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Kimberly Sellers
Professor of Statistics at Georgetown University
bootstrapping (statistics), chi-square distribution, conway–maxwell–poisson distribution, correlation and dependence, degrees of freedom (statistics), dependent and independent variables, errors and residuals, estimation theory, exponential family, fisher information, generalized linear model, likelihood function, likelihood-ratio test, logistic regression, mathematical optimization, maximum likelihood estimation, overdispersion, p-value, parameter, poisson distribution, regression analysis, standard error, statistics, sufficient statistic, variance
Deba Sahoo
SVP, Head of Product for Customer Journeys at Fidelity Investments
ai, ai4, ai4 2020, alexander tsyplikhin, alphadyne asset management, analytics, apple inc., artificial, artificial intelligence, automation, bank, business value, computer science, credibility, customer engagement, customer experience, customer relationship management, customer satisfaction, deba sahoo, design, digital transformation, fidelity, fidelity investments, finance, financial modeling, financial services, goal, google, graphcore, high-frequency trading, human, ibm, infidelity, intelligence, invest, investing, investment management, ioana boier, john finneran, kanye west, leadership, learning, learning curve, likelihood function, long tail, machine, machine learning, microsoft powerpoint, ml, monte carlo method, omnichannel, pattern recognition, pleasure, prediction, predictive analytics, recycling, risk, robotics, sales, sinequa, state street, strategic management, team, time, time series, unstructured data, victor martinez, world wide web
Davide Venturelli
Artificial Intelligence, Quantum Computing, Physics, Robotics, Entrepreneurship at USRA
algorithm, application software, bayesian inference, brute-force search, cluster analysis, computing, database index, estimation theory, gradient descent, likelihood function, linear programming, machine learning, mathematical optimization, matrix (mathematics), maximum a posteriori estimation, maximum likelihood estimation, mean, quantum computing, quantum superposition, sampling (statistics), software , statistical inference, statistics, support-vector machine, zero-sum game
Celeste Fralick
Chief Data Scientist at McAfee, LLC
adversarial machine learning, algorithm, automation, bias, chaos theory, computer security, cryptography , cumulative distribution function, customer satisfaction, cybercrime, cybersecurity, deepfake, devops, energy development, engineering, failure rate, function (mathematics), human, information, infosec, innovation, integral, intelligence, likelihood function, machine learning, maintenance (technical), mathematical model, mathematics, mean squared error, mind, nuclear warfare, prediction, probability distribution, psychological resilience, random variable, ransomware, reliability engineering, risk, robust statistics, rsa, rsac, rsaconference, science, security, self-driving car, social engineering (security), software quality, statistical classification, strong cryptography, technology, user interface, world war ii
San Gultekin
Ph.D., Research Scientist at Verizon Media
air pollution, algorithm, bomb, car, child, concentration, constitution of texas, earth, experiment, exponentiation, facebook, function (mathematics), hypothesis, inner product space, learning, likelihood function, mcdonald's, ratio, research, risk, sample size determination, solution, summation, texas, triangle
Peiyuan Zhu
Student at The University of British Columbia Department of Statistics
action film, bayesian inference, cluster analysis, data, database, determinism, experiment, infection, likelihood function, logarithm, markov chain monte carlo, mathematics, negative binomial distribution, number, observation, prediction, sample size determination, sampler (musical instrument), science, sequence, solid modeling, statistical inference, temperature, theory, truncation (statistics)
Julius von Kügelgen
PhD student at Max Planck Institute for Intelligent Systems, Tübingen Campus
algorithm, causality, cluster analysis, confounding, dependent and independent variables, determinism, disease, expectation–maximization algorithm, experiment, factorization, generative model, information, joint probability distribution, learning, likelihood function, logistic regression, prediction, random variable, regression analysis, semi-supervised learning, signs and symptoms, supervised learning, sympathy, unimodality
Marcelo Hartmann
Postdoctoral Researcher at University of Helsinki
algorithm, bohr model, color, expected value, experiment, fisher information, function (mathematics), human height, information, kullback–leibler divergence, language, likelihood function, machine learning, matrix (mathematics), mean, measurement, metre, parameter, plumbing, privacy, probability, probability distribution, randomness, sample space, sensor
Hermanni Hälvä
Student at University of Helsinki
