Professor at Department of Engineering Sciences,
Applied Materials Science UPPSALA University Sweden.
Professor Leifer has studied physics at the RWTH Aachen University. In his thesis at the Swiss Federal Institute of Technology in Lausanne (EPFL), he focused on materials analysis and in particular, electron microscopy techniques. During his work as a researcher at EPFL, he work on metallic multilayers, semiconductor nanostructures as well as on beam induced deposition techniques. At Uppsala University, Professor Leifer develops and refines advanced electron microscopy and Nano structuring techniques to understand and create materials which obtain their functionality on the micron down to the atomic scale. Professor Leifer has been a member of the CEI-Europe faculty since 2008. He has more than 200 research contributions. Currently he is working on four projects.
Dr. Raziq Yaqub
Department of Electrical Engineering and Computer Science
Alabama A & M University USA
Dr. Raziq Yaqub earned his Ph.D. in Wireless Communication from Keio University, Japan in 1998, MS in Electrical Engineering with distinction from UET Peshawar in 1993 and B.Sc. in Electrical Engineering with distinction from UET Peshawar in 1986. Also he is MBA in Marketing from Fairleigh Dickenson University, USA in 2004.
He is one of the pioneers of LTE/4G, and an inventor of numerous technologies of 4th Generation Wireless Communication, and Smart Grid. He invented 25 New Technologies and Filed Patents in FinTech and Cybersecurity Technologies in last 11 months. He received “Inventor of the Year Award” from the Governor of the State of New Jersey, USA, through Inventors Hall of Fame.
Dr. Yaqub remained an Executive Director of Toshiba America Research, Department head of NIKSUN University, headquartered in Princeton, New Jersey, where he lead educational efforts on Cybersecurity, and Big Data Analytics. He honored with “Corporate Award”, for Strategic Planning of IEEE 802.11n, by Toshiba Headquarters Japan. He also remained Sr. Consultant to the State of New Jersey, a spokesperson in 3GPP on behalf of Department of Homeland Security”, an Associate Professor at University of Tennessee, and currently an Associate Professor at Alabama A&M University, Huntsville. He is also Managing Partner of Del Terri, a Big Data Analytics venture and chairman MWIF where he honored with “Appreciation Award” by MWIF Board of Directors
Dr. Yaqub is skilled in teaching, conducting research, inventing technologies, developing solutions, and building industry academia collaboration. His academic efforts include developing from scratch, the new courses on “Smart Grid”, “4G Networks”, and “Advanced Metering Infrastructure and Cyber Security”. His research interest is in Big Data Analytics, 5G/4G/LTE, Smart Grid technologies (including Electric Vehicles, Renewable Energies, Smart Home Energy Management, etc.), and Cyber Security. He filed 34 patents (24 already issued), published numerous papers in international conferences, and submitted 150+ contributions in technical standards organizations. He remained a working group chairman in Mobile Wireless Internet Forum, Chairman IEEE Membership Development Committee, Chairman IEEE Award Committee, Rapporteur in 3GPP, keynote speaker, panelist, and guest speaker in numerous International conferences. He also conducted number of workshop/ seminars for educational and awareness purpose.
NIDHAL CARLA BOUAYNAYA
Department of Electrical and Computer Engineering
Signal processing; Computer vision; Big data analytics; Machine learning;
Optimization Education and Professional Experience: Nidhal Carla CV
He did his PhD in Electrical & Computer Engineering, from University of Illinois at Chicago, IL. His M.S in Pure Mathematics and Electrical & Computer Engineering from Illinois Institute of Technology, Chicago, IL Illinois Institute of Technology, Chicago, and BS degree from National School of Electrical Engineering, Computer Science and Telecommunications (ENSEA), France
He is serving as a Professor at Department of Electrical and Computer Engineering, Rowan University from Sep. 2013 – present. He worked as Associate Professor (Jul. 2013 – Aug. 2013) and Assistant Professor (Aug. 2007 – Jun. 2013) at Systems Engineering Department, University of Arkansas at Little Rock. He was research assistant in Multimedia Communications Lab Department of Electrical and Computer Engineering, University of Illinois at Chicago from Aug. 2003- Aug. 2007.
