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Research Manager
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
tiny-nn
June 19, 2023 – September 1, 2023
Intel device fast lightning neural network framework
View ProjectDeeperBiggerBetter_KDDCup
June 13, 2021 – June 16, 2021
Code release for the OGB KDD Cup for team DeeperBiggerBetter
View Projectsunets
April 27, 2018 – November 27, 2018
PyTorch Implementation of Stacked U-Nets (SUNets)
View Projectbiconvex-relaxation
April 4, 2018 – April 4, 2018
The code for the ECCV 2016 paper "Biconvex Relaxation for Semidefinite Programming in Computer Vision".
View ProjectDCC
March 1, 2018 – July 14, 2021
This repository contains the source code and data for reproducing results of Deep Continuous Clustering paper
View ProjectstableGAN
February 23, 2018 – April 21, 2019
The code for the ICLR 2018 paper "Stabilizing Adversarial Nets With Prediction Methods"
View ProjectWSC-SIIBP
February 11, 2018 – February 11, 2018
This repository contains the source code and data for reproducing results of "Weakly Supervised Learning of Objects, Attributes and their Associations", ECCV 2016 paper.
View ProjectCoLaMP
February 20, 2017 – June 4, 2017
The source code for the CVPR 2016 paper "Estimating Sparse Signals with Smooth Support via Convex Programming and Block Sparsity".
View ProjectCultural Fit Analysis
The candidate's projects are heavily research-oriented and academic, focusing on reproducing paper results. While this demonstrates strong technical depth, there is insufficient information to assess cultural fit for a typical industry Data Scientist role, which often requires product-focused development, stakeholder communication, and agile methodologies. The lack of diverse project types (e.g., deployment, A/B testing, business impact) suggests a potential gap in industry-specific experience.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. The candidate's profile primarily showcases technical project contributions without details on collaboration, problem-solving approaches, or communication in a team setting.