Ph.D. in the Department of Industrial and Systems Engineering Webinar

OK. And it's on.

Hello everyone.

Welcome to everyone joining.

Well, hello everyone.

We're still getting everything, but welcome to everyone joining the chat. I still see people rolling in. I'll be happy to chat with you all in a bit. It's exciting to meet all of you so.

Welcome.

And welcome, everyone. It's great to see everyone. And you do have the chat feature. So please utilize the chat. We will be trying to answer your questions and we'll get started momentarily. We'll let a few more people trickle in.

Awesome welcome.

All right, we'll get started in one minute.

OK, as people are trickling in, I would just like to say happy Halloween and welcome.

I am Jen Fazour, I'm the pH D Program Director in Industrial and Systems Engineering at RPI Rensselaer Polytechnic Institute and we're very excited to have you join us for this prospective PhD webinar. It is RP is 200th birthday, so we are celebrating 200 years of being a university and we are looking for.

Excellent people to join us in the future 200 years so.

We have some slides to share and we will talk about our research. So our plan for today is we're going to give you kind of a a very, very quick overview of ISE Industrial Systems Engineering at RPI. And then we have faculty that are going to talk briefly about their research. All of these faculty are looking for PhD students to join in Fall 2025. And so it's a quick overview.

But hopefully you understand what types of research are happening at our our programs and also get excited about different faculty because the PhD is a research degree. And so we'll emphasize that as well. And then we'll talk very briefly about kind of what is our PhD program and the application process and then hopefully have some time for question and answers. If you have questions, you're welcome to put them in the chat and we will try as much as possible to get them answered as.

We're talking, so please feel free to put your questions in the chat and then we will answer some vocally and some.

In in response in the chat as well.

OK. So RPI, it's located in Troy, NY, So we are in the state of New York and we are about two hours north of New York City, about 3 hours West of Boston and about 3 hours South Of Montreal.

We were established in 1824. Again, our 200th birthday is this year and we are the oldest school of engineering in the country. We are also classified as an R1 institution, which means that we have very high research activity. We have around over 700 PhD students on campus. Our department, Industrial and Systems Engineering, is housed within the School of Engineering.

Is also a very old program for industrial and systems engineering. It was founded in 1933.

So we have quite the legacy of industrial and systems engineering and we offer undergrad masters and PhD. Today we are going to talk primarily about our PhD program. If you have questions about our master's programs, we can also try to answer some of those as well in in the chat. But today is mainly about our PhD program. So I hope all of you are applying to, you know, multiple universities thinking about a PhD. What we are doing here is we really.

Make sure you have a good fit. So a PhD is a long endeavor. It's a important endeavor. And so we want to think about, you know, what type of people fit really well with our, our university. And we, I think have some unique advantages. Some unique advantages is that we have very close active mentorship. So here is a picture of all of us that informs informs is one of our.

National Society, we all attended this in Seattle last week.

And we had 10 pH. D students from our department attending that event. And we have very close mentorship where we're working with students one-on-one. There's high rates of funding and travel support for conferences. So we have funding for students. So as a percent of our PhD program, lots and lots of our students attended in forums or other conferences.

Another thing that's maybe different than some PhD programs is that we expect students to engage in research in the first year.

And we're able to do that because we have this close mentorship. So you get partnered with the faculty member and you start research right away. And we also, I think a nice thing about RPI is it's very collaborative. So we have students and faculty actively engaging with other departments. Just as an example, my own students this, my own PhD students this semester are taking classes in math, they're taking classes in industrial systems engineering, they're taking classes in electrical engineering, and they're taking classes in transportation engineering, all.

These to contribute kind of, you know, to their research. And it's common for us to have this kind of collaboration across campus.

The last thing I was going to say before I hand it off about research, specific research is that we have a ton of collaborations that are happening at RPI and we also have a lot of multiple university wide initiatives. So there's, you know, we're in engineering school, a very technical school and so there's a lot of.

Data a lot of machine learning a lot of optimization happening across across the university. Here are some examples of some university wide initiatives that I see is involved in We also have a quantum computer RP is the first and only university with a quantum computer on AUS campus. We also have very powerful supercomputers and we also have you know different kind of AI horizons scholar programs a lot of.

