UofT Home

Eric Yu  Ph.D. EY photo

   Research Interests

   Research Approach

As we create increasingly powerful technology systems, we are faced with potential side-effects that can be harmful or dangerous. Smart phones, the internet, cloud computing, machine learning, and all the other technologies that undergird today's digital world have supercharged the economy and have given us the numerous conveniences of modern living. Yet we worry about security attacks, privacy breaches, biased algorithms, potential displacement by robots and even loss of human dignity. Organizations struggle to remain nimble and resilient in the face of uncertainty and frequent disruptions, while deploying ever more complex software and information infrastructures.

   My research is premised on the idea that technology systems must be studied within a social context. To create effective and viable information technology systems, one must understand how the systems will impact or transform the surrounding social environment.

Prior work

   In earlier work, I developed the i* modeling framework -- an attempt to use Conceptual Modeling to bridge a social conception of the world and the design of information systems. i* adopts a rather simplistic view of the social world. It consists of actors who depend on each other to achieve what they want. Actors have freedom to choose among alternative means for achieving their goals, but their choices are constrained by mutual dependency relationships among actors. As technology options present themselves, actors seek to reconfigure relationships in ways that would advance their strategic interests.
   By applying i* modeling, one can explore the potential impacts of various system designs on stakeholders, in search of design options that would better meet the desires and aspirations of all concerned. i*-inspired social modeling has seen applications in business process analysis, requirements engineering, software development methodologies, information security and privacy, and other areas. Many researchers have incorporated social modeling into their modeling methods and techniques, and have enhanced and extended the original proposal in different directions. A version of i* is part of an international standard.

The Pressing Need

   Today's technology systems are becoming ever more entangled in human lives and social relationships, as they mediate more and more of our interactions, in work, play, or leisure. They know us more intimately than we know ourselves or each other, from the data they collect about us. Emerging technologies and trends like
virtual worlds and decentralized finance are taking us into uncharted waters. My current research agenda is to develop conceptual modeling techniques that are rich enough to help us analyze today's advancing technology systems in social context, so that we, stakeholders together with systems architects and designers, can choose the right technologies and make wise design choices to attain the desired positive benefits while avoiding the negative consequences.

An Approach Based on Conceptual Modeling and Requirements Engineering Techniques

   The i* framework was inspired by multi-agent concepts in classical AI, where agents are assumed to be rational and can reason about alternative ways of achieving goals. To be able to characterize and reason about today's digital technologies in social context, we expect to derive inspirations from theories and concepts from many disciplines, including social psychology, cognitive science, neuroscience, economics, political science, and others.
   The expected research outcome will include conceptual modeling language(s) (or features thereof), analysis techniques for drawing conclusions from models, and design techniques to guide the search for solutions to sociotechnical design problems. Knowledge catalogs to guide the modeling and design process may be compiled to provide easy access to pertinent knowledge from source disciplines. A sample of this research approach can be found in this doctoral thesis. Sample software tools supporting i* and related modeling techniques can be found on this iStar wiki page.

Recent Presentations:

Research Topic Areas

1. Creating responsible data science solutions

While machine learning has advanced by leaps and bounds technologically, harnessing the technology in application settings remains a challenge. Treating data science, ML, and AI purely as technology initiatives is bound to lead to deleterious effects in the human social arena. From problem formulation to solution development, such initiatives are fraught with pitfalls and typically require much iterative experimentation and exploration and ongoing adjustments. A data science initiative thus needs to be conceived of as a complex socio-technical endeavour, not only in terms of understanding the target application environment and how it will be transformed through the initiative, but also in its own operation as a project organization with specialized interests and skills intervening in the social environment of the target domain throughout the lifetime of the initiative. To advance the maturity of machine learning solutions development, new methods should address:
-    How to recognize the types of expertise and perspectives that each stakeholder - data scientists, machine learning engineers, data engineers, deployment specialists, subject matter experts, end users, business leaders, etc. - bring to an initiative? How to analyze relationships among them so as to identify issues and areas for improvement?
-    Can there be systematic techniques for problem understanding and characterization, analogous to those available for conventional information systems development?
-    What modeling representations and methods can facilitate mutual understanding of project objectives and solution alternative among  team members and other stakeholders?
-    How to reconcile disparate interests, such as concerns for bias and fairness, with accuracy or business efficiency? Are there heuristics or solution patterns that can guide the search for trade-offs that would be acceptable to all parties? Can such techniques facilitate adjustments and fine-tuning of the solution as situations evolve?
-    How to make the rapidly expanding knowledge base of solution techniques addressing critical ML issues such as model transparency and explainability readily accessible to solution developers and other stakeholders?
Relevant backgrounds: machine learning/AI, software development process and project organizations, requirements engineering (especially non-functional requirements), responsible data science, socio-technical analysis

