undergraduate research natural language processing

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Natural Language Processing

We rely on machines to understand human language and anticipate our instructions. Research in natural language processing seeks to build computers and autonomous systems that can understand and use human knowledge, primarily language and text. The goal of this research is to build intelligent systems that learn and communicate through language. Work in this area pushes the boundaries of artificial intelligence while also enabling advances in practical text processing applications that can have a broad impact on various real-world problems.

At Princeton, researchers develop novel algorithms, design new frameworks, and investigate theoretical foundations to tackle challenging problems in language understanding. Researchers draw on techniques like deep neural networks and reinforcement learning.

Associated Faculty

• Sanjeev Arora
• Adji Bousso Dieng
• Peter Henderson
• Karthik Narasimhan

Associated Graduate Students

• Adithya Bhaskar
• Howard Chen
• Ameet Deshpande
• Dan Friedman
• Vishvak Saivenkat Murahari
• Alexander Wettig
• Mengzhou Xia
• Zexuan Zhong

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Natural Language Processing

Our faculty are creating innovations that will enable more natural interaction between human and computers.

Researchers in the Natural Language Processing group work at the intersection of computer science, artificial intelligence, and computational linguistics. Projects in this area aim to understand how human language is used to communicate ideas and to develop technology for machine analysis, translation, and transformation of multilingual speech and text.

Computer scientists in this group work closely with researchers in other departments via the Whiting School’s Center for Language and Speech Processing .

Research Centers and Labs

Center for language and speech processing.

The CLSP conducts research across a broad spectrum of fundamental and applied topics, including acoustic processing, automatic speech recognition, big data, cognitive modeling, computational linguistics, information extraction, machine learning, machine translation, and text analysis.

Human Language Technology Center of Excellence

The HLTCOE focuses on advanced technology for automatically analyzing a wide range of speech, text, and document data in multiple languages.

Linguistics at JHU

Within the Department of Cognitive Science, linguistics research focuses on integrating formal linguistics within a broader cognitive science perspective by addressing questions about the nature of linguistic representations themselves, their processing, the architecture and learnability of the grammar, the implementation of linguistic theories in terms of neural computations, and language acquisition in the broader context of cognitive development.

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Daniel Khashabi

Philipp Koehn

Tom Lippincott

Benjamin Van Durme

David Yarowsky

Alan Yuille

Multi-language mastery: minimized hardware, maximized efficiency

Johns Hopkins computer scientists introduce a new method to reduce the size of multilingual language models.

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Natural Language Processing

Natural language processing helps computers comprehend, decipher, and manipulate text and spoken words—bridging the gap between human language and machine communication.

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PhD Students

• M. Omer Gul
• 9 receive 2023 Bowers CIS-LinkedIn grants
• Cornell natural language processing scholars win Best Paper at top conference
• Inaugural Grants Announced from Strategic Partnership with LinkedIn
• Joachims, Myers receive prestigious named professorships
• Trummer wins runner-up award at SIGMOD

Please visit the Cornell NLP Group website for more information.

Natural Language Processing

Our students and faculty are widely recognized as being among the best in the field, winning prestigious research fellowships and paper awards at all of the top venues. Moreover, our work has been featured in numerous local and national media outlets, including NPR , The NY Times , The Atlantic , and NewsWeek . Learn more about who we are and what we do in this exciting research environment!

