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Young Computer Scientists in Cuba Short of Opportunities
HAVANA (IPS) - Thousands of young Cubans are graduating in computer engineering, a sector the government decided to strengthen over the past decade. But their professional future is uncertain because of failures of organisation and of internet connectivity.
www.ipsnews.net/2013/03/young-computer-scientists-in-cuba...
2023 Winter Party
School of Electrical & Computer Engineering
College of Engineering
Georgia Institute of Technology
Source: livinghistories.newcastle.edu.au/nodes/view/52836
This photo appeared in the News, Volume 13, Number 13, August 10- 24, 1987. The text was:
"Staff Member's book for Engineers
Dr Peter Moylan, of the Department of Electrical and Computer Engineering, is the author of Assembly Language for Engineers, a book on the assembly language of the iAPX 86 series of microprocessors.
Dr Moylan explains in the book that languages for computer programming fall into two broad classes. The majority of programmers use ‘high-level’ language such as Pascal, which are useful for general-purpose programming. Assembly language is a more detailed notation, for use when it is necessary to communicate with the machine at a fundamental level. Although assembly languages are not much used for general-purpose computer programming, they are widely used for special applications like industrial control systems.
He says the topic of assembly language programming is typically taught at second or third year level in Computer Science and Computer Engineering.
In this University, the book will be used as a text in conjunction with major syllabus revisions to take effect in 1988. In the past, we have used computers such as the VAX and PDP-11 for assembly language teaching. We are now changing our orientation to concentrate on microprocessors, in recognition of the fact that this is where the main applications of assembly language programming now lie. The use of microprocessors also allows students to perform experiments which would not be safe – i.e. which would endanger the work of other users – on the central computers.
The author points put that a special feature of the book is the inclusion of material on multiprogramming and memory management. Most textbook authors omit these topics on the grounds that they are too difficult; but this leaves students poorly prepared for later work on computer operating systems and on control systems design. His philosophy is that this is precisely the material which is needed for engineers who will specialise in process automaton, a specialty which will receive more and more emphasis in planned improvements to the Computer Engineering degree course.
The publication is issued by Ellis Horwood Ltd., of Chichester, England."
This image was scanned from a photograph in the University's historical photographic collection held by Cultural Collections at the University of Newcastle, NSW, Australia.
If you have any information about this photograph, please contact us.
Research Professor in the Department of Electrical & Computer Engineering, University of California, Santa Barbara, and Professor Emeritus in the Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles
Monday, Feb. 29, 2018
Abstract:
The field of digital signal processing (DSP) has been a very active area of research and application for more than six decades. This broad development has paralleled in time the rapid development of high-speed electronic digital computers, microelectronics and integrated circuit fabrication technologies. An ever-increasing assortment of integrated circuits specifically tailored to perform common DSP functions is available to the design engineer as system building blocks or parts-in-trade. DSP methodologies have been applied to consumer electronics, communications, automotive electronics, instrumentation, medical electronics, tomography and acoustic imaging, cartography, seismology, speech recognition, robotics and other fields. In his talk, Dr. Mitra will provide a brief overview of the initial developments in DSP and review some of the important advances made during the nearly-60-year period of its growth, and will describe a number of its key applications. He will conclude with speculation on DSP’s future trends and directions.
Dr. Sanjit K. Mitra is a Research Professor in the Department of Electrical & Computer Engineering, University of California, Santa Barbara. Dr. Mitra has published over 700 papers in the areas of analog and digital signal processing, and image and video processing. He has also authored and co-authored twelve books, and holds six patents. Dr. Mitra has served IEEE in various capacities including service as the President of the IEEE Circuits & Systems Society in 1986.
Dr. Mitra has received many awards including the 2009 Athanasios Papoulis Award of the European Association for Signal Processing, the 2005 SPIE Technology Achievement Award of the International Society for Optical Engineers; the University Medal of the Slovak Technical University, Bratislava, Slovakia in 2005; the 2006 IEEE James H. Mulligan, Jr. Education Medal; and the 2013 IEEE Gustav Robert Kirchhoff Award. He is the co-recipient of the 2000 Blumlein-Browne-Willans Premium of the Institution of Electrical Engineers (London). He has been awarded Honorary Doctorate degrees from the Tampere University of Technology, Finland, the Technical University of Bucharest, Romania, and the Technical University of Iasi, Romania.
He is a member of the U.S. National Academy of Engineering, a member of the Norwegian Academy of Technological Sciences, an Academician of the Academy of Finland, a foreign member of the Finnish Academy of Sciences and Arts, a foreign member of the Croatian Academy of Sciences and Arts, international member of the Croatian Academy of Engineering and the Academy of Engineering, Mexico, and a Foreign Fellow of the National Academy of Sciences, India and the Indian National Academy of Engineering. Dr. Mitra is a Life Fellow of the IEEE.
