cv

I promise to thou - stranger on the internet - that I will keep this up to date 🤞🏽

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 @ USC and Research Intern @ Google Quantum AI.

Education

Work

  • 2025.08 - Present

    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.
    • Engineered a production-ready agentic AI framework with multi-agent coordination, evaluation, and tracing pipelines for autonomous calibration and diagnostics of superconducting quantum experiments.
    • Architected and deployed full-stack backend infrastructure—custom APIs, scalable databases, and containerized microservices on AWS—integrating Quantum Elements’ ML models, simulators, and databases with the AI agent ecosystem.
    • Built the infrastructure enabling autonomous execution of superconducting quantum experiments, allowing AI agents to calibrate, measure, diagnose, and analyze results through integrated MCP servers and ML-driven control workflows.
  • 2022.01 - Present

    Los Angeles, CA, USA

    Graduate Research Assistant
    Levenson-Falk Lab (LFL), University of Southern California
    • Lead developer of SQuADDS, an open-source platform accelerating quantum device design from weeks to minutes; widely adopted across research and industry labs for simulation, optimization, and fabrication-ready layout generation.
    • Developed and validated high-yield nanofabrication processes for nanobridge-SQUID resonators with 15 nm features, scaling functional device yield from <2% to >90%.
    • Designed and fabricated superconducting circuits including nanobridge-based resonators, offset-charge-sensitive transmons, and custom Josephson parametric amplifiers for probing quasiparticle dynamics and enhancing qubit readout.
    • Engineered full-stack experimental infrastructure—from cryogenic systems (Oxford Instruments, BlueFors) and microwave chains to automated measurement platforms using QUA, Labber, and AlazarTech hardware—integrating parametric sweeps, calibration routines, and advanced packaging for superconducting quantum devices.
    • Created a highly accurate Hidden Markov Model (HMM)-based inference pipeline to extract real-time quasiparticle occupation states from I/Q trajectory data, enabling detailed dynamic modeling of QP trapping and release.
    • Leading experiments on Andreev bound state spectroscopy and nanoSQUID-based QP traps to investigate and mitigate quasiparticle-induced decoherence in superconducting qubits.
    • Extended SQuADDS to support interpretable ML workflows using Kolmogorov-Arnold Networks (KANs) to learn mapping between device geometry and target Hamiltonians.
    • Collaborating with NVIDIA and Fermilab to develop explainable AI methods for quantum device design workflows, enabling physics-driven interpretability to uncover design principles and accelerate optimization of superconducting circuits.
    • Leading open-source integration of AWS Quantum's EM solver Palace with Quantum-Metal, enabling scalable cloud-based simulation pipelines for the quantum hardware community.
    • Designed and maintain HPC-based simulation and analysis pipelines for TB-scale datasets, optimizing for throughput, fault tolerance, and operational stability.
    • Mentored undergraduate researchers, master's students, and junior PhD students on quantum circuit design, measurement automation, and fabrication processes.
  • 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, addressing parameter identifiability and stochastic noise under multi-objective constraints.
    • Parallelized simulation-optimization workflows using Ray; achieved 10x+ speedup in sweep-based calibration experiments.
    • Designed tools to convert simulation output from the Intelligent Driver Model (IDM) into radar-style datasets for validation against real-world aggregate metrics.
    • Contributed to the Flow RL framework, enabling closed-loop learning in calibrated traffic environments; supported end-to-end tuning and evaluation.
    • Co-authored a peer-reviewed conference paper on microsimulation calibration using aggregate measurements.
    • Supervisors: Daniel Work, PhD. & George Gunter, PhD.
  • 2019.01 - 2020.06

    Lubbock, TX, USA

    Undergraduate Research Assistant
    Texas Tech Multidisciplinary Research in Transportation (TechMRT)
    • Developed an open-source simulator for heterogeneous AV/HV traffic using an extended Nagel-Schreckenberg CA model; supported both rule-based and learning agents.
    • Designed AV control strategies for shared-lane mobility and dynamic lane switching; revealed emergent behaviors like intelligent herding and platoon formation.
    • Integrated reinforcement learning for AVs to adapt to local density gradients; demonstrated benefits in flow stability and throughput in multi-lane networks.
    • Analyzed macro-scale flow metrics derived from microscopic simulation rules; co-authored a journal paper identifying system-wide effects of AV/HV composition.
    • Supervisor: Jia Li, PhD
  • 2018.11 - 2021.08