2. Human-AI collaborative work processes

With the rise of the data-driven computing paradigm and increasingly powerful machine learning algorithms, the roles of humans and machines are continually shifting. Current modeling techniques such as BPMN, suitable for facilitating the automation of routinized repetitive work, are inadequate for analyzing the new relationships in organization settings where humans and intelligent machines collaborate to achieve work objectives. A well-designed human-AI collaborative work process would aim to leverage the respective strengths of humans and machines while mitigating their weaknesses. Systems design today needs to consider the socio-technical relationship, such as trust, human and machine learning, and how much and what kinds of flexibility and discretion in human action and decision making to allow.   Some key research challenges for requirements modeling include:

-    How to model a software component or service that is capable of learning?
-    How to support analysis of varying degrees of automation, depending on the ability of algorithms to handle the task, and the confidence level of the human expert?
-    How to represent discretion, responsibility, and accountability?
-    How to model human learning, including their adaptation to ML/AI components with varying abilities? e.g, when they adopt workarounds.
Relevant backgrounds: business process management (BPM), machine learning/AI, computer-supported collaborative work

3. Architecting the cognitive enterprise

Enterprise modeling and architecture methods have expanded in recent years to support business and digital transformation, encompassing capability analysis, ecosystem relationships, business model innovation, and more. These methods however have yet to address the rapid rise of the data-driven paradigm of computing. Today, more and more organizations are adopting data-driven decision making as data from mobile, social, cloud, and sensor networks become readily available, in addition to traditional transactional data. Yet machine learning and AI are mostly deployed opportunistically in isolated applications rather than positioned strategically in the architecture of the overall enterprise. Data-driven computing can vastly augment the cognitive capabilities of an organization to sense and interpret the environment, complementing the power of traditional knowledge-based computing to automate operations. The business and enterprise architect today can conceive the enterprise as comprising of cognitive entities, realized by a mix of human and digital technologies, recognizing their respective capabilities and limitations. Research challenges include:

-    How to model the cognitive capabilities and characteristics of humans and machines in order to envision and architect the cognitive enterprise, so as to guide technical systems development and organization design?
-    How to characterize the nature of various types of machine learning and human learning?
-    What are the significant structural and dynamic relationships among cognitive actors within an enterprise that contribute to the cognitive capabilities of the enterprise as a whole? Can these type of relationships be generalized to encompass external actors.

Relevant backgrounds: enterprise information systems, enterprise modeling, organization theories and organization design, data-driven organization

4. Design for digital living

Early applications of information technology systems were primarily in the context of work settings. In those settings, goal modeling was able to provide useful analysis of the social context by considering how alternative system designs might help or hurt the interests of various stakeholders. Today, as digital technologies have become deeply entrenched into our personal and social lives, additional facets of analysis are needed to cover the complex sociotechnical nature of digital living.  
Research challenges include:

-    How to take into account psychological and social factors (e.g., emotions, norms and values) that enter into our interactions with and through digital technologies?
-    How to include the formation of beliefs and their transmission and diffusion through digital media?
-    How to characterize learning capabilities built into digital technologies and platforms, as well as human learning and behavioural change?
-    How to characterize notions of identity and autonomy, for individuals, groups, and communities?
-    How to characterize diversity, bias, and intersectionality?
-    How to take into account the effect of time, e.g., the immediacy of online interactions, cumulative effects over time
-    How to minimize modeling complexity and yet offer insightful analysis?
Relevant backgrounds: social psychology, cognitive science, value-sensitive systems design, systems dynamics

5. Tackling the grand challenges of technological society

Beyond workplaces and individual lives, software and digital technologies are also transforming societies and the planet on a grand scale. Much of our social ills can be traced to our voracious appetite for information and computation, threatening environmental sustainability, human justice and social coherence. Yet, appropriately conceived and designed digital technologies could potentially offer solutions to address or ameliorate some of these same challenges.The research challenge is to consider how modeling techniques can be adapted for addressing issues on a societal and planetary scale, in areas such as sustainability, public health, security, privacy and trust, and social coherence and governance.

Relevant backgrounds: complex adaptive systems, socio-ecology, agent-based simulation

 Find "Eric Yu" on  Google Scholar   DBLP Bibliography more DBLP   ACM Portal    LinkedIn   Facebook    


Joining us at U of T
We seek to recruit highly qualified individuals from Canada and from around the world.  We offer studentships and employment opportunities.  I will be happy to help you explore topics and programs that would suit your background and aspirations.