• Hannaneh Hajishirzi named 2020 Sloan Research Fellow , Allen School News, 2/12/20
• Yejin Choi recognized with Borg Early Career Award , Allen School News, 6/15/18
• Secrets of the $500K Amazon Alexa Prize winner: Inside the Univ. of Washington's 'socialbot' , GeekWire, 4/26/18
• Terra Blevins wins NSF Graduate Research Fellowship , Allen School News, 4/9/18
• Hannaneh Hajishirzi, Yoav Artzi earn Google Faculty Research Awards , Allen School News, 3/26/18
• Inside the Alexa Prize , WIRED, 2/27/18
• Eunsol Choi named 2018 Facebook Fellow , Allen School News, 1/18/18
• UW students teach Alexa to have a little chat with us , Seattle Times, 11/28/17
• New tool quantifies power imbalance between male and female characters in Hollywood movie scripts , UW News, 11/13/17
• UW CSE researchers win Best Paper Award at EMNLP 2016 , Allen School News, 10/14/16
• Luke Zettlemoyer wins Presidential Early Career Award for Scientists and Engineers , UW News, 2/18/16
• AI's 10 to Watch: Yejin Choi , IEEE Intelligent Systems, 1/22/16

Natural Language Processing and Information Retrieval

Wed 01.20.16

information retrieval, information retrieval models, information extraction, sentiment analysis, social media analysis, political text analysis, crowd-sourcing, conversational agents, syntactic and semantic parsing language, machine translation

Natural language processing (NLP) has a dynamic effect on the way humans and computers interact. At Khoury College of Computer Sciences, we build innovative semantic systems to tackle ever-increasing volumes of written and spoken language. Working from large, real-world datasets that include billions of webpages, social media posts, and digitized historical books and newspapers, we develop tools that transcribe and transform terabytes of written and spoken text into structured databases that can be searched and utilized by computers in new ways.

Areas of investigation

• Summarization
• Information retrieval
• Machine translation
• Social dynamics of language
• Inferring latent social networks
• Brain signal transcription
• Relational Agents Group
• Northeastern Natural Language Processing Research Group

At Khoury College, NLP researchers collaborate closely with experts in network science, signal processing, computational social science, and digital humanities. We strive to build more effective search engines, understand human societies, and facilitate communication among people and with increasingly intelligent machines. As a doctoral or graduate student, you’ll engage in real-world research efforts that encompass machine learning, data mining, and information retrieval in pursuit of these goals.

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Sy and Laurie Sternberg Interdisciplinary Associate Professor, Director - BS in Data Science Program

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Cornell NLP

The Cornell Natural Language Processing Group is a diverse team of researchers interested in computational models of human language and machine learning. We apply a computational lens to a broad set of problems in the areas of linguistic analysis, natural language understanding systems, social science, and humanities. We are a cross-campus group located both in Ithaca and the Tech campus in NYC . The group includes members from the departments of Computer Science , Information Science , and Linguistics .

A small sampling of problems we are interested in are (in alphabetical order): argumentation mining, grounded language learning, information and opinion extraction, lexicon and grammar induction, paraphrase acquisition, situated language understanding, syntactic parsing, question-answering, semantic parsing, sentiment analysis, similarity-based methods, and text summarization.

Related Links

• Computational Linguistics Lab and Computational Psycholinguistics Discussions group
• Machine Learning
• Artificial Intelligence
• Computer Science Department
• Information Science department
• Linguistics Department
• Psychology Department
• Cognitive Science Program
• Learning Machines Seminar

Related Links: Ithaca

Wenting Zhao named one of three AI2 Outstanding Interns of 2023

Claire Cardie elected fellow of the Association for the Advancement of Artificial Intelligence (AAAI)

Sasha Rush and colleagues from Hugging Face receive an outstanding main track runner-up paper award at NeurIPS 2023 [Dec 2023]

Jack Morris, Volodymyr Kuleshov, Vitaly Shmatikov, and Sasha Rush receive an outstanding paper award at EMNLP 2023 [Dec 2023]

GQ UK mentions Cornell memorable-movie-quotes paper [Sep 2023]

CS blog post summarizes research awards at ACL 2023 [Aug 2023]

Alumni Jack Hessel (AI2), Lillian Lee, and collaborators receive a best paper award at ACL 2023 [July 2023]

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Natural Language Processing

Drs. diane litman and rebecca hwa awarded pitt momentum funds scaling grant.

