RIDSAI November Seminar - FR

Event date(s): November 27, 2024
Time(s): 01:00 PM - 03:00 PM
Category: Both Campuses Fredericton Saint John
Location: Fredericton

Date: Nov. 27, 2024.  
Time: 1-3:00 p.m. 
Location: Research Commons Event Space (318), Harriet Irving Library, UNB Fredericton.

Kindly RSVP by Tuesday 26th November 2024, using the following links: 

For those attending in Person:
https://forms.office.com/r/Ufz6NvfPHP 

For those joining online or virtual:
https://forms.office.com/r/BPG4pME6DS 

Please share this invitation with colleagues and students who share an interest in Data Science and Artificial Intelligence research. Your participation is invaluable as we collectively navigate the frontiers of these transformative fields. 
Featured Speakers:

Title: Geeks with Empathy

Speaker: Jeremy Adamson

Abstract: The field of data science has evolved rapidly over the past decade, transforming from a niche technical skillset into a central pillar of decision-making across industries. As the practice matures, a key distinction has emerged between "doers" — those who write the code, build models, and create insights — and “advisors" — those who shape strategy, guide teams, and drive organizational impact through data. We will also discuss three guiding forces that will shape the future of data science over the next five years.

Bio: Jeremy Adamson is a leader in data and analytics strategy, and has a broad range of experience in aviation, energy, financial services, and public administration.  Jeremy has worked with several major organizations to help them establish a leadership position in data science and to unlock real business value using advanced analytics.

Topic: AI powered Social Agents 

Speaker: Dr. Debasmita Mukherjee

Abstract: This talk will focus the research being carried out in her lab on developing AI-powered social agents that can provide personalized assistance and companionship. These agents leverage advanced AI techniques like generative AI and deep learning to exhibit human-like behaviors and adapt to individual user preferences. By investigating factors such as recommendation style, explanatory behavior of the agent, and human behaviour and expectations, her research aims to build trust and enhance the effectiveness of human-agent interactions. At the intersection of engineering and sociology, the talk will provide insights into harnessing sociological trust factors based on gender and personality in the design of AI agents.
 
Bio: Dr. Debasmita Mukherjee is an Assistant Professor in the Department of Electrical and Computer Engineering and Dr. J. Herbert Smith Centre for Technology Management and Entrepreneurship at the UNB. She received her PhD in Mechanical Engineering from The University of British Columbia in 2023.
Motivated by the dynamics and seamless collaboration within human teams, as well as the potential for working alongside highly adaptable AI agents, her research endeavors to create a safety-centric and natural communication framework for human-agent interaction within social scenarios. Her primary focus involves enhancing trust in custom AI agents though study of analogic and analytic trust factors and personalizing deep learning models to user behaviors using affective computing, naturalistic feedback mechanisms, probabilistic approaches, explainable AI techniques.
With experience in both research and development, spanning roles as a Senior Engineer in a multinational corporation and within academic settings, Dr. Mukherjee specializes in designing models geared towards deployment in real-world environments. Her research has been patented, published in prestigious journals and international IEEE conference as well as covered in news and media outlets.

Title: SCIENCE. AI

Speaker: Stijn De Baerdemacker

Affiliation: UNB, Chemistry & RIDSAI

Abstract: The 2024 Nobel prizes in Physics & Chemistry not only celebrated the contributions of machine learning to the physical sciences, but it also generated food for thought and conversation.  Guided by some pertinent questions and comments from leading colleagues, I will discuss how machine learning has shaped and likely will shape the landscape in STEM, from my perspective as a quantum chemist and beyond.  

Bio: Stijn De Baerdemacker [he/him] is a Canada Research Chair T2 in Theoretical Chemistry and Associated Research Director of RIDSAI.  Research in his QuNB quantum chemistry lab at UNB revolves around the development of quantum many-body methods to compute molecular properties, the analysis and development of interpretable machine learning tools and the design of quantum computing circuits in the search for quantum advantage in chemistry.

Building: Research Commons Event Space (318), Harriet Irving Library, UNB Fredericton

Room Number: 318