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AI Technical Solutions | AI | Intel
Anisha is a passionate problem solver with an ever-growing skill set of an engineer. She has 7 years of Deep learning and Computer vision experience in academic and industry roles. She holds a Masters degree from Georgia Institute of Technology and is currently employed at Intel Corporation. Below are some of the many projects Anisha has completed using state of the art CV/DL algorithms: • Optimization of training and inference of DL models for best performance, • Implementation of Linear and Multiclass Classifiers, • Face Detection and Face Verification, • Scene Recognition, • Transfer learning. Industry Experience: • Successfully built and deployed models (Deep Learning models and classical Machine learning algorithms) on Python/C++, Tensorflow, Caffe, Keras, Theano, OpenCV, MKL, Scikit Learn, • Responsible for end-to-end system design including proof of concept, design, implementation, unit testing, and test automation, • Actively involved in leadership and event management. Connect with Anisha over email at anisha.gartia@gatech.edu Take a look at some of her projects on Github (https://github.com/anishagartia)!
Georgia Institute of Technology
Master of Science (M.S.), Electrical and Computer Engineering
January 1, 2015 – January 1, 2016
B. M. S. College of Engineering
Bachelor of Engineering (B.E.), Telecommunications Engineering
January 1, 2009 – January 1, 2013
National Public School, Koramangala
AISSE-10th Standard CBSE and AISSCE-12th Standard, Science- PCMB
January 1, 2006 – January 1, 2009
Intel Corporation
AI Accelerator Technical Solution Specialist
April 1, 2024 – Present
Intel Corporation
Worldwide GPU Technical Solution Specialist for AI and Media
January 1, 2023 – April 1, 2024
Intel Corporation
Artificial Intelligence - Technical Solutions Specialist
May 1, 2017 – December 1, 2022
Panasonic Automotive
Computer Vision Engineer
February 1, 2017 – May 1, 2017
Atlanta, GA
Silicon Labs
Application Engineering Intern
May 1, 2016 – August 1, 2016
Nashua, New Hampshire
Georgia Institute of Technology
Graduate Student
August 1, 2015 – December 1, 2016
Atlanta, Georgia
Georgia Institute of Technology
Graduate Student Researcher
August 1, 2015 – December 1, 2015
Atlanta, Georgia
Tata Consultancy Services
Assistant Systems Engineer
September 1, 2013 – July 1, 2015
Bengaluru Area, India
ZTE Corporation
Project Intern
September 1, 2012 – December 1, 2012
Bengaluru Area, India
Indian Institute of Technology, Bombay
Research Fellow
June 1, 2012 – August 1, 2012
Mumbai Area, India
POWERGRID
Intern
June 1, 2010 – August 1, 2010
Mumbai Area, India
Scene recognition with bag of words
October 1, 2016 – Present
- Implementation of image recognition to classify based on 15 scene database. - Implemented bag of words with linear SVM classifier. - Trained vocabulary using SIFT features, and k-means. - Implemented GIST descriptors, kernel codebook encoding for feature representation. - Implemented Fisher encoding with spatial pyramid for feature representation to result in best model.
Fundamental Matrix Estimation with RANSAC
September 1, 2016 – Present
- Implemented project for Computer Vision course. - Implemented Matlab code to estimate camera calibration, specifically estimation of camera projection matrix, and fundamental matrix. - Performed accurate estimation of camera projection matrix and the fundamental matrix can each be estimated using point correspondences related by epipolar lines on both the images. - Used linear regression to estimate the matrices. - Used RANSAC in conjuction with fundamental matrix to deal with outliers.
Local Feature Matching of images using SIFT
September 1, 2016 – Present
- Implemented Harris Corner Detector to get interest points corresponding to corner pixels. Designed to detect corners in multiple scales of the image. - Implemented SIFT algorithm for obtaining local feature descriptor of the corner points found earlier. Each corner point is described using Histogram of Gradients (HoG) of image patches surrounding it. - Implemented Feature Matching using nearest neighbour distance matching, and KNN search using k-d tree.