Collaboration with IBM and other industries, funding a lot of this work. So I know that's a very quick overview, but I want to take it is that RPI is a fantastic place. It's a fantastic place to be a faculty and I think a fantastic place to be a PhD student. So please consider us. And now what I'm going to do is I'm going to turn it over to Doctor James Bailey, who's going to talk about his research. And the, the primary part of this is we're going to hear from all of these faculty. Again, all of us are recruiting.

PhD students and in this SO please.

I'll turn it over. Doctor Bailey, are you?

Very old.

Yeah, yeah. Thank you, Doctor. So again, welcome to everyone. My name is James Bailey. I generally work in theoretical opposition and game theory. A little bit about me, my background is in a mix of industrial engineering, mathematics and computer science, although I generally work more in the game theory, economics, optimization domain.

That lower left is AQR code to all my personal website with papers, research areas, courses. I teach things along these lines.

Uh, I'll keep sort of my discussion a little bit short. I'm mainly recruiting for students related to three projects that I'm actively seeking funding in, specifically online optimization games, developing theoretical algorithms.

Designing techniques to mitigate wage theft and the efficient capture of evaders and and networks. I'll comment on each of these a little bit.

With respect to online optimization, this is probably the project I'm most excited about. I think like in 10 years, it has the most potential to make a big impact. But I look for ways to solve games, Not games like the sense of like video games, but more like adversarial systems where agents are competing against each other. And this actually has nice connections to modern ML techniques like generative adversarial neural networks, where you have two different algorithms sort of competing against each other.

And sort of where I work, this is a little bit more interesting is a couple of years ago I showed that there's this sort of nice connection between online optimization methods and games and physical Hamiltonian systems. And based off that with some of my students, I've been working on developing new techniques. A couple years ago we released an algorithms about 2 1/2 times faster than standard approaches. And 1st year PhD student of mine, we are about to submit a paper that's the first algorithm to ever achieve.

Finite time guarantees for solving these systems, so I will note.

We'll talk about this more the PhD program early later, but we like to see you guys get involved in research the very first year and hopefully all you'll see a lot of you'll see progress as you go along.

I also work in a little bit more of a pied area, again using game theory. We use designing techniques to mitigate wage theft. The idea is that we want to design communication mechanisms to decentralize wage debt in order to protect different workers. The idea is that if workers are aware of theft.

That can help them in negotiation stage. And we're using a lot of mathematical modeling techniques and optimization techniques, as well as some information theory to understand how the spread of information can be used to combat wage theft. This is also joint work with Doctor Chabdar, I think speaks next. So the final project that I'm really looking for students on actively now is optimal control for Pursuer Evader models. What you see in this figure is a graph. A network represents, you know, a road network, for instance.

And the idea is that there is someone in the system that is trying to evade capture.

Commonly described as cops and robbers problems, we're looking at a few twists of standard methods based off visibility, communication networks and multimodal vehicles. Things like incorporating unmanned Air Eagle vehicles into this problem.

And again, it's another mix of adversarial techniques where you know, the the the robbers trying to escape the cops and the cops are trying to capture the robbers, so.

Those are the three main projects I would say that I am looking for students for right now. In general, though I do have projects related to pathfinding robots. I do some work in voting and election theory.

I think I'm getting ready to start a proposal related to ML informed optimization. So.

I encourage you if you're interested in these areas to reach out to me. I would love talking to people more about, but I will turn it over to Doctor Chavdar next.

All right, Thank you. Let me share my screen.

All right, so hello everyone. I am Bahar Chaudhar. I'm an assistant professor at RPI in Industrial engineering department. My background, educational background, I see my bachelor's degree from Middle East Technical University from Meteor and my masters and PhD from Georgia Tech in industrial Engineering.

So a little bit very high level overview about my research is I work in the general area of operations research and supply chain management and logistics.

So more specifically on time sensitive problems and integrating human behavior. So there are two main concerns or issues that really motivate my research. The first issue is.

Handling the challenges in large scale problems in real time problems like the computational issues, how do we develop solution methodology so that we make decisions that are compatible with the requirements of real time decision making? And the second issue is that in classical models when we are solving problems, we ignore.

Behavioral issues like we make certain assumptions that lead to solutions that are not necessarily good in the long run. So my research focus on how do we integrate the current knowledge of human behavior into optimization model so that we can make a better decisions in the long run. So these are the application areas that I'm mainly focusing on. I work on last mile delivery. These are like pastoral delivery applications, disaster response and preparation.

