Nilansh khurana

Co-Founder

Leeds, England, United Kingdom4 yrs 1 mo experience

Most Likely To SwitchHighly Stable

Key Highlights

Expert in advanced deep learning models and techniques.
Proven leadership in AI-driven real-time applications.
Strong background in both technical and strategic roles.

Stackforce AI infers this person is a Data Science expert with a focus on AI-driven solutions in the Social Media industry.

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Skills

Core Skills

Deep LearningComputer Vision

Other Skills

Amazon Web Services (AWS)Android DevelopmentAndroid StudioArtificial Neural NetworksAttention MechanismsBERT (Language Model)Business DevelopmentCNNCaffeClient RelationsCommunicationData AnalyticsData ScienceDeep Learning Neural NetworksDeep Neural Networks (DNN)

About

As a Data Scientist, I excel in harnessing advanced deep learning state-of-the-art models like Attentions mechanism, Transformers, GAN, STN, RCNN to unlock valuable insights from any type of structured and unstructured data. Skilled in MLOps, I streamline processes and optimize workflows to drive efficiency and productivity. My passion lies in leading teams to leverage AI advancements for real-time applications, from predictive analysis to user-generated content understanding. With a strong background in Artificial Intelligence, I bring a unique blend of technical expertise and leadership skills to the table. My collaborative approach fosters innovation and empowers team members to thrive, while my strategic vision ensures alignment with organizational objectives.

Experience

Lexverify

2 roles

Lead AI Engineer / Chief Data Science Officer (Co-Founder)

Promoted

Jul 2023 – Present · 2 yrs 8 mos

Data Scientist

Feb 2022 – Aug 2023 · 1 yr 6 mos

Helixsmartlabs pvt ltd

Machine Learning Engineer

Sep 2020 – Aug 2021 · 11 mos · Delhi, India · Remote

Worked on Computer Vision and Image Processing using Deep Learning Neural Networks for designing real-time emotion recognition and emoji generation for a social media chatting application:
1. Worked on MobileNet pre-trained models trained on ImageNet dataset for identifying humans and their faces in a video frame.
2. Performed experiment using OpenPose, for identifying the major facial key points to detect a person’s expression.
3. Developed MLP, CNN and LSTM models for emotion recognition using speech/audio data.
4. Designed an efficient and optimized 13-layer CNN-based model for classifying different emotions based on a human facial expression and audio daya using Python, Keras, and TensorFlow.
5. Used tensorflow.js and OpenCV to enable the real-time implementation of the deployed model on the client side.
6. Worked on UNet-based architecture for performing semantic segmentation.

Computer VisionImage ProcessingDeep Learning Neural NetworksMobileNetOpenPoseMLP+9