Georgios Giannakis, PhD
Professor of Electrical and Computer Engineering at the University of Minnesota
Director of the Digital Technology Center
Monday, Nov. 18, 2019, 3:30 p.m.
Donna E. Shalala Student Center, Senate Room 302
1300 Miller Drive Coral Gables, FL 33146
Abstract
We live in an era of data deluge. Pervasive sensors collect massive amounts of information on every bit of our lives, churning out enormous streams of raw data in various formats. Mining information and learning from unprecedented volumes of data promises to limit the spread of epidemics and diseases, identify trends in financial markets, learn the dynamics of emergent social-computational systems, and also protect critical infrastructure including the smart grid and the Internet’s backbone network. While Big Data can be definitely perceived as a big blessing, big challenges also arise with large-scale datasets. This talk will overview challenges and opportunities emerging in the analytical and algorithmic foundations that are widely referred to as Data Science, and Network Science, the latter for data residing on graphs formed by agents that are interconnected (or networked) either physically or through their interdependencies.
Georgios Giannakis, PhD, received his diploma in Electrical Engineering from the Ntl. Tech. Univ. of Athens, Greece, 1981. From 1982 to 1986 he was with the Univ. of Southern California (USC), where he received his MSc. in Electrical Engineering, 1983, MSc. in Mathematics, 1986, and Ph.D. in Electrical Engr., 1986. He was with the U. of Virginia from 1987 to 1998, and since 1999 he has been a professor with the U. of Minnesota, where he holds a Chair in Wireless Communications, a University of Minnesota McKnight Presidential Chair in ECE, and serves as director of the Digital Technology Center. His general interests span the areas of communications, networking and statistical signal processing – subjects on which he has published more than 450 journal papers, 750 conference papers, 25 book chapters, two edited books and two research monographs (h-index 143). Current research focuses on data science and network science with applications to social, brain, and power networks with renewables. He is the (co-) inventor of 33 patents issued, and the (co-) recipient of 9 best journal paper awards from the IEEE Signal Processing (SP) and Communications Societies. He also received Technical Achievement Awards from the SP Society (2000), from EURASIP (2005), and the inaugural IEEE Fourier Tech. Field Award (2015). He is a Fellow of EURASIP, and has served the IEEE in various posts including that of a Distinguished Lecturer.
Ian Larson, Computer Engineering BSE Student, watches as members of TKO Graphics install the outer lining of the 2013 Solar Car in the Wilson Center on June 7, 2013.
Photo: Joseph Xu, Michigan Engineering Communications & Marketing
2023 Winter Party
School of Electrical & Computer Engineering
College of Engineering
Georgia Institute of Technology
2018 Roger Webb Awards
School of Electrical & Computer Engineering
College of Engineering
Georgia Institute of Technology
2023 Winter Party
School of Electrical & Computer Engineering
College of Engineering
Georgia Institute of Technology
2023 Winter Party
School of Electrical & Computer Engineering
College of Engineering
Georgia Institute of Technology
Georgios Giannakis, PhD
Professor of Electrical and Computer Engineering at the University of Minnesota
Director of the Digital Technology Center
Monday, Nov. 18, 2019, 3:30 p.m.
Donna E. Shalala Student Center, Senate Room 302
1300 Miller Drive Coral Gables, FL 33146
Abstract
We live in an era of data deluge. Pervasive sensors collect massive amounts of information on every bit of our lives, churning out enormous streams of raw data in various formats. Mining information and learning from unprecedented volumes of data promises to limit the spread of epidemics and diseases, identify trends in financial markets, learn the dynamics of emergent social-computational systems, and also protect critical infrastructure including the smart grid and the Internet’s backbone network. While Big Data can be definitely perceived as a big blessing, big challenges also arise with large-scale datasets. This talk will overview challenges and opportunities emerging in the analytical and algorithmic foundations that are widely referred to as Data Science, and Network Science, the latter for data residing on graphs formed by agents that are interconnected (or networked) either physically or through their interdependencies.
Georgios Giannakis, PhD, received his diploma in Electrical Engineering from the Ntl. Tech. Univ. of Athens, Greece, 1981. From 1982 to 1986 he was with the Univ. of Southern California (USC), where he received his MSc. in Electrical Engineering, 1983, MSc. in Mathematics, 1986, and Ph.D. in Electrical Engr., 1986. He was with the U. of Virginia from 1987 to 1998, and since 1999 he has been a professor with the U. of Minnesota, where he holds a Chair in Wireless Communications, a University of Minnesota McKnight Presidential Chair in ECE, and serves as director of the Digital Technology Center. His general interests span the areas of communications, networking and statistical signal processing – subjects on which he has published more than 450 journal papers, 750 conference papers, 25 book chapters, two edited books and two research monographs (h-index 143). Current research focuses on data science and network science with applications to social, brain, and power networks with renewables. He is the (co-) inventor of 33 patents issued, and the (co-) recipient of 9 best journal paper awards from the IEEE Signal Processing (SP) and Communications Societies. He also received Technical Achievement Awards from the SP Society (2000), from EURASIP (2005), and the inaugural IEEE Fourier Tech. Field Award (2015). He is a Fellow of EURASIP, and has served the IEEE in various posts including that of a Distinguished Lecturer.
