Deeban Ramalingam

PhD in Computer Science Student at Columbia University (joined Fall '21)

MS in Computer Science (with Thesis) at UCLA '21

BS in Computer Science (with Departmental Honors) &
Mathematics Minor at Cornell University '18

Education

I joined Columbia University in the Fall of 2021 as a PhD in Computer Science student with Advanced Standing. I conduct research in the Columbia University AlQuraishi Laboratory under the supervision of Professor Dr. Mohammed AlQuraishi.

I graduated from UCLA in June 2021 with my Master of Science in Computer Science degree (with Thesis). My Master's Thesis "Multi-modal Medical Imaging Registration" was published by ProQuest. My advisor was Professor Dr. Fabien Scalzo. My Graduate GPA was 3.957 / 4.000. I won the UCLA Engineering Achievement Award for Student Welfare. I am also a member of the UCLA Honor Society.

I received my Bachelor of Science in Computer Science with Departmental Honors, Cum Laude Designation, and a Minor in Pure Mathematics from Cornell University (Class of 2018). Upon admission, I was named a Meinig Family Cornell National Scholar and McMullen Cornell Engineering Dean Scholar. During my senior year, I was named a Thomas Dinwoodie McMullen Scholar. I am also a member of the Golden Key International Honor Society.

Research Interests

Machine Learning (applications in Communication and Radar systems, Computer Vision, Biology, Healthcare, Audio Recognition, Cloud Computing, and Economics), Networked Distributed Systems and Cloud Computing.

Societies / Honors

I was awarded Advanced Standing upon entering the PhD in Computer Science program at Columbia University. I am a member of the UCLA Honor Society. I won the UCLA Engineering Achievement Award for Student Welfare. I am also a member of the Meinig Family Cornell National Scholars, the McMullen Cornell Engineering Dean Scholars, and the Thomas Dinwoodie McMullen Scholars. I was placed on the Cornell Engineering Dean's List. I am also member of the Golden Key International Honor Society.

Publications

  1. B. Jamali, D. Ramalingam and A. Babakhani, “Intelligent Material Classification and Identification Using a Broadband Millimeter-Wave Frequency Comb Receiver,” in IEEE Sensors Letters, vol. 4, no. 7, pp. 1-4, July 2020, Art no. 3501104, doi: 10.1109/LSENS.2020.3002715.

  2. H. Rahmani, M. M. Archang, B. Jamali, M. Forghani, A. M. Ambrus, D. Ramalingam, Z. Sun, P. O. Scumpia, H. A. Coller and A. Babakhani, “Towards a Machine-Learning-Assisted Dielectric Sensing Platform for Point-of- Care Wound Monitoring,” in IEEE Sensors Letters, vol. 4, no. 6, pp. 1-4, June 2020, Art no. 5501004, doi: 10.1109/LSENS.2020.2999031.

  3. D. Ramalingam, C. H. Yoon and F. Poitevin, “Building Latent Spaces to Sort Massive X-ray Diffraction Datasets,” in Stanford-SLAC National Accelerator Laboratory, Aug 2020. Publishing of the poster in Progress.

  4. B. Jamali, D. Ramalingam and A. Babakhani, “Intelligent Material Classification with a Silicon-Based Millimeter-Wave Frequency Comb Receiver,” in UCLA, 2020. Poster published on my IEEE Research Paper.

  5. B. Jamali, D. Ramalingam and A. Babakhani, "Intelligent Material Classification and Identification Using a Broadband Millimeter-Wave Frequency Comb Receiver," 2020 IEEE SENSORS, 2020, pp. 1-1, doi: 10.1109/SENSORS47125.2020.9278697.

  6. Ramalingam, D. (2021). Multi-modal Medical Imaging Registration. UCLA. ProQuest ID: Ramalingam ucla 0031N 19625. Merritt ID: ark:/13030/m5k41vd4. Retrieved from https://escholarship.org/uc/item/6d50432c.

Research / Industrial Experience

I presently work with Dr. Mohammed AlQuraishi as a Graduate Research Assistant in the Columbia University AlQuraishi Laboratory. I conduct research related to machine learning and biology.

