[74]

Detection and Estimation Using Regularized Least Squares: Performance Analysis and Optimal Tuning Under Uncertainty

Signals, Information, and Algorithms Lab, MIT,  Feb 8, 2018

[73]

Detection and Estimation Using Regularized Least Squares: Performance Analysis and Optimal Tuning Under Uncertainty

Mathematical and Algorithmic Sciences Lab, Huawei Technologies Paris, France,  Jan 19, 2018

[72]

On The Use Of Structure In Signal Processing Analysis And Design, EE Program Seminar

King Abdullah University of Science and Technology KAUST,  Oct. 22, 2017

[71]

“Distribution Agnostic Structured Sparsity Recovery: Algorithms and Applications

Univ. de Nice Sophia-Antipolis, Nice,  Apr. 21, 2016

[70]

Ultra-wideband Communications and Localization: Challenges and Solutions

KAUST-NSF Conference, KAUST,  Mar. 16, 2016

[69]

Bounded Perturbation Regularization for Linear Least Squares Inverse Problems

Earth Sciences Seminar, KAUST,  Feb. 24, 2016

[68]

Bounded perturbation regularization for linear least squares inverse problems

Information Theory and Applications Symposium, San Diego,  Feb. 4, 2016

[67]

Distribution Agnostic Structured Sparsity Recovery: Algorithms and Applications

Department of Computer Science Colloquiumm Western Michigan University,  Jan. 26, 2016

[66]

Distribution Agnostic Structured Sparsity Recovery: Algorithms and Applications

Technische Universit¨at, Darmstadt, Germany,  Oct 15, 2014

[65]

Distribution Agnostic Structured Sparsity Recovery: Algorithms and Applications

Hungarian Academy of Sciences, Institute for Computer Science and Control, Budapest, Hungary,  July 23, 2014

[64]

Distribution Agnostic Structured Sparsity Recovery: Algorithms and Applications

Alcatel-Lucent Bell Labs, Paris, France,,  Jun 19, 2014

[63]

Distribution Agnostic Structured Sparsity Recovery: Algorithms and Applications

Technische Universit¨at, M¨unchen, Germany,  Jun 11, 2014

[62]

An Introduction to (Bayesian) Compressed Sensing with Applications in Communication, Signal and Image Processing

Universit´e Paris-Est Marne-La-Vall´ee, Paris, France,  May 31, 2014

[61]

Bayesian Sparse Recovery: A Distribution Agnostic Approach with Applications

VCC Summit, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia,  Apr 14, 2014

[60]

An Introduction to (Bayesian) Compressed Sensing with Applications in Communication and Signal Processing

TexasA&M University, Qatar,  Mar 31, 2014

[59]

Bayesian Sparse Recovery: A Distribution Agnostic Approach with Applications to PAPR Reduction in OFDM and Massive MIMO

INPT, Rabat, Morocco,  Mar 20, 2014

[58]

An Introduction to (Bayesian) Compressed Sensing with Applications in Communication and Signal Processing

SS5G 2014, SupCom, Tunisia,  Mar 17, 2014

[57]

Bayesian Sparse Recovery: Distribution Agnostic Approach with Applications to PAPR Reduction in OFDM and Massive MIMO

King Abdullah University of Science and Technology, Thuwal, Saudi Arabia,  Feb 8, 2014

[56]

Impulse Noise Estimation and Cancellation in OFDM Systems

ASSIA, Santa Clara, CA,  Apr. 4, 2013

[55]

Receiver-Based Bayesian PAPR Reduction in OFDM

Qualcomm, Santa Clara, CA,  Apr. 5, 2013

[54]

Structured Sparsity: Bayesian Recovery Algorithms and Applications

Keynote speech, WOSSPA, Algeries, Algeria,  May 2013

[53]

Distribution Agnostic Structured Sparsity Recovery Algorithms and Applications

SupCom, Tunisia,  May 17, 2013

[52]

Structured Sparsity: Bayesian Recovery Algorithms and Applications

University of Toronto,  June 6, 2013

[51]

Structured Sparsity: Bayesian Recovery Algorithms and Applications

University of Ontario Institute of Technology,  June 12, 2013

[50]