Along with collaborators Kevin Ashley, James Anderson, and Daqing He, Department of Computer Science faculty Diane Litman and Rebecca Hwa were awarded a 2022  Pitt Momentum Funds Scaling Grant for their work on the Center for Text Analytic Methods in Legal Studies .

Dr. Malihe Alikhani awarded best theme paper at ACL 2021

Dr. Malihe Alikhani (Assistant Professor, CS) was awarded the best theme paper at the  Annual Meeting of the Association for Computational Linguistics (ACL 2021) for her paper " Including Signed Languages in Natural Language Processing ." Dr. Alikhani worked with Dr.

Dr. Malihe Alikhani, Chloe Ciora and Nur Iren receive best paper award at INLG 2021

In conjunction with undergraduate students Chloe Ciora and Nur Iren, Dr. Malihe Alikhani (Assistant Professor, CS) was awarded the best paper award at the 14th  International Conference on Natural Language Generation for their paper "Examining Covert Gender Bias: A Case Study in Turkish and English Machine Translation Models." The work, which came out of Dr. Alikhani's AI ethics course from the spring 2021 semester, focuses on gender bias when translating from English to Turkish in different online translators.

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Zhaoyi (joey) hou, sina malakouti, kartik hans, michael miller yoder, lorraine (xiang) li.

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The aim of Natural Language Processing is to develop computational models for analysing and generating human language.  Research in the Department encompasses many areas of NLP, ranging from fundamental theory to real-world applications. 

The models we develop are mainly based on modern machine learning techniques.  On the theoretical side, we seek to understand the structure needed to represent language, how language is learned and processed by people, and how language varies between people and over time.  On the application side, the ALTA institute develops technology to support second language teaching and assessment.  Other researchers work on automated fact checking, dialogue systems, document summarisation and scientific text processing, as well as interdisciplinary work in various domains such as healthcare and cybercrime.  Collaborations with other departments are supported by Cambridge Language Sciences .

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Undergraduate Researchers Help Unlock Lessons of Machine Learning and AI

From large language models to brain-machine interfaces, students work with faculty on cutting-edge research.

UT College of Natural Sciences undergraduates Prasann Singhal, Nihita Sarma, Shankar Padmanabhan and Jennifer Mickel are conducting research into AI and its applications.

Even before The University of Texas at Austin declared 2024 the  Year of AI , artificial intelligence and machine learning had researchers across campus abuzz with activity. Undergraduates under the mentorship of professors in computer science and departments across campus are making their contributions in this fast-growing field. 

Prasann Singhal

Humanizing Responses 

Prasann Singhal, linguistics and computer science junior, works under Greg Durrett, professor of computer science, to research the intersection of machine learning and human feedback. Using data and human preferences, Singhal’s methods seek to tweak machine-learning models to create output more aligned with what a human would want or expect. This alignment attempts to learn appropriate functions in a semi-supervised way without explicit instruction, Singhal said. 

“A question like, ‘What are penguins?’ might produce an output that gives a scientific explanation of how penguins live their lives,” Singhal said. “That gets adjusted through technologies like ChatGPT to generate outputs that are more in line with what humans would expect and make them a little bit easier for the public to use.” 

Shankar Padmanabhan

Knowing More 

Also a student of Durrett’s, mathematics senior Shankar Padmanabhan, researches another side of generative AI: model editing and continual learning. 

“Language models such as ChatGPT are usually trained on a fixed dataset (e.g, the internet before fall 2021),” said Padmanabhan. “As a consequence, they lack knowledge of events happening after this cutoff. The field of continual learning attempts to determine the best way to edit the knowledge base of these models without affecting their knowledge of prior events.” 

Padmanabhan’s first paper discussed exploration of existing techniques, which he found were unable to make common-sense inferences. In a second paper, Padmanabhan found a method to fix discrepancies by placing a statement in context that outperformed prior models, he said. In December, Padmanabhan presented his research at the 2023 Neural Information Processing Systems conference, one of the biggest AI/machine learning conferences. Since then, Padmanabhan has been working on extending what he has learned to apply to information stored in languages other than English.

