Laboni765

I’m not sure whether UCSD has sent out all of its CSE PhD decisions yet. Admissions updates can take time, and not hearing back immediately does not necessarily confirm the outcome. I’d keep checking the application portal and the email address used for the application, including the spam folder. If the department’s published timeline has passed, a brief, polite message to the graduate admissions office may help clarify whether decisions are still being released. If anyone receives an update, it could be useful to share the date and the general research area or track, while keeping personal application details private. That may help other applicants understand how updates are being communicated.

“Starting with Cisco’s official blueprint sounds like a sensible approach. Since security carries the biggest share of the exam, giving it extra time makes sense. Did you practise these topics in a lab as well, or mainly use practice questions?”

Honestly, your profile doesn’t sound bland to me. You have a strong mix of research experience, publications, a relevant internship, and hands-on work with robotics. I wouldn’t worry too much about not having several first-author papers; what matters is being clear about your contribution and the research direction you want to pursue. I’d be careful about calling any of these schools “safe,” though. ML and robotics PhD admissions can depend a lot on advisor fit and which labs are taking students that year. I’d look closely at faculty working in robot learning, reinforcement learning, foundation models, and 3D data, then make sure your application explains why their work fits your interests. For NSF, try to make your research goals and broader-impact plans specific and realistic.

A BSIT does not automatically prevent you from applying to MSCS or PhD programs. Your 3.6 GPA and Mathematics minor are positives, especially with courses like linear algebra and real analysis. For an MSCS, the main thing to check is whether you have enough core CS coursework, such as data structures and algorithms, computer architecture, operating systems, and theory. Some programs may require you to complete prerequisite courses. For a PhD, the degree title matters even less than your preparation for research. Research experience, strong recommendation letters, relevant coursework, and evidence of your ability to do CS research will be important. So yes, you can be competitive, but it will depend heavily on the individual programs and how closely your coursework matches their requirements. I would look at the prerequisites for each program rather than ruling yourself out because your degree says BSIT.

It would be helpful to hear from anyone who has received an interview invitation from Columbia’s MS Journalism program. If you have heard back, sharing roughly when you submitted your application and when you received the interview invite could give other applicants a better idea of the timeline. Hopefully, decisions and interview invitations are being sent out in batches, so not hearing back yet doesn’t necessarily mean anything negative.

The funding timing seems like one of the biggest reasons the process feels inefficient. A student can have a strong research fit with a professor, but that doesn’t necessarily mean the professor has funding available when applications are open. I also think there’s a visibility problem. It’s difficult to know whether a faculty member is actively looking for students, especially when openings can depend on new grants or projects. A system that clearly showed active openings, research areas, funding status, and advisor interests could make the process much more targeted. Students would spend less time sending generic cold emails, while faculty might reach applicants who are a better research match. The challenge would probably be keeping that information current, since funding and availability can change quickly.

I think it’s still a bit early to read too much into the “under review” status, especially for a graduate program where applications can be reviewed in batches rather than strictly in submission order. If your application has been complete since December, I wouldn’t assume the lack of an update means anything negative. Decisions can depend on several things beyond the general admissions timeline, including supervisor availability and how the department handles funding and committee reviews. It would definitely be useful if other Fall 2024 applicants could share their timelines here, particularly: When they applied Whether their status changed from “under review” Whether they received an offer or rejection That would give everyone a clearer picture of how decisions are rolling out. Hopefully you’ll hear something soon, but at this stage the status alone probably doesn't tell you much.

I wouldn’t say you’re delusional or wasting money by applying. Your profile has some clear strengths, especially the strong upward academic trend and the A/A+ grades in relevant areas like linear algebra, probability, optimisation, ML, and statistical inference. That said, I wouldn’t assume the earlier grades are completely irrelevant just because they aren’t included in your GPA. Some programs may review the full transcript, and a C in Matrix Algebra plus a failed Discrete Mathematics attempt could raise questions for mathematically intensive ML programs. The important part is that your later performance provides a strong counterargument: you clearly improved and performed very well in more advanced quantitative and ML courses. Your research record and FAANG experience also strengthen the application considerably. For programs this competitive, admission is never guaranteed, but I’d definitely apply if the application costs are reasonable. I’d treat the most selective schools as reaches rather than assuming your early grades automatically disqualify you.

