Gaurav's 2nd 🧠

    MScAC

    2 part preparation

    • Behavioural
    • Technical

    Behavioural

    Introduce yourself

    Hi I am Gaurav Batra, a 2021 bachelors in technology, CS undergrad from IIIT Hyderabad. Since graduation, I have been working at a tech startup here in Bengaluru in their MLOps platform development team as a platform engineer.

    My day to day work mostly resolves in the backend side of things, my primary stack being Python and with a little bit of golang as and when required. Apart from this, I really like playing chess and have developed an interest for F1 recently.

    Where did you get to learn about this program?

    When I decided that I would pursue MS, I had a clear vision of the type of programs I was looking for. I wanted some program which provided me experience for the real world. MScAC is the perfect program for me, with it's 8 month course + 8 month industrial experience structure. [upon graduation I will have 8 months of Canadian work experience]

    What are your career goals and how will studying at MScAC help you?

    My goals post graduation is to be an industrial researcher leading innovative projects in AI, either within a tech company or through my own firm.

    • The MScAC program with it's perfect blend of academic and industrial training is the perfect medium to catapult me towards this vision of mine. This is in sharp contrast to most programs out there which are thesis based and train researchers to work in academia post graduation.
    • This program is effective for exploration of the field and bringing out the creativity of individual through applied research aimed at solving real world problems.
    • Also, the prestigious alumni network provides a perfect opportunity to connect with other like minded individuals. [diverse backgrounds]

    Why have you decided to study in Canada, specifically at University of Toronto?

    • Canada, specifically Toronto is a hub of startups and innovation.
    • Rich cultural diversity that would be difficult to find in my home country.
    • Excellent quality of life.
    • Canada has a rich history in AI reseach with the likes of [Yoshua Bengio]

    I see that you have done an internship at NVIDIA, can you briefly describe what you worked on?

    All Experiences:

    [Research]

    During the 3rd year of my undergrad, i had the opportunity to collaborate with Dr. Naresh Manwani in the MLL at our institute. We worked extensively in the domain of theoretical machine learning, specifically bandit feedback environments. We came up with a novel bandit environment named the dilute bandit environment, and developed a novel algorithm inspired by the Banditron for this environment.

    Our work was published in PRICAI, 2021. The reviewers suggested it's applicability across various sectors like advertisements and medical diagnosis.

    This was my first taste of research and I enjoyed every bit of it. My professor and I used to have long discussion session once/twice a week, wherein I used to present my learning and we used to brainstorm together on ideas.

    https://arxiv.org/abs/2105.08093

    [NVIDIA]

    I did an internship in the summer of 2019 at NVIDIA in their cloud gaming team. They were trying to develop their cloud gaming division - GeForce. The problem statement that I was presented was that of image smoothing so as to reduce the bits per pixel when the image is compressed using jpeg.

    We choose a research paper which proposed a CNN specifically for this very purpose. My internship was to implement the CNN tailored to their use case. At that time I was just about started out exploring the field of DL and this new experience gave me a really hands o experience on how these theoretical concepts are actually implemented and used in real world applications.

    Eventually, this was used in their video encoding module.

    [mention covid too]

    [Couture.ai]

    At Couture.ai I work in the MLOps platform team, responsible for adding functionality to the platform, catering to the challenges of scaling AI/ML applications. The platform lets you train, deploy ML models and AI applications, easing out the pains of data scientists in all aspects of their journey

    [Recent Work] From the last 6 months, we are working on architecting and developing a versatile model repository and serving layer designed to accommodate a wide variety of machine learning models. The model repository uses a version-controlled schema to ensure reproducibility and facilitate rollbacks. My designs also leveraged Kubernetes for auto-scaling and incorporated a robust API layer for effortless integration into third-party services like hugging face. These enhancements led to a 40% reduction in model deployment time and a 20% improvement in inference speed.

    Talk about one thing that you liked and disliked about any one of your internships.

    [Nvidia]

    • Pro
      • real life application
    • Con
      • A real novice [Undergrad research]
    • Pro
      • ample time, mostly theoretical
    • Con
      • no real world application as of now. [Couture.ai]
    • Pro
      • real world applications
    • Con
      • fast paced research

    Since you have interned at a company as well as a university, what according to you is the major difference between industry research and academic research?

    • Real world experience along with theoretical knowledge.
    • Hard deadlines and systematic way of working.
    • Practical approach to things

    What courses have you planned to enroll in MScAC if you get admitted?

    • Two required courses (1.0 FCEs): Communication for Computer Scientists (CSC 2701H) and Technical Entrepreneurship (CSC 2702H).

    CSC2515H - Introduction to Machine Learning, for its foundational machine learning insights, and CSC2516H - Neural Networks and Deep Learning, which align perfectly with my focus on advanced AI technologies.

    Have you seen what kind of companies host applied research internship opportunities for MScAC? This is another homework type question and again I had prepared a short list in my Notion page.

    • Tactual Labs Co.
    • Samsung AI
    • NVIDIA
    • ICICI Bank

    What do you do when you are not learning or working on projects? Chess and recent interest in F1

    Anything you would like to ask me about the MScAC program?

    This is a time when you should always ask questions as it shows that you are interested the MScAC program.

    • What is the cohort size?
    • Are all the companies that host applied research internship from the Greater Toronto area or do we need to relocate?
    • How do we complete the courses in the second semester during the internship if we end up relocating?

    Here are some insightful questions you could ask at the end of your interview that go beyond the logistics of the program and internship placements:

    1. Curriculum Evolution: "How has the MScAC program's curriculum evolved in recent years to adapt to the rapidly changing field of computer science?"

    2. Industry Collaboration: "Can you share some examples of how the program collaborates with industry partners to shape its curriculum and ensure it meets current industry needs?"

    3. Career Support: "What type of career support does the program offer to students, especially in terms of transitioning from academia to industry or exploring different career paths within computer science?"

    4. Research Opportunities: "Besides the applied research internship, what opportunities are available for students to engage in research, particularly in emerging areas of computer science?"

    5. Alumni Network: "How does the program facilitate networking with alumni, and what kind of roles are alumni typically taking up after graduating from the MScAC program?"

    6. Capstone Project: "Could you provide more details about the capstone project? How are the projects chosen, and what is the process for matching students with projects?"

    7. Program Outcomes: "What are some of the key outcomes the program aims to achieve for its students, and how does it measure the success of these outcomes?"

    8. Faculty Engagement: "How do students typically engage with faculty outside of the classroom setting, and what opportunities are there for mentorship or collaboration on projects?"

    Technical

    • LinkedList
    • Array
    • binary trees