1. Email: sheetaljantikar12@gmail.com SHEETAL D JANTIKAR 140 Horizon Ave, Mountain view,
Phone : 765-586-5555 CA-94035
Master of Science in Electrical and Computer Engineering|GPA-3.4/4.0 Aug 2014-May2016
Oklahoma State University, Stillwater, OK
Bachelor of Engineering in Instrumentation and Technology|GPA-8.2/10 Sep 2009-June 2013
Visweshwaraya Technological University, INDIA
SKILLS
C, C++, python, matlab, Simulink, LabVIEW, excel-VBA,MS office suit, UNIX, 8051 microcontroller, 8086 microprocessor.
CERTIFICATION: Stanford Universitycertified Machine learning Scientist.
 Designedandimplementedlinear regression, logistic regression, neural networks withbackpropagation, support vector machines on
example data sets.
 Analysisof machine learning performance withadvancedoptimizationandregularization methods withsupervisedandunsupervis ed
learning, building anomalydetection system withmultivariate Gaussian distribution.
PROJECTS
Artificial intelligence and MACHINE LEARNING PROJECT Jan 2015-May2015
 Implementedvarious supervisedandunsupervisedlearning algorithms such as K-means clustering, Gaussian regressionmodel and
Bayesiannon-parametric approach to determine the future state of a dynamic system.
 DesignedReinforcement learning algorithms for artificiallyintelligent systems such as MarkovDecisionProcesses, Value iteration,
TBVI (Transition basedValue Iteration), Q-learning, SARSA(state,action,reward,state,action)to determine the optimalpath ofa robot
PATH PLANNING OF AUTONOMOUS GOLF CART Sep 2014-Dec 2014
 The maingoalof the project wasto designalgorithm that can provide a safe andefficient path to the autonomous golf cart inthe
presence of obstacles and ensuring that this pathis followedthroughout its journey.
 Implementedbasic search algorithms viz. breadth-first search, depth-first search, RRT, A*, to determine the optimal path.
 Developedthe Dynamic RRT (rapidlyexploring randomtree) algorithm to plan andcontinuouslytrace the pathfrom the initialto the
goal location inC++ andsimulatedthe same inV-rep.
FAST CLASSIFICATION NETWORKS forhuman location analysis in a smart home environment Jan 2016-May2016
 The maingoalof the project wasto monitor the location ofa subject in real time bythe using the data from various PIR sensors
deployedinthe smart home bydesigning supervisedlearningclass of neural networks that can perform classification.
 Fast classification neural networks was designed to perform classificationoperation under the supervised learning rule using the
output data fromthe sensors ona data set inthe order of 1000s, achievinghighaccuracyunder the real-time demands.
ARTIFICIAL NEURAL NETWORKS Jan 2016-May2016
 Designedandimplementedmachine learningalgorithms suchas performance learning, perception rule, Widrow-Hoff rule andback-
propagation algorithms on multilayer networks for regression, functionapproximationand classification applications.
 Developed associative andcompetitive networks, including feature maps, radialbasisnetworks and learning vector quantization
under unsupervisedlearningto performpattern recognition, clustering and prediction.
 Analysisof neural network performance measures using Bayesianregularizationandearlystopping training methods, ensuring
network generalizationabilityinMATLAB.
SUPERVISED LEARNING THROUGH EXTREME LEARNING MACHINE ALGORITHM April 2016-May2016
 Implemented a new learningrule for neural networks called Extreme learning machine to work on data fitting, regression and
function approximationusing large chunk ofdata set inthe order of millions.
 Achieved better generalizedperformance andaccuracycompared to other learning algorithms witha speedefficiencyof lessthan
halfcompared to backpropagation based learning.
AUTOMATIC and DIGITAL CONTROL SYSTEMS Aug 2014-Dec 2014
 Analyzedthe stabilityof a system through the methods ofroot locus, Bode plots, andNyquist plots anddeterminedthe
controllabilityand observabilityof a systembyapplying Lyapunovstabilityanalysis.
 Designedthe digital control systems using Root locus and Frequencybasedmethods andpole placement bycompensatingfor phase
lead andphase lagbycomputing the transient andsteadystate response of a system.
OPTIMIZATION APPLICATIONS Aug 2015-Dec 2015
 Developedline search algorithms (goldensection, newtonsecant, successive quadratic) andoptimizers (leapfrogging, incremental
steepest descent, Hook-Jeeves, Levenberg-Marquardt) inVBA.
 Comparedandanalyzed various optimizers, convergence criteriaandobjective functions spreadover various applications.
DIGITAL SIGNAL PROCESSING Aug 2014-Dec 2014
 ImplementedButterworth, Chebyshevfilters under givenspecifications, workedon linear convolutionusingDFT and FFTusing
MATLAB.
 Determinedthe chirpedfrequencyandaliasedthe signals andperformed the sampling rate conversion (up sampling, down
sampling)using MATLAB.
GRID PIXEL MAPPING TECHNIQUE OF HIDING BLACK AND WHITE IMAGES Mar 2016-Apr 2016
 Developeda newtechnique of hiding black white imageswithin black andwhite imagesthroughgrid pixelmapping in applications
such as image steganography, watermarking, cyber securityetc.
 The performance metrics provedthe method to be successful in hidingthe imageswithout anydetection andalso appropriate
retrieval of the images at the receiver’s end waspossible.
WORKEXPERIENCE
VI Solutions, INDIA |Hardware Engineer-Intern Dec 2013-May2014
 Buildingcircuits byinterfacing withthe NI hardware tools (myDAQ, NI ELVISmx, digital multimeter, oscillioscope) and programming
in LabVIEW.
 Developedapplications usingthe GUI inLabVIEW.
 ProgrammingVIs using timedandevent structures in LabVIEW.