Muhammad Shafique (M’11 - SM’16) received his Ph.D. degree in Computer Science from the Karlsruhe Institute of Technology (KIT), Germany, in 2011. Afterwards, he established and led a highly recognized research group at KIT for several years as well as conducted impactful collaborative R&D activities across the globe. Besides co-founding a technology startup in Pakistan, he was also an initiator and team lead of an ICT R&D project. He has also established strong research ties with multiple universities in worldwide, where he has been actively co-supervising various R&D activities and student/research Theses since 2011, resulting in top-quality research outcome and scientific publications. Before KIT, he was with Streaming Networks Pvt. Ltd. where he was involved in research and development of video coding systems several years. In Oct.2016, he joined the Institute of Computer Engineering at the Faculty of Informatics, Technische Universität Wien (TU Wien), Vienna, Austria as a Full Professor of Computer Architecture and Robust, Energy-Efficient Technologies. Since Sep.2020, Dr. Shafique is with the New York University (NYU), where he is currently a Full Professor and the director of eBrain Lab at the NYU-Abu Dhabi in UAE, and a Global Network Professor at the Tandon School of Engineering, NYU-New York City in USA. He is also a Co-PI/Investigator in multiple NYUAD Centers, including Center of Artificial Intelligence and Robotics (CAIR), Center of Cyber Security (CCS), Center for InTeractIng urban nEtworkS (CITIES), and Center for Quantum and Topological Systems (CQTS).
Dr. Shafique has demonstrated success in obtaining prestigious grants, leading team-projects, meeting deadlines for demonstrations, motivating team members to peak performance levels, and completion of independent challenging tasks. His experience is corroborated by strong technical knowledge and an educational record (throughout Gold Medalist). He also possesses an in-depth understanding of various video coding standards and machine learning algorithms. His research interests are in AI & machine learning hardware and system-level design, brain-inspired computing, neuromorphic computing, approximate computing, quantum machine learning, cognitive autonomous systems, robotics, wearable healthcare, AI for healthcare, energy-efficient systems, robust computing, machine learning security and privacy, hardware security, emerging technologies, electronic design automation, FPGAs, MPSoCs, embedded systems, and quantum computing. His research has a special focus on cross-layer analysis, modeling, design, and optimization of computing and memory systems. The researched technologies and tools are deployed in application use cases from Internet-of-Things (IoT), Smart Cyber-Physical Systems (CPS), and ICT for Development (ICT4D) domains. 
Dr. Shafique has given several Keynotes, Invited Talks, and Tutorials at premier venues. He has also organized many special sessions at flagship conferences (like DAC, ICCAD, DATE, IOLTS, and ESWeek). He has served as the Associate Editor and Guest Editor of prestigious journals like IEEE Transactions on Computer Aided Design (TCAD), IEEE Design and Test Magazine (D&T), ACM Transactions on Embedded Computing (TECS), IEEE Transactions on Sustainable Computing (T-SUSC), and Elsevier MICPRO. He has served as the TPC Chair of several conferences like CODES+ISSS, IGSC, ISVLSI, PARMA-DITAM, RTML, ESTIMedia and LPDC; General Chair of ISVLSI, IGSC, DDECS and ESTIMedia; Track Chair at DAC, ICCAD, DATE, IOLTS, DSD and FDL; and PhD Forum Chair of ISVLSI. He has also served on the program committees of numerous prestigious IEEE/ACM conferences including ICCAD, DAC, MICRO, ISCA, DATE, CASES, ASPDAC, and FPL. He has been recognized as a member of the ACM TODAES Distinguished Review Board in 2022. He is a senior member of the IEEE and IEEE Signal Processing Society (SPS), and a professional member of the ACM, SIGARCH, SIGDA, SIGBED, and HIPEAC. He holds one US patent and has (co-)authored 7 Books, 20+ Book Chapters, 350+ papers in premier journals and conferences, and over 100 archive articles.
Dr. Shafique received the prestigious 2015 ACM/SIGDA Outstanding New Faculty Award, the AI-2000 Chip Technology Most Influential Scholar Award in 2020, 2022 and 2023, the ATRC’s ASPIRE Award for Research Excellence in 2021, six gold medals in his educational career, and several best paper awards and nominations at prestigious conferences like CODES+ISSS, DATE, DAC, ISLPED, and ICCAD, Best Master Thesis Award, DAC'14 Designer Track Best Poster Award, IEEE Transactions of Computer "Feature Paper of the Month" Awards, and Best Lecturer Award. His research work on aging optimization for GPUs featured as a Research Highlight in the Nature Electronics, Feb.2018 issue. Dr. Shafique was named in the NYU’s 2021 Faculty Honors List. His students have also secured many prestigious student and research awards in the research community.
 

