Thierry Bema is a 44-year-old French national with experience in data science and enterprise business intelligence projects using technologies like Spark, Hadoop, and Scala. He has created multinode clusters on Hadoop and run various financial market and machine learning applications including stock price prediction, portfolio risk calculation, and sentiment analysis. His background also includes 17 years of experience designing RFICs for wireless technologies.
The product was established to take advantage of the unique environment set by the convergence of the following three trends: large amounts of assetprice-relevant information becoming available, Big Data technologies making it possible to process and make sense of this information, and Machine Learning technics reaching a point where Artificial Intelligence can now be scalable. It systematically trades US equities based on rigorous machine learning, artificial intelligence, and statistical techniques to produce superior, uncorrelated returns. Also it uncovers non-obvious relationships by processing large amounts of unstructured data and leveraging computing brute force and employs disciplined and dynamic statistical Risk Management techniques to its portfolio.
Integrated Model Discovery and Self-Adaptation of RobotsPooyan Jamshidi
Machine learn models efficiently under budget constraints to adapt to perturbations such as environmental changes or changes in the internal resources.
Modern software-intensive systems are composed of components that are likely to change their behaviour over time (e.g., adding/removing components).
For software to continue to operate under such changes, the assumptions about parts of the system made at design time may not hold at runtime due to uncertainty.
Mechanisms must be put in place that can dynamically learn new models of these assumptions and use them to make decisions about missions, configurations, etc.
Розумне місто складається з великої кількостї технологій та компонентів. На перший погляд це схоже на велику купу розкиданих іграшок. Немає вимог, специфікацій, процессів, ресурсів. Нічний жах тестувальника або просто Black Box. Але це — прекрасний тренажер, щоб перевірити власну стійкість, розуміння теорії тестування та власну кваліфікацію. У цій доповіді Вікторія Таранюк будує чітку структуру із хаосу.
Цю доповідь представила Вікторія Таранюк (Associate Manager, Consultant, GlobalLogic) на GlobalLogic Kyiv QA Career Day 16 лютого 2019 року.
Відео: https://youtu.be/eu_39evZ7WQ
The product was established to take advantage of the unique environment set by the convergence of the following three trends: large amounts of assetprice-relevant information becoming available, Big Data technologies making it possible to process and make sense of this information, and Machine Learning technics reaching a point where Artificial Intelligence can now be scalable. It systematically trades US equities based on rigorous machine learning, artificial intelligence, and statistical techniques to produce superior, uncorrelated returns. Also it uncovers non-obvious relationships by processing large amounts of unstructured data and leveraging computing brute force and employs disciplined and dynamic statistical Risk Management techniques to its portfolio.
Integrated Model Discovery and Self-Adaptation of RobotsPooyan Jamshidi
Machine learn models efficiently under budget constraints to adapt to perturbations such as environmental changes or changes in the internal resources.
Modern software-intensive systems are composed of components that are likely to change their behaviour over time (e.g., adding/removing components).
For software to continue to operate under such changes, the assumptions about parts of the system made at design time may not hold at runtime due to uncertainty.
Mechanisms must be put in place that can dynamically learn new models of these assumptions and use them to make decisions about missions, configurations, etc.
Розумне місто складається з великої кількостї технологій та компонентів. На перший погляд це схоже на велику купу розкиданих іграшок. Немає вимог, специфікацій, процессів, ресурсів. Нічний жах тестувальника або просто Black Box. Але це — прекрасний тренажер, щоб перевірити власну стійкість, розуміння теорії тестування та власну кваліфікацію. У цій доповіді Вікторія Таранюк будує чітку структуру із хаосу.
Цю доповідь представила Вікторія Таранюк (Associate Manager, Consultant, GlobalLogic) на GlobalLogic Kyiv QA Career Day 16 лютого 2019 року.
Відео: https://youtu.be/eu_39evZ7WQ
This is a talk titled "Cloud-Based Services For Large Scale Analysis of Sequence & Expression Data: Lessons from Cistrack" that I gave at CAMDA 2009 on October 6, 2009.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
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Session 1 (January 14) –Transforming Experience into Future Opportunities Instructor: Joe Bucher, Assistant Director, School of Medicine Career Center
Topics to be addressed include:
Overview of course requirements and goals; introduction to Career of Choice Development Model;
Trainee best practices from CEO Program Alumni;
Creating goals for professional and academic success;
MBTI: Resources for exploring career of choice and internship possibilities.
