Undergraduate major: Bachelor’s in Electronics and Telecommunication

Undergraduate Institution: Sardar Patel Institute of Technology

First master’s degree: Master’s of Computer Science, Indian Institute of Technology Gandhinagar

Dhananjay is an Natural Language Processing MS student with a professional background in machine learning. Alongside being a student, Dhananjay is building an Alexa TaskBot and serves as a teaching assistant at UC Santa Cruz. Keep reading to learn more about Dhananjay’s diverse set of skills and future professional aspirations.

What led you to your interest in NLP? 

I worked at Compass as a machine learning engineer. I got a chance to work on NLP problems as part of the search team, helping real estate agents improve their searches. I realized I wanted to take a step further and do a deep dive into NLP, and that’s why I chose to do the NLP MS program.

What excites you about NLP development? 

The technology trends right now such as ChatGPT and transformers are exciting. I want to study it more and solve problems using them. It’s evolving technology, and we get new research every day with a lot of potential. I believe that it will be a crucial part of artificial intelligence in the future. Amazon Alexa or Siri are real time applications, and I can see how huge of an impact NLP can make in people’s lives.

For example, I am part of the Amazon Alexa TaskBot Challenge. I’m building the Alexa TaskBot that can solve DIY or recipe tasks for human users. This is a narrow scope, and I can see that it has a lot more potential to solve really complex problems.

Can you tell me more about the Amazon Alexa Taskbot challenge?

The Taskbot challenge is organized by Amazon each year where they select 10 teams across the globe. I’m really proud that we were 1 out of 10 selected. Our team is working with Professor Xin (Eric) Wang and a PhD student from Professor Wang’s research group. We gave a proposal to Amazon, and Amazon reviewed and selected teams based on the proposed solutions to the two problems: helping users with DIY tasks and recipes. Then we had a bootcamp in Seattle where we got training, guidelines, and tools to solve this problem.

We have ongoing work and meetings at the main campus, and I commute there weekly with the Silicon Valley Connector shuttle. We also have meetings with the Amazon Alexa team where they give us feedback on how our bot is performing, what improvements to make, as well as future steps to consider to excel in this competition.

What do you think about the program so far?

The program so far has been really good. I have completed six courses now. All of the six courses were directly related to NLP and ML, and I really like this program. We started with the basic foundations of ML and NLP, and we moved onto Transformers and Deep learning methodologies. I like the way the program has been structured.

We also have the NLP 280 Seminars. I really appreciate that these are included in this program. It gives us professional exposure to how NLP is actually happening within industry right now. A specific talk I really liked is about Grounded LLMs for Conversation Agents. The speaker had worked on Siri, Google Assistant, and Alexa. You can imagine how much potential NLP has because the person who has worked on major conversational AI systems is giving the talk.

Can you tell me more about being a TA on main campus? 

This is my third quarter as a teaching assistant for the class CSE 114 Programming Languages. This class is about functional programming using the Haskell programming language. A fun fact is that I had not learned this language before I got introduced to it for this TAship. It was like an additional course on top of NLP. It was not too difficult to learn because I had previously worked with multiple programming languages.

A TAship gives additional experience alongside your master’s degree. You work with the professor, and you help set up the assignments and support the students in the course. In my TA sections, I go over the concepts taught in a week, and I also go over upcoming assignments. I also hold office hours where students drop in to ask questions about assignments or difficult concepts.

What do you plan to do when you graduate?

My goal is to stay in the industry. My motivation for joining this program was to learn more about NLP and apply it to the industry. I want to get some hands-on exposure with NLP and use the tools or theories that I’m learning to practically solve problems. My long-term goal is to become an architect in NLP. I want to design an end-to-end architecture, taking a problem from the basics (for example, data analysis) through to production.

Undergraduate major: BTech Electrical, Electronics, and Communication Engineering

Undergraduate Institution: Jaypee Institute of Information Technology

Hometown: Kanpur, India

Passionate about NLP research, Shashwat aims to solve problems within NLP, as well as in society, using NLP technology and contribute to the rapid growth of the field. Shashwat also brings to the program interdisciplinary interests in linguistics and ethics in relation to AI/NLP. Read on to find out more about this member of the 2022-2023 cohort.

What led you to your interest in NLP?

I was intrigued by data science in my first year of undergrad. Once I started progressing through the field from data science to machine learning to deep learning, I realized that there is a huge scope in this field.

What excites you about NLP Development?

The most interesting thing about NLP is the current market and new innovations that are happening right now. There is no better time to study NLP like right now. There are all the upcoming large language models (LLM) like ChatGPT (we now have GPT-4), D5, and even Facebook is coming into this market as well. There is a huge scope for development and these models are not only used for NLP—they can be deployed in computer vision and signal processing. These are the key areas in NLP right now, and they are pioneering the future of technology.

