Photo credit: Mads Perch

Photo credit: Mads Perch

What I Do

I’m currently CEO at Signaloid. Up until September 2025, I was a full Professor (Chair of Physical Computation) in the Electrical Engineering Division of the Department of Engineering at the University of Cambridge, where I led the Physical Computation Laboratory (I joined the University of Cambridge as an Assistant Professor in 2017, got tenure in 2021, and was promoted to full professor in 2022). From 2022 to 2023, while on sabbatical from Cambridge, I was a Royal Academy of Engineering Enterprise Fellow, and from 2018 to 2021, jointly with my position at Cambridge, I was a faculty fellow at the Alan Turing Institute in London.

My research explores fundamental methods to use an understanding of the physical world to enable efficient computation that interacts with nature. My research results include processor and accelerator architectures for processing empirical data, computer architectures for computing on probability distribution representations, and novel devices (e.g., GFETs, memristors) for computation.

Some of my recent and ongoing work investigates new processor architectures, new hardware accelerators based on properties of novel devices (e.g., graphene field-effect transistors), new methods for learning models from physical sensor data in resource-constrained systems, and new methods for synthesizing state estimators (e.g., Kalman filters) and sensor fusion algorithms from physical system descriptions.

In addition to my primary activity of research, I am actively involved in research-driven education activities (both within the University as well as to a wider online audience) and science for public policy.

Capsule Biography

B.Sc., 1999 (Rutgers); M.Sc., 2001 (Rutgers); Ph.D., 2007 (Carnegie Mellon). In the summers of 1995, 1996, and 1999, I worked as an intern / engineer at Bell Labs (Murray Hill, NJ), first in the Microelectronics Division, and then in the Data Networking Division, on a project spun out by the research group that created the C programming language, the Unix, Inferno, and Plan 9 operating systems, and much more. I spent 2006–2008 at Technische Universiteit Eindhoven in the Netherlands, joined IBM Research in Zürich, Switzerland, as a permanent Research Staff Member from 2008–2012, and then joined Apple in Cupertino from 2012–2014. I moved back to academia in 2014: I was in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) from 2014-2017 and joined the University of Cambridge as a faculty member in 2017, eventually getting tenure in 2021 and being promoted to full professor in 2022. From 2018 to 2021 I was also a Faculty fellow at the Alan Turing Institute for Data Science and Artificial Intelligence in London. I am the founder of Signaloid, a startup developing a new kind of computing platform that performs arithmetic directly on discrete representations of continuous probability distributions. The platform has a broad class of applications, from engineering simulation to quantitative finance, to robotics.

Selected Recent Press

  1. June 2026: Real-time on-device dynamic quantification of sensor output uncertainty. Highlight article by Silvia Conti, about joint work with O. Kaparounakis that appeared in Nature Communications Engineering.

  2. May 2020: Long-form article in Wired Magazine by Kassia St Clair, about my collaboration with Peprally and the work on Crayon and Ishihara.

  3. November 2019: Article by the University of Cambridge Department of Engineering covering our work on Dimensional Function Synthesis in ACM Transactions on Embedded Computing Systems and the resulting Best Paper Award.

  4. August 2019: Article in Fast Company Magazine on collaborative work with Peprally, using anonymous color selections from the Specimen game for bespoke image compression.

 

Selected Recent Peer-Reviewed Research Publications

  1. G. Kirsten, M. Selby, J. Petangoda, O.H. Elias, J. Meech, and P. Stanley-Marbell. Theoretical Analysis of Thermodynamic Matrix Inversion: First-order Equivalence to Preconditioned Gradient Descent and Implications for Analog Computing. To appear, Nature Unconventional Computing, August 2026.

  2. O. Kaparounakis, P. Stanley-Marbell. Digital methods to quantify sensor output uncertainty in real time. In Nature Communications Engineering, May 2026.

  3. B. A. Bilgin, K. O. H. Elias, M. Selby, and P. Stanley-Marbell, "Quantization of probability distributions via divide-and-conquer: Convergence and error propagation under distributional arithmetic operations." In Methodology and Computing in Applied Probability 28, no. 2 (2026): 32, April 2026.

  4. O. Kaparounakis, Y. Zhang, and P. Stanley-Marbell. Approximating Analytically-Intractable Likelihood Densities with Deterministic Arithmetic for Optimal Particle Filtering. In IEEE Signal Processing Letters, February 2026

  5. Pei Mu, Nikolaos Mavrogeorgis, Christos Vasiladiotis, Vasileios Tsoutsouras, Orestis Kaparounakis, P. Stanley-Marbell, Antonio Barbalace. Cosense: Compiler optimizations using sensor technical specifications. In Proceedings of the 33rd ACM SIGPLAN International Conference on Compiler Construction, February 2024.