Talk a little bit about more about in a bit capacity allocation in service systems and supply chain resilience. And these are the main methodologies I'm commonly or currently using. Of course, that doesn't mean that this will. This is a limited list like as a problem requires different tools, different techniques like me and my students expand these as well, but mainly the methodologies the tools I use in my research is.

Are like mathematical modelling and optimization.

Routing techniques, Markov decision processes, game theory, and reinforcement learning. So when I'm recruiting students, it is important for me that or like to make sure that we have a good fit. The student enjoys or is excited to pick up these topics and learn like work in these areas. So a couple of the general research projects I'm working on and I'm looking for students are listed here.

So the first one is on infrastructure network restoration. So this is for disaster preparation and response. So the question is when we are expecting disruptions, disaster, which are going to affect the infrastructure systems, how do we prepare, how do we strengthen the network, how do we make facility location decisions? And also in the aftermath, how do we determine the operational management decisions like to repair and restore these networks with minimum negative impact and?

Project I am working on is like ambulance bus routing and hospital capacity allocation problem. So these are again on the humanitarian aspects and these routing problems, logistic problems are highly complex, complicated and we do not have good solution methodologies. And I'm also working on time sensitive problems, more on the parcel delivery applications. And in the second group in terms of like modelling human behaviour, there are different projects I'm focusing on. The 1st project is on capacity allocation.

Service systems, we prefer service delivery time. So these are queuing systems when customers have preferred service delivery times. And we consider many different problems based on like causing different types of customers, considering customer reaction in terms of like introducing endogenously and the genesity of the demand to the systems. And most of the applications come from like grocery shoppings, curbside pickup systems and Homeland Security. And finally like out some of my.

Focuses on understanding the impact of communication between customers in terms on on the operational system. So this is more about operationalizing the word of mouth communication and capturing the relationship between operational decisions and customer demand. So these are some of the projects I am working and looking for students so.

I will answer more questions later, but I think this is all I have. Thank you.

I I think Yinan, you can, uh.

Yes, yes.

Yeah, I think I will take the stage. So let me share the screen.

Yep.

Screen hope the screen shows up good. Hello everyone, my name is Ina Wong. Currently I'm assistant professor in the Department of Industrial and System Engineering at the Rensselaer Polytechnic Institute. And here I will give a quick summary of my research, which is I called it engineering driven artificial intelligence for the complex physical systems. So first of all, I will quickly introduce a little bit things about myself. So I did my bachelor in China.

University in electrical engineering automation and master in the Columbia University also in electrical engineering and start to do PhD in the industrial engineering at Virginia Tech. So I saw the questions about regarding the background to pursue a PhD in IE. So from my trajectory you can see I started in electrical engineering and finally be a PhD and faculty in IC. So IC is quite topic. So as long as you find a good fit of your expertise in this domain you can you're good to go.

For a pH D degree over here.

About my work experience for now, I started assisting professor since 2022 and previously I have a bunch of experience to work in the natural laboratories. So This is why I also encourage my students to do the internship into the National Laboratory, into the research lab in the industry to have them experience different kind of life and fight for them to choose their future career.

So more about my expertise and research interest. So my expertise is mainly focused on two aspects. The first part is the theoretical foundation in the statistics and also the machine learning, deep learning and reinforced learning. This is why I called the methodology perspective. And another part is the domain knowledge in the complex system, complex physical systems. The main physical complex physical system I'm working on is the manufacturing system. So in this case.

With the expertise.

This motivate my research interest focusing on the engineering driven machine learning, for example the circuit modeling, cyber attack detection and AI level design for the complex systems. I will give some specific examples in the following slides to show the detailed project I've worked on before and there are some research topics I've worked on previously and with a couple of publications going on. And I have to admit this is a little bit outdated because I didn't put my.

Students research output over here. So by the way, I have three students in my group and I think all of them have at least finished.

One project at component, so they're in a good progress of publication and graduation as well. So why we want to do the engineering driven AI. So here is a general hierarchical system, system level understanding of the manufacturing system. If we divide it into 3 levels, we may say OK in the hardware level, these are the operations, sensing system and edge devices in the manufacturing system try to do try to produce the specific products.

Try to collect the data and try to analyze the results in the process level. Different process of manufacturing in the manufacturing system will be collaborating together to generate the final product. In this case, it can be affirmative or subtractive, additive or assembly. Right? Additive manufacturing is a hot topic recently. You try to print out print out the products using this 3D printing hat. And if we consider the network level, it might connect to different branches or different sectors of industry.

