Machine Learning Part-Time

Mohammadreza Soltaninia

Contact Information

Email: ****.************@*****.***

Phone: 607-***-****

Location: NYC, United State

Github: https://github.com/natanil-m

LinkedIn: linkedin.com/in/mohammad-soltaninia-485398b8 Google Scholar: https://scholar.google.com/citations?user=HktZG5kAAAAJ Summary

Enthusiastic and versatile computer engineer with a strong background in data science, software development and high- performance computing. Passionate about pushing the boundaries of technology, particularly in the fields of high-performance machine learning. Eager to collaborate and contribute to innovative solutions with like-minded professionals. Education

Alfred University (AU), New York, US

Master of Electrical Engineering Jan 2023 - Present Research Focus: Machine Learning, Quantum Computing Advisor: Dr. Junpeng Zhan

GPA: 4/4

Amirkabir University of Technology (AUT), Tehran, Iran Bachelor of Computer Engineering Sep 2016 - Sep 2021 Focus Areas: Artificial Intelligence, Software Engineering GPA: 18.36/20 (equivalent to 3.93/4, with high honors) Work Experience (Detail in LinkedIn)

Nomyx, United States

Part-time Consultant Nov 2024 - Present

ForeQast, United States, Canada

Chief Technology Officer Jan 2024 - Oct 2024

Founder of Quantum Global Minimum Finder (NSF Granted), New York, United States Quantum Machine Learning Researcher Aug 2022 - Present Raimun Co., Tehran, Iran

Product Manager Jul 2020 - Apr 2021

IranTom Co, Tehran, Iran

Part-Time Full-Stack Developer Jul 2019 - Dec 2019 Butterfly Co, Tehran, Iran

Part-Time Full-Stack Developer Dec 2017 - Jul 2019 University of Isfahan, Isfahan, Iran

Programming Workshop Instructor (java) Jan 2017 - Jul 2017 Padideit Digital Agency, Isfahan, Iran

Programming Course Instructor (swift, iOS) Jan 2017 - Jul 2017 Setayesh Co, Isfahan, Iran

iOS Developer Intern Feb 2016 - Apr 2016

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Research Projects (2022 - Present)

Comparison of Quantum Simulators for Variational Quantum Search (NSF Funded, New York). . . . . . . . . Link: https://github.com/natanil-m/benchmark_vqs

• In-depth performance analysis of Variational Quantum Search

• Evaluation of quantum frameworks, backends, and hardware devices Quantum Global Minimum Finder (NSF Funded, New York). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Link: https://github.com/natanil-m/quantum_global_minimum_finder

• Design a novel two-stage Quantum Machine Learning Algorithm for non-convex optimization

• Secured funding to hire 5 students dedicated to applying the designed circuit for power system unit commitment. Quantum Factorization - Shallow Depth Factoring (NSF Funded, New York). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Link: https://github.com/natanil-m/variational_quantum_factorization

• Novel quantum approach with three key steps: Conversion, Quantum Feasibility Labeling (QFL), and Variational Quantum Search (VQS).

• Linear scalability with respect to circuit depth. Differential and Algebraic Equation Solver using Quantum Machine Learning (NSF Funded, New York) Link: https://github.com/natanil-m/quantum_dae_solver

• Develop new quantum algorithms for efficiently solving time-dependent nonlinear Differential Algebraic Equations (DAEs).

• Focus on high-dimensional simulation problems in large-scale power systems, including transient stability and N-k contingency analysis.

Academic Project Highlights (2016 - 2021)

AI-Powered Online Multiplayer Serious Game. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

• Designed and developed an intelligent and scalable online game for learning the Persian language.

• Implemented audio processing, microservices, NLP, and a speech-to-text model for speech recognition. High-Performance Determinant Calculator for Extremely Large Matrices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

• Implemented a high-performance function that calculates the determinant of very large matrices.

• The function utilizes both CPU (OpenMP) and GPU (CUDA) simultaneously to achieve maximum occupancy.