“In particular, if we inject some information in English into a language model, ideally it should be stored in a language-agnostic manner such that the model can use it even when it isn’t ‘talking’ in English,” Padmabhan explained. 

Nihita Sarma

Brain-Machine Interface 

AI also intersects with language in other research areas. Nihita Sarma, a computer science third-year student and member of Dean’s Scholars and Turing Scholars, researches the intersection of neuroscience and machine learning to understand language in the brain, working with Michael Mauk, professor of neuroscience, and Alexander Huth, an assistant professor of computer science and neuroscience. 

As research subjects listen to podcasts, they lie in an MRI machine and readings track their brain activity. These customized-to-the-subject readings are then used to train machine learning models called encoding models, and Sarma then passes them through decoding models. 

“My research is taking those encodings and trying to backtrack and figure out based on this neural representation — based on the brain activity that was going on at that moment — what could the person inside the MRI machine possibly have been thinking or listening to at that moment?” Sarma said. 

Along with gaining a better understanding of how language is represented in the brain, Sarma said the research has possible applications for a noninvasive communication tactic for people unable to speak or sign. 

“We would be able to decode what they’re thinking or what they’re trying to say, and allow them to communicate with the outside world,” Sarma said. 

Jennifer Mickel

Towards Ethical AI 

Inspired by the Netflix documentary “Coded Bias” about racial biases in facial recognition algorithms, Jennifer Mickel, a computer science fourth-year student, focuses her research on AI fairness. Biases in society are reflected within data, Mickel said, and a great deal of internet data represents a point of view centered on Western viewpoints. 

“We don’t see as much representation from people who are not in the West, and so that means that the ideas of the people whose data we’re using to train our models are replicated within those systems,” Mickel said. 

Improving fairness can involve using data and assessing an AI model’s performance in new and different ways. Much data is demographic-free due or has partial demographic information due to privacy or other barriers; nonetheless, many models perform better when given background information with demographics, Mickel said. That’s why Mickel works to improve models trained with demographic-free data. Using a system of finding intersections in data — and identifying and mitigating the effects of the worst-performing group — proves feasible for improving these models. 

“Practitioners would use our algorithm to find the groups and then they would retrain using that group information to improve the performance for the worst-performing group,” Mickel said. “They would just keep doing that until the worst-performing group was performing as they had hoped.” 

Early Starts 

Besides all studying AI, Singhal, Padmanabhan, Sarma and Mickel have something else in common: their undergraduate research exposure started within their first year and a half at UT. In fact, the University has more first- and second-year college students doing real-world research than any place in the country. 

Singhal said the early start afforded him the opportunity to learn his research interests and lead projects earlier in his research career. He also has formed strong connections with supportive lab members he’s able to ask for advice or learn from. 

“I’m an NLP [natural language processing] researcher,” Singhal said. “I really love most of NLP. So it’s always been very exciting, and it keeps me motivated to keep on doing research.”

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Natural Language Processing

Natural Language Processing

Natural Language Processing at RIT

Cecilia Alm , Professor 585-475-7327, [email protected]

    @nlprit

Natural language processing (NLP) and speech processing at RIT is a research-active area led by Dr. Cecilia Alm’s and Dr. Marcos Zampieri’s laboratories. The groups’ research  projects, supported by external or internal awards, provide graduate and undergraduate students with mentored experiential learning opportunities. 

NLP at RIT's curricular offerings include natural language processing and speech processing coursework for graduate and advanced undergraduate students. We welcome students and collaborators who are curious about natural language, artificial intelligence, and human cognition and who bring a passion for advancing science and technology.

Faculty Researchers

Cecilia Alm

Dr. Alm is PI for the NSF-funded REU Site Computational Sensing for Human-centered AI .

