Mikhail Soutchanski  

Mikhail Soutchanski, Professor

PhD in Artificial Intelligence, University of Toronto, Canada
M.Sc. (Diploma with Honors), Moscow Institute of Physics and Technology (Phys-Tech), National Research University.

Email:   Thank you for  not  sending me email!
(Exception: if you would like to discuss research over Skype or Zoom,
then please feel free to contact me using email address from my papers.)

Office: 245 Church Street, room ENG275 (NE corner, the 2nd floor)
Computer Science is located in George Vari ENG building.
The Department of Computer Science is part of the Faculty of Science.
Phone: (416) 979 5000 ext 55-7954   (leave voicemail)
General enquiries:
        Ms. Lori Fortune (416) 979-5000 ext.55-7411
        or Mr. Alex Zheltov (416) 979-5000 ext.55-7410

Mailing address:
245 Church Street, ENG281
Department of Computer Science
(former Ryerson) University (to be renamed soon)
Toronto, Ontario, M5B 2K3, Canada

A photo of Mikhail

A long-term mission of Soutchanski's Advanced Aritificial Intelligence (AI) Lab:
Theory is when you know everything but nothing works. Practice is when everything works but no one knows why. In our Advanced AI lab theory and practice are combined: computer programs that we develop have to work and we must know why. (Acknowledgement: this is an improvement of a well-known quote.)

Some of my Publications

Research interests

International students:
Unfortunately I am unable to respond to emails about graduate admission or possibility of working with me. Please contact the Ryerson School of Graduate Studies or the CS Graduate Program Assistant. If you have been admitted to Ryerson, please feel free to reach out if you're interested in discussing research opportunities in my group. I would strongly recommend to browse my recent research papers before you contact me and write why do you think our research interests match well. If you have published research papers, inform me.

Recent Teaching    

CPS 721:   Artificial Intelligence 1  , an undergraduate course (Fall 2021).
CP8314 (Advanced AI): Dynamic Systems in AI, a graduate course (Fall 2021)
CPS822     Artificial Intelligence 2: advanced undergraduate course (Winter 2022).

CPS 824 / CP8319:   Reinforcement Learning (Winter 2019).
A graduate / advanced undergraduate course.
CPS 815 / CP8201:   Topics in Algorithms ,   an undergraduate course (Fall 2019).
CPS 40A/B:   Undergraduate Thesis  , a two-term research oriented course (Winter 2019).
CP8310:   Directed Studies in Computer Science   (Fall 2016), a graduate course.
CPS 616:   Analysis of algorithms ,   an undergraduate course (Winter 2014).
CP8201:   Algorithms and Computability,   (Fall 2013), a graduate course.
CPS603:   Foundations of Semantic Technologies   (Winter 2011), an undergraduate/graduate course.
CPS 125:   Digital Computation and Programming   (Winter 2017), an undergraduate course.

WWW links

"In theory, theory and practice are the same. In practice, they are not."
"The proper method for inquiring after the properties of things is to deduce them from experiments."
"In questions of science, the authority of a thousand is not worth the humble reasoning of a single individual"
"An error does not become truth by reason of multiplied propagation, nor does truth become error because nobody sees it"
"The greatest enemy of knowledge is not ignorance, it is the illusion of knowledge"
"The truth is simple. If it was complicated, everyone would understand it."
"There is nothing more practical than good theory"
"We learn more and more about less and less, and less and less about more, until we know everything about nothing and nothing about everything."

Web mail