air conditioning, correlation and dependence, equation, exponential family, feature learning, function (mathematics), goal, hidden markov model, independent component analysis, interval (mathematics), latent variable, learning, lighting, likelihood function, markov chain, markov model, mean, nonlinear system, parameter, simulation, solution, stationary process, time series, truth, unsupervised learning
Ajay Jain
EECS PhD at UC Berkeley
algorithm, artificial neural network, autoregressive model, bit, color, communication, convolution, convolutional neural network, data compression, deep learning, holography, information, joint probability distribution, lighting, likelihood function, matrix (mathematics), matrix multiplication, maximum likelihood estimation, neural network, parameter, patch (computing), pixel, python (programming language), raster graphics, time series
Pawel Chilinski
UBS, Director at Quantitative Trader
computer, coronavirus disease 2019, correlation and dependence, covariance, data, dependent and independent variables, energy, equation, experiment, factorization, function (mathematics), gas, joint probability distribution, likelihood function, mathematics, matrix (mathematics), mean, monotonic function, parameter, probability density function, probability distribution, python (programming language), random variable, synthetic data, training, validation, and test sets
Soumyasundar Pal
Ph.D. student at McGill University
artificial neural network, atom, calculator, citation, convolution, data, dimension, experiment, future, graph (discrete mathematics), information, laptop, learning, lighting, likelihood function, machine, machine learning, metal, parameter, parametric model, patient, polynomial, proposition, question, solution
Ioan Gabriel Bucur
Machine Learning Engineer at Radboud University
aldh2, bayesian inference, cardiovascular disease, causal graph, causality, complexity, compound probability distribution, computational complexity theory, confounding, correlation and dependence, function (biology), function (mathematics), genome-wide association study, instrumental variables estimation, laplace's method, likelihood function, marginal likelihood, markov chain, markov chain monte carlo, mathematical optimization, mixture, normal distribution, parameter, risk, shrinkage (statistics)
Alan Yang
EE PhD at Stanford University
algorithm, artificial neural network, bayesian network, complexity, computational complexity, conditional independence, dependent and independent variables, dimension, distance, estimator, expected value, feature selection, lasso (statistics), likelihood function, mathematical optimization, metric (mathematics), mutual information, parameter, random variable, receiver operating characteristic, regularization (mathematics), statistic, statistics, time series, vector space
Sayak Ray Chowdhury
Postdoctoral Associate at Boston University
algorithm, analogy, approximation, astronomy , black box, carnival, color, compressor, conditional expectation, domestication, evaluation, expected value, experiment, function (mathematics), information, kansas, likelihood function, map, mathematical optimization, mean, prediction, space, statistical inference, uncertainty, weight
Marko Jarvenpaa
Postdoctoral research fellow at University of Oslo
cartesian coordinate system, computing, disco, epoxy, equation, evaluation, facebook, function (mathematics), gaussian process, innovation, likelihood function, mathematical optimization, normal distribution, parallel computing, parameter, playstation (console), price, probability, quantifier (logic), sampling (statistics), sequence, simulation, space, uncertainty, uncertainty quantification
Jan-Christian Huetter
Postdoctoral researcher at Broad Institute of MIT and Harvard
causal model, causality, equation, estimator, experiment, infidelity, lasso (statistics), likelihood function, linearity, logarithm, mathematical optimization, matrix (mathematics), maximum likelihood estimation, mean, minimax, minimax estimator, normal distribution, observation, random variable, sample size determination, sparse matrix, structural equation modeling, the matrix, universe, variable (mathematics)
Hanwen Xing
Student at University of Oxford
american broadcasting company, approximate bayesian computation, approximation, aquarium, average, bayesian inference, biopsy, cumulative distribution function, derivative, distortion, equation, function (mathematics), hawaii, inference, letter case, likelihood function, map, paper, parameter, probability, regression analysis, tattoo, truth, weather, weather forecasting
Refik Anadol
Media artist, director, and pioneer
3d computer graphics, art, bit, bit array, computer graphics, consciousness, function (mathematics), generative adversarial network, likelihood function, machine learning, many-worlds interpretation, matrix (mathematics), measurement in quantum mechanics, perlin noise, prediction, purpose: educate, quantum circuit, quantum computing, quantum entanglement, quantum memory, quantum network, quantum supremacy, reality, software , sycamore processor