Research Labs and Facilities

The Computational Linguistics and Speech Processing (CLaSP) lab at RIT is dedicated to advancing applied and theoretical research involving text, speech, dialogue, and multimodal data. Research in Alm’s lab includes projects focused on intelligent affective computing, humans’ roles in AI such as in interactive machine learning, language/multimodal sensing and systems that make inference from and support multimodal dialogue interactions.

Visit CLaSP Lab      

Language Technology Group

Members of the Language Technology Group work on various topics in computational linguistics and natural language processing. We are interested in collecting, processing, and analyzing large collections of texts from different domains (e.g. news, social media) written in various languages and dialects.

Current foci and recent projects in the group include language acquisition, readability, and educational applications; language variation and automatic language identification; user interaction in social media; and machine translation.

Latest News

December 6, 2023

Generative AI is changing education

Like many fields, the world of academia is wrestling with the challenges and opportunities presented by generative AI tools. While a few K-12 school districts, international universities, and businesses have attempted to ban the use of AI tools, RIT is acknowledging that it’s here to stay and can be used as a force for good.

October 2, 2023

Fall semester brings plentiful opportunities for international exchanges

As part of RIT’s ongoing commitment to academic and cultural exchanges, several cohorts of international students, including those from Ireland, Germany, and Indonesia, visited campus this fall. The visits were spearheaded, in part, by RIT Global, which has developed partnerships with nearly 100 countries.

December 14, 2022

RIT develops interdisciplinary master’s degree in artificial intelligence

RIT is offering a new master’s degree in artificial intelligence (AI). The program begins in fall 2023 and enrollment is now open. The Master of Science degree aims to prepare students from diverse educational backgrounds to become well-rounded AI professionals.

September 21, 2023

Teams present at Affective Computing and Intelligent Interaction conference

October 28, 2022

Giguere presents at NSF Research Traineeship Annual Meeting

August 4, 2022

Alm serves as tutorial co-chair for computational linguistics conference

March 29, 2022

Alm, Bailey organize workshop on computational sensing

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• Centre for Linguistic Research
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Natural language processing

The natural language processing group's research interests fall into a broad range of linguistic research topics

Natural Language Processing (NLP) is an interdisciplinary field that uses computational methods:

• To investigate the properties of written human language and to model the cognitive mechanisms underlying the understanding and production of written language (scientific focus)
• To develop novel practical applications involving the intelligent processing of written human language by computer (engineering focus).

The Sheffield University NLP research group's interests fall into the broad areas of Information Access, Language Resources and Architectures for NLP, Machine Translation, Detection of Text Reuse and Anomaly, as well as more Foundational Topics such as word sense disambiguation, semantics of time and events.

Research cluster coordinator

Professor rob gaizauskas.

Cluster members 

• Dr. Mark Hepple
• Professor Lucia Specia
• Dr. Mark Stevenson
• Dr. Andreas Vlachos
• Prof. Kalina Bontcheva
• Prof. Hamish Cunningham

Related information

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Research Areas

Natural Language Processing

The goal of natural language processing (NLP) research is to endow computers with the ability to understand and model human language.

We study both fundamental ideas and recent research trends in natural language processing, with a focus on both theoretical and practical aspects of various NLP techniques and applications.

Research in this area includes:

• Deep Learning for NLP
• Language Models
• Structure Prediction
• Knowledge Graphs
• Language Representation and Generation
• Information Extraction
• Language and Vision
• Question Answering
• Security and Privacy in NLP
• Model Robustness
• Model Interpretability
• Fairness in NLP
• Neural Symbolic Models
• Code Understanding and Generation
• NLP for Science, Biomedicine, Math, Economics, Blockchain
• NLP Systems
• Jiaxin Huang
• Chenguang Wang
• WashU NLP Group

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Natural Language Processing

Related faculty.

