Srijith Rajamohan, Ph.D.

AI/ML Scientist

Blacksburg, VA 24060

srijith.rajamohan@databricks.com

Education

2009-2014 Ph.D. in Computational Engineering The University of Tennessee
Dissertation: ”A StreamlineUpwind/ Petrov-Galerkin FEM based Time-Accurate Solution of 3D Time-Domain Maxwell’s Equations for Dispersive Materials”

2007-2009 MS in Electrical Engineering The Pennsylvania State University
Thesis: ”A Neural Network based classifier on the Cell Broadband Engine”

Core Competencies

Experience (selected)

2020-Present Senior Developer Advocate (Data Science & ML), Databricks, Blacksburg, VA

2014-2020 Computational Scientist, Advanced Research Computing, Blacksburg, VA

2009-2014 Graduate Research Assistant, SimCenter: Center of Excellence in Computational Engineering, The University of Tennessee, Chattanooga, TN

2007-2009 Graduate Student, The Microsystems Design Lab, The Pennsylvania State University, State College, PA

Sr. Developer Advocate (Data Science/Machine Learning)

My role as a Developer Advocate in Data Science allows me to serve as a thought leader in Machine learning and Data science, and educate the community about the state-of-the-art. This role allows me to engage in internal advocacy, and work cross-functionally across various units such as product management, product marketing, solutions, engineering and documentation. Some of my responsibilities include:

My technical areas of expertise here are Deep Learning for Natural Language Understanding (NLU), Bayesian inference and large-scale processing using PySpark.

Links: = Webpage of project

Projects

2021 Lead the DevRel efforts on Machine Learning at Databricks

2021 Apache Spark

2021 Lead the advocacy efforts on OSS MLflow

2020-Present Authored a set of three courses on Bayesian Inference titled ‘Introduction to Computational Statistics for Data Scientists’ on Coursera

2020-Present Articles on Data Science and Machine Learning

2020-Present Presentations/Talks
Machine Learning at Scale (Nov/Aug 2021)

Deep Learning at Scale at Databricks (Oct 2021)

MLflow for the ML Lifecycle (Nov 2021)

Bayesian Modeling of the Temporal Dynamics of COVID-19 using PyMC3 at the Data+AI Summit (Nov 2020)

Presented ‘Maintable HPC and Data Science with Python/C++’ at the National Center for Atmospheric Research (NCAR) (Mar 2021)

Computational Scientist

This role as a Computational Scientist involved providing Scientific Computing expertise, enabling High-Performance Computing and Visualization solutions and performing research in Machine Learning.

Links: = Webpage of project

Research Projects

2019-Present Interactive Network Analysis of Social Graphs

2018-Present Natural Language Processing for determining Political Affiliation

2019-Present Deep Learning lead for the project ‘Eye Gaze tracking for Surgical Training’

2019-2020 Co-PI on Jefferson National Lab funded project ‘Next-generation Visual Analysis Workspace for Multidimensional Nuclear Femtography Data’

2015-2020 General Dynamics Collaboration with the Discovery Analytics Center(VT)

2019-2020 Generative Methods for Stance Detection and Visualization

2019-2019 Reinforcement learning for Eye Tracking in Laparoscopic Surgery

2019 Cost analysis of On-premise Cloud vs. Public Cloud for Virginia Tech

2018-2019 ICAT SEAD Grant 2018

2018-2019 Scheduling and Visualization Application for Idaho National Lab

2016-2017 HNFE Project for Visualization of National Food and Beverage Endorsements

2016-2017 Interactive 3D Visualization for Nuclear Reactor Pool

Infrastructure projects

Cloud Computing

Scientific Visualization

Collaborative Computing Platforms

Miscellaneous

Graduate School

Highlighted is some of my research work conducted during my Masters and Ph.D. programs.

Graduate Research Assistant

2009-2014 Experience developing and maintaining a 3D Time domain Electromagnetic solver for open/closed boundary problems such as Radar Cross Sections, Waveguides, Frequency-dependent materials etc. Knowledge of Finite Element Method for solving electromagnetic problems involving time-dependent phenomena.

Graduate Student

2007-2009 I was a member of the Microsystems Design Lab at the Pennsylvania State University where I worked on accelerator technology.