So over this network level, it might happen some cyber attacks. We need to identify these attacks and to protect the entire system. There might be some data sharing across different sectors if they're working on the same product. There might be some knowledge sharing with respect to the, for example, anomaly detection, quality control as well as manufacturing process design. The the thing enable our research is we have a bunch of rich data collected from the manufacturing system.

Be the **** cloud can be the image can be the profile, but the **** cloud data is kind of a set of points measuring the position on the surface of product and image and tensor easy to understand is a time series or the screenshot at every single time. The profile is more like a time series skeletal data collected like the temperature, the humidity in the in the background. So with all the data, we can develop different AI methods. The problem is the traditional AI method may not work well in the specific manufacturing.

Men or any other specific domain, we may need to do some novel innovation in the AI methods to make it a good fit. We solve the problem in real practice and we can make innovation in the in the fundamental AI research. So these are all the basic ideas or high level ideas to motivate the research topics I'm working on. So I will give a quick review of let's say 4 topics or four papers that I've worked on before.

The first group is on the special temporal model and prediction.

So special temporal modeling essentially, if there's a special correlation every single time step, For example, if we look at the single source of leakage of toxic gas in the city space just described by every single screenshot, it can be a special distribution over the target city space. If we consider a sequence of this propagation process, there will be some temporal correlations as well. In this case, we try to establish a machine learning model.

Accurately capture this special temporal correlation and make the prediction in the future. In this case, it can inform the decision maker to make a good policy on execution plan and to give a good reference on which part of the city might be impacted by this toxic gas. This also applies to the application in the material degradation. Material degradation prediction. If we have a material with some initial contamination inside, where we apply a external force to destroy.

How this crack will be propagated over the internally and how the stress will be distributed? In this case, it is also a special temporal prediction problem. And the second part is more on the defect identification and root cause diagnosis. This is lies in the enormous action I mentioned before. It can be based on the image or can be based on the 30 point cloud. Depending on different types of data, we need to have different methods and also we need to consider.

On the edge device of the manufacturing system, the computational power might be limited in this case.

We need to control the computational complexity of the methods which motivate, motivate us to generate some efficient AI methods to solve this problem. So with all this, I think this summarizes the overview of myself of my research. And we're welcome to have new PhD students join the research group to contribute to this type of research. Thank you guys. If you have any questions and give me in the chat box or contact me in the e-mail.

And I will hand the stage.

Back to.

Diego, is Diego over here? Yes.

So I will Diego, are you on? OK, I will go next and then Diego is our last presenter, so.

So I am now coming at you, taking off my PhD director hat and coming at you as just a researcher. So as background, I've been at RPI for almost 10 years. It's a really great place to be a faculty. One reason is I like our.

Good interaction with PhD students and not, you know, just in my lab, but all my colleagues are really fantastic mentors of PhD students. As you can see, my background is in industrial engineering. For my bachelor's, I then went directly. There's been a lot of questions in the chat about can you go directly to our PhD program without a master's? Yes, that is possible. We did admit last year students that had both master's and direct from BSI did that, and then I also got a master's along the way.

That is also some questions in the chat. That is possible at RPI. You can enter the PhD program and get a master's along the way. That requires more coursework, but that is possible. So if you're interested more broadly, I have a pretty active website that has a lot more detail about my research and teaching. So I encourage you to check that out if you're interested. I am recruiting for students on two specific projects. One project.

Is a project funded by the Office of Naval Research. The the Pi is.

Patrick Brewer, Brewer, who is a civil engineer at Notre Dame. And the key idea here for this project is we are interested in basically model quantifying model uncertainty. And so our concept here is that all models are wrong, some are useful. And so a lot of my research is model focused. And here what we're trying to do is humans have to make a lot of decisions when they make engineering models, complex engineering models.

And so a lot of that information is discarded when we quantify uncertainty in our model predictions. And so this captures how do we capture human feedback and how certain they are in some of these model assumptions. And then this just got started, but the project will likely use Bayesian networks to combine all these different probabilities to get a model's representative ability. So I am actively recruiting for this project.

And So what kind of background do I want is someone that's interested in kind of how do we capture human behavior in a very statistical way? And then how do we model that in complex engineering systems modeling problems? So that's one project I'm interested in and recruiting for. And another project I'm recruiting PhD students for is a project that's ongoing, and it's actually a project called SWAP. This is a project funded by the National Science Foundation.

