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Jean-Jacques St. Leroux

Aspiring Optimization & Engineering Researcher

I am broadly interested in creating and harnessing computational advancements for nonlinear and stochastic optimization, and applying them in finance, electrical engineering, IT, operations research, and artificial intelligence.

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About Me

About Me

Me in San Francisco

I'm Jean-Jacques, an undergrad at tOSU.

I have dreams to impact optimization through cutting-edge software and hardware, and vice versa.

I've gained valuable insights from diverse experiences: designing software and IT for quantum physics research, developing risk & ML systems for a hedge fund, consulting on optimization algorithms for a private company, and interning at a leading bank to study risk analytics. See what I've learned along the way.

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Skills

Skills

Education & Skills

  • The Ohio State University

    B.Sc & B.S.B.A with Honors: Mathematics, Computer Information Systems, Class of 2026


    Involvements:
    • IEEE, Ohio State Chapter
    • INFORMS, Ohio State Chapter
    • Translational Data Analytics Institute
    • Software Engineering Club

    Honoraries:
    • 27th Honors Cohort
    • Buckeye Leadership Fellows
    • Gamma Iota Sigma

    Awards:
    • Dean's Promise Award
    • Geraldine H. Waring Award
    • Michael and Judith Heschel Award
    • Mortar Board Leadership Award
    • Project THRIVE (EY, KPMG, Deloitte, PwC Sponsored) Scholarship Group

  • Python

    AI (various), optimization, scientific computing, data science, back-end, networking, GUIs, scripting

  • R R logo

    Statistical modelling, data analysis and visualization, economics and financial modelling basics

  • Java My portrait

    Core Java, data structures & algorithms, 2D games, GUIs, I/O, threading, SQL interfacing

  • MATLAB Matlab Logo

    Mathematics & engineering research, scientific computing basics

  • C++

    Data structures and algorithms basics, sorting algorithms, SDL2

  • Git/GitHub

    What I use for most, but not all, of my projects!

  • C C Logo

    Used alongside Assembly in my self-study on kernel development, low-level interfacing; data structures and algorithms practice

  • x84-64

    In my free time, I self-study assembly to understand low-level computing, boot sector, compilers, and CPU registers/instructions

  • HTML/CSS

    What I used to build this website and my blog (TBD) alongside JavaScript! Also used in GUI development.

  • JavaScript

    What I used to build this website and my blog (TBD, node.js)!

  • Databases

    Various experiences using RDBMS (client-server, file-server) and NoSQL (document-store, wide-column)

Experience

Quantitative Research Intern (Risk Office)

New York, NY
June 2024 - August 2024

Quantitative research in risk modeling, machine learning development utilizing APIs, cloud computing, NoSQL.

Optimization Software Engineer

Columbus, OH
March 2024 - Present

Optimization software engineer & consultant for a private company, leveraging supercomputing, CPLEX, and cloud computing.

AI Engineer, Computer Engineer

Dayton, OH
January 2024 - Present

A startup venture with my brother to research integrating computer vision, NLP, LLM, ML/Deep Learning into cyber-physical systems.

Co-Founder, Researcher

Columbus, OH
August 2023 - Present

Co-Founder of a financial engineering research group, leading quantitative research, full-stack development, documentation, and much more.

Summer Analyst

New York, NY
June 2023 - August 2023

ABP 2023: Company rotations in risk management & securities services divisions within the investment bank.

Computer Engineering Intern

Columbus, OH
May 2022 - August 2022

Built out IT protocols, software packages, and GUIs to connect to / interact with hardware used to research topology in quantum materials.

A disclaimer about my skills:

I have a long way to go before I can say I'm an expert in everything mentioned above. But...

I can learn and relearn anything at any time. Software is a tool -- it's a way we interact with computers to do what we need them to. I simply ask computers to do quite a bit for me, from websites to machine learning to database interaction and dynamic memory management. Hence I've picked up foundational skills in a few 21st century languages. But, I am not claiming to be 100% proficient in all of them.

