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Basics

Name Sadman Ahmed Shanto
Email shanto@usc.edu
Url https://sadmanahmedshanto.com
Summary Physics PhD Candidate @ USC

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 for quantum device design, simulation, and fabrication-readiness; accelerated design iteration from weeks to minutes and adopted across multiple labs.
    • Developed and validated custom high-yield fabrication processes for nanobridge-SQUID resonators (15 nm features), increasing 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 systems, including cryogenic infrastructure (Oxford Instruments, BlueFors), filtering and microwave chains, CPW PCB design, mounting, and advanced packaging techniques.
    • Built automated measurement infrastructure using QUA, Labber, and AlazarTech acquisition systems; integrated parametric sweeps and custom calibration routines across multiple platforms.
    • 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.
    • 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; built DAQ systems using Arduino, CAMAC crates, and custom PCBs with wireless synchronization.
    • 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 inferred photon time-of-propagation to reconstruct 3D tomograms from 2D detector arrays.
    • 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
QUA
scqubits
Cirq
PennyLane
KLayout
Labber API
Circuit Pro
Simulation and Modeling
Ansys HFSS
Ansys Q3D
AWS Palace
COMSOL
Elmer
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
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