Suryakiran S
Analyst, Software Engineer at Goldman Sachs
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolSuryakiran S is a versatile software engineer and analyst with nine years of experience building production ML and data platforms, currently at Goldman Sachs after recent AI/ML work at AT&T Labs and NYU. He bridges research and production—having led a no-code data science platform on Azure Databricks, improved object-detection accuracy by 10% in industry projects, and automated cash-application workflows that halved manual effort. A Courant MSCS student with strong academic credentials and teaching experience in probability, linear algebra, and computer vision, he combines deep technical breadth (Python, Java, C++, Go, Spark) with practical delivery. Suryaakiran’s work often targets developer productivity and operational wins—examples include Java parsers that raised data accuracy to 95% and CI/CD-backed APIs that sped deployments. Curious about LLMs and real-world impact, he enjoys turning complex data problems into elegant, user-facing solutions.
9 years of coding experience
3 years of employment as a software developer
Master of Science - MS, Computer Science, 3.85/4.00, Master of Science - MS, Computer Science, 3.85/4.00 at New York UniversityBachelor of Engineering - BE, Computer Science and Engineering, 9.1/10.0, Bachelor of Engineering - BE, Computer Science and Engineering, 9.1/10.0 at Thiagarajar College of EngineeringISC, ISC at Lakshmi school
English, Tamil
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (5)
training-data10
point-to-point10
linear-regression10
imitation-learning9
python2
Programming languages (2)
Github contributions (5)
suryakiranmg/My-Projects-DoctoralCoursework
May 2018 - Jul 2024
Contributions:92 pushes, 12 branches in 6 years 3 months
suryakiranmg/Dynamic-Movement-Primitives-and-Imitation-Learning-Robotics
Mar 2019 - Oct 2021
Dynamic movement primitives (DMPs) are a method of trajectory control/planning from Stefan Schaal’s lab. Complex movements have long been thought to be composed of sets of primitive action ‘building blocks’ executed in sequence and \ or in parallel, and DMPs are a proposed mathematical formalization of these primitives. The difference between DMPs and previously proposed building blocks is that each DMP is a nonlinear dynamical system. The basic idea is that you take a dynamical system with well specified, stable behavior and add another term that makes it follow some interesting trajectory as it goes about its business. The DMP differential equations (Transformation System, Canonical System, Non-linear Function) realize a general way of generating point-to-point movements. Imitation learning using linear regression is performed to compute the weight factor W from a demonstrated trajectory dataset, given by a teacher. The quality of the imitation is evaluated by comparing the training data with the data generated by the DMP.
Contributions:7 commits, 17 pushes, 1 branch in 2 years 7 months
imitation-learninglinear-regressiontraining-datadynamic-movement-primitivepoint-to-point
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.