Hi! I'm Sepehr a junior pursuing a bachelors in Computer Science and Mathematics at Lake Forest College. I'm driven by a mix of human-centered AI and creative problem-solving. While most of my coursework and projects lean toward deep learning and AI engineering, I've also worked on full-stack development (MERN), GPU computing with CUDA, and basic quantum computing using Qiskit. I'm fascinated by the "sweet spot" where algorithmic automation meets human innovation, building models that don't just process data, but expand our capacity for creativity.

My current research interest is in the intersection of deep learning and combinatorial optimization. I am particularly interested in leveraging diverse architectures, ranging from reinforcement learning to computer vision, as tools to develop neural heuristics for NP-hard problems in mathematics and computer science.

I thrive in the algorithmic world, but I'm just as interested in how technology connects with human innovation. I'm always eager to connect, learn, and collaborate on interesting problems in CS and Math, so please feel free to reach out!

Current Endeavours

Courses:

• CSCI 417: Algorithms & Algorithm Analysis
• MATH 450: Mathematical Statistics
• MATH 491-2: Tensors & Tensor Geometry
• MATH 499: Great Theorems of Mathematics

This semester my main focus is on my bachelor's thesis. which explores combining Transformers and Geometric Deep Learning to evaluate the computational complexity of generating Gröbner bases. My research involves learning a performance metric for Buchberger's algorithm, which is then integrated into a Proximal Policy Optimization (PPO) agent to develop an optimal generator selection strategy. While a niche area of study, I am excited by its potential to resolve computational bottlenecks in various fields, including efficient motion planning in robotics, faster geometric solving in computer vision, and robust risk modeling in quantitative finance.

To prepare myself for my thesis, I spent some time with IVA by Cox et al. last semester, which beyond just algebraic geometry, sparked a fascination with tensor geometry; leading me down the rabbit hole of studying Landsberg's Tensors this semester. I'm also diving into higher-level statistics this term right after studying probability theory; curious to see how these concepts bridge over to my previous work in Bayesian ML. To round things out, I'm taking an algorithms course, which hopefully adds some pure-CS time to my schedule, and a course dedicated to proving "Great Theorems" to satisfy my math tooth.

Additionally, I work as a part-time software engineer at the Applied Data Center, building internal tools for usage across various departments. I am also a teaching assistant for multiple CS/Math courses.

Projects

• Quantum Circuit Optimization: a deep reinforcement learning approach to synthesizing the shortest quantum circuit for a target state or operator with high fidelity.
• Heart: heart murmur, gallops, etc. detection pipeline from heart sound recordings.
• Unjumble: implementation and novel evaluation of search algorithms solving a word jumble puzzle.
• CaeLLM: an LLM built from the ground up, using gpt-oss and qwen3 weights.(on-going)
• Enrollment Cliff: an ML approach to analyzing enrollment trends and US college closures.
• Randomity: a novel Python package for generating and assessing randomness.
• Objectness: an enhanced pipeline for object and saliency detection
• Here is Waldo: finding Waldo with a novel Transfer Learning approach
• Context Color: intelligently colorize BW images through controlled CNN hallucination
• Semantique: NLP model for starring movie reviews
• Video Store: a DBMS for a movie rental store chain
• Hemo Lens: blood cells analysis pipeline using classical methods
• Stop the Churn: retention prediction tool
• Caelum-Lang: self-designed compiler (on-going)
• Tracking CO2 Footprint: carbon emissions trend prediction
• Two Faces of the Moon: Pink Floyd, statistically
• Radioactive Modeling: simulating nuclear decay
• Trash Inference: understanding Baltimorians, from their trash
• Gas Today: exploring imputing prices in time series data
• Algorithming Sudoku: a backtracking solution to create and solve sudoku puzzles (on-going)

Teaching

[Current Office Hours]

Teaching Assistant, CSCI 450: Computer Vision & Machine Learning [Dr. Sugata Banerji, Spring '26, Lake Forest College]

Teaching Assistant, CSCI 325: Artificial Intelligence [Dr. Sara Jamshidi, Spring '26, Lake Forest College]

Teaching Assistant, MATH 231: Linear Algebra [Dr. Sara Jamshidi, Spring '26, Lake Forest College]

Teaching Assistant, MATH 230: Abstract & Discrete Mathematics [Dr. Arthur Bousquet, Spring '26, Lake Forest College]

Grader, MATH 574: Bayesian Computational Statistics [Dr. Sara Jamshidi, Fall '25, Illinois Institute of Technology]

Teaching Assistant, CSCI 317: Data Structures & Algorithms [Dr. Sugata Banerji, Fall '25, Lake Forest College]

Teaching Assistant, MATH 250: Statistical Programming [Dr. Andrew Gard, Fall '25, Lake Forest College]

Teaching Assistant, CSCI 250: Programming for Data Applications [Dr. Arthur Bousquet, Fall '25, Lake Forest College]

Teaching Assistant, MATH 230: Abstract & Discrete Mathematics [Dr. Sara Jamshidi, Fall '25, Lake Forest College]

Teaching Assistant, MATH 150: Probability & Statistics [Dr. Tung Nguyen, Summer '25, Lake Forest College]

Teaching Assistant, MATH/CSCI 240: Computational Mathematics [Dr. Sara Jamshidi, Spring '25, Lake Forest College]

Teaching Assistant, CSCI 112: Functional Programming [Dr. Tung Nguyen, Spring '25, Lake Forest College]

Teaching Assistant, CSCI 107: Web Programming [Dr. Tung Nguyen, Spring '25, Lake Forest College]

Teaching Assistant, CSCI 112: Functional Programming [Dr. Tung Nguyen, Fall '24, Lake Forest College]

Teaching Assistant, CSCI 112: Functional Programming [Zoya Mirza, Spring '24, Lake Forest College]

Team Mentor, Rescuer Robotics Team [Salam Dibaji Highschool, Summer '23, FIRA Cup]

Industry Work

Software Engineer (Part-time), Applied Data Center [Summer '25 - ongoing]

Software Engineer (Intern), Tenacity AI [Summer '24]

Software Engineer (Intern), Elite Engineering Solution [Summer '23]

Software Engineer (Intern), Lavan Energy Group [Summer '22]

Publications

A Python package for generating and assessing randomness. [Python Package Index (PyPI), 2025, Sepehr Akbari]

Comparing Insertion Complexity in AVL and Splay BSTs [Unpublished Manuscript, 2022, Sepehr Akbari]

Modeling a Chicken Egg [Unpublished Manuscript, 2022, Sepehr Akbari]

Past Courses

CSCI 450: Computer Vision & Machine Learning
MATH 491-1: Computational Algebraic Geometry
MATH 350: Mathematical Probability
CSCI 327: Database Systems
CSCI 325: Artificial Intelligence
CSCI 317: Data Structures & Algorithms
CSCI 270: Web Development
MATH 250: Statistical Programming
CSCI 250: Programming for Data Applications
MATH/CSCI 240: Computational Mathematics
MATH/CSCI 231: Linear Algebra
MATH 230: Abstract & Discrete Mathematics
CSCI 212: Object-Oriented Programming
MATH 150: Probability & Statistics
MATH 111: Calculus II
MATH 110: Calculus I
CSCI 107: Web Programming