Structured Sparsity: Bayesian Recovery Algorithms and Applications

E´cole Polytechnique de Montre´al, Montreal,  June 13, 2013

[49]

Structured Sparsity: Bayesian Recovery Algorithms and Applications

Georgia Institute of Technology,  June 17, 2013

[48]

Structured Sparsity: Bayesian Recovery Algorithms and Applications

The University of Akron, Akron, Ohio,  June 20, 2013

[47]

Bayesian Estimaton of Sparse Signals with Applications in Signal Processing and Communications

A tutorial at EUSIPCO, Marrakesh,  Sep. 9, 2013

[46]

A Bayesian Approach to multi-channel (Blind) Deconvolution

KFUPM-GA Tech workshop, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia,  Dec. 17, 2012

[45]

Compressed Sensing: An overview and an application to Seismic Deconvolution

Earth Sciences Seminar, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia,  Nov. 6, 2012

[44]

Structure Based Bayesian Sparse Reconstruction

Electrical Engineering Department, University of Akron, Akron, Ohio,  August 24, 2012.

[43]

Structure Based Bayesian Sparse Reconstruction

Electrical Engineering Department Northwestern University, Evanston, IL,  July 11, 2012

[42]

Structure Based Bayesian Sparse Reconstruction

Electrical Engineering Department American University of Beirut, Lebanon,  May 11, 2012

[41]

Combating Impairments of OFDM Systems: A Model Reduction Approach

Electrical Engineering Department King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia,  Jan. 4, 2012

[40]

Combating Impairments of OFDM Systems Electrical Engineering Department

Masdar Institute, Abu Dhabi, United ArabEmirates,  Oct. 13, 2011

[39]

Progress in Collaboration between KFUPM & KAUST

KFUPM’s International Advisory Board at KAUST, Thuwal, Saudi Arabia,  Jan. 12, 2010

[38]

A Model Reduction Approach for OFDM Channel Estimation Under High Mobility Conditions

Electrical Engineering Department, King Fahd University of Petroleum and Minerals,  Mar. 1, 2011

[37]

An Overview of KFUPM

King Abdullah University of Science & Technology,  Dec. 1, 2010

[36]

An Overview of Research Interests and Contributions

KFUPM’s International Advisory Board, SABIC Head Quarters, Riyadh, Saudi Arabia,  Jan. 12, 2009

[35]

Combating Some Impairments of OFDM Systems: A Model Reduction Approach

Electrical Engineering Department, Stanford University,  Aug. 30, 2010

[34]

The Potential of Compressive Sensing in (Seismic) Signal Processing [Abstract]

Workshop on KFUPM-GA Tech Joint Research Program, King Fahd University of Petroleum and Minerals,  Jun. 21, 2010

[33]

Indefinite quadratic forms in Gaussian random variables: Distribution, scaling, and applications

Electrical Engineering Department, Texas A & M Qatar,  Jun. 3rd, 2009

[32]

Writing with two languages: $yMb0ls & Words

Electrical Engineering Department, King Fahd University of Petroleum and Minerals,  Apr. 7, 2009

[31]

Indefinite quadratic forms in Gaussian random variables: Distribution, scaling, and applications

Electrical Engineering Department, American University of Beirut,  Feb. 19, 2009

[30]

Indefinite quadratic forms in Gaussian random variables: Distribution, scaling, and application to the broadcast channel

Electrical Engineering Department, University of Texas at Dallas, TX,  Sep. 4, 2008

[29]

Indefinite quadratic forms in Gaussian random variables: Distribution, scaling, and application to the broadcast channel

Electrical Engineering Department, Smart Antenna Research Group, Stanford University, CA,  Aug. 22, 2008

[28]

Scaling laws of multiple antenna (group) broadcast channels

Electrical Engineering Department, University of California at Irvine, CA,  Jun. 18, 2008

[27]

Scaling laws of multiple antenna (group) broadcast channels

Electrical Engineering Department, University of Southern California, CA,  Feb. 20, 2008

[26]

(Semi) blind channel identification and equalization in OFDM

Babak Hassibi’s Research Group, Electrical Engineering Department, California Institute of Technology, Pasadena, CA,  Feb. 15, 2008

[25]

Scaling laws of multiple antenna group-broadcast channels

Ecole Sup´erieure ´ dElectricit´e (Sup´elec), Paris, France,  Jul. 6, 2007

[24]

How much does correlation affect the sum-rate of MIMO downlink channels?