Dr. Ling Wang
Department of Automation
Shanghai Key Laboratory of Power Station Automation Technology （Mailbox 49）, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, 200072, China
Dr. Ling Wang earned his Ph.D in Control Theory and Control Engineering from East China University of Science and Technology, Shanghai China in 2007 and Bachelors in Automation from the same university in 2002. Since 2007 he is serving as an associate professor in department of Automation of Shanghai University His research interests include; meta-heuristic optimization algorithms, and their applications. The main focus of his work includes; Particle Swarm Optimization, Ant Colony Optimization, Differential Evolution and Quantum Evolutionary Algorithm and their applications to engineering, such as fault diagnosis and intelligent control, currently his research focus is deployment optimization of industrial wireless sensor network, controller design for ultra-supercritical power units. He has more than 50 research publications in impact factor journals and international conferences.
Metaheuristic Approaches in Exploring Smart and Optimized Solutions for Enhancing Energy Efficiency:
Optimization is very important for energy systems, and now metaheuristics are widely adopted to improve the efficiency and effectiveness. The applications of metaheuristic-based approaches are firstly reviewed and few important metaheuristics, like Particle Swarm Optimization, Ant Colony Optimization, Harmony Search, as well as an emergent highly effective algorithm named Human Learning Optimization (HLO), are introduced. The controller design of energy systems based on metaheuristics are specially focused and analyzed, and a novel framework based intelligent data-driven control is then presented which can directly design the optimal controller based on the input and output data without the information of the model. Finally, some real applications are introduced.
Muhammad Khawar Islam Muhammad Khawar Islam CV
1. Ph.D Electrical Engineering 1998 University of New South Wales, Sydney, Australia.
2. M.Eng. Sc. (Dig. Comm.) 1993 University of New South Wales, Sydney, Australia.
3. B. Sc. Eng. (GOLD MEDAL) 1987 University College of Engineering, Mirpur (AJ&K.)
Post Doctoral Research:
20th August 2004 to 31 st May 2006, City University of Hong Kong, HONG KONG.
Experience (September 1987 to date)
1. Present position:
4th March 2015 to Date, Professor, (Electrical Engineering), Taibah University, Kingdom of Saudi Arabia.
2. 1st November 2012 to February 2015, Dean/Director, Faculty of Engineering & Technology (Professor in Electrical Engineering), University of Gujrat, Pakistan,
3. 22nd July 2010 to 31 st October 2012, Chairman/Professor, Department of Telecommunication Engineering, University of Engineering & Technology, Taxila, Pakistan.
4. 10th November 2006 to 21st July 2010, Professor, Software Engineering, University of Engineering & Technology, Taxila, Pakistan.
5. 1st June 2006 to 8th November 2006, Professor, Electrical Engineering, University of Azad Jammu & Kashmir, Mirpur Campus, (AJ&K)
6. 20th August 2004 to 31st May 2006, Research Fellow at Optoelectronics Research Centre, Department of Electronic Engineering, City University of Hong Kong, HONG KONG.
7. 1st September 2001 to 18th August 2004 , Professor/Chairman Department of Computer Sciences & Information Technology, University of Azad Jammu & Kashmir, Mirpur Campus, (AJ&K)
8. September 1987 to August 2001, worked as Lecturer, Assistant Professor and Associate Professor, University College of Engineering, Mirpur (AJ&K)
He has produced three PhDs under his supervision.
Alexander Von Humboldt Research Fellow Dept. of Physics, Univ. of Konstanz, Germany
Interfaces and stability of perovskite solar cells,
Optical properties of mixed halide perovskites, Light emitting diodes, Azhar Fakharuddin CV
made of halide perovskites, Nanostructures: designs and properties.
