SPS Student Services Committee Webinar: Effectively Applying for Industrial Research

Date: 15 May 2024
Time: 11:00 AM ET (New York Time)
Presenter(s): Dr. Atulya Yellepeddi, Dr. Ravi Kiran Raman


Research roles in industry hold a unique position of bridging the technology development expectations of industry with the research driven agenda of academia. While trying to develop technology that is path breaking to address various challenges, industrial research is also tied to different expectations of timeline, practicality, cost-benefit tradeoffs, and efficacy. Thus, applications for such roles, especially in the signal processing and machine learning space, should best reflect such expectations rather than being limited to a typical engineering role in industry.

Over time we have reviewed different styles of applications and CVs running from those that are too concise to those that are overloaded with detail. In this talk the presenters will provide some insights on application styles that are most effective in communicating an applicant’s skill set generally in a CV, but more generally in an application. They will provide some synthetic examples and narrate their experiences, providing an overview of typical expectations and how to address them effectively in a job application.


Atulya YellepeddiAtulya Yellepeddi received the Ph. D. from the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution in 2016 in machine learning for underwater communication.

He is currently a Director of Research at the Analog Garage, which is the internal incubator of Analog Devices. In his time at ADI, he has worked on system architecture and algorithm design in a variety of fields, including depth sensing cameras, Lidar and Radar systems, and most recently, green energy systems.

Dr. Yellepeddi is broadly interested in the development of algorithms in data-deficient regimes and in the application of intelligence for green energy applications.


Ravi Kiran RamanRavi Kiran Raman received the Ph. D. from the University of Illinois, Urban-Champaign in 2019 where he worked on Information Theoretic analysis of statistical inference algorithms and scalability of large-scale networked systems.

He is currently a Lead Research Scientist at the Analog Garage, which is the internal incubator of Analog Devices Inc. At ADI he has worked on ML and signal processing algorithm research for various fields including battery monitoring, spoken language understanding, Lidar systems, and noise detection for amplifiers.

Dr. Raman’s interests include the design of computation and data-efficient statistical inference and ML algorithms focused on intelligence at the edge.