  6. N. J. Tye, S. Hofmann, and P. Stanley-Marbell. Materials and devices as solutions to computational problems in machine learning. In Nature Electronics 6 (7), 479-490, July 2023.

  7. Vasileios Tsoutsouras, Orestis Kaparounakis, Bilgesu Bilgin, Chatura Samarakoon, James Meech, Jan Heck, P. Stanley-Marbell. The laplace microarchitecture for tracking data uncertainty. In IEEE Micro 42 (4), 78-86, April 2022.

  8. R Hopper, D Popa, F Udrea, SZ Ali, P. Stanley-Marbell. Miniaturized thermal acoustic gas sensor based on a CMOS microhotplate and MEMS microphone. In Nature Scientific reports 12 (1), 1690, February 2022.

  9. J. T. Meech, P. Stanley-Marbell. An algorithm for sensor data uncertainty quantification. In IEEE Sensors Letters 6 (1), 1-4, December 2021.

  10. T. Newton, J. T. Meech, P. Stanley-Marbell. Machine learning for sensor transducer conversion routines. In IEEE embedded systems letters 14 (2), 75-78, November 2021.

  11. Vasileios Tsoutsouras, Orestis Kaparounakis, Bilgesu Bilgin, Chatura Samarakoon, James Meech, Jan Heck, P. Stanley-Marbell The laplace microarchitecture for tracking data uncertainty and its implementation in a RISC-V processor. In MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture, October 2021.

  12. V. Tsoutsouras, S. Willis, P. Stanley-Marbell. Deriving equations from sensor data using dimensional function synthesis. In Communications of the ACM 64 (7), 91-99, June 2021.

  13. B. A. Bilgin and P. Stanley-Marbell, “Probabilistic Value-Deviation-Bounded Source-Dependent Bit-Level Channel Adaptation for Approximate Communication". In IEEE Transactions on Computers, 2020.

  14. N. J. Tye, J. T. Meech, B. A. Bilgin, and P. Stanley-Marbell, “A Mixed-Signal Architecture for Generating Non-Uniform Random Variates Using Graphene Field-Effect Transistors". In Journal of Signal Processing Systems, October 2020.

  15. C. Samarakoon, G. Amaratunga, and P. Stanley-Marbell. “Content-Aware Automated Parameter Tuning for Approximate Color Transforms”. In 22nd International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI), October 2020.

  16. M. Pirron, D. Zufferey, and P. Stanley-Marbell. “Automated Controller and Sensor Configuration Synthesis using Dimensional Analysis”. In IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), October 2020.

  17. J. T. Meech and P. Stanley-Marbell, “Efficient Programmable Random Variate Generation Accelerator from Sensor Noise". In IEEE Embedded Systems Letters, June 2020.

  18. N. J. Tye, J. T. Meech, B. A. Bilgin, and P. Stanley-Marbell, "A System for Generating Non-Uniform Random Variates Using Graphene Field-Effect Transistors". In 31st IEEE International Conference on Application-specific Systems, Architectures and Processors, (8 pages), July 2020.

  19. P. Stanley-Marbell, A. Alaghi, M. Carbin, E. Darulova, L. Dolecek, A. Gerstlauer, G. Gillani, D. Jevdjic, T. Moreau, M. Cacciotti, A. Daglis, N. Enright Jerger, B. Falsafi, A. Misailovic, A. Sampson, and D. Zufferey, "Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms".  In ACM Computing Surveys vol. 53, no. 3, article 51, 2020. (38 pages). (Nominated for best paper award.)

  20. P. Stanley-Marbell and M. Rinard, "Warp: A Hardware Platform for Efficient Multimodal Sensing With Adaptive Approximation. In IEEE Micro, vol. 40, no. 1, pp. 57-66, (10 pages), Jan.-Feb. 2020.

  21. Y. Wang, S. Willis, V. Tsoutsouras, and P. Stanley-Marbell, “Deriving Equations from Sensor Data Using Dimensional Function Synthesis". In ACM Transactions on Embedded Computing Systems (best paper award winner), vol. 18, issue 5s, (22 pages) October 2019.

Complete publication list.


Selected Recent Preprints

  1. K. O. H. Elias, M. Selby, and P. Stanley-Marbell, "Distributional Computational Graphs: Error Bounds." arXiv preprint arXiv:2601.16250, January 2026.