Bluetooth network-security-seminar-reportROHIT SAGAR
basic network to protect blue-tooth from any un-authorised persons and devices ;its vital importance is to protect and send the data with or without any encrypted key
3 reasons to pick a time series platform for monitoring dev ops driven contai...DevOps.com
In this webinar, Navdeep Sidhu, Head of Product Marketing at InfluxData, will review why you should use a Time Series Database (TSDB) for your important times series data and not one of the traditional datastore you may have used in the past. Join us to learn why you should consider implementing a new monitoring strategy as you upgrade your application architecture.
This is a talk titled "Cloud-Based Services For Large Scale Analysis of Sequence & Expression Data: Lessons from Cistrack" that I gave at CAMDA 2009 on October 6, 2009.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
Лучшие онлайн игры на BMOG.RU
новые новинки mmorpg 2012
transformers mmo
браузерные mmofps
mmorpg игры на пк новые
mmo от mindark
играть через браузер в космическую игру mmorpg
хорошая mmorpg yf fylhjbl
online mmorpg чтобы играли девушки
mmorpg для зыз
mmorpg игры 2010 бесплатно
анимэшное mmorpg
браузерные mmorpg wow
mmo help
www mmorpg ru
mmorpg star wars clone
mmo символ табуляции
mmorpg в жанре фэнтези рейтинг
mmorpg nbgf gotika
русские mmorpg 2013
mmo champion computer of the month
все в контакте mmorpg
mmorpg 2012 года новинки hd
mmorpg в стиле 60-х
популярные ники для mmorpg
mmorpg star wars онлайн
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в какую mmorpg
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Session 1 (January 14) –Transforming Experience into Future Opportunities Instructor: Joe Bucher, Assistant Director, School of Medicine Career Center
Topics to be addressed include:
Overview of course requirements and goals; introduction to Career of Choice Development Model;
Trainee best practices from CEO Program Alumni;
Creating goals for professional and academic success;
MBTI: Resources for exploring career of choice and internship possibilities.
Bluetooth network-security-seminar-reportROHIT SAGAR
basic network to protect blue-tooth from any un-authorised persons and devices ;its vital importance is to protect and send the data with or without any encrypted key
3 reasons to pick a time series platform for monitoring dev ops driven contai...DevOps.com
In this webinar, Navdeep Sidhu, Head of Product Marketing at InfluxData, will review why you should use a Time Series Database (TSDB) for your important times series data and not one of the traditional datastore you may have used in the past. Join us to learn why you should consider implementing a new monitoring strategy as you upgrade your application architecture.
WSO2 Machine Learner takes data one step further, pairing data gathering and analytics with predictive intelligence: this helps you understand not just the present, but to predict scenarios and generate solutions for the future.
Real-Time Simulation for MBSE of Synchrophasor SystemsLuigi Vanfretti
This talk starts by exploring how electrical power systems are increasingly becoming digitalized, leading to their transformation into a class of cyber-physical systems (a system of systems) where the electrical grid merges with ubiquitous information and communication technologies (ICT).
This type of complex systems present unprecedented challenges in their operation and control, and due to unknown interactions with ICT, require new concepts, methods and tools to facilitate their operational design, manufacturing (of components), and testing/verification/validation of their performance.
Inspired by the tremendous advantages of the model-based system engineering (MBSE) framework developed by the aerospace and military communities, this talk will highlight the challenges to adopt MBSE for electrical power grids. MBSE is not only a framework to deal with all the phases of putting in place complex systems-of-systems, but also provides a foundation for the democratization of technology - both software and hardware.
