Talk Title:

Investigating the Capabilities of Large Language Models in Financial and Legal Analytics Tasks

Talk Summary:

Large language models (LLMs), such as ChatGPT and Llama-2, have achieved state-of-the-art performance in many natural language processing (NLP) and related tasks. In this talk, I will discuss our recent investigation of LLMs’ capabilities and limitations in several typical financial and legal tasks. For the financial applications, we explored areas such as the Chartered Financial Analyst (CFA) examination, sentiment analysis, and financial question answering, while in the legal domain, we focused on legal citation analysis. Through detailed experiments, we will demonstrate that, although current LLMs have achieved impressive performance, they still face significant limitations in these tasks.

Important time and date info:

• Date: April 26, 2024
• Time: 11:00 a.m. – 12:00 p.m. EST

Brock University Campus

Room:  Pond Inlet

Speaker info:

Xiaodan Zhu is an Associate Professor and Mitchell Professor in the Department of Electrical and Computer Engineering and at the Ingenuity Labs Research Institute at Queen’s University. He is also a Faculty Affiliate at the Vector Institute for Artificial Intelligence. His recent research interests are in Natural Language Processing (NLP), Deep Learning, and Artificial Intelligence. He is also interested in applying NLP to medical, financial and legal applications. For this research communities, Xiaodan has served as a chair for the 33rd Canadian Conference on Artificial Intelligence. He served on the ACL ’19 and COLING ’20 Best Paper Selection Committees. He has held many other senior roles in the NLP and AI communities. Xiaodan is a recipient of the JP Morgan Faculty Research Award for 2022 and 2023 and NEC Labs America’s Faculty Research Award in 2021.

Talk Title:

Cryptologic History & Canada

Talk Summary:

This presentation will shed light on a number of the most meaningful instances involving cryptologic history and Canada through a selection of events, its people and artifacts. Although a good number will be concentrated in World War 2 and the Cold War, instances from the 18^th and 19^th centuries will also be shared. As one example, what role, if any, did Canada play in the breaking of Enigma in World War 2? There will also be a cursory glance at where cryptologic artifacts can be found in present day Canada.

Important time and date info:

• Date: January 26, 2024
• Time: 1:00 p.m. – 2:00 p.m. EST

Brock University Campus

Room:  Ranking Family Pavilion 214/215

Speaker info:

Richard Brisson is a graduate from the University of Ottawa (BSc Math-Physics in 1978 and MSc Systems Science in 1980). Upon graduation, he was hired by Canada’s Communications Security Establishment (CSE) from which he retired in 2011. His career at CSE largely encompassed various fields of Mathematics and Computer Science — he is also a graduate of NSA’s three-year Cryptologic Mathematics Program (1984-1987).

Although most of his papers are of a classified nature, Brisson and his colleague François Théberge co-published an unclassified pamphlet titled “An Overview of the History of Cryptology” in the early 2000s.

Over the last 30 years, Brisson has been collecting vintage cryptographic and clandestine artifacts dating up to and including the Cold War. Key World War II artifacts in the collection include a three-rotor Wehrmacht Enigma, a four-rotor Kriegsmarine Enigma and a commercial K-Enigma, which were manufactured in Germany and sold to Switzerland.

He has given numerous talks on Enigma at various institutions and conferences including the University of Calgary, University of Waterloo, Carleton University, University of Ottawa, Cryptologic Symposium in Charlotte SC USA, Canadian Association for Security and Intelligence Studies in Kingston, etc.

Brisson has had a passion for understanding how vintage cryptographic systems function at the algorithmic level, understanding their strengths and weaknesses, developing software simulators and also writing applications that attempt to exploit/break some of these vintage systems including the German WW2 Enigma. 

Through the years, he has had a number of partnerships with various museums in the display of artifacts including the Diefenbunker (Cold War Bunker in Carp near Ottawa), National Museum of Science and Technology (in Ottawa), the National Cryptologic Museum (Fort Meade, Maryland USA), and the Canada Aviation and Space Museum in Ottawa with a forthcoming spring 2024 Cold War exhibit. Portions of his collection can be viewed at the ultrasecret.ca website. 

Talk Title:

Big data analytics and artificial intelligence in cancer research and precision medicine.

Talk Summary:

Big data analytics and artificial intelligence (AI) has grown exponentially over the past decade in oncology and precision medicine. The increased data volume and capacity for data aggregation and analytics power, along with decreasing costs of genome sequencing has spurred the growth in bioinformatics and need for novel tools to extract meaningful patterns imbedded in these data from multiple sources and of varying types. The big data and AI tools have already created significant impact in many fields of medicine. However, the data complexity and multi-dimensionality in medicine has led to technical challenges in developing and validating AI solutions that generalize to diverse populations and imped the progress in their implementation in clinical practice due to imbalance in data distribution across population demography and data sparsity. This leads to the unconscious biases in the generated models and algorithms. In this talk, the speaker will discuss major applications of AI in cancer research and precision medicine, major challenges in implementation of AI in the clinical applications.