Summer Intern

2008 Performed code maintenance and bug fixes on proprietary embedded system firmware at Arris Corporation.

Teaching

2019 SuperComputing 2019 short talks : Talks@VT series

2019 Presented work on Stance Detection using Deep Learning at the AI4Good workshop held at PEARC19

2018 SuperComputing 2018 short talks : Talks@VT series

2018 Taught ‘Text Summarization with Word Embeddings using PyTorch’ for CS4984/5984 in Fall 2018

2016 Workshops for the Industrial and Systems Engineering Dept.

2016 Taught undergraduate class ‘CS1064: Introduction to Python’ in the Spring 2016 semester

2016 Taught ’Introduction to OpenACC’ lecture for undergraduate class ‘CMDA 3634: Comp Sci Foundations for CMDA’

2018 Taught workshop titled ’Introduction to ARC Cloud using OpenStack for Machine Learning’

2015-2019 Taught Networked Learning Initiative Seminar classes 2015, 2016, 2017 and 2018

2015-2016 Taught XSEDE workshops in 2015, 2016

2017-2018 PEARC workshops in 2017, 2018

2015-Present Mentored graduate and undergraduate students on various funded projects

Publications

*- Chaitanya S. Kulkarni, Tianzi Wang, Nathan Lau, Jacob Hartman-Kenzler, Sarah E. Parker, Srijith Rajamohan, Laura E. Barnes, Shawn D. Safford, Applying Deep Learning to Provide Eye- Gaze Guidance for the Peg Transfer Task, Jan 1 2021, 16th Academic Surgical Congress

- Srijith Rajamohan, Robert Settlage Informing the On/Off-prem Cloud Discussion in Higher Education, PEARC20, ACM, Portland

- Robert Settlage, Srijith Rajamohan Enabling AI/DL Workloads on HPC Infrastructure through Containers and Open OnDemand. HPCKP20, High-Performance Computing Knowledge Meeting, Barcelona, July 2020

- Rincón-Gallardo Patiño, Sofía, Srijith Rajamohan, Kathleen Meaney, Eloise Coupey, Elena Serrano, Valisa E. Hedrick, Fabio da Silva Gomes, Nicholas Polys, and Vivica Kraak. Development of a Responsible Policy Index to Improve Statutory and Self-Regulatory Policies that Protect Children’s Diet and Health in the America’s Region. International Journal of Environmental Research and Public Health 17, no. 2 (2020): 495.

- Robert Settlage, Srijith Rajamohan, Kevin Lahmers2, Alan Chalker3, Eric Franz3, Steve Gallo4, David Hudak3. Portals for Interactive Steering of HPC Workflows. Nov 2019, Third Workshop on Interactive High-Performance Computing, SC19

- Srijith Rajamohan, Alana Romanella, Amit Ramesh. A Weakly-Supervised Attention-based Visualization Tool for Assessing Political Affiliation. Aug 2019, arXiv:1908.02282 [cs.CL], https:// arxiv.org/abs/1908.02282

- Zhou, M., Rajamohan, S., Hedrick, V., Rincón-Gallardo Patiño, S., Abidi, F., Polys, N., & Kraak, V. (2019). Mapping the Celebrity Endorsement of Branded Food and Beverage Products and Marketing Campaigns in the United States, 1990–2017 ,International journal of environmental research and public health 16.19 (2019): 3743

- Valerio Mascolino, Alireza Haghighat, Nicholas Polys, Nathan J. Roskoff, and Srijith Rajamohan. 2019. A Collaborative Virtual Reality System (VRS) with X3D Visualization for RAPID, The 24th International Conference on 3D Web Technology (Web3D ’19), ACM, New York, NY, USA, 1-8.

- Srijith Rajamohan and Faiz Abidi, Web-based Visualization and Querying of Food and Beverage Endorsements by Celebrities, PEARC19, ACM, Chicago

- Rajamohan, S., Romanella, A., Ramesh, A., A Human-in-the-Loop Deep Learning Based Document Tagging for Stance Detection, CHCI 2019: Algorithms that make you think, Blacksburg.

- Rajamohan,S. and Anderson, W.K. A Modified Streamline Upwind/Petrov-Galerkin Stabilization Matrix for Time-Domain FEM, ACES 2018, Denver

- Rajamohan,S. and Anderson, W.K. Using an Approximate Streamline Upwind/Petrov-Galerkin Stabilization Matrix for the Solution of Maxwell’s Equations in Dispersive Materials, ACES 2018, Denver.

- Abidi, F., Polys, N., Rajamohan, S., Arsenault, L., Mohammed, A. (2018, April). Remote high performance visualization of big data for immersive science. In Proceedings of the High Performance Computing Symposium (p. 5). Society for Computer Simulation International.

- Zhou M, Kraak VI, Rajamohan S, Abidi F, Polys N. Mapping the Celebrity Marketing of Branded Food and Beverage Products in the United States: Policy Implications and Research Needs. 15th World Congress on Public Health. April 3-7, 2017. Melbourne, Victoria, Australia

- Nicholas Polys, Ayat Mohammed, Jagathshree Iyer, Peter Radics, Faiz Abidi, Lance Arsenault, and Srijith Rajamohan. Immersive Analytics: Crossing the Gulfs with High-Performance Visualization. IEEE VR 2016 Workshop on Immersive Analytics

- Rajamohan,S and Anderson, W.K , HPC for Legacy EM Code, a Mixed Language Approach using CUDA. Applied Computational Electromagnetic Society 2012, Volume: GPU for CEM.

- Porting Algorithms to the IBM Cell Processor - an FFT case study. Penn State Research Symposium 2009.