About

The page provides information about the New York University Stern Center for Research Computing (SCRC), which is devoted to providing world-class computational facilities and services to researchers at the Stern School of Business. The center offers a variety of services including a moderately sized Slurm HPC cluster, Cloud Computing (Virtual Machines), data acquisition and storage, research software, and access to WRDS (Wharton Research Data System). The research software suite is designed to facilitate advanced computational research and data analysis, while the datasets are sourced from diverse disciplines through collaborations with data repositories, platforms, and academic institutions. The compute services and storage systems support faculty and researchers' projects with high-speed, robust, and scalable solutions. The page does not contain specific biographical information about Professor Peter Lakner, including his research focus, background, or key contributions.

Research topics

• Computer Science
• Mathematics
• Statistics
• Mathematical optimization
• Artificial Intelligence
• Machine Learning
• Operating system
• Combinatorics
• Engineering
• Computer network
• Geometry
• Distributed computing
• Mathematical analysis

Selected publications

• Optimal cash management using impulse control

  Indagationes Mathematicae · 2023 · 1 citations

  1st authorCorresponding
  • Computer Science
  • Mathematics
  • Mathematical optimization
  PublisherDOI

• Reflected Brownian motion with drift in a wedge

  Queueing Systems · 2023 · 2 citations

  1st authorCorresponding
  • Computer Science
  • Mathematics
  • Mathematical analysis
  PublisherDOI

• Reflected Brownian Motion with Drift in a Wedge

  arXiv (Cornell University) · 2022-04-22

  preprintOpen access1st authorCorresponding

  We study reflecting Brownian motion with drift constrained to a wedge in the plane. Our first set of results provide necessary and sufficient conditions for existence and uniqueness of a solution to the corresponding submartingale problem with drift, and show that its solution possesses the Markov and Feller properties. Next, we study a version of the problem with absorption at the vertex of the wedge. In this case, we provide a condition for existence and uniqueness of a solution to the problem and some results on the probability of the vertex being reached.

  PublisherOA PDFDOI

• LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and Trajectory Prediction

  arXiv (Cornell University) · 2022 · 13 citations

  • Computer Science
  • Computer Science
  • Artificial Intelligence

  Multi-agent trajectory prediction is a fundamental problem in autonomous driving. The key challenges in prediction are accurately anticipating the behavior of surrounding agents and understanding the scene context. To address these problems, we propose LatentFormer, a transformer-based model for predicting future vehicle trajectories. The proposed method leverages a novel technique for modeling interactions among dynamic objects in the scene. Contrary to many existing approaches which model cross-agent interactions during the observation time, our method additionally exploits the future states of the agents. This is accomplished using a hierarchical attention mechanism where the evolving states of the agents autoregressively control the contributions of past trajectories and scene encodings in the final prediction. Furthermore, we propose a multi-resolution map encoding scheme that relies on a vision transformer module to effectively capture both local and global scene context to guide the generation of more admissible future trajectories. We evaluate the proposed method on the nuScenes benchmark dataset and show that our approach achieves state-of-the-art performance and improves upon trajectory metrics by up to 40%. We further investigate the contributions of various components of the proposed technique via extensive ablation studies.

  PublisherOA PDFDOI

• Optimal cash management using impulse control

  arXiv (Cornell University) · 2022-06-08

  preprintOpen access1st authorCorresponding

  We consider the impulse control of Levy processes under the infinite horizon, discounted cost criterion. Our motivating example is the cash management problem in which a controller is charged a fixed plus proportional cost for adding to or withdrawing from his/her reserve, plus an opportunity cost for keeping any cash on hand. Our main result is to provide a verification theorem for the optimality of control band policies in this scenario. We also analyze the transient and steady-state behavior of the controlled process under control band policies and explicitly solve for the optimal policy in the case in which the Levy process to be controlled is the sum of a Brownian motion with drift and a compound Poisson process with exponentially distributed jump sizes.