Important time and date info:

• Date: Wednesday, October 25th, 2023
• Time: 11:00 a.m. – 12:00 p.m. EDT

Brock University Campus

Room:  Ranking Family Pavilion 215

Speaker info:

Dr. Youlian Pan is an international expert in integrative pattern recognition from big data in Life Sciences. He has authored and co-authored over 80 refereed articles and created significant applications of data mining, machine learning, AI and bioinformatics in genomics, transcriptomics and systems biology of cancers, neurodegenerative diseases, plants’ embryogenesis and interaction with environment. Dr. Pan is a Senior Research Officer at the National Research Council Canada and an Adjunct Professor at the University of Victoria. He received his PhD in Biology and Master of Computer Science from Dalhousie University. He has served at various capacities in editorial board of six international journals, such as Journal of Computations & Modeling, Open Medical informatics, and Frontiers in Genetics, Microbiology and Plant Sciences.

Talk Title:

Be Ready for AI Third Wave!?

Talk Summary:

We have witnessed the achievements of AI research and its impact on human life and society over the past few decades. Since its inception at the Dartmouth Conference 1956, AI has gone through several boom and bust cycles.  Each bust pushed AI research from one boom to yet another prosperous boom. For example, the most recent bust from 1987 to 1993 led to another AI research boom which has lasted almost 20 years (2000-2019).  During the last two decades, many dreams in AI research have become reality. However, today AI development run into many barriers in meeting the expectations of public in the commercialization AI technologies such as safe-driving, self-abstracting, self-learning, etc. In this talk the speaker will explore where is boundary to impede the AI advancement by reviewing the AI history, and the major achievements from the first and second AI waves. Then speaker will share the vision of next AI wave in order to be ready for AI third wave. We believe in that the AI third wave is coming and will be a much stronger torrent！

Important time and date info:

• Date: Friday, March 24th, 2023
• Time: 10:00 a.m. – 11:00 a.m. EDT

Brock University Campus

Room:  Ranking Family Pavilion 302 (upper level)

Speaker info:

Dr. Chunsheng Yang is a Fellow of the Canadian Academy of Engineering (FCAE) and Fellow of the Asia-Pacific Artificial Intelligence Association (FAAIA).   He is a Principal Research Officer at the National Research Council Canada and a distinguished Professor with Nagoya Institute of Technology (Japan).  Dr. Yang is an Adjunct Professor with Carleton University (Canada), Chongqing University (China), and Beijing Normal University (China). He is interested in data science, machine learning, hybrid reasoning, intelligent systems, digital twins and Prognostic and Health Management (PHM). He received an Hons. B.Sc. in Electronic Engineering from Harbin Engineering University, China, an M.Sc. in computer science from Shanghai Jiao Tong University, China, and a Ph.D. from the National Hiroshima University, Japan. He worked with Fujitsu Inc., Japan, as a Senior Engineer and engaged on the development of intelligent traffic management for ATM backbone telecommunication networks.  He was an Assistant Professor at Shanghai Jiao Tong University from 1986 to 1990 working on Hypercube Distributed Computer Systems. Yang has been the author for 226 technical papers (book chapters) and reporters published in the referred journals and conference proceedings. Dr. Yang is a Program Co-Chair of the 20th IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD 2016) and a Program Co-Chair for the 17^th International Conference on Industry and Engineering Applications of Artificial Intelligence and Expert Systems in 2004. He is also a guest editor for the International Journal of Applied Intelligence and the Journal of Clustering Computing. He severs scientific advisor for several instructions such as NSERC, Irish Research Council, etc.

Talk Title:

Bounding treatment effects with deep generative models

Talk Summary:

In this talk, I’ll focus on the problem of partial identification, the estimation of bounds on the treatment effects from observational data. Although studied using discrete treatment variables or in specific causal graphs, partial identification has been recently explored using tools from deep generative modeling. I’ll describe a new method for partial identification of average treatment effects (ATEs) in general causal graphs using implicit generative models comprising continuous and discrete random variables. Since ATE with continuous treatment is generally non-regular, we leverage the partial derivatives of response functions to define a regular approximation of ATE, a quantity we call uniform average treatment derivative (UATD). The resulting algorithm converges to tight bounds on ATE in linear structural causal models (SCMs). For nonlinear SCMs, empirically, that using UATD leads to tighter and more stable bounds than methods that directly optimize the ATE. This is joint work with Vahid Belazadeh and Vasilis Syrgkanis.