For theory, I’d put more weight on research fit and advisor availability than on overall department size or general prestige. A larger department like UT Austin can give you a broader selection of courses, students, and research areas, while a smaller program can sometimes make it easier to build a close relationship with a particular research group. I’d compare the faculty working in your specific area rather than judging “theory” as one category. Complexity, algorithms, cryptography, computational geometry, and related areas can have very different strengths. For an academic career, your advisor, research output, collaborations, and recommendation letters will generally matter much more than a small difference in overall university prestige. If you’re also considering industry later, both schools are strong enough that I wouldn’t make prestige the deciding factor. I’d look at where several faculty members match your interests and where current students seem to be getting the kind of research opportunities you want.

For the areas you mentioned, I’d probably lean toward Python. It is flexible for scientific computing and works well for data analysis, machine learning, and processing experimental data such as spectra. MATLAB can also be a good choice, especially if it is already used in your department or research group. Maple is particularly useful when symbolic mathematics is a major part of the work. For your interests, I’d choose based partly on what tools are commonly used by the researchers you want to work with. That can be more important than finding one language that is universally “best.”

Yes, it is absolutely possible to build a research career from a mid-tier university. The university’s reputation can influence opportunities, especially for highly competitive academic positions, but it is not the only factor that matters. For a research-focused career, I would pay particular attention to the advisor and research environment. Working with a professor you already know, whose research genuinely interests you, can be a significant advantage if they are actively publishing and have strong collaborations. During a PhD, things such as quality of publications, research experience, conference participation, collaborations, and recommendation letters can become much more important than the name of the institution alone. I would not choose a program solely because it has a higher ranking if the research fit and mentorship are substantially worse. If your goal is to stay in research, having an advisor who can help you develop a strong research profile may be more valuable than choosing a more prestigious school with a poor fit.

Given the $20k scholarship and the shorter program, Cornell seems like the stronger choice unless there is something specific about CMU's program that better matches your career goals. Both schools have strong names in computer science, so I would not put too much weight on the difference in overall rankings or general name recognition. For a professional master's, the more important questions are what courses you can take, the recruiting opportunities, and the total cost. The 16-month CMU program may give you more time to network and search for jobs, which is a real advantage. On the other hand, paying significantly more for those extra months only makes sense if you expect CMU's program, location, or recruiting network to provide opportunities you are unlikely to get at Cornell. I would compare the actual total cost and employment outcomes before deciding. If the career opportunities look similar, the Cornell scholarship is difficult to ignore.

Your profile looks like it is moving in a good direction, especially given that you still have time before applying. The strongest part is probably the research experience that connects directly to your stated interests. A few things I would focus on: Try to make your research experience as substantial as possible rather than collecting many short projects. A strong letter from a professor who knows your research work well can be more valuable than a letter based mainly on reputation. If the IoT project leads to a publication, that could strengthen your application, but I would not treat publication as the only measure of a successful research experience. Your GPA is something worth improving if possible, particularly in core CS and math courses. For research-focused graduate programs, the fit between your interests, experience, and the faculty you want to work with will also matter a lot. Your current profile seems to have a clear direction, so I would focus on building depth and being able to explain your research interests and contributions clearly.

I’m interested in getting into cybersecurity, but I’m still trying to figure out which area would be a good starting point. I’ve been looking into things like network security, cloud security, and penetration testing. They all seem interesting, but I’m not sure whether it’s better to explore a little of everything first or choose one area and build deeper skills there. For people who are already studying cybersecurity or working in the field, how did you decide which area to focus on? And are there any fundamentals you’d recommend learning before specializing? I’d appreciate hearing about your experiences and what you wish you had known when you were starting out.

If you are just starting, I would focus less on finding the “perfect” course and more on choosing one clear learning path. For general programming, Python is often a good starting point because the syntax is relatively easy to understand. If you are interested in websites, start with HTML, CSS, and JavaScript. The best course is usually one that includes practice and small projects rather than only video lectures. Try to build simple things as you learn, even if they are basic at first. It also helps to avoid jumping between several courses. Pick one beginner-friendly course, complete a reasonable section of it, and then decide what area of programming interests you most.