And it's a collaboration between faculty at RPI as well as faculty at WPI, Worcester Polytech Institute. And the key idea here is we are having nonprofits exchange resources among themselves. And so the background is a very industrial and systems engineering tool, which is combinatorial optimization. So we have an integer program behind the scenes. But also, to be honest, the math is the hard part or is the easy part of this problem.

The humans and using the system is the hard part. So why I say that is we have social scientists that are working on this project as well. Sarah Stanley and Eunice Telai are professors in social science. And so if you're interested in this project, what I'm looking for is someone to really get involved in a very multidisciplinary problem. We are actually utilizing this system with nonprofits. So we need to do the math, but we also need to communicate math to non math.

People and so that's kind of the the system I'm looking for for for this project. I do research on lots of other things. If you're interested, please reach check out my website and I am recruiting students for the cycle. So with that, I will turn it over to my colleague Diego. So thank you.

Yes.

Thank. Thank you. So that is our research backgrounds. There's been a lot of questions in the chat, which is fantastic. A lot of questions around, you know, is this a central decision or a decision? So we as the committee, who are all the people that are on this webinar is the committee. And so you all need to apply centrally. However, it makes sense that you should have a good fit with the faculty member. So if you're interested in any of the stuff that.

Faculty have presented. I strongly encourage you to write that in your statement of purpose. I'm specifically interested to work with Professor Moran because I'm interested in his integer programming work. And when you apply, there is a box you can click that says you know, what are you interested in. And those fields kind of map to all of our different fields. So I strongly encourage you to have a good fit with research in our department, but you don't have to directly.

Correspond with the faculty ahead of time. If you want to, you can, but that I will say my thing is please apply. I will evaluate people after they've applied, you know, So hopefully that answers some, some of your, your questions. What I have now for the last kind of remaining is to talk more broadly about the PhD program at RPI.

And so one thing to note is when you apply, you need to apply if you're interested in the PhD in our department.

To decision sciences and engineering systems. This is our PhD degree. OK, so when you apply, please apply to decision sciences and engineering systems. And what does our, our field do? You know, we create and apply new mathematical, computational, statistical approaches to solve novel cutting edges problems in ISE. As you just saw, the breadth of our research is there. There's a lot of breath, but we're all, you know, creating and applying new mathematical, computational and statistical.

To different, you know, cutting edge problems. In terms of our graduates, we are successful in placing them in kind of their chosen career paths in academia, government labs or industry. And I want to emphasize their chosen career paths. Students come in wanting to do different things. Some students want to go into academia, some want to go into industry, some want to go into government labs. We try really hard to make sure we support them in those endeavors.

It is helpful if you know the answer to this question.

To put this on your statement of purpose, I am very interested in getting a pH D because I want to do this.

You might not know, you might not know for sure. That's OK. Also, I'm interested at this time to say I'm interested in these different things. We have experience and evidence that we have students in all of these times. We also have a very good track record of graduating students. So the typical completion for a PhD is four to five years. And you know, our department really conducts, you know, high quality, impactful research. But we provide critical feedback and a supportive environment. One thing I like about.

Is that we do hard stuff, but we help you do hard stuff, OK, So we want you to do impactful things, but we want to support you in those endeavours.

So in terms of requirements, So in terms of our requirements, someone asked about courses. There are course requirements. However, the PhD is a research degree. The key way you get a PhD is you make intellectual contributions to the body of knowledge, right?

And so that is what we're looking for. Can you be a good researcher? Can you contribute intellectual merit to the body of knowledge in industrial and systems engineering? And so the skills to be successful vary and may not be uniformly known to the students. We will try to talk a little bit more about that when we talk about what you should put in your statement of purpose and things like that. One thing that's unique about our program is that students are involved in research from day one. So we are a relatively small.

Which means that we can mentor students to do research right away. We also, you do need to take courses, but it's personalized to meet your research needs. So what this is, is there's a course about research contributions and then you work with your advisor, your research advisor to find courses you need for your research. And we do have a qualifying exam that's at the end of the first year, but it's not a traditional like test exam. It is a research document demonstrating your ability to conduct.

Play.

