About

I am Mohammadreza Daneshvaramoli, a Ph.D. student in Computer Science at the University of Massachusetts Amherst since Spring 2023. I received my B.Sc. and M.Sc. in Computer Engineering (Software Engineering) from Sharif University of Technology.

My research lies at the intersection of online algorithms and machine-learning predictions. I study how predictions produced by learning systems can be incorporated into the design of online and approximation algorithms to improve performance while preserving strong worst-case guarantees.

In particular, I work on learning-augmented algorithms for problems such as:

• Online knapsack
• Secretary selection
• k-server
• Scheduling and resource-allocation tasks

My research focuses on:

• Consistency–robustness trade-offs
• Fairness in online decision-making
• Reliability under uncertainty and prediction errors
• Optimization with partial or noisy information

My goal is to develop theoretically grounded algorithms that bridge machine learning, online optimization, and algorithmic systems, enabling practical improvements in areas such as scheduling, caching, and resource management.

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 Projects

PhD Research projects, University of Massachusetts Amherst, 2024–2025

• Near-Optimal Consistency-Robustness Trade-Offs for Learning-Augmented Online Knapsack Problems. 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.ITCS 2026.
  • 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.ITCS 2026.
  • Proposed a new hiring problem where you can use machine-learned 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.
• Calibrated Predictions for Learning-Augmented Caching. Ongoing research
  • Designing online caching algorithms that leverage calibrated probabilistic machine-learned predictions for page reuse, and proving competitive guarantees that depend on calibration error.
  • The work introduces a binary prediction framework for caching and develops phase-based analyses connecting miscalibration to additional cache faults.

Pre-PhD Research projects, Institute for Research in Fundamental Sciences, 2020–2022

• On using Monte-Carlo tree search to solve puzzles. ICCTA 2021.
  • Applied Monte Carlo Tree Search (MCTS) with domain-specific heuristics to solve diverse classes of constraint satisfaction problems, showcasing adaptability across complex optimization tasks.
• A Study on Non-overlapping Multi-agent Pathfinding. FICC 2022.
  • 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.
• Multi-agent non-overlapping pathfinding with monte-carlo tree search. IEEE 2019.
  • 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.
• Decentralized communication-less multi-agent task assignment with cooperative Monte-Carlo tree search. IEEE 2020.
  • 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.
• Unlucky explorer: a complete non-overlapping map exploration. WSSE 2021.
  • 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.

MSc and BSc thesis, Sharif University of Technology

• 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

Achievements

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.

Contacts

Email: FirstName.LastName@gmail.com