Call for Papers DSLW 2022: 2022 IEEE Data Science and Learning Workshop
The DSLW team is inviting you to submit regular papers to the 2022 IEEE Data Science & Learning Workshop (DSLW 2022), a workshop organized by the IEEE Signal Processing Society (supported by the SPS Data Science Initiative). The workshop aims to bring together researchers in academia and industry to share the most recent and exciting advances in data science, learning theory and applications.
Call for Papers DSLW 2022: 2022 IEEE Data Science and Learning Workshop
The DSLW team is inviting you to submit regular papers to the 2022 IEEE Data Science & Learning Workshop (DSLW 2022), a workshop organized by the IEEE Signal Processing Society (supported by the SPS Data Science Initiative). The workshop aims to bring together researchers in academia and industry to share the most recent and exciting advances in data science, learning theory and applications.
2020 IEEE Medal & Recognition Recipients Honored Online
Ramalingam Chellappa Receives IEEE Jack S. Kilby Signal Processing Medal . IEEE Life Fellow Ramalingam Chellappa has received this year’s IEEE Jack S. Kilby Signal Processing Medal "for contributions to image and video processing, especially applications to face recognition."
Call for Papers DSLW 2021 - Deadline: 15 October 2020
The DSLW team is inviting you to submit regular papers to the 2021 IEEE Data Science & Learning Workshop (DSLW 2021), a new workshop organized by the IEEE Signal Processing Society. The workshop aims to bring together researchers in academia and industry to share the most recent and exciting advances in data science, learning theory and applications.
Call for Papers DSLW 2021 - Deadline: 15 October 2020
The DSLW team is inviting you to submit regular papers to the 2021 IEEE Data Science & Learning Workshop (DSLW 2021), a new workshop organized by the IEEE Signal Processing Society. The workshop aims to bring together researchers in academia and industry to share the most recent and exciting advances in data science, learning theory and applications.