Independent research and we will talk more about that once you would get admitted and we provide lots of feedback about this. And so you know, one of the things that you would go through, you know, at the end of your first year you would have already done some research and you have a written document demonstrating your ability. This is something you work very directly with your faculty advisor and you determine courses also by your faculty advisor. Then you would do a candidacy which would present.

Your your dissertation research and a doctoral defense. So I just want to emphasize that this is a research degree and that's what I have for our department. Aiden I know is on here from grad admissions. There's been a lot of questions about questions about GRE GRE and you know, is your application in English transcripts and things like that. If you have questions about the actual process, you can e-mail this e-mail address.

In terms of what we expect for the application if you scan this QR code.

This will take you to how you apply. So I know there's a lot of them. Yes, you will be waived. An application fee will be waived for attending today's event. What this means is if you registered with your e-mail for today's event, that needs to be the same e-mail you apply and you can use it for one application so that you will be waived if you use the same e-mail address that you registered for this webinar and your application. What you need is you need ACV or a resume. You need a personal.

And two letters of recommendation, test scores, English proficiency scores if applicable. The GRE is not required, but I would say it's strongly encouraged. So you do not need to take the GRE, but we strongly encourage it. And you need transcripts, they can be unofficial at application review. They need to be submitted if you are accepted the official documents.

Again, this QR code gives you access to this information which is on online.

In terms of there's been questions about how do you pay for Graduate School? So one advantage of our program, which is smaller is that we, any students we admit, we provide full funding for. So we only admit students we are able to fund. What does that mean? You will be a combination of a teaching assistant and a research assistant as a PhD student. So this would usually you are a teaching assistant, which means you're helping with office hours and grading in classes and research assistance.

You're contributing to one of the research projects of a faculty member. This is why it matters what faculty members you work with. You should be interested in their research topic for this this funding. But we only admit students that we can be fully funded if you come with a fellowship or something like that. That's the other way that students are funded in our program. What does this mean? This means your tuition is paid for and you get a small stipend. Information about this is on this QR code. If you go there, there's more detail about.

That there are some student fees and things like that that you need to pay for, but tuition is covered and you do get a small stipend. More information is on this QR code. Again, if you have questions about the like the application process, the nitty gritty, you are welcome to send an e-mail here. You also can schedule an admission meeting. So these are people, folks outside of the IC department, but they're specifically if you have questions about grad admissions. So here is another QR code that you can.

Schedule an admissions meeting if you have specific questions that we didn't get answered in our chat. OK, I know we don't have a crazy event, but we do have some time. And so I'm going to turn it over. James, do you want to talk kind of about your application process and what we're really looking for in this application process? And others can chime in as well, but James, if you want to start.

James, you're muted.

Bye.

By the way, is this being recorded for everyone?

Yes.

Perfect, perfect. I see that question a few times. I didn't want to answer it because I wasn't sure. We will post the recording on our department website, which someone will share in the link in some bit. But for the rest of the time, we want to focus on both addressing questions and also helping to make sure that you're able to design your application process. The way that's sort of consistent with what we're looking for in general is faculty. And part of this deals with things like we want to understand why you want to pursue PhD. I'll ask some of my colleagues to share that for a bit, but we'll also talk a little bit about.

Looking in terms of backgrounds, personal statements, recommendation letters, and perhaps most importantly, interest in technical ability, but would anyone like to comment on why they pursued a PhD and what they look for in their application process?

OK, I guess I can go first. I mean, it definitely depends on the individual, so you don't need to have the same answer, right? But for me?

I always enjoyed a lot problem solving, finding my own problems and like doing this deep work to find like how to like develop different solution methodologies. And I started PhD immediately after undergrad. So like some people do masters, they have more experience, they know what they are going to do.

More for me, I knew I wanted to work on like really like network related problems and I knew I really enjoy research. So that was my main motivation to pursue PhD and I was very excited and determined for a like academic career.

Anyone else interesting going next?

I think I can continue. So again, why I want to do a PHDI think I I know this after I get some research experience during during my my master. So I started from the master degree in the States and I work in the lab for like 10 months to finish one project prior to PhD and figured out OK, this is something I want to continue to work on besides instead of directly going to the industry.

After the master program and after entering the PhD program and I gradually figured out my my interest is more in academia and I want to be a faculty member and this further solidify my interest in the PhD program. So I think there is a gradual process to figure out the personal interest. You can dispose yourself with some research, with research experience and see whether it is good fit for yourself whether you enjoy the life like this. Yes, this is my answer.