This event aims to provide valuable knowledge and foster discussions that will benefit our students and faculty members alike. It's an excellent opportunity to gain insights into the semiconductor industry, network with professionals, and contribute to the growth of design verification in Pakistan. 

The panelists include engineers from:

DreamBig Semiconductors
Rapid Silicon
10xEngineers

In this panel discussion, engineers will discuss.

1. The current status of design verification practices in Pakistan.

2. The gaps and challenges we need to overcome to stay competitive in the semiconductor industry.

3. Strategies to enhance our capabilities and innovation in design verification.

4. Career opportunities and skill sets required to excel in this domain.

Aqsa Naeem is a Postdoctoral Research Fellow in the Department of Energy Sciences and Engineering at Stanford University. Her current research centers on utilizing data analytics to analyze energy consumption in buildings and support urban planners in enhancing building energy efficiency and resilience.
Naeem obtained her Ph.D. in Electrical Engineering from Lahore University of Management Sciences, Pakistan, where she worked on designing resilient and cost-effective microgrids to promote the adoption of renewable energy systems in the power sector. 

Dr. Humayun Irshad is a seasoned Machine Learning and Computer Vision Leader with expertise in Machine Learning and Deep Learning techniques. With a strong domain knowledge and a proven track record, He have successfully designed, developed, and supported innovative AI applications across diverse industries, catering to clients globally.

His focus lies in delivering cutting-edge Computer Vision and Data Science products, including robust APIs for image recognition, classification, object detection, and segmentation in both 2D and 3D images/videos. He have worked on a wide range of applications spanning climate analysis, satellite imagery, medical imaging, facial recognition, self-driving vehicles, as well as retail and fashion industries.

Over the course of his career spanning more than a decade, he have gained extensive experience in applying and training deep learning algorithms such as Convolutional Neural Networks (CNN), Region-based CNN (RCNN), Long Short-Term Memory (LSTM), Transformers and Stable Diffusion. Additionally, he possess expertise in image processing techniques, feature extraction, pattern recognition, feature selection, regularization methods, boosting methodologies, generalized regression models, data visualization, analytic approximation, enumerative and graphical combinatorics. He have also worked on advanced implementations of operator algebras, probability theory, and statistics.

With a passion for staying at the forefront of the field, He is well-versed in the latest advancements in Machine Learning and Computer Vision. Dr. Humayun Irshad's goal is to utilize his skills and knowledge to drive innovation, solve complex challenges, and deliver exceptional results for his clients and partners.

Dr. Muhammad Bilal is an educator, researcher and a maker. His research interests include Digital Image/Signal Processing, Machine Learning/AI, Digital/Analog circuit design, Embedded systems and Robotics. He is an Associate Professor in the Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, KSA. Prior to joining KAU in 2014, he worked as a post-doctoral researcher at KAIST, South Korea. He obtained his PhD in EE from LUMS SBASSE in 2013.

Huawei is one of the leading global information and communications technology (ICT) giants and developing significant share including but not limited to software and energy sector. Huawei offers an enormous range of courses and certifications and partners with other institutes for exam trainings. The Department of Electrical Engineering, SSE-LUMS has also established Huawei Authorized Information and Network Academy (HAINA) in partnership through MoU with Al-Khawarizmi Institute of Computer Science (KICS) that is currently supervising the Huawei academy partnership programs in Pakistan. The EE Dept., LUMS aims to offer training courses for career certifications. See this brief video presentation for overall and general functioning of any HAINA academy. Initially, the academy is offering exam training course for “Huawei Certified ICT Associate (HCIA) - Datacom”. The HCIA-Datacom is a newly designed course outline from Huawei that has replaced the previous HCIA-Routing & Switching certification adding more diverse ICT skill set. Please visit https://academy-huawei.lums.edu.pk/ for more information.