What do you think about the program so far?

To be honest, I was a little skeptical when I initially joined because NLP is a very specific field when we talk about AI. But as I’m progressing in this course, I feel that the curriculum offered is absolutely amazing. We started with the initial primer in data science, then moved to Pytorch (which is the technology used for deep learning development), and now we are moving onto transformers (the architecture that GPTs are based on). Progressing onto the Capstone project, I feel it is very important to apply what we are learning on business-use cases. The fact that this course is being offered at very limited universities right now makes it amazing.

What classes are you taking right now? 

I am currently taking NLP 244 Advanced Machine Learning for Natural Language Processing where we learn about the latest ventures in NLP and how research has taken place. The fact that developments that we are studying right now did not even exist 5 years ago makes it really exceptional. I am also taking NLP 267 Machine Translation which I feel is a core subject of NLP, and it’s great how we are learning so much about it. NLP 202 is more on linguistics and how language processing works.

Can you expand more on how linguistics plays into NLP?

Most of the time, when we talk about AI and deep learning we only talk about models and technical stuff like Pytorch. To excel in NLP, it is important to get into linguistics, such as understanding Chomsky’s rules on language. These are essential when we are traversing through this field.

What would you like to do after you complete your NLP degree? 

Speaking specifically as an engineer, my goals have always been to get into research and work on cutting edge technology, solving pressing problems in NLP as well as in society. There is a huge debate going on about ethics and morality, and I feel this is where I can contribute as an NLP engineer.

Can you elaborate more on the ethics and morality in NLP?

We are going through an age of generative AI in the form of text, image, audio. Current models often hallucinate and give random pieces of information. In this huge pool of data, we have to point out and create a disparity between real and unreal information. On the data side of deep learning, we have to ask how we are getting the data and ensure that the data we are collecting is unbiased. It is important to focus on these issues as an engineer and draw a clear line of ethics and morality to understand whether or not what we are doing is right or wrong.

Aside from NLP, what are your other interests and hobbies? 

After I’m done with my academics, I try to work out. I am really into music. Right now I can only listen to it, but usually I would play piano or unwind through composing pieces. Other than that, I am also interested in Formula 1 and football (soccer).

Undergraduate major: BE Computer Science

Undergraduate Institution: BITS-Pilani

Hometown: Noeda, Inda

After 4 years of working in industry, Sugam comes to the NLP MS program with a drive to learn about NLP theory and how to build NLP models. As a Student Ambassador, he brings people together, socially and academically, and hopes to eventually apply his NLP skills to building user-centered applications in languages other than English.

What led you to your interest in NLP? 

I did my internship in my last year of undergrad at Samsung R&D, Bangalore. My intern project was working on a machine learning-based feature for mobile phones. After that internship, I really understood that the industry is slowly moving towards machine learning, and that I needed a theoretical background as well.

After I joined my full time role at Samsung R&D, my primary job was to put a language model on a mobile phone. Then I got introduced to different text-classification paradigms, and then transformers. I eventually moved onto another project on semantic search at a different company. That was 2020. And then NLP was taking off: GPT-3 was released and the buzz was high. But these large NLP models were still not in the hands of actual users.

I kept thinking about one thing: I know how to use a lot of models, I know how to read papers, but I don’t know what it takes to build them. I didn’t really understand why things are the way they are, and I realized that I needed to go back to academia to study from experts to understand the natural progression of NLP. The NLP MS program structures it for you, provides you with guidance, and answers so many questions for you which will otherwise take days to solve.

What do you think about the program so far?

Even though I had four years experience using, developing, and studying NLP applications, the NLP 201 series kind of took me by surprise by how little I knew about NLP prior to the deep learning era. There were a lot of things which Professor Jeff Flanigan explained that I still understand because of the manner in which he explains things. I don’t think I will forget the basic concepts that he taught. I really appreciate the way he explains nuances in NLP and how they fit into today’s GPT-era models.

A second experience is from NLP 244. I follow NLP stuff on Twitter, and a paper came out in January. During our next class, Professor Delip Rao was talking about the paper that was released just two weeks ago. He explained the paper and put it into a framework and the entire course itself. I don’t know if there’s any program in the world that has a two-week turnaround time.

Can you tell me about your Student Ambassador experience?

I wanted not just academic excellence, but I also wanted to contribute and find a sense of belonging and create a community. I joined the Student Ambassador Program, and I joined the Community Organizer team. We organized hang-outs like movie nights and dinners, and I made friends which I hope will stay beyond the program. I got to know people outside of academic interactions. It is really fun, and I hope to continue doing that.