  PublisherOA PDFDOI

• Graph-SIM: A Graph-based Spatiotemporal Interaction Modelling for Pedestrian Action Prediction

  2021-05-30 · 22 citations

  article

  One of the most crucial yet challenging tasks for autonomous vehicles in urban environments is predicting the future behaviour of nearby pedestrians, especially at points of crossing. Predicting behaviour depends on many social and environmental factors, particularly interactions between road users. Capturing such interactions requires a global view of the scene and dynamics of the road users in three-dimensional space. This information, however, is missing from the current pedestrian behaviour benchmark datasets. Motivated by these challenges, we propose 1) a novel graph-based model for predicting pedestrian crossing action. Our method models pedestrians’ interactions with nearby road users through clustering and relative importance weighting of interactions using features obtained from the bird’s-eye-view. 2) We introduce a new dataset that provides 3D bounding box and pedestrian behavioural annotations for the existing nuScenes dataset. On the new data, our approach achieves state-of-the-art performance by improving on various metrics by more than 15% in comparison to existing methods. The dataset is available at https://github.com/huawei-noah/datasets/PePScenes.

  PublisherDOI

• On the optimality of the earliest due date rule in stochastic scheduling and in queueing

  European Journal of Operational Research · 2021 · 8 citations

  • Computer Science
  • Computer Science
  • Mathematical optimization
  PublisherDOI

• On Applications of the Earliest Due Date Rule in Stochastic Scheduling and in Queueing

  SSRN Electronic Journal · 2020-01-01

  articleOpen access
  PublisherDOI

• PePScenes: A Novel Dataset and Baseline for Pedestrian Action Prediction in 3D

  arXiv (Cornell University) · 2020-12-14 · 8 citations

  preprintOpen access

  Predicting the behavior of road users, particularly pedestrians, is vital for safe motion planning in the context of autonomous driving systems. Traditionally, pedestrian behavior prediction has been realized in terms of forecasting future trajectories. However, recent evidence suggests that predicting higher-level actions, such as crossing the road, can help improve trajectory forecasting and planning tasks accordingly. There are a number of existing datasets that cater to the development of pedestrian action prediction algorithms, however, they lack certain characteristics, such as bird's eye view semantic map information, 3D locations of objects in the scene, etc., which are crucial in the autonomous driving context. To this end, we propose a new pedestrian action prediction dataset created by adding per-frame 2D/3D bounding box and behavioral annotations to the popular autonomous driving dataset, nuScenes. In addition, we propose a hybrid neural network architecture that incorporates various data modalities for predicting pedestrian crossing action. By evaluating our model on the newly proposed dataset, the contribution of different data modalities to the prediction task is revealed. The dataset is available at https://github.com/huawei-noah/PePScenes.

  PublisherOA PDFDOI

• Graph-SIM: A Graph-based Spatiotemporal Interaction Modelling for Pedestrian Action Prediction

  arXiv (Cornell University) · 2020-12-03 · 4 citations

  preprintOpen access

  One of the most crucial yet challenging tasks for autonomous vehicles in urban environments is predicting the future behaviour of nearby pedestrians, especially at points of crossing. Predicting behaviour depends on many social and environmental factors, particularly interactions between road users. Capturing such interactions requires a global view of the scene and dynamics of the road users in three-dimensional space. This information, however, is missing from the current pedestrian behaviour benchmark datasets. Motivated by these challenges, we propose 1) a novel graph-based model for predicting pedestrian crossing action. Our method models pedestrians' interactions with nearby road users through clustering and relative importance weighting of interactions using features obtained from the bird's-eye-view. 2) We introduce a new dataset that provides 3D bounding box and pedestrian behavioural annotations for the existing nuScenes dataset. On the new data, our approach achieves state-of-the-art performance by improving on various metrics by more than 15% in comparison to existing methods. The dataset is available at https://github.com/huawei-noah/datasets/PePScenes.

  PublisherOA PDFDOI

Frequent coauthors

• Josh Reed

  New York University

  11 shared

• Abel Cadenillas

  University of Alberta

  6 shared

• Michael Pinedo

  New York University

  6 shared

• Anand Srinivasan

  5 shared

• Ashay Kadam

  5 shared

• Michael Pinedo

  4 shared

• Bert Zwart

  3 shared

• Florian Simatos

  École Nationale de l’Aviation Civile

  3 shared