Princeton, NJ, United States of America

Ioannis Akrotirianakis

This inventor holds 11 USPTO granted patents and 8 published patent applications and 3 EPO patents. Top assignees: Siemens Aktiengesellschaft, Siemens Medical Solutions USA, Inc.. Active years: 2014-2022.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Ioannis Akrotirianakis. Retrieved from https://idiyas.com/inventor/ioannis-akrotirianakis

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile

USPTO Granted Patents = 11 

% Patents Active = 72.7

 

Average Co-Inventor Count = 3.0

ph-index = 4

Forward Citations = 23(Granted Patents)


Location History:

  • Plainsboro, NJ (US) (2014 - 2015)
  • Princeton, NJ (US) (2017 - 2022)

Company Filing History:


Years Active: 2014-2022

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11 patents (USPTO):Explore Patents

Title: Innovations and Contributions of Ioannis Akrotirianakis

Introduction

Ioannis Akrotirianakis is a notable inventor based in Princeton, NJ (US), recognized for his significant contributions to the fields of industrial process scheduling and deep learning optimization. With a total of 11 patents to his name, he has made strides in enhancing energy efficiency and computational methods.

Latest Patents

One of his latest patents focuses on energy-efficient scheduling of industrial processes by reducing idle time and tardiness of jobs. This computer-implemented method involves receiving jobs to be executed on machines within a manufacturing facility. A job schedule is generated based on an optimization function that minimizes total energy costs for all machines during a specified time horizon. The energy cost at each time step is calculated based on various modes of machine operation, including sleeping, stand-by, and processing modes. The jobs are executed on the machines according to the generated job schedule.

Another significant patent addresses efficient calculations of negative curvature in a Hessian-free deep learning framework. This method for training a deep learning network includes defining a loss function and receiving training samples. An optimization method is then applied to iteratively minimize the loss function. Each iteration involves using an eigCG solver to determine a descent direction, which helps prevent the optimization method from converging to saddle points. Curvilinear and adaptive line-searches guide the optimization method to a local minimum, ensuring effective training of the deep learning network.

Career Highlights

Ioannis has worked with prominent companies such as Siemens Aktiengesellschaft and Siemens Medical Solutions USA, Inc. His experience in these organizations has contributed to his expertise in industrial processes and advanced computational techniques.

Collaborations

Throughout his career, Ioannis has collaborated with talented individuals, including Amit Chakraborty and Rodrigo Carrasco. These collaborations have further enriched his work and innovations.

Conclusion

Ioannis Akrotirianakis stands out as an influential inventor whose work in energy-efficient scheduling and deep learning optimization continues to impact various industries. His contributions reflect a commitment to innovation and excellence in technology.

Profile summary based on public USPTO records.
Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]
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