Education and research experience
1. Alexander Von Humboldt Since 1 August 2016 Dept. of Physics, Univ. of Konstanz, Germany Research Fellow (Interfaces in perovskite solar cells)
2. Postdoctoral Research Fellow 1 June 2015-May 2016 Universiti Malaysia Pahang, Malaysia (Organic solar cells) Doctor of Philosophy Dec 2011-Jan 2015 Universiti Malaysia Pahang, Malaysia (Advanced Materials)
3. Master of Engineering Jul 2009-Sep 2011 Universiti Malaysia Pahang, Malaysia (Electronic)
4. Bachelor of Engineering Mar 2004-Aug 2008 Mehran University of Engineering & Technology, (Electronic) Sindh, Pakistan.
1. Qamar wali, A. Fakharuddin, R. Jose, Z. M. M. Yusoff., “SnO2 Multichannel nanotubes” , patent applied, 2014.
2. R. Jose, A. Fakharuddin, Z. Khalidin, “Nanowires based dye-solar modules”, patent applied. 2014.
3. R. Jose, A. Fakharuddin, Z. Khalidin, M. M. Yusoff, “ A dye sensitized solar cell device”, PI2013000529 (6 March
Reviewer for peer reviewed journals
Reviewing jobs includes high impact peer reviewed journals such as Nanoscale, Scientific Reports, JPCL, Solar
Energy, APL Materials, PCCP, Crystals, and Material Science and Engineering Reports.
“How Can Deep Learning Create a Smarter Grid?”
Dr. Nidhal Bouaynaya, Dept. of Electrical and Computer Engineering, Rowan University
Deep learning technology is empowering many aspects of modern society, including web searches, social networking, recommendation systems; and it is increasingly beingimplemented in consumer products, such as camera and smart phones. Deep learning allowscomputational models that are composed of multiple processing layers, to learn nonlinear representations of data with multiple levels of abstraction, and thus outperform traditional machine learning systems in classification and prediction accuracies. Applications of deep learning to the electric power grid are still embryonic. Historical data could be capitalized to learn powerful machine learning models to cope with the high uncertainty of the electrical patterns. In this talk, we will explore, in the smart grid context, the benefits of using Deep Reinforcement Learning, a hybrid type of methods that combines Reinforcement Learning with Deep Learning, to perform on-line optimization of schedules for building energy management systems beyond what traditional models would offer.
Dr. Nidhal Carla Bouaynaya received the B.S. degree in Electrical and Computer Engineering from the National School of Electrical Engineering, Computer Science and Telecommunications (ENSEA), France, in 2002, the M.S. degree in Electrical and Computer Engineering from the Illinois Institute of Technology, Chicago, IL, in 2002, the M.S. diploma (DEA) in Signal and Image Processing from ENSEA, France, in 2003, the M.S. degree in Mathematics and the Ph.D. degree in Electrical and Computer Engineering from the University of Illinois at Chicago, Chicago, IL, in 2007. From August 2007 till August 2013, she was an Assistant then Associate Professor with the Department of Systems Engineering at the University of Arkansas at Little Rock, Little Rock, USA. In September 2013, she joined the Department of Electrical and Computer Engineering at Rowan University, New Jersey, USA, where she is currently an Associate Professor. Her research interests are in signal, image and video processing, dynamical systems and machine learning. Dr. Bouay naya won the Best Student Paper Award at SPIE Conference on Visual Communication and Image Processing (SPIE VCIP’06) in 2006, the Best Paper Award at the IEEE International Workshop on Genomic Signal Processing and Statistics in 2013 (GENSIPS’13) and the Runner-up Best Paper Award at the IEEE International Conference on Bioinformatics and Biomedicine in 2015 (BIBM’15). She is also the winner of the UALR Faculty Excellence Award in Research in 2013 and the 2017 winner of Rowan Excellence Award in Research. Her research is funded by the United States National Science Foundation (NSF), United States National Institutes of Health (NIH), New Jersey Department of Transportation (NJ DoT), United States Department of Agriculture (USDA) and the United States Federal Aviation Administration (FAA). She is also interested in entrepreneurial endeavors. In 2017, she Co-founded and is Chief Technology Officer (CTO) of MRIMATH, LLC, a start-up company in medical imaging.