I.

To build on that a little bit, I actually decided I want to be a professor. I think my sophomore year of college. I got involved in some research project related operations research and I really enjoyed the independent research element along with just the, the, the ability to creatively pursue a, a project sort of of my choosing. And I will say that we look for all of those details in your application package. I mean, you obviously should have some passion for something. For me, I originally had a major passion for graph theory. I didn't end up.

Graph theory, but it sort of got me more involved with different areas. But having some sort of area your interest and excited about to do independent research on is important and that should show up in your personal statement. But on top of that, we really look for the capacity, the ability to do research, independent research. So sometimes I see a few questions about masters or masters. To be honest, the best part about masters is if you do that master's thesis because you have a research experience.

And we do have plenty of people that they get pH DS without masters. They have often will have other research experiences undergrads as as.

Workers in private industry and if you have a speak to that in your personal statement, it's great. I would highly encourage you to try to find a reference writer that can speak strongly about your ability to research. Hopefully you have someone having one strong reference writer that's able to talk to your ability to independent work can be really sort of sort of helpful. But in general in that personal statement, we are looking for a mix of.

You know, you're sort of passionate about at some level and you have the ability to actually contribute to that field so.

Does anyone else have more data?

I, I mean, I will just say, you know, so why pursue APHDI? Think a key thing is this is a research degree and I want to just emphasize that, right. So if you, why preserve PhD as you want to do research in some capacity, why did I pursue APHD? Because I wanted to be a faculty member and that, you know, you required a PhD to be a faculty member. But I also really liked, like Bahar mentioned, like I like hard problems.

Like, I like really getting into things. I I like, you know, that kind of aspect. And I will say one of the things we're looking for, like we don't know the answer to it. If we knew the answer to it, it wouldn't be research. So there's no answer at the back of the book, right? So things that I think we're really looking for in applications and why you pursued PhD is you like this uncertainty, you like not knowing what the answer is and, and you are going to figure this out. I mean, I think that's a good reason to pursue a PhD. And I want to emphasize, you know, you can get a PhD.

And go get amazing jobs in industry, national labs or academia. But these jobs are looking for different things than if you didn't have a PhD. They want you to be able to solve problems that haven't even been defined yet. It's less go do these things and what should the problem be that we're studying, right? It's a much more open-ended. And those are the types of skills people want in these jobs that aren't even just in academia and so.

Tying that to in your application material, I think you should have a reason to do a PhD Honestly, APHD is hard. There are times when it's hard. If you don't have a reason why, that is usually not a great application. So as much as possible, you should have a reason why you're doing a PhD. And in your personal statement you should describe that. And I would emphasize this is less about when I was a child, I learned all these things. I wanted this like what about it now?

Undergrad or your masters or in your job where you said you wanted to do something that was more research focused, that should be what you're describing in your personal statements. I don't know, I'll stop there. And James, do you want to keep the conversation going?

Diego, did you have anything to add in?

So I think with the rest of time we'll focus on questions. So if you want to pop in, we'll try to address the ones we haven't. The first thing I want to really mention is quite a few have asked about background, your undergraduate degree. I will comment again, we're looking for technical ability. So almost in some sense taking some high level math courses is almost just as important as what degree you have as an undergrad.

In terms of common backgrounds, mechanical engineering is actually very common background for a lot of IE faculty.

As is math. I know a few people in physics, but there there's fewer that like transition. I think you have the skill set, it's just, you know, we can't always convince physics students to join us, but.

Yeah, I will. Computer science and electrical engineering as well. So I guess it is up to you to determine because in different areas may have different like requirements in different like departments like countries, right? If you look at our papers, then it's going to give you a signal like is this something I can learn, pick up quicker? We are not necessarily looking for people who have the exact skill set, right? Because you will be taking a lot of.

And you will be developing that. It's more about.

Are you going to succeed? We will be able to learn those skills in a reasonable time with reasonable effort. So that's I think one thing important like you should be able to signal in your application that you did well in the past and you are going to do well in the future.

James.

There's also been quite a few comments on GRE and GPA specific questions. I will commently evaluate things very holistically. The GRE is great for establishing some like lower level of background. So we do encourage you to take it, but it's not required. Similarly, we don't have a set background for GPA. We'll evaluate your package. Obviously more GPA is, you know, more good.

I'll tell you that I had a 383 GPA undergrad and I got into Georgia Tech based almost solely off research experience.

