u/SnooSongs2979

just realized today that I left a post-it note on my wall that I use as a todo list. I forgot to take it down and it was there during my exam. I think Honorlock may have even seen it during the webcam screening of the room.

Does anyone know how long OSI or course moderators usually take to respond once a case is opened? I'm asking here instead of the course EdStem thread since I don't want to raise unnecessary suspicion and cause myself trouble.

It was a genuine mistake.

I'm finishing an MS CS at a top US university, with an ECE undergrad from a Tier 1/2 Indian institution. I did well overall but excelled specifically in Digital Electronics, Computer Architecture, Control Systems, and Power Systems. Weak performance in Analog, DSP, and EM, combined with limited well paid electronics opportunities at the time, pushed me toward CS.

I've spent a few years in software specializing in Distributed Systems with a flair of Security and ML, with hands on experience in C++ and Rust and some research background. I entered the industry at a good time but CS feels increasingly unstable, with constant layoff anxiety and leetcode grinding that I'd rather not sustain long term.

I'm considering moving back to electronics. Roles that interest me include Embedded Systems, RTL/FPGA design, firmware engineering, SoC architecture, verification engineering, and compiler or toolchain work adjacent to hardware. Digital hardware feels timely given AI compute demand and seems more stable by nature. I don't mind a pay cut for that stability.

Alternatively I'm open to leveraging my ML and Distributed Systems background in hardware adjacent roles like ML Systems engineering, though I'm unsure how that compares to pure hardware in terms of job stability.

A second MS ECE is also on the table. My first MS cost me almost nothing due to funding, I have savings, and friends with similar profiles got into Columbia, UPenn, and UCSD. No visa concerns as I'm a US Citizen so I can target the US job market freely.

Not sure which direction makes the most sense. Looking for honest advice on the viable paths and how to prioritize.

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I'm finishing an MS CS at a top US university, with an ECE undergrad from a Tier 1/2 Indian institution. I did fine overall but enjoyed Digital Electronics, Computer Architecture, Control Systems, and Power Systems. Weak performance in Analog, DSP, and EM, combined with limited well paid electronics opportunities at the time, pushed me toward CS.

I've spent a few years in software specializing in Distributed Systems with a flair of ML and Security, with hands on experience in C++ and Rust and some research background. I entered the industry at a good time but CS feels increasingly unstable, with constant layoff anxiety and leetcode grinding that I'd rather not sustain long term.

I'm considering moving back to electronics. Roles that interest me include Embedded Systems, RTL/FPGA design, firmware engineering, SoC architecture, verification engineering, and compiler or toolchain work adjacent to hardware. Digital hardware feels timely given AI compute demand and seems more stable by nature. I don't mind a pay cut for that stability.

Alternatively I'm open to leveraging my ML and Distributed Systems background in hardware adjacent roles like ML Systems engineering, though I'm unsure how that compares to pure hardware in terms of job stability.

A second MS ECE is also on the table. My first MS cost me almost nothing due to funding, I have savings, and friends with similar profiles got into Columbia, UPenn, and UCSD. No visa concerns as I'm a US Citizen so I can target the US job market freely.

Not sure which direction makes the most sense. Looking for honest advice on the viable paths and how to prioritize.

NLP is turning out to be unnecessarily difficult. The questions are vaguely framed, it's all closed book for a course which was supposed to be open book. How can the same questions be applied to a different environment? Even the marks gained are confusing, a lot of students are already complaining about the quizzes' grading. The credit awarded on some of the questions doesn't make sense to me although we have it partially correct and no mistakes made. Even the TAs aren't properly responsive too.

This is just 1 week into the course, and I already regret taking the course. I thought people from the previous semester were whining but they were correct.

I've taken ML, GA, OS, Information Security and ML4T so far and NLP is probably the worst course so far. GA and ML were run far better than this course.

Professor Joyner please take a look into this course and introduce some improvements while the semester is still young.