From January 2021 to July 2021, I worked as a Software Consultant from California for Idaho Digital Learning Academy (IDLA), where I developed software to complement online education for the state of Idaho.

During the Summer of 2020, I worked as a Linac Coherent Light Source (LCLS) Graduate Research Intern at the Stanford-SLAC National Accelerator Laboratory with Dr. Chun Yoon and Dr. Frederic Poitevin, where I developed Machine Learning methods for X-ray Free-electron Lasers. I presented my research to Dr. Mike Dunne, Director of LCLS. The publishing of the research poster is in progress. A slide highlighting the research was submitted to Stanford-SLAC for the Science Advisory Committee talk.

During my first year at UCLA, I worked with Dr. Aydin Babakhani and Dr. Babak Jamali as a Graduate Research Assistant in the UCLA Integrated Sensors Laboratory. I published 2 papers in the IEEE Sensors Letters 2020 Journal. Both publications have also been posted in the UCLA Integrated Sensors Laboratory website (Department of Electrical and Computer Engineering). A poster was published on my IEEE Research Paper. My IEEE Research Paper was published in the 2020 IEEE SENSORS Conference.

I also worked as a Graduate Research Assistant under Dr. Aydogan Ozcan and Dr. Yair Rivenson in the UCLA Bio- & Nanophotonics Laboratory. I optimized the preprocessing of biological image data for Machine Learning methods. I will be listed as a co-author in a future publication.

During the Summer of 2019, I worked as a Software Consultant from California for Idaho Digital Learning Academy (IDLA). I developed software to complement online education for the state of Idaho.

That summer I was also a CryoEM & Bioimaging Research Intern at the Stanford-SLAC National Accelerator Laboratory. I worked with Dr. Cornelius Gati and Dr. Frederic Poitevin to develop Machine Learning methods for Cryogenic Electron Microscopy (CryoEM).

From August 2018 to March 2019, I worked as a Software Engineer at Microsoft Azure R&D, where I developed software for the networking infrastructure for the Microsoft Azure cloud.

During the Summer of 2017, I worked as a Software Engineering Intern under Mr. Geoff Outhred at Microsoft Azure R&D. I designed a Multi-tenant Container-based Application Layer Load Balancing-as-a-Service (https://goo.gl/pu25TN). My proof-of-concept trumped existing application load balancers in functionality and speed. I presented my internship project to Azure Networking and was invited to work as a full-time Software Engineer after graduating from Cornell University.

During the Summer of 2016, I worked as a Software Engineering Intern under Mr. Vijay Ramadoss at NVIDIA. I designed a debugging tool to communicate with HyperVisors on the NVIDIA Grid Network (NGN) cloud compute clusters. I also designed an automation pipeline to handle efficient deployments to storage nodes in NGN.

During the Summer of 2015, I worked as a Software Engineering Intern under Mr. Roger Baird at Hewlett-Packard R&D Laboratory. I developed a feature for JetAdvantage Management (JAM). JAM allows customers to centrally and remotely manage their fleet of print devices over a network. My feature allows customers to make changes to the configuration of any JAM client, critical for effective communication between JAM in the cloud and a JAM client within the customer’s corporate firewall.

During the Spring of 2015, I worked with Dr. Graeme Bailey on a Masters of Engineering Research Project: Media Enabled Research Interface and Database (MERID). MERID enables researchers to run surveys and research investigations with respondents. Investigations dealt with the subtleties of inter-orchestral communication between musicians during a performance. Research was done jointly with the University of Oxford. For my research project, I designed a real-time subscribe/broadcast event-driven communication framework.

During the Summer of 2014, I worked as a Software Engineering Intern under Mr. Tim Ramey at WhiteCloud Analytics. I developed software to complement hospital administration and healthcare analytics.

From February 2012 to June 2014, I was a Software Engineering Intern under Mr. Ryan Gravette at IDLA. I presented my first educational web application at the age of 16 to the Idaho Senate Education Committee and received a Letter of Recommendation from one of the Senators (https://goo.gl/fUpCac).

Contact Information

Personal Email: rdeeban@gmail.com

Columbia University Email: d.ramalingam@columbia.edu