Supriya Gadi Patil

I completed Research-based Master’s degree in Computer Science in June 2023 from Simon Fraser University, advised by Manolis Savva.

My expertise is in Data Analysis and Machine Learning domain. I have 8 years of experience in Data Analysis and around 2 years in Software Engineering, making me proficient in both analytical methodologies and software development.

I am looking for a full-time role as a Data Analyst / Software Engineer / Machine Learning Engineer. If your team has any open position that aligns with my profile, please reach out to me.

Please find my resume here[Last updated: Sept 2023].

Updates:

• [* 7 Nov 2023*] 🏆 Completed 200 LeetCode problems (LeetCode profile)

• [Sept 2023] 🌱 Completed Certificate Course: Generative AI with Large Language Models(LLM) (Certificate)

• [Aug 2023] 🌱 Completed Certificate Course: MLOps - CI/CD for ML (Certificate)

• [June 2023] Paper accepted at Computer Graphics Forum (CGF) 2023: “Advances in Data-Driven Analysis and Synthesis of 3D Indoor Scenes”. [Paper]

• [April 2023] Defended M.Sc.-Thesis degree. Here is a link to my thesis.

• [April 2023] Paper accepted for Oral Presentation at Conference on Robots and Vision (CRV) 2023: “Evaluating 3D Shape Analysis Methods for Robustness to Rotation Invariance”. [Paper] [Project website]

• [April 2023] Check out our new survey paper: Advances in Data-Driven Analysis and Synthesis of 3D Indoor Scenes

• [Dec 2022] Completed research internship at Huawei.

• [8-11 August, 2022] Student Volunteer for SIGGRAPH 2022 in-person conference in Vancouver.

• [May 2022] Started working as a Research Intern in Xuebin Qin’s team at Huawei Research Center, Vancouver.

• [August 2019] Joined as a graduate student in the GrUVi lab at Simon Fraser University, Vancouver, Canada (SFU), advised by Prof. Manolis Savva.

• [4 March 2019 - 31 May 2019] Completed Research Internship at Max Planck Institute for Software Systems (MPI-SWS), working with Prof. Manuel Gomez Rodriguez

• [7 Aug 2018] Our team(me and Rashmi Narvekar) was ranked 104 out of 394 teams in Kaggle Challenge: The 2nd YouTube-8M Video Understanding Challenge. We achieved Global Average Precision (GAP) score of 0.82853 on private leaderboard. Link to our code

• [25 Jun 2018] Successfully defended my MTech !! [Thesis:Undestanding Graph Data Through Deep Leaning Lens], [Presentation Slides]

• [7-14 Apr 2018] Participated in Honeywell Aerospace Automation Challenge 2018 Hack-A-Thon.

• [14 Mar 2018] Wrote blog post on Paper Summary #2: Convolutional Neural Network on Graphs with Fast Localized Spectral Filtering (NIPS 2016).

• [8 Mar 2018] Gave a talk on Geometric Deep Learning at CS5480-Deep Learning course. [Slides]

• [23 Feb 2018] Wrote a blog post on Paper Summary #1: Learning Convolutional Neural Network for Graphs (ICML2016).

• [22 Feb 2018] Wrote a blog on CNN for Graph: Notes on IPAM UCLA talk- Part II.

• [17 Feb 2018] wrote a blog on CNN for Graph: Notes on IPAM UCLA talk- Part I.

• [24-25 Jan 2018] Attended Microsoft Academic Research Summit 2018 at IIIT Hyderabad.

• [11-13 Jan 2018] Presented my research work “STWalk: Learning Trajectory Representations in Temporal Graphs” at CoDS-COMAD 2018.