  2. J. Couchman and P. Stanley-Marbell. Uncertainty Propagation in Finite Impulse Response Filters: Evaluating the Gaussian Assumption. arXiv preprint arXiv:2510.11384, December 2025.

  3. O. Kaparounakis, Y. Zhang, and P. Stanley-Marbell, "Approximating Analytically-Intractable Likelihood Densities with Deterministic Arithmetic for Optimal Particle Filtering." arXiv preprint arXiv:2512.01023, November 2025.

  4. G. Kirsten, B. A. Bilgin, J. Petangoda, and P. Stanley-Marbell. "A Tensor Train Approach for Deterministic Arithmetic Operations on Discrete Representations of Probability Distributions." arXiv preprint arXiv:2508.06303, October 2025.

  5. O. Kaparounakis, and P. Stanley-Marbell, "Efficient Digital Methods to Quantify Sensor Output Uncertainty." arXiv preprint arXiv:2509.21311, September 2025.

  6. J. Petangoda, C. Samarakoon, J. Meech, D. T. Kanapram, H. Toshani, N. J. Tye, V. Tsoutsouras, and P. Stanley-Marbell, "The Monte Carlo Method and New Device and Architectural Techniques for Accelerating It." arXiv preprint arXiv:2508.07457, August 2025.

  7. J. Couchman, O. Kaparounakis, C. Samarakoon, and P. Stanley-Marbell. Simulated Eyeblink Artifact Removal with ICA: Effect of Measurement Uncertainty. arXiv preprint arXiv:2410.03261, October 2024.

  8. J. T. Meech, V. Tsoutsouras, and P. Stanley-Marbell. Electron-Tunnelling-Noise Programmable Random Variate Accelerator for Monte Carlo Sampling. arXiv preprint arXiv:2403.16421, March 2024.

  9. J. T. Meech, V Tsoutsouras, P. Stanley-Marbell. The data conversion bottleneck in analog computing accelerators arXiv preprint arXiv:2308.01719, August 2023.

  10. J. T. Meech, V. Tsoutsouras, and P. Stanley-Marbell. The Data Movement Bottleneck: Theoretical Shortcomings of Analog Optical Fourier Transform and Convolution Computing Accelerators. ArXiv.

  11. N. J. Tye, A. W. Tadbier, S. Hofmann, P. Stanley-Marbell. Gfet lab: A graphene field-effect transistor tcad tool arXiv preprint arXiv:2206.13239, June 2022.

  12. N. Tye, S. Hofmann, P. Stanley-Marbell. Bridging the band gap: What device physicists need to know about machine learning arXiv preprint arXiv:2110.05910, October 2021.

  13. O. Kaparounakis, V. Tsoutsouras, D. Soudris, and P. Stanley-Marbell, "Automated Physics-Derived Code Generation for Sensor Fusion and State Estimation". Available as arXiv preprint (11 pages), 2020.

  14. B. A. Bilgin and P. Stanley-Marbell, “Probabilistic Value-Deviation-Bounded Source-Dependent Bit-Level Channel Adaptation for Approximate Communication" arXiv:2009.07811, September 2020.

  15. C. Samarakoon, G. Amaratunga, and P. Stanley-Marbell. “Inferring Human Observer Spectral Sensitivities from Video Game Data”, arXiv:2007.00490, July 2020.

  16. C. Samarakoon, G. Amaratunga, and P. Stanley-Marbell. “Content-Aware Automated Parameter Tuning for Approximate Color Transforms”, arXiv:2007.00494, July 2020.

  17. N. J. Tye, J. T. Meech, B. A. Bilgin, and P. Stanley-Marbell. “A System for Generating Non-Uniform Random Variates using Graphene Field-Effect Transistors”, arXiv:2004.14111, April 2020.

  18. O. Kaparounakis, V. Tsoutsouras, D. Soudris, and P. Stanley-Marbell, “Automated Physics-Derived Code Generation for Sensor Fusion and State Estimation”, arXiv:2004.13873, April 2020.

  19. V. Tsoutsouras, M. Vigdorchik, and P. Stanley-Marbell, “Synthesizing Compact Hardware for Accelerating Inference from Physical Signals in Sensors”, arXiv:2002.01241, February 2020.

  20. J. Meech and P. Stanley-Marbell, “Efficient Programmable Random Variate Generation Accelerator from Sensor Noise”, arXiv:2001.05400, February 2020.

  21. V. Tsoutsouras, J. Story, and P. Stanley-Marbell, “Payload-Mass-Aware Trajectory Planning on Multi-User Autonomous Unmanned Aerial Vehicles”, arXiv:2001.02531, January 2020.

