Multi-Agent Orchestration (LangGraph)
RAG
Fine-tuning (LoRA/PEFT)
Evals & Tracing (LangSmith)
Tool calling (SKILLS.md, MCP)

Languages

Bengali
Native speaker
English
Bilingual
Hindi
Intermediate
Urdu
Intermediate

Publications

References

Professor Eli Levenson-Falk
Email: elevenso@usc.edu,
Office: SSC 222
Phone: (213) 740-0163
Professor Nural Akchurin
Email: Nural.Akchurin@ttu.edu
Office: 39 Science Building
Phone: (806) 834-8838