Sinan Can Gülan

I'm a

About

Undergrad & Software Engineer

Hi, I'm a current undergraduate at McGill, studying Computer Science with a minor in Mathematics.

  • City: Montreal, Canada
  • Phone: +1 438 866 3807
  • University: McGill University
  • E-mail: sinan.gulan@mail.mcgill.ca

Courses

Applied Machine Learning

COMP 551

This class is the first graduate-level course I had at McGill. It is an intensive introduction to supervised machine learning models as well as an overview of the history and context of AI.

Key takeaway: Since machines can seamlessly consume and manipulate incredible amounts of data, they can outperform humans on complex tasks, given the task is well-defined. However, the dream of a generalized AI seems far away.

Operating Systems

COMP 310

This class served sort of as a de-mystification of smart machines that are omnipresent in our daily lives. It was interesting exploring the frontier between software & hardware.

Key takeaway: Amdahl's law, which states that adding more processors to complete a job has diminishing returns. I've seen that this principle could be applied to almost any other domain in life.

Distributed Systems

COMP 512

This class was a implementation-based, somewhat low-level (although very theoretical at times) course, which I enjoyed quite a bit. I got to learn about and implement the Paxos consensus algorithm, which was quite challenging but also rewarding.

Key takeaway: Theory only gets you so far: great products require a lot of engineering.

Compiler Design

COMP 520

This course brought many of the seemingly independent concepts studied throughout my time in University together. I got to learn a new language (Scala), to write a Compiler for a subset of C and developed a solid understanding of what's happening under the hood.

Key takeaway: Testing is key.

Applied Machine Learning

COMP 551

This class is the first graduate-level course I had at McGill. It is an intensive introduction to supervised machine learning models as well as an overview of the history and context of AI.

Key takeaway: Since machines can seamlessly consume and manipulate incredible amounts of data, they can outperform humans on complex tasks, given the task is well-defined. However, the dream of a generalized AI seems far away.

Operating Systems

COMP 310

This class served sort of as a de-mystification of smart machines that are omnipresent in our daily lives. It was interesting exploring the frontier between software & hardware.

Key takeaway: Amdahl's law, which states that adding more processors to complete a job has diminishing returns. I've seen that this principle could be applied to almost any other domain in life.

Distributed Systems

COMP 512

This class was a implementation-based, somewhat low-level (although very theoretical at times) course, which I enjoyed quite a bit. I got to learn about and implement the Paxos consensus algorithm, which was quite challenging but also rewarding.

Key takeaway: Theory only gets you so far: great products require a lot of engineering.

Compiler Design

COMP 520

This course brought many of the seemingly independent concepts studied throughout my time in University together. I got to learn a new language (Scala), to write a Compiler for a subset of C and developed a solid understanding of what's happening under the hood.

Key takeaway: Testing is key.

Applied Machine Learning

COMP 551

This class is the first graduate-level course I had at McGill. It is an intensive introduction to supervised machine learning models as well as an overview of the history and context of AI.

Key takeaway: Since machines can seamlessly consume and manipulate incredible amounts of data, they can outperform humans on complex tasks, given the task is well-defined. However, the dream of a generalized AI seems far away.

Operating Systems

COMP 310

This class served sort of as a de-mystification of smart machines that are omnipresent in our daily lives. It was interesting exploring the frontier between software & hardware.

Key takeaway: Amdahl's law, which states that adding more processors to complete a job has diminishing returns. I've seen that this principle could be applied to almost any other domain in life.

Distributed Systems

COMP 512

This class was a implementation-based, somewhat low-level (although very theoretical at times) course, which I enjoyed quite a bit. I got to learn about and implement the Paxos consensus algorithm, which was quite challenging but also rewarding.

Key takeaway: Theory only gets you so far: great products require a lot of engineering.

Compiler Design

COMP 520

This course brought many of the seemingly independent concepts studied throughout my time in University together. I got to learn a new language (Scala), to write a Compiler for a subset of C and developed a solid understanding of what's happening under the hood.

Key takeaway: Testing is key.