Complete publication list.

Recent International Research Events Organized

Recent Professional Service

  • Program committee, USENIX/ACM European Conference on Computer Systems (EuroSys), 2020.

  • Co-Organizer, Dagstuhl International Workshop 20222 on Approximate Systems, Schloss Dagstuhl – Leibniz-Zentrum für Informatik, May 2020.

  • Executive Committee, EPSRC Connected Everything NetworkPlus, 2019 to present.

  • Steering Committee, EPSRC Centre for Doctoral Training in Sensor Technologies and Applications (Sensor CDT) 2019 to present.

  • Associate Editor, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2019 to present.

  • Vice Chair, ACM Special Interest Group on Operating Systems (SIGOPS), 2019 to present.

  • Steering committee, University of Cambridge Trust and Technology Initiative, 2017 to present.

  • Steering committee, USENIX/ACM Hot Topics in Operating Systems (HotOS XVII), 2017, 2019.

  • Program committee, USENIX/ACM European Conference on Computer Systems (EuroSys), 2019.

  • Program committee, IEEE Symposium on High Performance Computer Architecture (HPCA), 2019.

  • Program committee, ACM/IEEE International Symposium on Computer Architecture (ISCA), 2018.

  • Program committee, ACM/IEEE Intl. Conf. on Formal Methods & Models for Sys. Design, 2018.

  • Program committee, IEEE/ACM Intl. Conference on Computer-Aided Design (ICCAD), 2018.

  • Program committee, USENIX/ACM European Conference on Computer Systems (EuroSys), 2018.

  • Program committee, 8th Workshop on Systems for Multi-core and Heterogeneous Architectures (SFMA), 2018.

  • Co-organizer, Swiss National Science Foundation (SNF) International Exploratory Workshop on Theory and Practice of Error-Efficient Computing, 2017.

  • Sponsorships chair, USENIX/ACM Hot Topics in Operating Systems (HotOS XVI), 2017.

  • Posters chair, USENIX/ACM European Conference on Computer Systems (EuroSys), 2017.

  • Program committee, IEEE/ACM Intl. Conference on Computer-Aided Design (ICCAD), 2017.

Selected Recent Granted Patents

  1. P. Stanley-Marbell and M. Rinard, “System, method, and apparatus for reducing power dissipation of sensor data for bit-serial communication”, US Patent number 10,601,452, granted 24th March 2020.

  2. P. Stanley-Marbell and M. Rinard, “Method and Apparatus for Reducing Sensor Power Dissipation". US Patent number 10,539,419, granted 21st January 2020.

  3. D. Chan, J. Iarocci, G. Kapoor, K. Wan, P. Stanley-Marbell et al., “Initiating background updates based on user activity”. US Patent 10,223,156, granted 5th March 2019.

  4. P. Stanley-Marbell and M. Rinard, “System, method, and apparatus for reducing power dissipation of sensor data on bit-serial communication interfaces". US Patent US 10,135,471, granted 20th November 2018.

  5. C. de la Cropte de Chanterac, P. Stanley-Marbell, K. Venkatraman, G. Kapoor, “Smart advice to charge notification”, US Patent US 10,083,105, granted 25th September 2018.

  6. P. Stanley-Marbell, G. Kapoor, and U. Vaishampayan), "Dynamic adjustment of mobile device based on voter feedback". US Patent 9,813,990, granted 7th November 2017.

  7. P. Stanley-Marbell, G. Kapoor, and U. Vaishampayan. "Dynamic Adjustment of Mobile Device Based on Adaptive Prediction of System Events". US Patent Number 9,465,679, granted 11th October 2016.

  8. P. Stanley-Marbell, G. Kapoor, and U. Vaishampayan. "Dynamic Adjustment of Mobile Device Based on System Events". US Patent Number 9,462,965, granted 11th October 2016.

  9. P. Stanley-Marbell, G. Kapoor, and U. Vaishampayan. "Dynamic Adjustment of Mobile Device Based on Thermal Events". US Patent Number 9,432,839, granted August 30, 2016.

  10. J. Wood, K. Vyas, A. Vyrros, G. Kapoor, P. Stanley-Marbell et al. "Push notification initiated background updates". US Patent Number 9,392,393, granted July 12, 2016.

  11. P. Stanley-Marbell, G. Kapoor, K.-M. Wan, and J. Andrews. "Dynamic adjustment of mobile device based on user activity". US Patent Number 9,256,484, granted February 9, 2016.

Complete publication list.

 

Selected Recent Education Activities

I am passionate about enabling learning.