Post-Doctoral Fellows - You will hold key responsibilities in a research project team.  Good research and writing skills are required.  Excellent vehicle for launching a research career.  Competitive salaries.  Teaching duties pay extra.

Ph.D. Students - You should have an excellent academic record, a Master's degree from a recognized program, and deep interest and commitment in pursuing research.  Writing skills are important.

Masters Students - The Master of Information (MI) degree offers professional education in the study of information in a multidisciplinary context.  A thesis option is available. For students interested in specialized interest areas of faculty members, Reading Courses are sometimes offered.

In my capacity as a cross-appointed Faculty Member at the Department of Computer Science, I also supervise:

Ph.D. Students in Computer Science

M. Sc. Students in Computer Science

Bachelors Thesis in Engineering Science

I will be happy to talk with you if you find my research areas to be of interest.

Summer Studentships
We typically have openings for several summer positions in research projects for senior undergrads.  You should have high academic standing.  This is an excellent opportunity for learning about the research environment and graduate school while being gainfully employed.  Part-time employment during the school year may also be available.  Masters students interested in contributing to our research projects are also welcome.  Please send me your resume by e-mail to register your interest.  Having some of the following as background would be helpful but not essential:

However, enthusiasm, self-motivation, and dedication are essential :-)

Student Projects / Thesis Topics
There are many interesting thesis or research project topics under the research areas within my research interests. I will be happy to provide further detail to help you explore topics that would suit your background and aspirations.

Recent Phd theses

Recent Master's theses

Thanks to all current and past students, visiting scholars, and other team members who contributed to the research.

Rohith Sothilingam
Zhuoran Jiang
Ling Ding
Albert Tzu-Yu Tai
Ling Long
Difei Chen
Vik Pant
Amy Kwan
Zia Babar
Jens Gulden
Navid Mahlouji
Mohammad Danesh
Nazanin Khosravani-Tehrani
Azadeh Nasiri
Soroosh Nalchigar
Jiaying Evan Dai
Mahsa Sadi
Luiz Gustavo Fonseca Ferreira
Jiaqi Yan
Yanghuixin Haley Liu
Jian Wang
Arnon Sturn
Denys Pavlov
David Jorjani
Stephanie Deng
Divyajyoti Sasmal
Davide Calvaresi
Li Yao
Sadra Abrishamkar
Nikoo Nasser
Maryam Fazel-Zarandi
Andrew Hilts
Milene Serrano
Azalia Shamsaei
Xiaoxue Andrea Deng
Samer Abdulhadi
Michaël Petit
Alejandro Mate
Jose-Norberto Mazon
Xinjun Mao
Jihyun Won
Kelvin Ng
Monica Olinescu
Lina Zhai
Lysanne Lessard
Reza Manbachi
Hesam Chiniforooshan
Golnaz Elahi
Yong Du
Imran Kabir
Alireza Moayerzadeh
Ali Akhavan
Amy Lo
Reza Samavi
Catalan Bidian
Faranak Farzad
Vic Chung
Nidhi Sachdev
Parsa Shabani
Chris Cocca
Frank Zhihua Hu
James Zheng Li
Xinjun Mao
Bas van der Raadt
Zhifeng Liu
Jean Yuntian Fan
Subhas Misra
Jia Song
Joanna Churbaji
Jiang Chen
Yue Sun
Jennifer Horkoff
Jane Zheng You
Majed Al-Shawa
Min Qi 
Sharon Bider
Cara Ying Li 
Bowen Hui
Paul Chong
Kelvin Yuen
Sarah Mak
Sara Maharaj
Nick Cheung
Cindy Lun
Daniel Gross
Mike Higginson
Joseph Makuch
Tyronne Mayadunne 
Ying Shi
Wincy Chan
Niloo Hodjati
Mike Bissener
Constant Backes
Godfrey Cheng
Seyil Yoon
Patrick Premont
Nelson Yu
Conan Chan
Vincent Wu
Jane Foo
Nick Zahariadis 
Angela Lee
Chen Wang
Mark Maguire
Fabian Tell
Oscar Sjøden
Jelena Ivanesevic

Post-Doctoral Fellows and Research Associates

     Affiliated Groups
I am a Faculty Member at the Faculty of Information, with a cross-appointment at the Department of Computer Science. I am an Adjunct Professor at the University of Ottawa and a member of OCICS.