Assel Altayeva
Data Quality Lead, Strategy & Planning at Peabody
administrative data, adolescence, algorithm, autoregressive model, birth control, blockchain, caesarean section, calibration, causality, child development, childbirth, clinical trial, communication protocol, computer network, conversation, cryptocurrency, data science, differential privacy, economic inequality, encryption, epidemiology, ethereum, federated learning, function (mathematics), gaussian function, gestational age, health, hyperparameter optimization, hypothesis, information, information privacy, information security, life expectancy, likelihood function, likelihood-ratio test, liverpool, machine learning, maximum likelihood estimation, mean, mental health, model selection, mother, multimodal systems, multivariate normal distribution, national health service (england), neural network, normal distribution, parameter, poverty, predictive modelling, pregnancy, privacy, privacy-enhancing technologies, public housing in the united kingdom, reason, research, risk, science, sensitivity and specificity, server (computing), social work, society for worldwide interbank financial telecommunication, statistical causality, statistics, substance abuse, teenage pregnancy, time series, tropical medicine, variance, vector autoregression, violence, white noise
Anna Zaremba
Lead Data Scientist at Hunter Labs Tech
adjacency matrix, analogy, autism spectrum, autoregressive model, bit, blockchain, brain, calibration, causality, communication, computer vision, cryptocurrency, data science, deep learning, detecting fake news online, dimensionality reduction, ethereum, fake news, function (mathematics), gaussian function, gender, graph (discrete mathematics), hyperparameter optimization, hypothesis, image segmentation, information, integer programming, likelihood function, likelihood-ratio test, linear programming, mathematical optimization, matrix (mathematics), maximum likelihood estimation, model selection, multimodal systems, multivariate normal distribution, neuroscience, normal distribution, parameter, predictive modelling, research, science, semi-supervised learning, social network, statistical causality, time, time series, trace (linear algebra), vector autoregression, white noise
Tanvi Anand
Data Science Consultant at Fractal
algorithm, api, big data, blood donation, blood transfusion, data analysis, data science 2020, data science in social media, facebook, gender, google search, hashtag, learning, likelihood function, livestreaming, machine learning, news, privacy, research, sentiment analysis, sexual orientation, social media, social media analytics, social media analytics python , social media data analytics, social media data analytics tools, social media data analytics tutorial, social media data science, social network, social network analysis, twitter, whatsapp, wids 2020, wids mumbai 2020, wids worldwide, youtube
Julian Sara Joseph
Senior Full Stack Engineer at Somnoware Healthcare Systems
effective data visualization in the era of covid-19, ai, algorithm, api, artificial intelligence, basic reproduction number, big data, blog, blood donation, blood transfusion, bootstrapping (statistics), constrained optimization, convex function, covid-19, covid-19 data visualization, covid-19 data analysis, covid-19 modelling, covid-19 visualization, data analysis, data science, data science 2020, data science conference 2020, data science in social media, decision tree learning, deep learning, dependent and independent variables, derivative, determinism, differential equation, equation, errors and residuals, estimator, facebook, function (mathematics), gender, genie (feral child), google search, gradient descent, hashtag, hessian matrix, hyperparameter (machine learning), hyperparameter optimization, initial condition, instagram, lasso (statistics), learning, likelihood function, livestreaming, logistic regression, loss function, machine learning, mathematical model, mathematical optimization, music, neural network, news, odds ratio, ordinary differential equation, overfitting, parameter, podcast, privacy, random forest, regression analysis, research, sentiment analysis, sexual orientation, sine, social media, social media analytics, social media analytics python , social media data analytics, social media data analytics tools, social media data analytics tutorial, social media data science, social network, social network analysis, spotify, statistical classification, statistics, supervised learning, twitter, variance, video, whatsapp, wids 2020, wids mumbai 2020, wids worldwide, woman in data science , woman in data science mmmbai , women in ds, women in tech, women in technology, youtube
Madhavi Kaivalya Kandalam
Chief Data Scientist at Loylty Rewards