Eligibility Criteria
Currently, there are no official eligibility pre-requisites from Huawei for registering HCIA Datacom exam. It is expected that candidates are already aware of the general working of computers, mobile devices, and internet. Research Assistants (RAs) and Teaching Assistants (TAs) in LUMS should have prior consent of their supervisors before enrolling.

Details on the course fee structure
Course outline:
•    Fundamentals of information and communication technology
•    Routing & Switching
•    Network management and security
•    IPv6
•    Software Defined Networks (SDN) and Automation programming
•    See Official Course Outline for further details

Fee Structure: Rs. 15,000 for Off-Campus
Rs. 10,000 for On-Campus (students/staff/faculty)

The above fee structure is 25% and 50% discount respectively for opening batches.

Dates and Deadlines
Registration: 18th April 2024. Fee Payment: 24th April 2024
HCIA Datacom | May 2024 Batch 

Contact Details
Email: academy.huawei@lums.edu.pk 
Phone: +92-42-35608000 (3527) - (9:00 AM to 5:00 PM)
            +92-42-35608000 (3528) - (9:00 AM to 5:00 PM)

Cell: 0331-5480059
Address:  Opposite Sector U, DHA Lahore

The MLSH seminars are targeted towards individuals engaged in research-based activities in the hydrological sciences and will be delivered by scientists who have produced acclaimed research in the field. The concurrent emergence of new technologies, advancements in data-science and new approaches to modelling have expanded the possibility frontier for effective management of our water resources. Not only this, but these developments are uncovering new insights and spawning new discoveries that change our fundamental understanding of the Earth's hydrosphere. "Models, Learning and Sensing in Hydrology" is aimed at propagating the science and techniques of exactly this field of research.

The full schedule for the webinars can be found here, which also includes recordings of past sessions. A link for attending will be sent via email to registered individuals. Colleagues who register once can attend all future talks without the need to register again.

Speaker Introduction: Weiyu Li has received her PhD in Energy Science and Engineering from Stanford University. Her research focuses on data-assimilation and parameter estimation in environmental applications, aiming to provide science-based estimation of evapotranspiration from soil moisture measurements and the quantification of uncertainty inherent in such estimators. Her other research interests include modeling and simulation of electrochemical transport in batteries and biomedical modeling. Prior to her doctoral studies, Weiyu Li obtained her M.Sc. degree in Mechanical and Aerospace Engineering from Princeton University. Weiyu Li is the recipient of the Siebel Scholars Award in Energy Science, class of 2023. Furthermore, she has received prestigious awards, including the Henry J. Ramey Fellowship Award for outstanding research in the Department of Energy Science and Engineering at Stanford University, as well as the Princeton University Fellowship in Natural Sciences and Engineering.

The Electrical Engineering Department at LUMS is organising the ‘LUMS Electrical Engineering Graduate Open House-2023’ on Saturday, February 18th 2023, at SBASSE, LUMS. We invite prospective students to explore our university facilities and various graduate programs offered by the EE department, including:

• Intelligent Systems
• Machine Learning
• Communications and Control
• Electronics and Embedded Systems
• Modern Power and Energy Systems
• Photonics

Also, the participants will have,

• Campus Tour
• Lab Visits
• Free Refreshment
• Social Hour
• Meet-up with EE Faculty

Please register yourself today. 

