Our research focuses in the theory and application of machine learning, in particular applied to problems in remote sensing and robotics. A primary goal of our ongoing research is to develop practical probabilistic methods to enable agents to sense, learn and act in complex, dynamic environments. This poses difficult challenges to the algorithms, in particular: scaling up to high-dimensional, high-volume data sets; constructing sparse representations of dynamic data; combining multiple sensor modalities; and extracting complex knowledge from sensor data. We have been working to address those challenges in the context of various applications, from environment monitoring to disaster management, medical imaging and mineral exploration.

    We investigate the following three lines of research.

    Remote Sensing: hyperspectral image and signal processing, lidar, radar, data fusion

    Robotics: autonomous mobile robots, sensing, perception, learning, mapping

    Machine Learning: probabilistic graphical models, nonparametric Bayesian methods, reinforcement learning

    Our research group is affiliated with the Digital Imaging and Remote Sensing (DIRS) Laboratory.


    - Apr/15: Our 2nd workshop on Alternative Sensing for Robot Perception organized with Thierry Peynot, Teresa Vidal-Calleja and Peter Corke for IROS 2015 in Hamburg, Germany has been accepted.

    - Apr/15: Zeeshan Amin will present our Husky UGV robot at Imagine RIT 2015, with help from our PhD candidates Utsav Gewali, Yilong Liang, Yang Hu and Yansong Liu.

    - Sep/14: Richard Murphy's paper has been accepted for publication in the journal Remote Sensing.

    - Jun/14: We have been awarded an Amazon Web Services (AWS) in Education Research Grant in the Machine Learning category in the amount of $10,000 in AWS credits.

    - Mar/14: Sildomar Monteiro gave a talk at the Center for Imaging Sciences seminar series, watch the online video here.

    - Jan/14: Sildomar Monteiro is a guest editor of the Journal of Field Robotics special issue on "Alternative Sensing Techniques for Robot Perception" with Alonzo Kelly (CMU), Michel Devy (LAAS) and Thierry Peynot (QUT)


    We are always looking for highly motivated graduate and undergraduate students to join our research group. Students with a strong interest in conducting research in machine learning, robotics and/or remote sensing are encouraged to contact Dr. Monteiro via email with a curriculum vitae and a description of interests.

    • PhD Students: We welcome enthusiastic and motivated PhD candidates already enrolled or accepted in one of RIT's PhD programs.
    • Masters students: We welcome talented engineering and imaging science students who have taken and excelled in at least one of their focus area courses.
    • Undergraduate Students: We welcome motivated students in the robotics or computer engineering options to join our group.

    • Prospective Students: Applicants interested in applying for the PhD or MS program at RIT are advised to apply directly via the RIT Graduate Programs Office. Our research group is affiliated with the PhD programs in Engineering and Imaging Sciences.