With over 13 years of experience in the telecom, wireless network, marketing analytics, and data science industries, Reza has strong backgrounds in: 1) AI/ML models for telecom applications 2) signal processing & baseban…
With over 13 years of experience in the telecom, wireless network, marketing analytics, and data science industries, Reza has strong backgrounds in: 1) AI/ML models for telecom applications 2) signal processing & baseband algorithms (channel coding & modulation, time/frequency synchronization, channel equalizers, channel estimation, etc) 3) applied machine learning & data analytics including traditional machine learning approaches, reinforcement learning, deep learning, Bayesian machine learning, convex optimization, distributed online learning 4) adaptive signal processing, wireless network positioning, and network synchronization 5) marketing mixture model (MMM), digital attribution, budget optimization for marketing channels 6) leading and mentoring small groups of juniors 7) problem solving and technical writing/presentation skills Personal attitude: hard working, quick learner, approachable, and highly organized with a proven track record of both independent and team working capabilities.
<p>Developing AI models for Softband to estimate TA<br>
Contributing to open radio-access network (ORAN) project to bring AI/ML to various layers/services<br>
Mobility prediction via ML<br>
Developing AI based positioning app for cloud RAN<br>
Developing AI models to predict KPIs & the location of antenna from P3 and ITK data sets<br>
Developing AI models on MDT data set to estimate the location of antennas<br>
Collaborating with DE team to bring AI to production (MLOps)<br>
Real time traffic prediction per cell for various time horizon (1 hour and 24 hours ahead of time)<br>
IP flow size prediction via sequence based modelling to optimize cuup resources<br>
Improving carrier aggregation (CA) using AI model: predicting the future volume of UE’s flow to help CA consumer to make the activation/deactivation intelligently</p>
Role: Senior data scientists
Responsibility: design and develop ML models for telecom use cases to improve the quality of experiences, decrease the complexity of RAN system, examples are cell traffic predictions, mobility prediction, etc
<p>Designing an advanced and robust positioning algorithm for localizing commercial drones<br>
Classifying drones (from received radio signals) via convolutional deep learning</p>
<p>Developing an extremely fast and robust attribution algorithm able to handle thousands of touchpoints based on cooperative game theory<br>
Developing a new probabilistic model to automatically address the ordering of touchpoints<br>
Applying a new predictive modelling into attribution problem in order to include time as a new dimension to the problem<br>
Developing a new marketing mixture modelling type algorithm to resolve the ambiguity in attribution applied to different unlinked platforms, e.g., facebook , google<br>
Developing a new model to investigate the causal effect of changes<br>
Contributing and helping tech team in building a new marketing platform, offering automatic Adwords bidding, automatic bid modifier based on online attribution</p>
<p>Data gathering and coding for various sources of data<br>
Developing new marketing mixture models via advanced statistical learning techniques<br>
Developing new algorithms for media budget optimization using convex programming<br>
Developing a method to get optimal mix of TV and online media to maximize reach<br>
Providing technical reports to different clients including suggestions on media budget allocation</p>
<p>Developing learning algorithms to capture seasonality effect in predicting conversion rate and conversion values for google Adwords data<br>
Solving an inference problem on aggregation levels to estimate individual levels<br>
Exploratory data analytics to extract and analyze hidden patterns in historical data<br>
Developing learning algorithms taking seasonality into account to improve predictions<br>
Designing and developing fitting problems to extract the relation between hourly and daily observations on historical data</p>
<p>Developing various physical layer algorithms for different wire & wireless projects<br>
TCM (2& 4D TCM) and Bit-Interleaved TCM (BICM) for voice band modem<br>
Timing and Frequency synchronization techniques for Turbo-Coded systems<br>
Implementing turbo decoders (MAP, Log-MAP, Max Log-MAP, and SOVA algorithms) for Inmarsat receiver<br>
Developing channel decoders, demodulation, channel estimation, time & frequency asynchronization for the GSM receiver<br>
Implementing baseband physical layer algorithms for GEO-Mobile Radio (GMR)<br>
Design and implementation of the physical layer for V.34 half duplex (V.34 Super G3 Fax)<br>
Evaluation of various Timing Recovery Algorithms such as Muler& Muler, Gardner, Meyer, Godard, feedforward algorithms, and etc for real baseband data<br>
Design and implementation of a real time OQPSK for Inmarsat receiver<br>
Developing a new efficient algorithm for phase noise mitigation in OFDM for DRM standard</p>
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