The Impact

A distinctive event space that will define the Waterloo student journey–from visiting high school student to engaged undergraduate to returning alumni.

An awe-inspiring entrance to M4 that conveys the bold spirit of the Faculty.

At the intersection of four Math buildings, the Nexus will be a space where faculty, staff, and students meet and community is formed.

Hands-on, exploratory learning to develop versatile, entrepreneurial and collaborative leaders.
 
With the creation of M4, the Faculty of Mathematics aims to take the next step in our experiential education tradition. It will house the Polymorphic Classroom, which will feature moveable, modular desks and chairs, wall-to-wall whiteboards and screens and integrated cameras, video and immersive audio for true hybrid learning experiences with on-site and off-site participation. The modular furniture and advanced technology give teachers the ability to ‘flip’ the classroom, shifting quickly from a lecture-style setup to small breakout groups. The goal is to create media-rich, self-directed experiences that transition students from a passive to an active mode of learning and instill in them a problem-solving mindset. 
 
The Polymorphic Classroom will provide an ideal venue for the CEMC to host outreach events and school visits and for the Math Innovation Office to host workshops, presentations and mentoring events. This classroom represents a bold new stage in the Faculty of Mathematics’ experiential tradition and will help us develop the versatile and entrepreneurial graduates our world needs.

Vibrant, comfortable spaces to enrich the student experience and foster wellness.
 
Numerous student lounge spaces throughout the building allow math students to gather, socialize, and build connections with peers. These spaces will foster a sense of community within the Faculty, leading to enhanced collaboration and support among students.

This efficient data center will power research across the University while creating advances in sustainable computing.

The AI, machine learning and data science technologies revolutionizing society consume huge amounts of energy. To continue benefitting from these technologies while reducing their ecological footprint, we urgently need breakthroughs in green computing.
 
The Green Room is a state-of-the-art facility in M4 that will power large-scale computations across the Faculty while allowing researchers to study and improve resource efficiency. Here, researchers will explore and document the various relationships and trade-offs between resource input and application objectives for large-scale computing. With access to the full technology stack–including hardware, system-level software, and network configuration–they can experiment with different component arrangements to optimize resource use. The goal is to develop practical guidelines for improving resource-conscious computing that can be widely adopted across society.
 
The Green Room represents a bold step towards green computing and a much-needed upgrade to the Faculty’s current computing infrastructure. It will help attract leading researchers from around the world, drive pioneering research at the intersection of STEM disciplines and foster new breakthroughs in resource-conscious computing.
 

Securing our data and safeguarding our privacy.

The Cryptography, Security, and Privacy (CrySP) research group carries out research in a wide variety of topics, from designing cryptographic protocols to the evaluation of their effectiveness and usability in deployed systems. Some examples are:

• (Distributed) cryptographic protocols. Designing interactive protocols to enable secure communication, such as key agreement protocols, key distribution schemes, secret sharing schemes, identification schemes, broadcast encryption and oblivious transfer.
• Efficient cryptographic algorithms and their implementation.
• Designing and analyzing cryptographic primitives such as block and stream ciphers, public-key encryption schemes, signature schemes, message authentication codes, key establishment protocols, and pairing-based cryptography.
• Cryptographic hash functions. Analyzing the security of iterated design techniques and the random oracle model and constructing families of universal hash functions.
• Privacy-preserving communications networks. Creating privacy-preserving communications networks with better security, privacy, efficiency, and scalability properties than existing ones.
• Off-the-record messaging. Improving the user interface, robustness, and group communication abilities of Off-the-Record Messaging, or OTR.
• See the CrySP website for more.

Enhancing computer systems and networks to be more secure, reliable and efficient.

The focus of the Systems and Networking Group is the design, implementation, analysis, and evaluation of computer systems and networks that knit together disparate underlying components to present a cohesive operating environment for task-specific applications.
 
The group’s members study systems ranging from single-processor, standalone embedded systems to large-scale highly-networked distributed systems. Research activity similarly covers a broad variety of topics: parallel hardware, systems software, data-centre networking and data-centre systems, wireless networks, energy systems, and wide-area networks.
 
They use many approaches in their work, reflecting varied expertise. This includes workload characterization, architectural design, algorithm design, theoretical analysis, simulation, and the design, implementation and experimental evaluation of prototype systems.

Solving fundamental problems to realize the full potential of data science.
 
Despite having more data at our disposal than ever before, we are still learning how best to process and understand it, so that we can solve pressing global problems. In this context, new fundamental questions in data science have emerged: What are the trade-offs between statistical and computational power when tackling complex data? How do we understand the remarkable power of stochastic approximation algorithms and neural networks? How hard are the typical high-dimensional optimization and sampling problems that we face? 
 
The Mathematical Foundations of Data Science Lab, led by Dr. Aukosh Jagannath, Canada Research Chair in Mathematical Foundations of Data Science and an Assistant Professor in the Department of Statistics and Actuarial Science, will tackle these and related questions using recent advances in probability theory. These tools have had remarkable success in applications to data science in recent years but this research is only in its infancy. 
 
The Lab will put Canada at the forefront of this exciting new field while developing the data scientists of tomorrow. 

Developing methods to better manage and mitigate risk associated with complex data and systems.
 
Major events like the 2007 subprime mortgage crisis and the COVID-19 pandemic point to the importance of quantitative risk management for the modern world. This field enables individuals, financial institutions and policy-makers to understand, communicate, analyze and manage various types of risk. Yet, existing methods for risk management are not sufficient for a world of increasingly complex data, risks and systems. 
 
The Quantitative Risk Lab will improve society's understanding and mitigation of risks by advancing the theory and quantitative methods that will better address this complexity. Led by Dr. Ruodu Wang, a professor of actuarial science and quantitative finance and Canada Research Chair in Quantitative Risk Management, the lab will pursue the following objectives:

1) propose and analyze risk measures and risk assessment procedures in optimization, portfolio analysis and decision making under uncertainty.

2) develop high-dimensional dependence modelling and mass transportation, with applications in finance, economics, and large-scale testing.

3) design methods for reliable and robust inference with e-values in multiple testing and backtesting risk measures.