3 citations · 4 across the 12 of their papers we have counts for
13 papers
Cross-disorder Learning of Imaging-derived Joint Mophometric Markers in Alzheimer's and Parkinson's Diseases
Yuji Zhao, Boris Gutman
As two most prevalent neurodegenerative diseases (NDDs), Alzheimer's disease (AD) and Parkinson's disease (PD) shared commonalities both in symptomology and pathophysiology. Howeve…
Simplifying Flow Matching Transformations with Low-Rank Mixture Models
Liam A. Kruse, Houjun Liu, Alexandros E. Tzikas +2
Normalizing flows are powerful generative models that learn an invertible mapping between complex data distributions and simple latent distributions, typically a standard normal de…
Optimizing Lithium Production Decisions under Geological, Demand, and Pricing Uncertainties: A POMDP Framework for Multi-Objective Decision Making
Anna C. Edmonds, Mansur M. Arief, Robert J. Moss +2
Decision making in lithium production is challenging, whether from an investor's perspective or a strategic production standpoint. Determining which mines to open and when to open…
Deepbullwhip: An Open-Source Simulation and Benchmarking for Multi-Echelon Bullwhip Analyses
Mansur M. Arief
The bullwhip effect remains operationally persistent despite decades of analytical research. Two computational deficiencies hinder progress: the absence of modular open-source simu…
AI-Driven Optimization under Uncertainty for Mineral Processing Operations
William Xu, Amir Eskanlou, Mansur Arief +2
The global capacity for mineral processing must expand rapidly to meet the demand for critical minerals, which are essential for building the clean energy technologies necessary to…
Resource Allocation under Stochastic Demands using Shrinking Horizon Optimization
Alexandros E. Tzikas, Nazim Kemal Ure, Mansur Arief +2
We consider the problem of optimally allocating a limited number of resources across time to maximize revenue under stochastic demands. This formulation is relevant in various area…
General-Purpose Robotic Navigation via LVLM-Orchestrated Perception, Reasoning, and Acting
Bernard Lange, Anil Yildiz, Mansur Arief +3
Developing general-purpose navigation policies for unknown environments remains a core challenge in robotics. Most existing systems rely on task-specific neural networks and fixed…
Managing Geological Uncertainty in Critical Mineral Supply Chains: A POMDP Approach with Application to U.S. Lithium Resources
Mansur Arief, Yasmine Alonso, CJ Oshiro +5
The world is entering an unprecedented period of critical mineral demand, driven by the global transition to renewable energy technologies and electric vehicles. This transition pr…
Enhanced Importance Sampling through Latent Space Exploration in Normalizing Flows
Liam A. Kruse, Alexandros E. Tzikas, Harrison Delecki +2
Importance sampling is a rare event simulation technique used in Monte Carlo simulations to bias the sampling distribution towards the rare event of interest. By assigning appropri…
Sliced Distribution Matching based on Cumulative Distribution Functions with Applications to Control
Alexandros E. Tzikas, Arec Jamgochian, Nazim Kemal Ure +2
Computing the similarity between two probability distributions is a recurring theme across control. We introduce a unified family of distances between the probability distributions…
Optimal Control of Mechanical Ventilators with Learned Respiratory Dynamics
Isaac Ronald Ward, Dylan M. Asmar, Mansur Arief +2
Deciding on appropriate mechanical ventilator management strategies significantly impacts the health outcomes for patients with respiratory diseases. Acute Respiratory Distress Syn…
Importance Sampling-Guided Meta-Training for Intelligent Agents in Highly Interactive Environments
Mansur Arief, Mike Timmerman, Jiachen Li +2
Training intelligent agents to navigate highly interactive environments presents significant challenges. While guided meta reinforcement learning (RL) approach that first trains a…
Diffusion-Based Failure Sampling for Evaluating Safety-Critical Autonomous Systems
Harrison Delecki, Marc R. Schlichting, Mansur Arief +3
Validating safety-critical autonomous systems in high-dimensional domains such as robotics presents a significant challenge. Existing black-box approaches based on Markov chain Mon…