The training will offer a balanced mix of lectures on the theory of glaciers and their modelling and practical sessions with the OGGM model. It will cover the following topics:

• General introduction to glaciers
• Climatic mass-balance: Processes and modelling
• Practicals: Temperature index modelling with OGGM
• Ice flow: Processes and modelling.
• Practicals: Numerics of simple differential equations, flowline modelling with OGGM
• Glacier system modelling: Glacier-climate interactions, coupling, uncertainties
• Practicals: real glacier experiments with OGGM.
• Regional hydrology with a focus on the Upper Indus Basin
• Glaciers in the hydrosphere: Glacier runoff
• Practicals: peak-water and runoff partitioning with OGGM
• Scientific programming with the Python programming language and practising open-source software development on github
• Excursion: Visit the LUMS campus and research facilities, outdoor field visit to relevant site

Workshop Dates: 10 March 2023 to 16 March 2023

Application deadline: 07 February 2023

Announcement of selected participants: 10 February 2023

For more details click here 

Usman A. Fiaz received his bachelor’s (BS) and master's (MS) degrees in Electrical Engineering from Pakistan Institute of Engineering and Applied Sciences (PIEAS) and King Abdullah University of Science and Technology (KAUST), respectively, in 2015 and 2017. He obtained his doctoral degree (PhD), also in Electrical Engineering, from the University of Maryland, College Park (UMD) in 2022, with a specialization in Robotics, Control, and Learning. Currently, he is a Postdoctoral Fellow in Autonomy and Cyber-Physical Systems (CPS) at the National Institute of Standards and Technology (NIST), USA. He also holds an affiliate appointment with the Department of Electrical and Computer Engineering (ECE) and the Institute for Systems Research (ISR), at the University of Maryland, College Park (UMD). 
Dr. Fiaz’s research interests lie in some of the modern areas of control theory, robotics, and machine learning with special emphasis on autonomous multiagent systems. More specifically, he is interested in designing theory, algorithms, and physical implementations for achieving assured autonomy in multiagent systems, such as teams of autonomous robots and vehicles (across all terrains and space), that can provide simultaneous assurances on: for example, safety and finite-time completion etc., during various complex tasks. In addition to his contributions to academia and research, Dr. Fiaz also has an extended history of collaborations with world-renowned industry. He has held visiting research positions at Intel (2021), ABB (2020), Nokia Bell Labs (2019), Mitsubishi Electric Research Labs (2018), and CERN (2014). 
Dr. Fiaz is a member of the IEEE, the IEEE Robotics and Automation Society, and the IEEE Control Systems Society. He is a recipient of the Ann G. Wylie Dissertation Fellowship (2022), the Michael J. Pelczar Award for Graduate Excellence (2021), the Future Faculty Fellowship (2021), and the Outstanding Graduate Assistant Award (2018) from the University of Maryland; the Outstanding Achievement in Robotic Orchestration Award from Nokia Bell Labs (2019), the IFAC Young Author Award (Finalist) at the IFAC Mechatronics Symposium (2019), a 2nd Runner-up finish and a Bronze Medal at the MBZIRC Robotics Challenge (2017), and the President's Gold Medal for achieving the highest distinction during his BS from Pakistan Institute of Engineering and Applied Sciences (2015).
 

Asad Abidi received the BSc degree in Electrical Engineering from Imperial College, London, in 1976, and the Ph.D. from the University of California, Berkeley, in 1982. He worked at Bell Laboratories, Murray Hill until 1985, and then joined the faculty of the University of California, Los Angeles, where he is Distinguished Chancellor’s Professor of Electrical Engineering. With his students, he has developed many of the radio circuits and architectures that enable today’s mobile devices. Among other awards, Professor Abidi has received the 2008 IEEE Donald O. Pederson Award in Solid-State Circuits and the Best Paper Award from the IEEE Journal of Solid-State Circuits in 2012. The University of California, Berkeley’s Department of EECS recognized him as a Distinguished Alumnus in 2015. He was elected Fellow of IEEE in 1996, Member of the US National Academy of Engineering, and Fellow of TWAS, the world academy of sciences. Professor Abidi holds the Abdus Salam Chair at the SBASSE School of Science & Engineering, LUMS, Lahore.

The event is open to the public. Visitors from outside LUMS can register at: 

https://form.jotform.com/221710914790050.