The talk will illustrate the foundations that have been built by the presenter's research over the last 7 years, placed within the context of MBSE, with focus on areas of power engineering. Some of these foundations and contributions include the OpenIPSL, RaPId, SD3K, BableFish and Khorjin open source software developed and distributed online by the research group, and available at: https://github.com/ALSETLab
Model-Simulation-and-Measurement-Based Systems Engineering of Power System Sy...Luigi Vanfretti
This talk starts by exploring how electrical power systems are increasingly becoming digitalized, leading to their transformation into a class of cyber-physical systems (a system of systems) where the electrical grid merges with ubiquitous information and communication technologies (ICT).
This type of complex systems present unprecedented challenges in their operation and control, and due to unknown interactions with ICT, require new concepts, methods and tools to facilitate their operational design, manufacturing (of components), and testing/verification/validation of their performance.
Inspired by the tremendous advantages of the model-based system engineering (MBSE) framework developed by the aerospace and military communities, this talk will highlight the challenges to adopt MBSE for electrical power grids. MBSE is not only a framework to deal with all the phases of putting in place complex systems-of-systems, but also provides a foundation for the democratization of technology - both software and hardware.
The talk will illustrate the foundations that have been built by the presenter's research over the last 7 years, placed within the context of MBSE, with focus on areas of power engineering. Some of these foundations and contributions include the OpenIPSL, RaPId, SD3K, BableFish and Khorjin open source software developed and distributed online by the research group, and available at: https://github.com/ALSETLab
WSO2Con USA 2017: Discover Data That Matters: Deep Dive into WSO2 AnalyticsWSO2
Today’s digital businesses are flooding with big and fast-moving data. The ability to trawl this data ocean and identify actionable insights can deliver a competitive advantage to any organization. WSO2 Analytics enables businesses to do just that by providing real-time, interactive, predictive and batch analysis capabilities together.
In this hands on session we will
Plug in the WSO2 Analytics platform to some common business use cases
Showcase the numerous capabilities of the platform
Demonstrate how to collect data and analyze, predict and communicate effectively
Shceduling iot application on cloud computingEman Ahmed
Resource scheduling considers the execution time of every distinct workload, but most importantly, the overall performance is also based on type of workload i.e. with different QoS requirements (heterogeneous workloads) and with similar QoS requirements (homogenous workloads).
Stream processing IoT time series data with Kafka & InfluxDB | Al Sargent, In...HostedbyConfluent
Time series data is everywhere -- connected IoT devices, application monitoring & observability platforms, and more. What makes time series datastreams challenging is that they often have orders of magnitude more data than other workloads, with millions of time series datapoints being quite common. Given its ability to ingest high volumes of data, Kafka is a natural part of any data architecture handling large volumes of time series telemetry, specifically as an intermediate buffer before that data is persisted in InfluxDB for processing, analysis, and use in other applications. In this session, we will show you how you can stream time series data to your IoT application using Kafka queues and InfluxDB, drawing upon deployments done at Hulu and Wayfair that allow both to ingest 1 million metrics per second. Once this session is complete, you’ll be able to connect a Kafka queue to an InfluxDB instance as the beginning of your own time series data pipeline.
1. Thierry Bema
Age 44
Nationality :French
Address : 3 rue Crespieres
78580 Les Alluets le Roi (France)
Mobile : 0785866639
E-mail:thierry.bema@gmail.com
Bringing my full experiences in Enterprise oriented on BUSINESS
INTELLIGENT CORE UNIT gained after 17months of complete Achievements
of entire Scalable Projects in Data Science from Abstract Concepts to the
Real World solutions ( BIG DATA HADOOP YARN MESOS SPARK with the
Powerful Reactive programming Actor Akka Multithreading and Scala
Functional Programming for Machine Learning as being my key point
interests in our fast moving world of Big Data today including Graph