Institute Eurcom, Sophia-Antipolis, France,  Jun. 21, 2007

[23]

The potential of adaptive filtering for seismic signal processing

Research Institute, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia,  May 15, 2007

[22]

Broadcasting data to multiple user groups: Information theoretic investigation of the wide band case

Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia`,  May 1st, 2007

[21]

Opportunistic scheduling in wireless networks: An overview of issues and design considerations

(jointly with Dr. Yahya Al-Harthi (KFUPM) and Dr. Mohamed-Slim Alouini (Texas A & M Qatar), Tutorial at the International Symposium on Signal Processing and its Applications (ISSPA 2007), Sharjah, UAE,  Feb. 11, 2007

[20]

Employing undergraduates as teaching assistants at KFUPM

Deanship of Academic Development, Center of Teaching and Learning, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia,  Jan. 16, 2007

[19]

The effect of spatial correlation on the capacity of MIMO broadcast channels with partial side information

Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia,  Jan. 13, 2007

[18]

How much does correlation affect the sum-rate of MIMO downlink channels?

Electrical Engineering Department, Imperial College, London, UK,  Nov. 23, 2006

[17]

A unified approach to mean-square analysis of adaptive filters

Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia,  Nov. 20, 2006

[16]

How much does correlation affect the sum-rate of MIMO downlink channels?

Research Department, Intel Corporation, Santa Clara, CA,  Aug. 22, 2006

[15]

Broadcasting data to multiple user groups: An information theoretic investigation

Babak Hassibi’s Research Group, Electrical Engineering Department, California Institute of Technology, Pasadena, CA,  Jul. 29, 2006

[14]

A framework for the estimation of time-variant channels in OFDM

Delft Technical University, Delft, the Netherlands,  Jun. 9th, 2006

[13]

A forward backward Kalman for the estimation of time-variant channels in OFDM

Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia,  Nov. 16, 2005

[12]

A framework for the estimation of time-variant channels in OFDM

the University of New Louvain, Belgium,  Nov. 2nd, 2005

[11]

A unified approach to mean-square analysis of adaptive filters

the University of New Louvain, Belgium,  Nov. 2nd, 2005

[10]

A framework for the estimation of time-variant channels in OFDM

Telecommunications Research Center, Vienna, Austria,  Oct. 28, 2005

[9]

Wireless broadband networks–WIMAX: A contrast and a complement to WiFi

(jointly with Dr. Salam Zummo) Internet and Communications Engineering Technical Exchange Meeting (e-CETEM), Saudi Aramco, Dhahran, Saudi Arabia,  Sep. 19, 2005

[8]

A unified approach for transient analysis of adaptive filters

Babak Hassibi’s Research Group, Electrical Engineering Department, California Institute of Technology, Pasadena,  Mar. 25th, 2005

[7]

Receiver design for MIMO-OFDM transmission over time-variant frequency selective channels

Standards Group, Qualcomm Corporation, San Diego,  Jun. 18th, 2004

[6]

Receiver design for MIMO-OFDM transmission over time-variant frequency selective channels

Communications Systems Lab., Texas Instruments, Dallas, TX,  Feb. 23, 2004

[5]

Adaptive semi-blind receiver for MIMO-OFDM transmission

ATHEROS Communications, Sunnyvale, CA,  Dec. 23, 2003

[4]

Receiver design for MIMO OFDM transmission over time-variant channels

TZero Technologies Inc., Sunnyvale, CA,  Jan. 27, 2004

[3]

An OFDM receiver for MIMO OFDM transmission over wireless channels

Intel Corporation, Sunnyvale, CA,  Dec. 19, 2003

[2]

A semi-blind algorithm for OFDM transmission over wireless channels

Stanford Networking Research Group, Stanford University,  Apr. 10, 2003

[1]

Adaptive algorithms for wireless channel estimation

Qualcomm Technology Ventures,” Qualcomm Corporation, San Diego,  Apr. 3, 2003