Important time and date info:

• Date: Friday, March 17th, 2023
• Time: 11:00 p.m. – 12:00 p.m. EDT

Brock University Campus

Room:  Ranking Family Pavilion 302 (upper level)

Speaker info:

Rahul G. Krishnan is an Assistant Professor of Computer Science and Laboratory Medicine and Pathobiology. He holds a CIFAR AI Chair at the Vector Institute. His research interests lie at the intersection of decision making using data with a focus on problems in healthcare. Prior to joining the faculty at UofT, he was a Senior Researcher at Microsoft Research New England. He received his MS from New York University and his PhD in Electrical Engineering and Computer Science from MIT.

Talk Title:

Action-Based Networks: A Robust Framework for Synthesizing Complex Networks using Multiple Connection Mechanisms

Talk Summary:

Social contact, biological interactions, brain structure/function, and IT infrastructure are examples of real-world systems that can be modeled as complex networks, which are ill-defined mathematical objects that exist as a subset of all graphs. These networks have non-trivial topological properties that typically occur when the network is a model of a real-world system, and analysis of the network or its model can reveal new scientific and practical insights into the system.

Important time and date info:

• Date: Thursday, December 1st, 2022
• Time: 11:00 p.m. – 12:00 p.m. EST

Speaker info:

Mario is an Associate Professor of Industrial Engineering at Purdue University, currently on sabbatical leave at The University of Toronto.  His research focuses on computational aspects of two primary areas: complex systems and operations research.

Talk Title:

Using Mitacs Funding to Support Research and Innovation Collaborations

Talk Summary:

Mitacs is a national not-for-profit funding agency working to encourage collaborations in research and innovation between academic and non-academic partners in Canada. This presentation will discuss how Mitacs funding programs, and extended eligibility for interns and partners, can foster collaborations and enhance research, and how faculty and students can use these to enhance their research and innovation work.

Important time and date info:

• Date: Monday, March. 10th, 2022
• Time: 12:00 p.m. – 1:00 p.m. EST

Speaker info:

Dr. Greg MacNeill, completed his PhD in molecular and cellular biology from the University of Guelph, and is now a business development specialist with Mitacs, co-funded with Brock’s Office of Research Services. Prior to his PhD, he worked as an environmental consultant in a research-based start-up, focused the remediation of petroleum impacted soils.

Talk Title:

Where can I apply data science skills for public health?

Talk Summary:

After having learned data science skills, the next step is to understand where one can practically apply these skills. Powerful as it is, artificial intelligence may not apply to all problems and, when it does, applicable techniques can differ depending on the type of data. In this talk, Julie will present her work on omics data and the recent global efforts on SARS-Cov-2 genomic data to give a sense of how data science can contribute to public health.

Important time and date info:

• Date: May 28, 2021
• Time: 11:00 a.m. EST
• Speaker Contact:  Julie Chih-yu Chen, PhD, Data Scientist/Research Biologist, National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, Manitoba

Speaker info:

Dr. Julie Chih-yu Chen heads Data Sciences within the Bioinformatics section at the National Microbiology Laboratory, Public Health Agency of Canada. At work, she leads the ongoing learning of biological/omics data science, and applies Statistical and Machine Learning approaches to microbial omics datasets in tackling biological and public health challenges. The interdisciplinary nature of her career path is largely shaped by her training in both Molecular Biology (MSc, UofT 2011) and Bioinformatics (PhD, UBC 2016) as well as collaborations with researchers in biological and computational fields. She led a team that won the CAMDA international data analytical challenge on metagenomic geolocation in 2019. With the interest in addressing biological challenges, her scientific contribution for the past 10 years typically involves an examination of omics data for pre-processing, visualization, and extraction of biological information using data science techniques for the challenge at hand.

Talk Title:

Computer Vision and Machine Learning Modelling for Livestock Phenomics

Talk Summary:

This presentation will introduce current studies carried out in my lab at University of Guelph focused on applications of computer vision and machine learning on livestock phenomics. The presentation will outline some challenges encountered during the data acquisition and modelling stages, preliminary results and potential avenues for future research projects.

Important time and date info:

• Date: Monday, Nov. 8th, 2021
• Time: 10:00 – 11:00 a.m. EST
• Speaker Contact: Dr. Dan Tulpan is an Assistant Professor in Computational Biology and Bioinformatics in the Department of Animal Biosciences at the University of Guelph.

Speaker info:

Dan Tulpan’s research interests range from computational biology and bioinformatics to machine learning and computer vision.  His expertise has applications across a broad range of topics,but at U of G, Dan is applying his skills to livestock breeding and other areas of animal science.