PeopleofSuperintelligence

Researcher

Daniel E. Clark

Also known as Daniel Clark

Daniel E. Clark is a statistical signal-processing researcher whose work connects multi-target tracking and sensor management with AI and autonomous systems. He earned a PhD at Heriot-Watt University in 2006, worked as a research associate at Cambridge before taking a Heriot-Watt lectureship, and led Télécom SudParis’s TIPIC team. He coauthored a multi-target filter with linear complexity under stated assumptions in 2018, led an information-theoretic sensor-management project for autonomy in 2023, and published a sensor-selection analysis in 2024 and a point-process paper in 2026. As of October 2026, he is a Professor at the University of Southampton.

Works
308
Citations
16,886
h-index
60
Badges

Publication numbers from OpenAlex, as of 22 October 2026.

Timeline

  1. 2006

    A PhD in multi-target tracking

    In 2006, he earns a PhD at Heriot-Watt University with a thesis titled “Multiple Target Tracking with the Probability Hypothesis Density Filter.”

  2. 2007

    Research associate in Cambridge

    In 2007, he works as a research associate in the Signal Processing Lab at the University of Cambridge before taking a lectureship at Heriot-Watt.

  3. 2018

    A linear-complexity filter

    In 2018, he and Jérémie Houssineau publish “Multi-target filtering with linearized complexity” in IEEE Transactions on Signal Processing. The method estimates multiple targets from partial and corrupted observations by combining multiple-hypothesis tracking with point-process methods. Under the paper’s stated assumptions and approximations, its complexity can be linear in the numbers of tracks and observations.

  4. 2021

    Directing the TIPIC team

    In 2021, he directs the Information Processing for Images and Communications (TIPIC) team in Télécom SudParis’s Communications, Images and Information Processing (CITI) department and SAMOVAR laboratory. The team recruits an associate professor in applied probability and statistical signal processing for work with applications including AI and data science.

  5. 2023

    Sensor management for autonomy

    In 2023, he leads a University of Southampton PhD project on information-theoretic sensor management. It develops algorithms that help autonomous systems make decisions from sensor data, manage sensing and communication resources, and adapt surveillance to dynamic environments.

  6. 2024

    Sensor selection by information bounds

    In 2024, he coauthors “An Analysis of the Mutual Information Upper Bound for Sensor-Subset Selection” with Idyano Leroy, Augustin A. Saucan, and Yohan Petetin. The paper analyzes information loss when sensor selection uses an information upper bound in place of exact mutual information.

  7. 2026

    A point-process cumulant hierarchy

    In 2026, he publishes “From the GNZ identity to a Dyson–Schwinger cumulant hierarchy for point processes” in Statistics & Probability Letters.

  8. As of October 2026

    Professor at Southampton

    As of October 2026, he is a Professor at the University of Southampton.

Career

  1. Since 2023University of SouthamptonProject Lead
  2. From 2021Communications, Images and Information Processing DepartmentDirector
  3. 2021–2022Télécom SudParisDirector of TIPIC team
  4. 2017–2019CNRS
  5. 2017–2022Institut Polytechnique de Paris
  6. 2008–2012Imperial College London
  7. 2008–2015Stanford University
  8. 2007University of CambridgeResearch Associate
  9. 2005–2017Heriot-Watt UniversityLecturer

Education

  1. 2006Heriot-Watt UniversityPhD

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

  1. 20% of people at companies who shape AI also hold a university role, led by Google and Stanford UniversityIncludes Stanford University. We estimate that 539 of the 6,766 people with a known current organization (8%) hold both a university role and a company role, including 20% of people at companies and 13% of people at universities.
  2. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes University of Southampton. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.
  3. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes University of Southampton. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  4. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes University of Southampton. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

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  1. Technical Seminar by Dr Daniel Clark ewh.ieee.org. Checked 22 October 2026.
  2. Daniel E. Clark csauthors.net. Checked 22 October 2026.
  3. [1404.7408] Multi-target filtering with linearised complexity ar5iv.labs.arxiv.org. Checked 22 October 2026.
  4. Associate Professor in Applied Mathematics, Probability, and Statistics signalprocessingsociety.org. Checked 22 October 2026.
  5. Information theoretic methods for sensor management southampton.ac.uk. Checked 22 October 2026.
  6. An Analysis of the Mutual Information Upper Bound for Sensor-Subset Selection researchportal.ip-paris.fr. Checked 22 October 2026.
  7. Doctor Daniel Clark southampton.ac.uk. Checked 22 October 2026.