What about the NLP paper reading group?

This is pretty close to my heart. Back when I was working at Samsung, there was probably not a single university that was offering a dedicated single course for NLP let alone an entire program. It was difficult to understand the developments in NLP. I decided to host a monthly paper session which became a community-oriented thing. We had volunteers every month speak about the things they were working on. This was another way of team bonding. I started a knowledge-sharing group immediately after joining my next company as well.

When I came to the NLP program, I noticed that there’s a new paper every two weeks. The NLP MS curriculum teaches the conceptual arguments made in papers, but in order to learn about the nuances in execution, one has to read the paper itself. That is where the success lies. I approached the Ambassador team with the idea of a paper reading group, and everyone was interested. It has been great, and I hope the program continues to do it.

What do you plan to do when you graduate?

NLP technology is not in the hands of users. It’s benefiting people who were already part of the AI world or the computer world, but it doesn’t reach people beyond the existing digital population. I want to work with companies that are trying to get more people using AI products, so that actual people can benefit.

Are there technical or societal problems that you hope to solve in the future?

The first problem I want to solve is the language problem. I come from a land of 100+ languages. I am not familiar with any AI model that is working on a large scale with regional languages in India. My mother uses her phone, but she doesn’t use any utility or productivity apps because she is not really familiar with English. My goal is to help build apps which can bring people like my mother to use AI products because they offer tremendous value. It’s my dream to move towards an inclusive language ecosystem.

The second thing that is close to my heart is educating more and more people. I love teaching. Even here at UCSC, I’m a tutor for a web application course, and yesterday I received an email from a student saying that the suggestions I gave them helped them finish their assignment on time. It’s the most wonderful feeling. I could honestly say that it’s more important than anything else to me, to see people achieve the best they can.

In India, I see a gap in the quality of education that I received versus the quality of education that most people are going to get. It will be like that for at least the next 10 to 20 years, unless people change something about it. I’m seeing AI models starting to solve math and physics problems, and I just hope I can work on an idea to use those AI capabilities to scale up the quality of education.

Undergraduate major: BE Computer Engineering

Undergraduate Institution: Pune Institute of Computer Technology

Hometown: Mumbai, India

Driven by his interest in NLP, Mridul joined the NLP program after working in the gaming industry for 4 years. He hopes to eventually apply the skills gained in the program to an engineering role in NLP or machine learning. He currently serves as a program Student Ambassador on the social media team.

What led you to your interest in NLP? 

After my Bachelors in 2018, I worked with Ubisoft Entertainment. My responsibilities included developing automation tools using Reinforcement learning, computer vision, and natural language processing. I did a project that was related to analyzing sentiments of players on various communities and discussion forums for newly launched games. The number of players in gaming has been on an exponential rise, and analyzing that data has been critical for gaming companies. That project sparked my interest in NLP, and I decided to pursue it for my master’s.

What excites you about NLP development? 

Gaming definitely interests me. I am also interested in the field of finance and how NLP is being used in that industry. These are the directions that I would like to take in my career path.

What do you think about the program so far?

It’s been a great learning curve for me. When I came here, I had very little knowledge of NLP, and it was high-level. But after going through the program, I now have a theoretical and mathematical understanding of concepts. As a researcher or working in industry, having that theoretical knowledge really differentiates you. One course in particular–Deep Learning for NLP that I took in Fall–gave a tremendous boost to my learning curve, and that experience was awesome.

Can you tell me about your Student Ambassador experience?

My responsibility as a Student Ambassador is to develop content and promote our NLP program on various social media platforms. I also post about uses of NLP and how it is being applied to industries. I look at the first post I made versus the posts I make now, and I can see growth in the content I am making. There is a huge difference in the way things are framed on various platforms, and every social media platform requires different content.

I currently focus on Reddit and Twitter, simultaneously sometimes creating posts on LinkedIn and Facebook as well. The audience is different. For example, on Reddit, I focus on the NLP seminar, and I go to subreddit groups to talk about technical content like machine translation and transformers. On Twitter, I focus more on events at Silicon Valley Campus like internship fairs or gaming events. Being a Student Ambassador helps me build on my communication skills and grow as a content creator. This experience teaches you to market yourself to the outside world and professionals.

What do you plan to do when you graduate?

I am open to engineering and research roles, but I am more focused on engineering roles in the NLP field around NLP and machine learning. I’m also open to roles in reinforcement learning or computer vision as a data science or machine learning engineer.

Are there technical or societal problems that you hope to solve in the future?

Data in the gaming community has been on an exponential rise. It is important to shape this data so that it can be analyzed by gaming companies for their benefit. I look forward to using the knowledge I have gained in the NLP program and applying it to analyze games.