ai, artificial intelligence, bootstrapping (statistics), constrained optimization, convex function, data science, data science conference 2020, decision tree learning, deep learning, derivative, determinism, errors and residuals, gradient descent, hessian matrix, lasso (statistics), likelihood function, logistic regression, loss function, machine learning, mathematical optimization, neural network, odds ratio, overfitting, random forest, regression analysis, statistical classification, statistics, supervised learning, variance, wids 2020, wids mumbai 2020, wids worldwide, woman in data science , women in tech, women in technology
Susan Athey
Economics of Technology Professor at Stanford Graduate School of Business
aaai, aaai 20, active learning, ai, analogy, artificial intelligence, artificial neural network, bayes estimator, bayesian inference, bayesian machine learning, behavioral economics, blockchain, causality, city university of new york, cloud computing, cnn, collaborative filtering, competition law, conference, confidence interval, consumer choice, credibility, credit score, deep learning, demand, demand curve, digitization, dimensionality reduction machine learning, discrete choice, econometrics, economics, education, educational technology, effect size, experiment, facebook, field experiment, future, genetic algorithm in machine learning, hypothesis, innovation, insight, institute, instrumental variables estimation, intel, investment, knowledge, likelihood function, logistic function, logistic regression, machine learning, macroeconomics, mathematical model, matrix factorization (recommender systems), maximum likelihood estimation, mergers and acquisitions, microsoft, milken, mind, money, monte carlo method, motivation, multi-armed bandit, nvidia, optimization, p-value, perceptron in machine learning, power (statistics), prediction, price discrimination, price–earnings ratio, privacy, productivity, profit (economics), prototype, python, quasi-experiment, recommender system, regression analysis, regression discontinuity design, reinforcement learning, research, revealed preference, rnn, science, self-driving car, sensitivity analysis, social science, speech recognition, standard deviation, startup company, statistical inference, statistics, tech, training, validation, and test sets, utility, utility maximization problem, valuation (finance), variational bayesian methods
Cheng Zhang
Principal Researcher at Microsoft
bayes estimator, bayesian inference, behavioral economics, consumer choice, credibility, deep learning, demand, economics, experiment, likelihood function, logistic function, machine learning, matrix factorization (recommender systems), maximum likelihood estimation, monte carlo method, quasi-experiment, research, revealed preference, sensitivity analysis, statistical inference, statistics, training, validation, and test sets, utility, utility maximization problem
Elias Bareinboim
Assistant Professor at Columbia University
bayes estimator, bayesian inference, behavioral economics, consumer choice, credibility, deep learning, demand, economics, experiment, likelihood function, logistic function, machine learning, matrix factorization (recommender systems), maximum likelihood estimation, monte carlo method, quasi-experiment, research, revealed preference, sensitivity analysis, statistical inference, statistics, training, validation, and test sets, utility, utility maximization problem
Amit Sharma
Senior Researcher at Microsoft
bayes estimator, bayesian inference, behavioral economics, consumer choice, credibility, deep learning, demand, economics, experiment, likelihood function, logistic function, machine learning, matrix factorization (recommender systems), maximum likelihood estimation, monte carlo method, quasi-experiment, research, revealed preference, sensitivity analysis, statistical inference, statistics, training, validation, and test sets, utility, utility maximization problem
Jana Diesner
Associate Professor at University of Illinois at Urbana-Champaign
accuracy and precision, bias, big data, bigdata2020, computer science, data analysis, data science, decision-making, dependent and independent variables, effect size, gender, gender role, good and evil, information sciences, innovation, likelihood function, machine learning, observation, policy, prediction, product development, public engagement, reliability (statistics), research, research and development, sampling (statistics), sentiment analysis, sexism, simpson's paradox, small business innovation research, small business technology transfer, social media, statistics, stereotype, the research park at the university of illinois
Natalia Culakova
Data scientist and engineer at nPlan
accuracy and precision, artificial neural network, average, binary classification, calibration, data mining, data science, forecasting, ground truth, likelihood function, logistic regression, machine learning, mean, monte carlo method, neural network, neuroimaging, nothing, prediction, regression analysis, reliability engineering, research, simulation, softmax function, software engineering, statistical classification, temperature, theory, weighted arithmetic mean
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