Srijith Rajamohan, Ph.D.

AI Lead

Washington, DC

Education

2009-2014 Ph.D. in Computational Engineering The University of Tennessee

2007-2009 MS in Electrical Engineering The Pennsylvania State University

Leadership

Core Competencies

Machine Learning Deep Learning (NLP)
Reinforcement Learning Graph Learning
Statistical Learning Bayesian Learning
MLOps Data platforms
GenAI Information Visualization

Experience (selected)

2022-Present Staff AI Research Scientist, Sage AI

2022-2022 Senior Data Scientist, NerdWallet

2020-2022 Senior Developer Advocate (Data Science & ML), Databricks

2014-2020 Computational Scientist, Virginia Tech

2009-2014 Graduate Research Assistant, SimCenter: Center of Excellence in Computational Engineering, UT

Staff AI Research Scientist

I currently lead the data science and research portfolio within product teams at Sage AI. My responsibilities include finding novel solutions to difficult problems by interfacing cross-collaboratively across teams with Product Managers, Data Scientists, ML Engineers and business stakeholders to solve novel business problems.

Links: = Webpage of project

Projects

Thought leadership

Jan 2024 - Present

Tech lead for domain-specific LLM training (Collaboration with AWS)

July 2023 - Present

Retrieval Augmented Generation (RAG) - LLM for Q&A (Patent filing in progress)

June 2023 - Sep 2023

Tech lead for cashflow forecasting

Mar 2023 - Present

Generative AI - LLM-based model for response generation (Patent pending)

Mar 2023 - Jun 2023

Intent detection from text using classical ML

Feb 2023 - Mar 2023

Applied Reinforcement Learning for Financial Applications

September 2022 - Feb 2022

Identifying Recurring Trusted Transactions from vendor buyer interactions (Patent pending)

September 2022 - October 2022 Graph Neural Networks for Financial Applications

Sr. Data Scientist

In the data science team at Nerdwallet, I design experiments and perform modeling to solve data science problems for business units such as lead optimization. I also interface with the data engineering and ML infrastructure teams to optimize ML workflows.

Links: = Webpage of project

Projects

April 2022 - June 2022 Contextual bandits for product placement/ordering

May 2022 - July 2022 User understanding for prospective and propensity models

April 2022 - July 2022 ML monitoring for production pipelines

June 2022 - Jul 2022 ML job submission and workflow management

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 also allows me to engage in internal advocacy, and work cross-functionally across various units such as product management, product marketing, solutions and engineering. 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

2020-2021 Authored & open-sourced a set of courses ‘Introduction to Computational Statistics for Data Scientists’

2020-2022 Articles on Data Science and Machine Learning

2020-2022 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

2019-2020 Interactive Network Analysis of Social Graphs

2018-2020 Determining political affiliation from short texts using stance detection(NLP)

2019-2020 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

Select research work conducted during my Masters and Ph.D. programs.

Graduate Research Assistant

2009-2014 Experience developing and maintaining a parallel 3D time domain electromagnetic solver using the finite element method for open/closed boundary problems such as radar cross sections, waveguides, frequency-dependent materials etc.

Graduate Student

2007-2009 At the Microsystems Design Lab at the Pennsylvania State University, I implemented a neural network for skin-tone detection on the IBM Cell and which resulted in a 23x speedup.

Teaching

2018-2019 SuperComputing 2018-209 short talks : Talks@VT series

2016 - 2018 Lectures and workshops at Virginia Tech

2015-2019 Select seminar classes taught at Virginia Tech

2015-2018 XSEDE conference workshops

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.