So we do evaluate everything in your package.

If you are excited about projects, if you have a good technical background, we might not pay as much attention to GPA in that case, if it's lower, you know. So don't let that hesitate cause hesitancy in sending in your applications.

Mm-hmm.

Does anyone see other major questions we've missed? Is there anything else in the chat?

I guess someone go ahead.

Uh, go ahead, Go ahead, Doctor Schelder.

No, some of them are about like Jerry having masters so people are not required. You can still apply one thing maybe again like iterating what James Richfield it is about providing a good holistic overview of right you are preparing your application package. We do not know your if you prepared innovative.

We learn you have like what you are good at and that's going to help. So basically like provide all the related information and present it in a beta like we have a good understanding of your background.

And we also use this when we are like, I think one of the questions was do are you assigned to an advisor? So that is not how it works. So we try to make sure that we find a good fit between the advisor and the student. So it is important for this reason you express interest who you are in your application package, who you are interested in working with. Although it is a committed decision like that helps us like to pay closer attention and evaluate accordingly.

To build on that a little bit more, in your application you'll be able to select like faculty that you're interested in working with that isn't binding or anything. For instance, one of my students, I think only selected doctor presor on his thing and then we met during the application process and made it off and been doing pretty well. But during the interview process, you will be speaking with us individually. And if you get admitted, most likely you've already had very detailed conversations about what you're going to do. Doesn't necessarily mean you're, you're, you know, stuck with that person, but we like to try to find.

Early on so you know exactly what you're getting into and you're hopefully excited to get started in research on day one.

Uh, you?

Jen, you seem like you had something you were going to say as well.

Mm-hmm. Yeah.

In Oh good.

And I wanted to work, yeah, I wanted something we we keep mentioning fit because we really take it very seriously. We spend a lot of time evaluate the applications, we interview with the applicants to make sure that it is going to be a very good fit both for the students and us. And we work closely with our students. It's a huge investment for both sides, like a four to five year, very close advising from our side and for students taking several courses, working on something we want to.

Sure that everyone is happy, everyone achieves their goal and their successful. So we really support our students very closely. So This is why it is very important for us to make sure that we will once we admit you and if you come here you will be successful, happy and when you are graduating you will be, you will have all the opportunities that you can get.

Yeah, One more thing I know, here's the couple of questions mentioning about the, the amount of support or Stephen every single month, whether he's good enough or to support your life and local area. So I would say our PhD Stephen is enough for the students to to live in the Troy space and we I think Troy is a good place to live with affordable living expense and also a good distance to go to the big cities.

Like New York City, Boston and Montreal.

So if I were Steven, I don't have a specific number but I remember it stays around like 2000 after text at least. So please correct me if I'm wrong. So so it definitely a good a good amount of money to to live in local area.

And I will also say that it is competitive with every university in the country. Unfortunately, graduate students aren't paid for it, but yeah.

Yeah.

So thank you all for you. I don't know if you can see this, we are one of 275 of you have attended, which is really, really fantastic. We really, really hope you apply. So as a reminder, our PhD is in decision sciences and engineering systems. The deadline is January 1st. We do encourage, you know, earlier application, but if you get it by January 1st, you will have full consideration.

And really?

Please apply. We will post this recording on our website and please apply to our PhD program.

One thing, we had a link for them to submit their questions later.

Right, OK.

Right.

I don't have that quick available but yes we did but I don't have it quickly available.

I think we can probably send out a link for other questions based off the registration, right?

Yup, Yup.

Joke.

So we'll also send you a follow up link to the e-mail you registered with, including a form to ask additional questions. We'll try to address you all, of course, welcome to reach out to faculty individually. I encourage you to use those mainly for targeted specific questions about research. For general questions, the grad admissions process hands a lot of things. For instance, the waiver question, I think they're automatically applied. That showed up a few times.

But yeah, thank you everyone for for joining us so.

Thank you.

Thank you.

Thank you.

Thank you. Bye.

See all.

OK. Thank you.

And the recording will also be posted on the department website in case you want to watch it later on. Let me go and resend that link to the people still here.

Currently, our last year's webinar is up, umm, but you'll also be able to view the new one once, uh, it's compressed and we posted. So, uh, thanks a lot to everyone. I hope to see your applications. I hope to talk to you all individually later on through the interview process. But umm, it was a pleasure chatting with all of you.