With that said, it is imperative to me I become proficient at whatever is needed to create research. So, I've done some work to earn these skills. See more about my motivations and projects below.

Research

Research

My research interests

  • Stochastic Optimization

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    Stochastic Optimization

    Most of the world's problems aren't deterministic. Hence, it's imperative we view problems stochastically.

    My specific interests pertaining to stochastic optimization are algorithms & metaheuristics. I enjoy thinking about genetic algorithms, simulated annealing, particle swarm optimization, and ant colony optimization in the context of stochastic and nonlinear optimization problems. I find it fascinating how we can leverage these techniques for improving convergence speed, scalability, and robustness in handling complex & high-dimensional problems.

  • HPC-Driven, RTO Solvers

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    High Performance Computing-Driven and Real-Time Optimization Solvers

    I.e., making solvers in optimization work in real-time situations through powerful computers.

    I find it fascinating to ponder parallel/distributed algorithms capable of solving large-scale stochastic & nonlinear optimization problems. I hope to explore techniques for load balancing, communication minimization, and fault tolerance in optimization frameworks to leverage modern computer architectures. I explore this through my part-time work in cyber-physical systems, where algorithms interact with physical processes.

  • Nonlinear Optimization

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    Nonlinear Optimization

    The challenge of nonlinear optimization is a challenging, yet fruitful field for my studies.

    I took a course called ISE 7200 at Ohio State in Spring 2024 which changed my life. Surrounded by graduate students, I learned the facets of nonlinear optimization: convex analysis separation theorems, FOOC, algorithms (convergence, strong convextiy, Lipschitz continuity, gradient descent, Newton), and more. Now, I aim to implement nonlinear optimization into my work to model & solve such problems.

  • Optimization in Practice

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    Optimization in Practice

    Compiling my research interests to solve real-world problems.

    The reason why I study what I do is because I quickly got attached to the mission statement of optimization & O.R. when I discovered the subjects. Though fields like applied math and computer science are interesting to me, nothing beats studying optimization's real-world impact. I hope to conduct studies on portfolio construction, control, and ML, but also look "backwards" on technologies driving optimization as I mentioned before.

Projects

Projects

"The Best Snake"

C++, Java, Python graphics libraries comparative analysis

"The Best Snake" (03/2024)

"The Best Snake" analyzes memory usage of the "Snake" game implemented identically in Java, Python, and C++ using their respective graphics libraries (AWT/Swing, SDL, Tkinter) on my 2020 MacBook Air (i3). I added memory profiling tools in each language to monitor/compare CPU memory consumption of gameplay. My analysis sheds light on efficiencies of each language's graphics library and provides insights into memory management.

Genetic Algorithm in C++

A non-exhaustive recreation of a genetic algorithm with OOP.

Genetic Algorithm in C++ (02/2024)

I optimized a genetic algorithm I saw online by leveraging memory management techniques like std::move and emplace_back, improving performance for potentially complex objects. I integrated fitness evaluation into my constructor method to reduce iteration overhead. The original experiment sorted its entire solution vector, but I employed std::nth_element, which is useful for large datasets. I also used CLI (Bash) to execute my program.

OB-Stick

Predicting candlestick charts with KNN and Logistic Regression.

OB-Stick (11/2023)

OB-Stick applies logistic regression and KNN to predict "gravestone" candlestick patterns in financial data based on a mathematical algorithm I invented. Logistic regression is tuned with hyperparameters, KNN is trained with SMOTE for addressing class imbalance. Evaluation involves metrics like accuracy, confusion matrices, and ROC curves. Motivated by HFT applications. Used AWS to incorporate into front-end app with Dart and Flutter (front-end unfinished).

Contact Me

Contact Me

Feel free to reach out about research, Obie-McFarland, Daytime, or anything else.