     Research Partners and Sponsors
Current and past sponsors and partners:

Research Projects
I am a Principal or Co-Investigator in the following projects:

Selected Publications     
See also  "Eric Yu"  on   Google Scholar   DBLP Bibliography  more DBLP   ACM Portal     
A roadmap to learn about i*

(pdf) (ps) (html)   downloadable in Acrobat pdf, Postscript, HTML formats respectively.
  The version indicated in smaller font may be of lesser print quality.
(iel)  on IEEExplorer Electronic Library (click through for U of Toronto users)
(acm)  on ACM Digital Library (click through for U of Toronto users)
(ut)   accessible via UofT E-journals
LNCS or LNAI    downloadable from Springer (pdf) for U of Toronto users and other subscribers, abstracts only for others
LNBIP series from Springer.
Google Scholar


  • [istarbook]  E. Yu, P. Giorgini, N. Maiden, J. Mylopoulos (eds)
  • Social Modeling for Requirements Engineering 
    Cambridge, MA: MIT Press. 2011.  ISBN: 978-0-262-24055-0
(amazon) (MIT Press
Sneak preview - Chapter One.   

                Modeling for Requirements Engineering (Cooperative
                Information Systems)

  • [JMfest]  A. T. Borgida, V. Chaudhri, P. Giorgini, E. S. Yu (eds)
  • Conceptual Modeling: Foundations and Applications - Essays in Honor of John Mylopoulos (festschrift)  
    LNCS volume 5600. Springer, 2009.  530 pp. ISBN 978-3-642-02462-7.
(doi) (amazon)
Sneak preview - Chapter 7 Social Modeling and i* by Eric Yu.   (pdf) (doi)  
jm book cover

  • [ER08proc]  Q. Li, S. Spaccapietra, E. Yu, A. Olive
  • Conceptual Modeling - ER 2008 
    27th International Conference on Conceptual Modeling, Proceedings, Barcelona, Spain, October 2008
    LNCS volume 5231. Springer, 2008.  550 pp. ISBN 978-3-540-87876-6.
(doi) (amazon)
The image
                cannot be displayed, because it contains errors.

  • [NFRbook]  L. Chung, B.A. Nixon, E. Yu, J. Mylopoulos
  • Non-Functional Requirements in Software Engineering (Monograph)
    Kluwer Academic Publishers, 2000.  472 pp. ISBN 0-7923-8666-3.  (amazon)
       early versions of Ch 2 (ps) (pdf), Ch 3 (ps)

Articles in journals, conference and workshop proceedings, and book chapters
Copyright Notice

Papers published by the Association for Computing Machinery (ACM) are Copyright © by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or permissions@acm.org.

Papers published by the Institute of Electrical and Electronics Engineers, Inc. (IEEE) are Copyright © by IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Papers published in the Requirements Engineering Journal, the LNCS series, and the LNBIP series are Copyright © by Springer-Verlag.

(click here for a roadmap to learn about i*)

Papers on Non-Functional Requirements may be found in the DKBS ftp directory.  Softgoal modelling and reasoning originated from the NFR framework.  Details may be found in the NFRbook.

See the i*  and  GRL webpages for more details.



The OME tool has now been superseded by the open source OpenOME tool.
See the i* tools page on the i* wiki for many software tools developed by other research groups to support i* modeling and analysis.

Standardization Activities

  • ITU-T Z.151 (2008-11).   i* is the basis for GRL (Goal-oriented Requirements Language), which together with UCM (Use Case Maps), constitute the User Requirements Notation URN.  URN was adopted as an international standard in November 2008. The full standards document "User Requirements Notation (URN) – Language definition" may be downloaded from here
  • ITU-T Z.150 (2003-08).  This international standard defines the requirements for a user requirements notation. The full standards document "User Requirements Notation (URN) – Language requirements and framework" may be downloaded from here.
                Telecommunication Union
ITU is the UN agency for information and communication technologies

IJAOSE cover

  • Journal on Data Semantics
    Member of Editorial Board, since May 2006.
Journal on Data Semantics XII

International Journal of
                Information System Modeling and Design (IJISMD)

  • IET Software
    Published by the Institution of Engineering and Technology (U.K.)
    (formerly Institution of Electrical Engineers), and the British Computer Society. 
    Member of Editorial Board, since June 2005.

IET Software Cover

Course Material


eric -dot- yu  -at- utoronto -dot- ca 

University of Toronto

Faculty of Information
140 St. George St.
Toronto, Ontario, M5S 3G6, CANADA

(416) 978-3107 (416) 978-8942


This page last modified on: May 27, 2022