From left, Skylar Lennon, an undergraduate in computer engineering, Cody Dempster, a master’s student in electrical and computer engineering, Parth Raut, a master’s student in computer science and engineering, Rebecca Lara, an undergraduate in mechanical engineering, and Harrison Keller, a first year engineering undergraduate, gather around Michaela Garvey, a graduate student in space engineering, as she completes a test run of the Supermileage vehicle in the parking lot across from the Ford Motor Company Robotics Building on the North Campus of the University of Michigan in Ann Arbor on Saturday, April 8, 2023.
This year the Supermileage team has more than 30 members from ten different majors. They designed two vehicles this season, the Maple and the Magnolia. One is a internal combustion engine vehicle for the urban concept category, with a goal of 500 miles per gallon, the other is for the electric prototype category with a target efficiency of 10,000 miles per gallon.
Photo: Brenda Ahearn/University of Michigan, College of Engineering, Communications and Marketing
Georgios Giannakis, PhD
Professor of Electrical and Computer Engineering at the University of Minnesota
Director of the Digital Technology Center
Monday, Nov. 18, 2019, 3:30 p.m.
Donna E. Shalala Student Center, Senate Room 302
1300 Miller Drive Coral Gables, FL 33146
Abstract
We live in an era of data deluge. Pervasive sensors collect massive amounts of information on every bit of our lives, churning out enormous streams of raw data in various formats. Mining information and learning from unprecedented volumes of data promises to limit the spread of epidemics and diseases, identify trends in financial markets, learn the dynamics of emergent social-computational systems, and also protect critical infrastructure including the smart grid and the Internet’s backbone network. While Big Data can be definitely perceived as a big blessing, big challenges also arise with large-scale datasets. This talk will overview challenges and opportunities emerging in the analytical and algorithmic foundations that are widely referred to as Data Science, and Network Science, the latter for data residing on graphs formed by agents that are interconnected (or networked) either physically or through their interdependencies.
Georgios Giannakis, PhD, received his diploma in Electrical Engineering from the Ntl. Tech. Univ. of Athens, Greece, 1981. From 1982 to 1986 he was with the Univ. of Southern California (USC), where he received his MSc. in Electrical Engineering, 1983, MSc. in Mathematics, 1986, and Ph.D. in Electrical Engr., 1986. He was with the U. of Virginia from 1987 to 1998, and since 1999 he has been a professor with the U. of Minnesota, where he holds a Chair in Wireless Communications, a University of Minnesota McKnight Presidential Chair in ECE, and serves as director of the Digital Technology Center. His general interests span the areas of communications, networking and statistical signal processing – subjects on which he has published more than 450 journal papers, 750 conference papers, 25 book chapters, two edited books and two research monographs (h-index 143). Current research focuses on data science and network science with applications to social, brain, and power networks with renewables. He is the (co-) inventor of 33 patents issued, and the (co-) recipient of 9 best journal paper awards from the IEEE Signal Processing (SP) and Communications Societies. He also received Technical Achievement Awards from the SP Society (2000), from EURASIP (2005), and the inaugural IEEE Fourier Tech. Field Award (2015). He is a Fellow of EURASIP, and has served the IEEE in various posts including that of a Distinguished Lecturer.
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Georgios Giannakis, PhD
Professor of Electrical and Computer Engineering at the University of Minnesota
Director of the Digital Technology Center
Monday, Nov. 18, 2019, 3:30 p.m.
Donna E. Shalala Student Center, Senate Room 302
1300 Miller Drive Coral Gables, FL 33146
Abstract
We live in an era of data deluge. Pervasive sensors collect massive amounts of information on every bit of our lives, churning out enormous streams of raw data in various formats. Mining information and learning from unprecedented volumes of data promises to limit the spread of epidemics and diseases, identify trends in financial markets, learn the dynamics of emergent social-computational systems, and also protect critical infrastructure including the smart grid and the Internet’s backbone network. While Big Data can be definitely perceived as a big blessing, big challenges also arise with large-scale datasets. This talk will overview challenges and opportunities emerging in the analytical and algorithmic foundations that are widely referred to as Data Science, and Network Science, the latter for data residing on graphs formed by agents that are interconnected (or networked) either physically or through their interdependencies.
