ISL-Talks

 

  1. On Regularization Tuning

    Electrical & Computer Engineering Department, University of Toronto, Toronto, Canada, Jul 29, 2019. 

  2. Distributed Agnostic Structured Sparsity Recovery: Algorithms and Applications

    Department de geenie electrique, Ecole de technologie superieure, Montreal, Canada, Jul 24, 2019.

  3. Aerial Data Aggregation in IoT Networks: A Novel Paradigm with Application to Forest Fire Fighting

    KAUST Research Conference on Sustainable Urban-Enviornmental Future, Thuwal, Saudi Arabia, Apr 3, 2019

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

    Institut National Polytechnique de Toulouse (ENSEEIHT), Toulouse, France, Mar 29, 2019.

  5. A Large Dimensional Study of Regularized Discriminant Analysis Classifiers

    Statistics and Data Science Workshop

    King Abdullah University of Science and Technology (KAUST), Saudi Arabia, Nov 13, 2018. 

  6. A Large Dimensional Study of Regularized Discriminant Analysis Classifiers

    Statistics and Data Science Worksho, Indian Institute of Science, Bangalore, India, Jul 19, 2018. 

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

     Communications Research Lab, Ilmenau University of Technology, Ilmenau, Germany, Aug 9, 2018. 
  8. Detection and Estimation Using Regularized Least Squares: Performance Analysis and Optimal Tuning Under Uncertainty

     France Research Center, Huawei Technologies, France, Jan 19, 2018. 

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

    Electrical Engineering Department, University of California at Davis,Davis, CA, USA, Jun 14, 2018.

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

    EECS, University of California at Irvine, Irvine, CA, USA, Jun 6, 2018. 
  11. Detection and Estimation Using Regularized Least Squares: Performance Analysis and Optimal Tuning Under Uncertainty

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

  12. 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

  13. 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

  14. “Distribution Agnostic Structured Sparsity Recovery: Algorithms and Applications

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

  15. Ultra-wideband Communications and Localization: Challenges and Solutions

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

  16. Bounded Perturbation Regularization for Linear Least Squares Inverse Problems

    Earth Sciences Seminar, KAUST, Feb. 24, 2016

  17. Bounded perturbation regularization for linear least squares inverse problems

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

  18. Distribution Agnostic Structured Sparsity Recovery: Algorithms and Applications

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

  19. Distribution Agnostic Structured Sparsity Recovery: Algorithms and Applications

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

  20. Distribution Agnostic Structured Sparsity Recovery: Algorithms and Applications

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

  21. Distribution Agnostic Structured Sparsity Recovery: Algorithms and Applications

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

  22. Distribution Agnostic Structured Sparsity Recovery: Algorithms and Applications

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

  23. 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

  24. Bayesian Sparse Recovery: A Distribution Agnostic Approach with Applications

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

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

    TexasA&M University, Qatar, Mar 31, 2014

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

    INPT, Rabat, Morocco, Mar 20, 2014

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

    SS5G 2014, SupCom, Tunisia, Mar 17, 2014

  28. 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

  29. Impulse Noise Estimation and Cancellation in OFDM Systems

    ASSIA, Santa Clara, CA, Apr. 4, 2013

  30. Receiver-Based Bayesian PAPR Reduction in OFDM

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

  31. Structured Sparsity: Bayesian Recovery Algorithms and Applications

    Keynote speech, WOSSPA, Algeries, Algeria, May 2013

  32. Distribution Agnostic Structured Sparsity Recovery Algorithms and Applications

    SupCom, Tunisia, May 17, 2013

  33. Structured Sparsity: Bayesian Recovery Algorithms and Applications

    University of Toronto, June 6, 2013

  34. Structured Sparsity: Bayesian Recovery Algorithms and Applications

    University of Ontario Institute of Technology, June 12, 2013

  35. Structured Sparsity: Bayesian Recovery Algorithms and Applications

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

  36. Structured Sparsity: Bayesian Recovery Algorithms and Applications

    Georgia Institute of Technology, June 17, 2013

  37. Structured Sparsity: Bayesian Recovery Algorithms and Applications

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

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

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

  39. 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

  40. 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

  41. Structure Based Bayesian Sparse Reconstruction

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

  42. Structure Based Bayesian Sparse Reconstruction

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

  43. Structure Based Bayesian Sparse Reconstruction

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

  44. 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

  45. Combating Impairments of OFDM Systems Electrical Engineering Department

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

  46. Progress in Collaboration between KFUPM & KAUST

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

  47. 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

  48. An Overview of KFUPM

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

  49. An Overview of Research Interests and Contributions

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

  50. Combating Some Impairments of OFDM Systems: A Model Reduction Approach

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

  51. 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

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

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

  53. Writing with two languages: $yMb0ls & Words

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

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

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

  55. 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

  56. 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

  57. Scaling laws of multiple antenna (group) broadcast channels

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

  58. Scaling laws of multiple antenna (group) broadcast channels

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

  59. (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

  60. Scaling laws of multiple antenna group-broadcast channels

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

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

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

  62. The potential of adaptive filtering for seismic signal processing

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

  63. 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

  64. 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

  65. 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

  66. 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

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

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

  68. 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

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

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

  70. 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

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

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

  72. 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

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

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

  74. A unified approach to mean-square analysis of adaptive filters

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

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

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

  76. 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

  77. 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

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

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

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

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

  80. Adaptive semi-blind receiver for MIMO-OFDM transmission

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

  81. Receiver design for MIMO OFDM transmission over time-variant channels

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

  82. An OFDM receiver for MIMO OFDM transmission over wireless channels

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

  83. A semi-blind algorithm for OFDM transmission over wireless channels

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

  84. Adaptive algorithms for wireless channel estimation

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