Processing, Streaming, SQL sustained by Python) which are based on my
self starter motivation attitude strongly inspired by AMERICAN SCIENTIFIC
CULTURE after a long career on Telecom Industry as being RFIC &
Microwaves Engineer
ARCHITECTURE IMPLEMENTATION
Creating and Building a Multinode Cluster (Ubuntu VMs) on Hadoop-2.7.2
with YARN and Spark1.6.0 Scalable through scripts based on Docker with
Serf Agent as the Orchestration communication between nodes
APPLICATIONS RUNNING ON THE CLUSTER (JFreeChart , Zepplin,
Breeze, Apache Common Math Libraries,Twitter scalding ,Akka,
Anaconda : IPython Notebook :NumPy,Scipy,Pandas,scikit-learn,h5py)
FINANCIAL MARKET PROJECTS :
Scalable Framework (Spark Akka Scala)
-Stock Price Prediction using full order from NYSE with Spark and Decision
Tree Algorithm for Classification (Spark Mlib :Multiclass Classification)
-Akka Cluster for Portfolio Market Risk Calculation which is depending on
factor forecasts (single node and multiple nodes) by using MonteCarlo
-Net Asset Stock Market Application Concurrent (with GUI) :
Taking a list of Stock ticker symbols along with the units of stock users hold
and tells them the stock value of their investments of the current date
UnSupervised Learning
-Extracting clusters from stock price action during a period by selecting the
appropriate features prior to clustering and time window to operate on by
using K-Means Algorithm
-Expectation Maximization (EM) Algorithm to estimate the maximum likelihood
observation based on posterior probability (stock prices from a couple industries movement)
OBJECTIVES
Starting
date :07/10 /2014
SYSTEM ENGINEER DATA SCIENCE
KEY ACHIEVEMENTS
2. Supervised Learning :
-Predicting the direction of change in the stock price movement based on
price index, change in Federal Fund rate and Gross Domestic Product by
using Naive Bayes Algorithm
-Computing of Single Variate Linear Regression of the price of Ticker
symbol over a period
- Predicting of price change between two consecutive trading session with
Multilinear Regression
-Prediction of positive price Variation for specific Ticker symbol given its
Volatility and Trading Volume by using Logistic Regression
Kernel Models and Support Vector Machines ( LIBSVM)
-Applying the Binary Support Vector Classifier to estimate the risk for a
company to eliminate its dividend based on realtime change in stock price
over the last few months (long term debt , Equity Ratio , Dividend coverage
ratio, Annual Dividend yield , Operating profit marging
Artificial Neural Networks (Multilayer Perceptron)
Understanding the correlation factors between Exchange rate of some
currencies ,the spot price of gold and S & P500 index (Extracting one or more
regressive models)
Genetic Algorithms :(Evolution Computing)
Chromosome genetic encoding of Trading Strategy , set of two signals to
predict the sudden relative decrease of price Security :relative Volume with
a condition and Relative Volatility with a condition
Reinforcement Learning (Q-Learning)
Option Trading , computing the best strategy to trade certain types of
options given some market conditions and trading data
VARIOUS PROJECTS IN DIFFERENT TOPICS
GraphX :( Healthcare)
-Query to track the source of Epidemic (Progression of Infection disease )
based on number of days exposure to other patient
Energy :
-Predicting Global energy demand using the available data the energy usage
on the last past years ,in order to enable companies effectively handle
energy demand (Using Brownian Motion continuos time Stochastic Process
by which drift in data is Standard Mean and Volatility in data is Standard
Deviation
AirLine : (on IPython notebook PySpark parallel distributed computation
accros the cluster on YARN )
-Computing the mean delay for take off and landing
Recommandation
Movielens data set
Classification Decision Tree
Titanic data set
Ending
date :17/12/2015
3. Project « LTE FONTEND RFIC 4G HARDWARE DESIGN » ( FREELANCE )
Project « Dual Band WLAN /WIMAX RFIC TRASNCEIVER) » (INFINEON:
INTEL MOBILE COMMUNICATION: Munich) VERIFICATION RF ENGINEER (
RFIC )
Project « UMTS BLUETOOTH » (FREELANCE)
Project « GSM DCS PCS » SAGEM ( SAFRAN )
Project « GSM DCS PCS » WAVECOMSA
Master in Microwave’s Circuits and Components ( University of
Montpellier 1998)
FINANCE ,GEOLOGY ,HISTORY , SPORTS (Running )
Starting date
07/2009/
Ending date
01/2014
Sarting date
11/2008
Ending date
08/2006
Starting date
6/2006
Ending date
9/2002
Starting date
11/2001
Ending date
7/2002
Starting date
8/2001
Ending date
1/200
RELEVANT EXPERIENCES MICROWAVES RFICS
DIPLOMA
INTERESTS