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About
Siddharth Chamarthy is an accomplished Engagement Manager at DIA Associates in Brooklyn, New York, where he focuses on leveraging machine learning to derive strategic insights from data. With a Master of Science in Mechanical Engineering from Columbia University, Siddharth specializes in predictive analytics, notably enhancing marketing strategies for sports organizations by categorizing fans based on their engagement and preferences.
In his current role, he oversees complex data ingestion and database management projects, demonstrating strong technical skills in programming and robotics, particularly with Arduino. Prior to DIA Associates, he spent over four years as a Research Engineer at Quadrus Medical Technologies, where he led the development of innovative medical products, including a kidney injury prediction algorithm and an emergency ventilator. His work resulted in significant advancements in predictive healthcare technologies, achieving impressive accuracy rates in clinical predictions.
Siddharth is also an active member of the IEEE EMBS, contributing to the Technical Committee on Cardiopulmonary Systems and Physiology-Based Engineering. His diverse skill set encompasses data analysis, system integration, and the development of advanced analytical solutions, which he applies to support both organizational goals and client needs.
With over 1,249 connections on LinkedIn, Siddharth actively engages with the professional community, celebrating achievements and fostering collaboration. His commitment to innovation and excellence positions him as a key player in the intersection of technology, healthcare, and data analytics.Shyamakrishna Siddharth Chamarthy is an accomplished professional with extensive expertise in robotics, data science, machine learning, and biomedical research. With a Master of Science in Mechanical Engineering, specializing in Robotics and Controls from Columbia University, and a Bachelor of Technology in Mechanical Engineering from Amrita School of Engineering, he has consistently demonstrated proficiency in cutting-edge technologies. Currently serving as an Engagement Manager at DIAAssociates in New York City, he leads a team of data scientists and engineers in developing machine learning models for advanced marketing strategies, leveraging tools such as Python, PySpark, SQL, AWS Redshift, and Sagemaker. His contributions include rank-ordering sports fans based on scoring probabilities, optimizing pricing strategies using secondary resale market insights, and spearheading data visualization platforms utilizing ticketing analytics. Additionally, he is an IEEE EMBS Member of the Technical Committee on Cardiopulmonary Systems & Physiology-Based Engineering, actively involved in reviewing research papers and presenting findings at international symposiums. His research background is extensive, having worked as a Senior Research Engineer at Quadrus Medical Technologies, where he developed emergency smart ventilators, remote patient monitoring systems, and data acquisition tools. His work on lung emulators integrated with medical devices has significantly contributed to respiratory performance analysis and patient care. Siddharth has also demonstrated expertise in predictive healthcare analytics, implementing machine learning algorithms for predicting Acute Kidney Injury and kidney disease progression, achieving high accuracy using neural networks and gradient boosting techniques. Furthermore, he has developed electronic health records (EHR) data extraction applications using Python, FHIR, and SQL, integrating them with leading medical databases such as EPIC, Cerner, and Allscripts. His experience as a Graduate Researcher at Columbia University further highlights his proficiency in applied robotics, exoskeleton control, and augmented reality for gait training. Notable achievements include implementing real-time parameter estimation for lung models, designing control algorithms for robotic systems, and publishing impactful research in high-impact journals such as IEEE Transactions on Neural Systems and Rehabilitation Engineering and Chemical Engineering Science. Siddharth's technical acumen spans a broad spectrum of programming languages, simulation tools, and prototyping techniques, including Python, TensorFlow, C++, MATLAB, Simulink, SolidWorks, and ANSYS. His work in machine learning-driven applications, predictive analytics, and control systems has been instrumental in advancing research in healthcare, automation, and robotics. With numerous publications, contributions to open-source projects, and accolades such as an honorary mention at the NYC Media Lab for his AR feedback application on HoloLens, he continues to drive innovation at the intersection of machine learning, healthcare, and robotics. His projects, ranging from Kalman filter-based meteorite tracking to PID-controlled drone stabilization and brain-computer interface EEG signal classification, demonstrate a well-rounded expertise in AI-driven systems and control engineering. His strong leadership in research and applied technology makes him a valuable contributor to the fields of robotics, biomedical engineering, and artificial intelligence-driven analytics.