Georgios Giannakis, PhD, received his diploma in Electrical Engineering from the Ntl. Tech. Univ. of Athens, Greece, 1981. From 1982 to 1986 he was with the Univ. of Southern California (USC), where he received his MSc. in Electrical Engineering, 1983, MSc. in Mathematics, 1986, and Ph.D. in Electrical Engr., 1986. He was with the U. of Virginia from 1987 to 1998, and since 1999 he has been a professor with the U. of Minnesota, where he holds a Chair in Wireless Communications, a University of Minnesota McKnight Presidential Chair in ECE, and serves as director of the Digital Technology Center. His general interests span the areas of communications, networking and statistical signal processing – subjects on which he has published more than 450 journal papers, 750 conference papers, 25 book chapters, two edited books and two research monographs (h-index 143). Current research focuses on data science and network science with applications to social, brain, and power networks with renewables. He is the (co-) inventor of 33 patents issued, and the (co-) recipient of 9 best journal paper awards from the IEEE Signal Processing (SP) and Communications Societies. He also received Technical Achievement Awards from the SP Society (2000), from EURASIP (2005), and the inaugural IEEE Fourier Tech. Field Award (2015). He is a Fellow of EURASIP, and has served the IEEE in various posts including that of a Distinguished Lecturer.
Georgios Giannakis, PhD
Professor of Electrical and Computer Engineering at the University of Minnesota
Director of the Digital Technology Center
Monday, Nov. 18, 2019, 3:30 p.m.
Donna E. Shalala Student Center, Senate Room 302
1300 Miller Drive Coral Gables, FL 33146
Abstract
We live in an era of data deluge. Pervasive sensors collect massive amounts of information on every bit of our lives, churning out enormous streams of raw data in various formats. Mining information and learning from unprecedented volumes of data promises to limit the spread of epidemics and diseases, identify trends in financial markets, learn the dynamics of emergent social-computational systems, and also protect critical infrastructure including the smart grid and the Internet’s backbone network. While Big Data can be definitely perceived as a big blessing, big challenges also arise with large-scale datasets. This talk will overview challenges and opportunities emerging in the analytical and algorithmic foundations that are widely referred to as Data Science, and Network Science, the latter for data residing on graphs formed by agents that are interconnected (or networked) either physically or through their interdependencies.
Georgios Giannakis, PhD, received his diploma in Electrical Engineering from the Ntl. Tech. Univ. of Athens, Greece, 1981. From 1982 to 1986 he was with the Univ. of Southern California (USC), where he received his MSc. in Electrical Engineering, 1983, MSc. in Mathematics, 1986, and Ph.D. in Electrical Engr., 1986. He was with the U. of Virginia from 1987 to 1998, and since 1999 he has been a professor with the U. of Minnesota, where he holds a Chair in Wireless Communications, a University of Minnesota McKnight Presidential Chair in ECE, and serves as director of the Digital Technology Center. His general interests span the areas of communications, networking and statistical signal processing – subjects on which he has published more than 450 journal papers, 750 conference papers, 25 book chapters, two edited books and two research monographs (h-index 143). Current research focuses on data science and network science with applications to social, brain, and power networks with renewables. He is the (co-) inventor of 33 patents issued, and the (co-) recipient of 9 best journal paper awards from the IEEE Signal Processing (SP) and Communications Societies. He also received Technical Achievement Awards from the SP Society (2000), from EURASIP (2005), and the inaugural IEEE Fourier Tech. Field Award (2015). He is a Fellow of EURASIP, and has served the IEEE in various posts including that of a Distinguished Lecturer.
Purdue University’s Elmore Family School of Electrical and Computer Engineering today (Sept. 29) celebrated the completion of new, cutting-edge research space. The Chiminski Family Collaborative Research Hub is located on the second floor of the Materials and Electrical Engineering Building (MSEE). It was made possible by a generous gift from alumnus John R. Chiminski and his wife Laura A. Chiminski.
Dr. Guang Gao, a distinguished professor of electrical and computer engineering, along with Professor Roberto Giorgi, an associate professor at the Università degli Studi di Siena in Siena, Italy and primary investigator (Coordinator / Scientific Manager) of the TeraFlux project. Pictured here with Ken Barner, chair of the Department of Electrical and Computer Engineering. The TeraFlux project seeks to exploit dataflow parallelism in teradevice computing and propose a complete solution to harness large-scale parallelism in an efficient way. The University of Delaware recently joined the TeraFlux project and received a grant connected to the project from the EU.
Purdue University’s Elmore Family School of Electrical and Computer Engineering today (Sept. 29) celebrated the completion of new, cutting-edge research space. The Chiminski Family Collaborative Research Hub is located on the second floor of the Materials and Electrical Engineering Building (MSEE). It was made possible by a generous gift from alumnus John R. Chiminski and his wife Laura A. Chiminski.