    Lubbock, TX, USA

    Undergraduate Research Assistant
    Advanced Particle Detector Laboratory (APDL), Texas Tech University
    • Led end-to-end design of optical system upgrades for muon telescopes; developed custom Winston cones that improved signal efficiency from 20% to 78%.
    • Co-designed and assembled both SiPM- and PMT-based muon telescopes; machined 50+ scintillator bars; engineered calibration and installation of 40 SiPMs and 44 PMTs.
    • Built DAQ systems using Arduino, CAMAC crates, and custom PCBs with wireless synchronization; implemented multithreaded sync and FPGA logic to reduce channel deadtime by 300x.
    • Wrote real-time data acquisition and analysis software, converting raw readout into muon flux maps; deployed automated pipelines on the university HPC, with cloud-style report generation.
    • Built and validated full Geant4-based Monte Carlo simulation of the experimental system, including physics modeling of photon scattering and muon interactions.
    • Developed a custom ML architecture that uses TDC-based photon time-of-propagation measurements to infer depth information, enabling 3D tomographic reconstruction from 2D detector plane data.
    • Implemented RNN/LSTM models to recover missing hit data, improving data efficiency and significantly enhancing image resolution from sparse muon events.
    • Actively involved in multiple data-taking campaigns, including first experimental run; maintained 24/7 operations and emergency response support.
    • Presented work at national conferences, winning multiple awards for technical talks and posters on detector design and reconstruction algorithms.
    • Co-authored peer-reviewed publications on both the initial prototype and next-generation telescope design.
    • Supervisors: Shuichi Kunori, PhD. & Nural Akchurin, PhD.

Talks

Conferences

Awards

Skills

Programming Languages
Python
C++
C
Bash/ZSH
Lua
Java
Matlab
R
Julia
TeX
Go
Swift
Mathematica
Rust
Quantum Computing Software
Qiskit
Qiskit Metal
Quantum-Metal
QUA
scqubits
Cirq
PennyLane
Labber
AlazarTech PCIe Digitizer
OPX+
Zurich Instruments
Layout & Verification
gdsfactory
gdspy
gplugins
KLayout
kfactory
KQCircuits
phidl
Siemens nmDRC
SiEPIC-Tools
SQDMetal
Simulation and Modeling
Ansys HFSS
Ansys Q3D
AWR Office
COMSOL
Elmer
Keysight ADS
meep
Palace
tidy3d
Quantum Computing Control Systems
Quantum Machines OPX
Zurich Instruments
QICK
Cryogenic and Vacuum Systems
Bluefors
Oxford Instruments
Turbo and Scroll Pumps
Helium Compressors
Leak Detectors
Packaging
Wirebonding
Soldering
CPW and Microstrip Design
RF Packaging
Cavity Design
Electroplating
Fabrication and Cleanroom
EBL
Laser Lithography
Photolithography
SEM
FIB
STM
E-Beam Evaporation
Thermal Evaporation
ALD
Sputtering
RIE
ICP Etching
Dicing
Lift-off
Wet Etching
Dry Etching
CMP
Class 100/1000 Cleanroom
Profilometry
Process Optimization
Metrology
Recipe Calibration
Machine Learning and AI
TensorFlow
PyTorch
Keras
Scikit-learn
XGBoost
HuggingFace
Transformers
Ray
AutoML
JAX
LightGBM
Hyperopt
HMMLearn
pyKAN
interpret
TFX
Data Analysis
Numpy
Pandas
Vaex
Matplotlib
Seaborn
Plotly
Dask
Apache Spark
SQL
Dash
StatsModels
SymPy
High-Performance Computing & Cloud Platforms
AWS
GCP
Azure
Docker
Kubernetes
OpenMPI
SLURM
Ray
Dask
TensorFlow Extended (TFX)
Database and Big Data
PostgreSQL
MongoDB
Apache Hadoop
Apache Spark
AWS RDS
DevOps and Version Control
Git
GitHub
GitLab
CI/CD Pipelines
Docker
Kubernetes
Digital Electronics & Embedded Systems
FPGA Design
Verilog
Vivado Design Suite
KiCad
LTspice
Arduino
Raspberry Pi
PCB Design
Siemens nmDRC
Web Development and APIs
HTML
CSS
JavaScript
Node.js
Flask
FastAPI
Django
REST APIs
GraphQL
WebSockets

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