About
I am Mohammadreza Daneshvaramoli and I am a Ph.D. student at the University of Massachusetts Amherst since Spring 2023. I obtained my BSc and MSc in Computer Engineering(Software Engineering) from Sharif University of Technology. My PhD research involves improving online decision-making processes, learning algorithms, algorithms with uncertainty, and optimization.
Skills
Programming Languages
C
C++
Java
Python
Pascal
Delphi
C#
Matlab
80x86
Racket
Lisp
HTML
CSS
LaTeX
MIPS
Verilog
VHDL
SQL
Technical
Matlab
Visual Studio
Eclipse
CLion
PyCharm
Git
OOP
Research Skills
Learning Algorithms
Artificial Intelligence
Machine Learning
Optimization
Monte-Carlo Tree Search
Pathfinding Algorithms
Algorithms with Uncertainty
Data Analysis
Graph Theory
Design Algorithms
Web Application Development
Achievements
Olympiads
- Gold medal in Iran National Mathematics Olympiad, Tehran, Iran, 2013
- Gold medal in Iran National Computer Science Olympiad for undergraduate students. Iran, 2019
- Silver Medal in 6th International Tournament of Young Mathematicians, Germany, 2014 (Certificate)
- Ranked 2nd in the annual Iranian nationwide universities’ M.Sc. entrance exam (Konkoor), among more than 16,000 students in Software Engineering major, Iran, 2019
Programming Contests
- Ranked 2nd team in qualifying round of Artificial Intelligent programming tournament MIT BattleCode held by MIT, and Ranked 13th among more than 500 teams from all around the world and won $1000, USA, Feb 2019.
- Ranked 1st team in Sharif Artificial Intelligence Challenge, Iran, Mar 2016. (Certificate)
- Ranked 3rd team in a National Data Mining Programming Contest and won prize equivalent to $700, Iran, Nov 2017.
- Ranked 2nd team in the National Presidential Election Prediction Competition and won prize equivalent to $2000. Using Natural Language Processing and statistical methods, we built an AI system that predicted the 2017 Iranian presidential election results with 96 percent accuracy, Iran, May 2017. (Certificate)
- Ranked 1st team in Makeathon Start-Up competition at Sharif University and won prize equivalent to $2000. We developed a new social media and presented its app, Iran, Mar 2017.
- Ranked 4th team in an annual contest of Sharif Artificial Intelligence Programming Challenge, Iran, Mar 2017. (Certificate)
Research Projects
PhD projects, University of Massachusetts Amherst, 2024–2025
- Near-Optimal Consistency-Robustness Trade-Offs for Learning-Augmented Online Knapsack Problems. Accepted to ICML 2025.
- Developed learning-augmented online knapsack algorithms utilizing minimal predictive input—specifically, a single value or interval estimating the minimum value of items accepted by an optimal offline solution—achieving consistency and robustness.
- Fairness in the k-Server Problem. Submitted to ITCS 2025.
- The goal is to ensure no server bears excessive load while serving incoming requests (fairness notion) in a metric space.
- Proved that there is no deterministic online algorithm that achieves a competitive ratio less than the diameter of the metric space while also being fair. Also, showed that for any non-constant k in a tree graph, the Double Coverage algorithm is not fair.
- The Secretary Problem with Predictions and Chosen Order. Submitted to ITCS 2025.
- Proposed a new hiring problem where you can use predictions and even choose interview order, with an algorithm that stays strong even if predictions are wrong.
- Navigable Graphs for High-Dimensional Nearest Neighbor Search. Ongoing research
- Developing scalable navigable graph structures for high-dimensional nearest neighbor search, with a focus on establishing rigorous theoretical performance bounds for graph-based retrieval methods.
MSc and BSc thesis, Sharif University of Technology, Tehran, Iran
- K-strong conflict-free assignment to regions, MSc thesis. Assigned resources ensuring optimality and balance among regions. May 2022
- Path-finding of a self-moving agent, BSc thesis. Developed an algorithm for a single mobile agent to move efficiently. May 2020
Technical Projects
Imperial College London, web application developer intern, Department of Natural Sciences, London, UK, Summer 2018
- Developed a web application to estimate exposure to laboratory hazards, focusing on numerical analysis.
- Designed an application to perform risk assessments for organic solvent purifications, improving lab safety and efficiency.
Institute for Research in Fundamental Sciences, full time researcher, Tehran, Iran, 2020 – 2022
- Applied Monte Carlo Tree Search (MCTS) with domain-specific heuristics to solve diverse classes of constraint satisfaction problems, showcasing adaptability across complex optimization tasks (ICCTA 2021)
- Engineered an optimized Monte Carlo Tree Search (MCTS) algorithm for large-scale multi-agent pathfinding, delivering faster runtimes and lower memory usage for complex optimization tasks (FICC 2022)
- Developed an optimized Monte Carlo Tree Search (MCTS) framework with a 2D search space to efficiently handle large-scale optimization problems, achieving quadratic-time performance with linear memory use (IEEE 2019)
- Designed and implemented a scalable decentralized algorithm for multi-agent task allocation that eliminates the need for inter-agent communication, improving efficiency and robustness in distributed systems (IEEE 2020)
- Introduced the Maze Dash puzzle and developed a hybrid approach using Monte Carlo Tree Search (MCTS) and SAT to compute Hamiltonian paths, optimizing MCTS for faster and more accurate solutions (WSSE 2021)
Contacts
Email: FirstName.LastName@gmail.com