Skills & Expertise
Keras
PySpark
Redshift
C++
C
SQL
Python
Linux
C#
Data Analysis
Matlab
Data Science
Solidworks
MATLAB
AutoCAD
Arduino
Tensorflow
Raspberry Pi
Git
Simulink
Inventor
Blender
Arduino IDE
Cura
ROS
AWS Sagemaker
Fusion360
3D Builder
Visualization Tool Kit
ANSYS CFD
ANSYS CFX
ANSYS ICEM-CFD
ANSYS MECHANICAL APDL
Simplify3d
Sli3er
PrusaSlicer
HL7 Fundamentals
Computational Motion Planning
Sequence Modeling
Research Interests
Augmented Reality
product design
Robotics
Programming
Mechanical Engineering
Data analysis
Data Science
Data Visualization
Database Management System
Product Development
Product Management
Artificial Intelligence (AI)
Data Engineering
data science
Biomedical Research
Predictive Analytics
System Integration
Machine Learning Models
Ticketing Analytics
Gradient Boosting
EHR Data Extraction
Advanced Marketing Strategies
Analytical Solutions
AI-Driven Systems
Connect With Me
Experience
Engagement Manager
- Ticketing Analytics & Insights for improved marketing Python | PySpark | SQL | AWS Redshift | Sagemaker | ML Flow Led a team of data scientists and engineers to develop machine learning models for advanced marketing strategies Implemented predictive algorithms using end-to-end ML Flow. Rank ordered sports fans based on scoring probabilities and provided segments to stakeholders Driven revenue for different teams in the league through providing marketing strategies using insights from fan and ticketing engagement activities Data Ingestion and manipulation Python | PySpark | SQL | AWS Redshift | Sagemaker Assisted in the ingestion of data from various data vendors Led data quality checks and provided reports by comparing with external golden records Manipulated the data to provide aggregated custom reports and data visualizations to lead presentations to stakeholders Led the development of the data and visualization platforms using ticketing data. Development of pricing optimization strategies Python | PySpark | SQL | AWS Redshift | Sagemaker | ML Flow Research and provide insights in the secondary resale market to get supply and demand analytics on tickets Provide pricing strategies based on fan engagements at the team stadiums
Education
Columbia University (CU)
Projects
Surgical Instrument Delivery Device [S.I.D.D.] - Robot Manipulation Project
Designed a robotics gripper to pick up and handout thin surgical instruments using passive mechanisms. Developed the ROS interface to manipulate the instruments.
Certificates & Licenses (1)
Data or Specimens Only Research
This is to certify that:
Siddharth Chamarthy
Has completed the following CITI Program course:
Human Research
(Curriculum Group)
Data or Specimens Only Research
(Course Learner Group)
1 - Basic Course
(Stage)
Under requirements set by:
Completion Date 08-Jul-2022
Expiration Date 07-Jul-2025
Record ID 35361923
Not valid for renewal of
certification through CME.
Massachusetts Institute of Technology Affiliates
Professional Memberships (1)
IEEE EMBS TC on Cardiopulmonary Systems and Physiology-Based Engineering
Country: United States
Peer-Reviewed Articles (47)
The integration of machine learning (ML) into healthcare robotics has emerged as a transformative factor, significantly enhancing the decision-making capabilities of robots used in clinical settings. With an increasing demand...
Surgical robotics have emerged as a transformative force in healthcare, promising to improve the accuracy, efficiency, and outcomes of surgical procedures. With the advent of next-generation surgical robotics, intelligent systems...
The increasing global population of elderly and disabled individuals presents significant challenges in healthcare, particularly concerning mobility, independence, and overall quality of life. Wearable robotics have emerged as a promising...
The use of robotic systems in healthcare has gained significant momentum, offering unparalleled benefits such as enhanced precision, reduced recovery times, and minimized human errors. However, these advancements also bring...
Soft robotics is a rapidly emerging field that has gained significant attention for its potential applications in healthcare, especially for minimally invasive procedures and enhancing patient comfort. These robots, designed...
Role in Research Journals (11)
Guest Editor
IJRAR
Guest Editor
IJRAR
Guest Editor
IJRAR
Guest Editor
IJRAR
Guest Editor
IJRAR
Publications (3)
Individuals with chronic hemiparesis post-stroke exhibit gait impairments that require functional rehabilitation through training. Exoskeletal robotic assistive devices can provide a user with continu...
Visual and force feedback are common elements in rehabilitation robotics, but visual feedback is difficult to provide in over-ground mobile exoskeleton systems. This letter aims to provide a method to...
This paper presents a numerical investigation to estimate pressure drop in fluid flow through reticulated ideal and randomized porous structures. The 3D open-cell foam geometries are constructed using...
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