• Optimized the code by leveraging profiling tools, including Nsight Monitor and Intel VTune. Machine Learning-Powered Document Search Engine. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

• Designed and implemented a machine learning-based search engine, utilizing NLP algorithms for efficient document retrieval from multiple sources.

MIPS CPU ISA Extension & Educational Simulator Project. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

• Implemented an assembler and simulator to extend MIPS CPU Instruction Set Architecture (ISA).

• Requested for use in teaching due to its educational value. Enhancing Operating System Scheduling Policies Project. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

• Modified the kernel of the xv6 operating system to incorporate different scheduling policies.

• Gathered statistical data to compare the performance differences between the scheduling policies. Technical Skills

OS: GNU/Linux, MacOS, Windows

Programming Languages: C/C++, python, dart (flutter), javascript, java, swift, RISK assembly(ARM), latex Frameworks, Platforms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C/C++: CUDA, OpenMP, Intel Parallel Studio, Nsight Monitor Python: Qiskit, Pennylane, PyCUDA, TensorCircuit, Tensorflow, PyTprch, Jax, Cirq, Qulacs, Scikit-learn Javascript: Nodejs, Expressjs, Socket.io

Databases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MongoDB, Redis, MySQL, PostgreSQL, SQLite

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Others. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Networking: GraphQL, RestAPI, RabbitMQ, gRPC

Deployment: NCSA Delta High Performance Computing, Docker, GitHub, Gitflow Advanced Coursework

• Quantum Computing • Advanced Optimization

• Quantum Machine Learning • Advanced Multicore Programming (CUDA, OpenMP)

• Advanced Linear Algebra • Advanced Information Retrieval (NLP)

• Data Mining • Advanced Computational Intelligence (AI) Service to the Profession

• Provided guidance and mentorship to Alfred University undergraduate students participating in a programming competition.

• Volunteered to instruct business professors at Alfred University, New York, in Python and Data Mining.

• Assisted in organizing research seminar series at Alfred University, New York.

• Conducted programming workshops at Amirkabir University of Technology, Iran.

• Assisted in organizing and delivering TED Talks and seminars at University of Isfahan, Iran. Achievements

• 2024: Successfully submitted and received funding from ISO New England for a quantum machine learning proposal, enabling the hiring of five students to work on the practical applications of my research, New York, United States.

• 2023: First-place winner in the Programming Contest hosted by Alfred University, New York, United States.

• 2021: Attained a TOEFL Writing score of 27 out of 30.

• 2018: First-place winner in the Spaghetti Programming Contest hosted by Sharif University of Technology, Tehran, Iran.

• 2015: Awarded the Gold Medal for excelling in the advanced-maze-solving challenge at the RoboCup, Tehran, Iran. Publications

1. M. Soltaninia and J. Zhan, “Comparison of Quantum Simulators for Variational Quantum Search: A Benchmark Study,” accepted by the 27th Annual IEEE High Performance Extreme Computing Conference, Boston, MA, September 25-29, 2023. Available online.

2. M. Soltaninia and J. Zhan, “Quantum Global Minimum Finder based on Variational Quantum Search,” (In-Press) Scientific Journal, 2024. Available online

3. M. Soltaninia and J. Zhan, “Quantum Neural Networks for Solving Power System Transient Simulation Problem,” Submitted to IEEE Transactions on Power Systems, 2024. Available online 4. I.K. Tutul, S. Karimi, M. Soltaninia, and J. Zhan, “Shallow Depth Factoring Based on Quantum Feasibility Labeling and Variational Quantum Search,” To be sumbitted to IEEE Transactions on Quantum Engineering, 2024. Available online. Conference & Presentations

1. Engaged in the Q2B23 SILICON VALLEY conference held in Santa Clara, California, 2023. 2. Participated in the Workshop on Mathematical Aspects of Quantum Learning at UCLA, Los Angeles, California, 2023. 3. Presented a Quantum Machine Learning poster at the Rochester Institute of Technology (RIT), Rochester, New York, 2023.

4. Presented a Quantum Benchmarking poster at the 27th Annual IEEE High-Performance Extreme Computing Conference in Boston, MA, sponsored by Nvidia, 2023.

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