Alexander Ihler

About

Alexander Ihler is a Professor in the Department of Computer Science at the University of California, Irvine. He received his Ph.D. in Electrical Engineering and Computer Science from MIT in 2005 and holds a B.S. with honors from Caltech, earned in 1998. His research focuses on machine learning, graphical models, and algorithms for both exact and approximate inference. His work has applications in sensor networks, computer vision, data mining, and computational biology. Professor Ihler has been recognized with an NSF CAREER award and has received several best paper awards at conferences including NIPS, IPSN, and AISTATS.

Research topics

• Computer Science
• Artificial Intelligence
• Computer network
• Machine Learning
• Data Mining
• Distributed computing
• Transport engineering
• Engineering

Selected publications

• Multi-Lane Short-Term Traffic Forecasting With Convolutional LSTM Network

  IEEE Access · 2020 · 78 citations

  Senior authorCorresponding

  Short-term traffic prediction consists a crucial component in intelligent transportation systems. With the explosion of automated traffic monitoring sensors and the flourishing of deep learning techniques, a growing body of deep neural network models have been employed to tackle this problem. In particular, convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks have demonstrated their advantages in modeling and predicting the spatiotemporal evolution of traffic…

  DOI

• A Cross-Layer, Mobility, and Congestion-Aware Routing Protocol for UAV Networks

  IEEE Transactions on Aerospace and Electronic Systems · 2022 · 32 citations

  Autonomous and decentralized unmanned aerial vehicle (UAV) networks are used in many applications, including disaster management, environment monitoring, and surveillance. The performance of the routing protocols in these networks drops significantly due to frequent route breaks and traffic congestion at high node speeds. This is partly because the routing metrics used in many of the existing protocols are sensitive to the intra-flow interference after the source node starts transmission on the…

  PublisherDOI

• A Connectivity-Aware Pheromone Mobility Model for Autonomous UAV Networks

  2023 · 14 citations

  UAV networks consisting of reduced size, weight, and power (low SWaP) fixed-wing UAVs are used for various applications such as search and rescue, surveillance, and tracking. To carry out these operations efficiently, there is a need to develop scalable, decentralized autonomous UAV network architectures with high network connectivity. However, the area coverage and the network connectivity requirements exhibit a trade-off. In this paper, a connectivity-aware pheromone mobility (CAP) model is de…

  PublisherDOI

• Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients

  2020-12-01 · 8 citations

  articleOpen access

  The recent increase in volume and complexity of available astronomical data has led to a wide use of supervised machine learning techniques. Active learning strategies have been proposed as an alternative to optimize the distribution of scarce labeling resources. However, due to the specific conditions in which labels can be acquired, fundamental assumptions, such as sample representativeness and labeling cost stability cannot be fulfilled. The Recommendation System for Spectroscopic followup (R…

  PublisherOA PDFDOI

• A Deep-$Q$-Learning-Based Base-Station-Connectivity-Aware Decentralized Pheromone Mobility Model for Autonomous UAV Networks

  IEEE Transactions on Aerospace and Electronic Systems · 2024-07-29 · 6 citations

  article

  Wireless networks consisting of low size, weight, and power, fixed-wing unmanned aerial vehicles (UAVs) are used in many applications, such as search, monitoring, and information gathering of inaccessible areas, in which UAVs sense within an area and forward the information, in a multihop manner, to an aerial base station (BS). Robustly performing these tasks requires the UAV network to be decentralized, autonomous, and scalable. An important tradeoff is between area coverage and connectivity: f…

  PublisherDOI

Recent grants

• CAREER: Estimation and Decisions in Graphical Models

  NSF · $442k · 2013–2019

Frequent coauthors

• Rina Dechter

  33 shared

• Alan S. Willsky

  23 shared

• John W. Fisher

  Massachusetts Institute of Technology

  22 shared

• Padhraic Smyth

  22 shared

• Radu Marinescu

  17 shared

• Junkyu Lee

  University of Essex

  14 shared

• Julian Yarkony

  10 shared

• Sunil Kumar

  10 shared

Education

• PhD, EECS

  Massachusetts Institute of Technology

  2005

• BS, EE & Mathematics

  California Institute of Technology

  1998

Awards & honors

• NSF CAREER Award

Similar researchers at University of California, Irvine

• Pierre Baldih-121
• Gene Tsudikh-82