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Matthew J. Burlick
Assistant Professor
Stevens Institute of Technology
Department of Computer Science
Castle Point on Hudson
Hoboken, NJ 07030, USA

Office: Lieb 214
Email: mburlick@stevens.edu
Phone: 201-216-5321
Office hours: By appointment.

Bio | Teaching | Research | Publications


Bio

I am currently an Assistant Professor in the Computer Science Department at Stevens Institute of Technology in Hoboken, NJ. I joined Stevens as an Assistant Teaching Professor after completing my Ph.D at Stevens in Computer Science in 2013. My current duties include teaching undergraduate courses, providing undergraduate advisement, and participating in curriculum development.

Teaching

Below is a list of the courses I am or have taught at Stevens, including a sample syllabus for each.

Research

Generally speaking my research interest include topics in Computer Vision and Machine Learning, in particular understanding video semantics and interpreting human annotation. Below is a list of projects I have worked on, both past and present:

Semantics-Based Video Indexing using Tracked Objects
Using object trackers, we discover video semantics for tasks including video segmentation, story discovery, video matching and searching. Some parts of this work are included in my current Thesis work.
Multi-Modal Fusion for Voice Activity Detection
Using classification from both acoustic and visual modalities, we provide a novel late-fusion classifier that aides in instantaneous voice activity detection (VAD) in the presence of low acoustic SNR.
SCAR Tracking
By tracking on small, salient face patches, we provide a face tracker that is more robust to noise such as illumination and occlusion. Furthermore our algorithms provide mechanisms to detection failure, resulting in object re-acquisition, dynamic model update, or true loss-of-target.
Shotgun Learning
A novel semi-supervised learning method using temporally consistent data to label streams instead of individual images to build a training set
X-Reality
Real-time visualization of tracking objects in a virual environment. This allows the user to interact with the environment in a previously impossible manner. Doing so we can build behavorial modals for environments
Beamforming
Using hydrophone data and statistical correction methods, we determine the direction of arrival of ship traffic and establish a guarenteed capture probability.
Ground Truths of Subjective Tasks
Acquired ground truth data from several subjects for use in creating a probabilistic model for the segmentation of several videos.

Publications

  • George Kamberov, Matt Burlick, Lazaros Karydas, Olga Koteoglou, "Unsupervised Detection of Video Sub-scenes", International Conference on Patter Recognition (ICPR), 2014

  • Matt Burlick, "A Bottom-Up Extraction of Atomic Feature Vectors and Action Sequences for Video Representation", Ph.D. Dissertation, 2013.

  • M. Burlick, O. Koteoglou, L. Karydas, and G. Kamberov "Leveraging Crowsourced Data for Creating Temporal Segmentation Ground Truths of Subjective Tasks", CVPR Workship on Ground Truth, 2013 [pdf]

  • Matt Burlick, Dimitrios Dimitriadis, Eric Zavesky, "On the Improvement of Multimodal Voice Activity Detection", Interspeech, 2013.

  • G. Kamberov, M. Burlick, L. Karydas and O. Koteoglou, "SCAR: Dynamic adaptation for person detection and persistence analysis in unconstrained videos", International Symposium on Visual Computing (ISVC), 2012 [pdf]

  • George Kamberov, Gerda Kamberova, Matt Burlick, Lazaros Karydas, Bart Luczynski, "Track Analysis, Data Cleansing, and Labeling", International Symposium on Visual Computing (ISVC), 2011. [pdf]

  • Barry Bunin, Alexander Sutin, George Kamberov, Heui-Seol Roh, Bart Luczynski, and Matt Burlick, "Fusion of acoustic measurements with video surveillance for estuarine threat detection", Proc. SPIE, Vol 6945, 694514 (2008) [pdf]

  • George Kamberov, Matt Burlick, Bart Luczynski, and Rob Bader, "Automatic Real-Time Analysis and Anomaly Detection of Estuary Traffic Via Video-Acoustic Sensor Fusion", MSL Annual Report, Chapter 7 (2008)

  • Matt Burlick, Gerda Kamberova, George Kamberov, "Mini-max rules and estimation for scene and context description", in preparation for submission.

  • Maxim Serebrennik, Matt Burlick, Bart Luczynski, Lazaros Karydas, George Kamberov, "X-reality: fusing long range real-time sensors ", in preparation for submission.

© Matt Burlick, 2014