Face Verification using CNN with Siamese Networks
January 1, 2016 – May 1, 2016
- This project involves implementing a convolution neural network(CNN) that learns a model to detect faces in images, and classify two images of a single person to a single class. This CNN, when used with a siamese network, allows us to recognise two images as of the same person even though the model may not have come across it during training. - Thus, CNN in Siamese architecture learns a model that computes loss function to assign unseen facial images of the same person to a single category, and different people to different categories. We achieved 0.9946 Train Accuracy and 0.9284 test Accuracy with AT&T dataset for face verification.
Implementation of Linear and Multiclass Classifiers
January 1, 2016 – May 1, 2016
- Implemented linear classification algorithms PCA, Naïve Baye’s, and LDA on python. - Implemented classification of data using Decision trees, Random Forest, Boosting, and Bagging. - Implementation of k-NN classifier by learning k and distance metric, Hidden Markov Models to determine states of an observation sequence using forward, Viterbi, and Baum Welch algorithms.
Simulation of Cache System, and Scheduling Techniques
January 1, 2016 – May 1, 2016
- Designed a simulation for Three level Cache Memory System in Object oriented C++. - The three level of the cache include victim cache, Write back- write allocate policy, and LRU policy - Designed a simulator for Tomasulo dynamic scheduling technique, with ROB and checkpoint repair algorithms.
Implementation of Path Oriented Decision Making Algorithm
September 1, 2015 – Present
The objective of this project was to implement Path Oriented Decision Making (PODEM) for testing of digital circuits. The final system was able to accept an input circuit and generate test vectors for detection of specific or all stuck-at-faults in the circuit. The above was achieved in Matlab.
Memory System Design and Arithmetic Unit Design
September 1, 2015 – December 1, 2015
The objective of this project was to design an SRAM Array, and an Adder unit for addition of two 8-bit binary numbers. This required design of SRAM array with peripherals, latches, circuit to drive highly capacitive load, and arithmetic logic.The above schematic and layout was achieved using Cadence.
Microprocessor based Line Differential Protection using OFC
February 1, 2013 – June 1, 2013
Successfully completed the circuit design, microcontroller programming and prototype development of a system which provides line differential protection to a line by the use of fibre optic communication. Undertaken at Power Systems Operation Corporation, Powergrid, Bangalore. Publication: “Microcontroller Based Line Differential Protection Using Fibre Optic Communication” Conference - IEEE Innovative Smart Grid Technologies Asia 2013 – Transmission Track
Configuration and Commissioning of SDH
September 1, 2012 – December 1, 2012
Successfully completed the configuration of a SDH system with 4 and 6 nodes. The system enables adding and dropping of lines as per requirement. Optic fibres were used to establish the connectivity. Undertaken at ZTE Communication, Bangalore
Sensors based on Fibre-Optic Sagnac Interferometry
June 1, 2012 – August 1, 2012
Research project which describes how sagnac interferometry can help the use of optic fibre as a sensor. Focuses on the different type of sensors viz., temperature sensor, acoustic sensor etc. Undertaken at Indian Institute of Technology, Bombay.
Cultural Fit Analysis
The candidate's project portfolio is heavily skewed towards Computer Vision, Machine Learning, and embedded systems, which aligns well with a data-intensive role. Their experience at Intel as a Technical Solution Specialist for AI and Media, and previous Computer Vision Engineer role at Panasonic Automotive, indicates a strong interest and capability in advanced analytical and AI-related fields. However, the projects are predominantly academic or personal, and while technically deep, they lack explicit mention of collaborative team environments or business impact, which could be a point for further exploration regarding cultural fit in a corporate data analyst setting. The target role 'Data Analyst' is broad, and while the candidate has strong analytical skills, the direct alignment with typical business intelligence or statistical analysis tools (e.g., SQL, Tableau, advanced Excel) is not explicitly demonstrated in the provided data.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving aptitude and a hands-on approach to technical challenges. Their experience at Intel as a Technical Solution Specialist suggests good communication and customer engagement skills, which are valuable for operational fit. The diverse range of projects also points to adaptability and a continuous learning mindset.