Events

Join us for our next online workshop “Python Basics“, followed by a 30-minutes socialization and Q&A session. This course is part of the AI & Optimization Crash Course Series. Registration is free but required for all participants. A Zoom link will be sent out to the email provided in the registration form.

When: October 19th at 5-6 pm EDT (GMT -4)
Instructor: Moira MacNeil (Email)
Topics:

• • • Introduction to dynamic languages
    • Variable types
    • Flow control
    • Functions, modules, and classes (including some useful existing modules)

Click here for registration.

 

Join us for the upcoming online event, a series of workshops covering various topics in AI and optimization. The courses are introductory and no prior knowledge is assumed. Also, you will be entered in a random draw to win one of six $25 gift cards. Full information on the courses can be found below. All courses will be followed by a 30-minutes socialization and Q&A session. Registration is free but required for all participants. Before the start of each course, a Zoom link will be sent out to the email provided in the registration form.

Click here for registration.

Python Basics
October 19th at 5-6 pm EDT (GMT -4)
Instructor: Moira MacNeil (Email)

Topics:

• • Introduction to dynamic languages
  • Variable types
  • Flow control
  • Functions, modules, and classes (including some useful existing modules)

Data Engineering in Python
October 20th at 5-6 pm EDT (GMT -4)
Instructor: Aida Khayatian (Bio)

Topics:

• •  Python libraries for data analysis
  •  Reading data
  • Inspecting and aggregating data with Pandas
  • Exporting data with Pandas

Data Science in Python
October 21st-22nd at 5-6 pm EDT (GMT -4)
Instructor: Javad Soltani Rad (Bio)

‌‌Day 1 topics:

• •  Data visualization in Python
  •  Introduction to Machine Learning (ML)
  • Preparing data for ML models
  • Classification example in Python

Day 2 topics:

• •  Regression example in Python
  •  Unsupervised Learning (clustering) example in Python
  • Introduction to Deep Learning
  • Python tools for industrializing ML applications

Reinforcement Learning
October 26th-27th at 5-6 pm EDT (GMT -4)
Instructor: Peyman Kafaei (Bio)

Day 1 topics:

• •  Reinforcement Learning (RL) vs Supervised/Unsupervised Learning
  •  Introduction to RL
  • Markov decision processes
  • Elements of RL

Day 2 topics:

• • Model free prediction and control
  • Monte Carlo methods
  •  Temporal difference learning
  • Tabular mehtods vs approximation methods

Operations Research in Python
October 28th-29th at 5-6 pm EDT (GMT -4)
Instructor: Maryam Daryalal (Bio)

Day 1 topics:

• • Install and setup Gurobi in Python
  • Solve a linear programming model with Gurobi
  • Input/output management
  • Model modification
  • Warm start for LP/IP models
  • Handling infeasibility

Day 2 topics:

• •  Branch & bound for IP models
  •  Callbacks in branch and bound
  •  Access to information of nodes while solving
  •  Modify the model during solve
  • A sneak peek at Column Generation in Gurobi

The University of Toronto Operations Research Group (UTORG) is hosting a lunch talk by Mahsa Hosseini. The talk is entitled “Efficient Repositioning in Car Sharing Networks”. Lunch and coffee will be provided. Hope to see you there!

 

Who: Mahsa Hosseini, Ph.D. candidate, University of Toronto   When: Wednesday, March 18th @ 12:10pm – 1:00pm Where: GB173

Bio-sketch: Mahsa Hosseini is currently a Ph.D. candidate at the Rotman School of Management, University of Toronto. She is supervised by Prof. Joseph Milner and Prof. Gonzalo Romero. She holds an MBA from the Department of Management and Economics at Sharif University of Technology. Mahsa’s research interests include stochastic control, stochastic modeling, and theory of Markov decision process. Abstract: We propose a modelling framework to study the dynamic problem of repositioning vehicles using steady-state behaviour for a network with centralized control and uncertain, unbalanced demand. To provide interpretable strategic level reposition policies, we use the structure of the underlying transition matrix to estimate the relocation gradient as a function of the current number of vehicles in each location. In particular, we project the full-dimensional problem of finding optimal open-loop relocation policy onto the lower-dimensional space of reposition decisions only. Then we segment this space and estimate the local change to the steady-state and interpret the meaning of these gradients via absorbing Markov chain concepts. These gradients then provide a state-dependent heuristic for the dynamic vehicle repositioning problem.

 

The University of Toronto Operations Research Group (UTORG) is hosting a lunch talk by Hamed Pouya. The talk is entitled “Wavelength Defragmentation Problem in Optical Networks”. Lunch and coffee will be provided. Hope to see you there!

Who: Hamed Pouya, Postdoc, University of Toronto   When: Thursday, January 24th @ 12:00pm – 1:00pm Where: BA8256

Bio-sketch:  I am a Ph.D. student at Concordia University, Montreal. I will start my postdoc from March in U of T with Dr. Merve Bodur. I have been collaborating with Ciena Co. (a world leading company in telecommunications) since 2015. My focus in Ph.D. is on using decomposition methods to tackle large scale problems in the telecommunication industry. Abstract:  Future optical networks, in particular, Software Defined Optical Networks (SDONs), are expected to provide reconfigurable services while maintaining an efficient usage of wavelength resources. I’ll present a make before break wavelength defragmentation process, which provides the best possible lightpath network provisioning, i.e., with minimum bandwidth requirement. We propose an original solution scheme which, at defragmentation times, (i) computes an optimal lightpath provisioning, (ii) check if it is made before break reachable from the fragmented current one, (iii) if not, go on with an iterative process which recomputes a lightpath provisioning using a Nested Column Generation scheme subject to additional constraints for eliminating the rerouting deadlocks.

 

The University of Toronto Operations Research Group (UTORG) is hosting a Meet & Eat with Professor Jamol Pender, of the Cornell University, following his talk titled “Queueing Theory in the Age of Smartphone Technology”. Lunch and coffee will be served. We hope to see you there!

Please note that the Meet & Eat will follow immediately after the talk which will be held on Nov 29th, at 12:00 pm-1:00 pm in MB128. For more information regarding the talk, please visit https://www.mie.utoronto.ca/events/or-seminar-series-jamol-pender/

When: Thursday Nov 29th @ 1:30pm – 2:30 pm Where: BA 5281

Bio-sketch: Jamol Pender is an Assistant Professor in the School of Operations Research and Information Engineering at Cornell University. He received his PhD in Operations Research and Financial Engineering in 2013 at Princeton University. His primary research interests involve the stochastic analysis and optimal control of queueing networks with time varying rates. He is also broadly interested in the applications of queueing networks in the study of service systems, collaborative economies, smartphone networks, healthcare, and transportation systems.