Gina-Anne Levow

Shane Steinert-Threlkeld

Latest News

• Alumna Lesley Carmichael (September 3, 2021)
• Featured language:  Kham (May 10, 2021)
• UW Linguistics graduate student awarded MSR Graduate Women's Scholarship (February 12, 2015)

Related Research

• C.m. Downey, Shannon Drizin, Levon Haroutunian, and Shivin Thukral. 2022. Multilingual unsupervised sequence segmentation transfers to extremely low-resource languages. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages 5331–5346, Dublin, Ireland. Association for Computational Linguistics.
• C.m. Downey, Fei Xia, Gina-Anne Levow, and Shane Steinert-Threlkeld. 2022. A Masked Segmental Language Model for Unsupervised Natural Language Segmentation. In Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 39–50, Seattle, Washington. Association for Computational Linguistics
• Song, Yan, et al. "Summarizing Medical Conversations via Identifying Important Utterances."  Proceedings of the 28th International Conference on Computational Linguistics . 2020.
• Chen, Guimin, Yuanhe Tian, and Yan Song. "Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional Networks."  Proceedings of the 28th International Conference on Computational Linguistics . 2020.
• Tian, Yuanhe, Yan Song, and Fei Xia. "Joint Chinese Word Segmentation and Part-of-speech Tagging via Multi-channel Attention of Character N-grams."  Proceedings of the 28th International Conference on Computational Linguistics . 2020.
• Nie, Yuyang, et al. "Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information."  Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings . 2020.
• Nie, Yuyang, et al. "Named Entity Recognition for Social Media Texts with Semantic Augmentation."  Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . 2020.
• Tian, Yuanhe, et al. "Improving biomedical named entity recognition with syntactic information."  BMC Bioinformatics  21, 539 (2020)
• Tian, Yuanhe, et al. "Improving Constituency Parsing with Span Attention."  Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings . 2020.
• Tian, Yuanhe, Yan Song, and Fei Xia. "Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks."  Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . 2020.
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Natural language processing, faculty working in this area.

How to Apply

Bsai roadmap: sample course sequence, additional major in artificial intelligence, artificial intelligence minor, undergraduate catalog, artificial intelligence program.

Reid Simmons, Director of the BSAI program (NSH 3213) Kaleigh Mitchell, Program Administrator (GHC 4113) www.cs.cmu.edu/bs-in-artificial-intelligence

Carnegie Mellon University has led the world in artificial intelligence education and innovation since the field was created. It's only natural, then, that the School of Computer Science would offer the nation's first bachelor's degree in Artificial Intelligence, which started in Fall 2018.

The BSAI program gives students the in-depth knowledge needed to transform large amounts of data into actionable decisions. The program and its curriculum focus on how complex inputs — such as vision, language and huge databases — can be used to make decisions or enhance human capabilities. The curriculum includes coursework in computer science, math, statistics, computational modeling, machine learning and symbolic computation. Because Carnegie Mellon is devoted to AI for social good, students will also take courses in ethics and social responsibility, with the option to participate in independent study projects that change the world for the better — in areas like healthcare, transportation and education.

Just as AI unites disciplines from machine learning to natural language processing, instruction in the BSAI program includes faculty members from the school's Computer Science Department, Human-Computer Interaction Institute, Institute for Software Research, Language Technologies Institute, Machine Learning Department and Robotics Institute.

Students in the BSAI program within the School of Computer Science are expected to acquire the following skills upon graduation:

• Understand how to distill a real-world challenge as an artificial intelligence problem, involving explicit representation and learning of symbolic and numeric models; reasoning about such models; and using such models for decision making, action selection, and interaction with humans.
• Design, analyze, implement, and use state-of-the-art AI and machine learning techniques for dealing with real-world data, including data involving vision, language, perception, and uncertainty.
• Master the core concepts of computer science, with emphasis on data structures, programming, computing systems, and algorithm design, performance, and correctness across a variety of metrics (e.g., time, space, parallel vs. sequential implementation, what is computable).
• Master the fundamentals of discrete mathematics, logic, theorem proving and explanation, probability and statistics, and optimization.
• Describe, specify, and develop large-scale, open-ended artificial intelligence systems subject constraints such as performance, available data, and need for transparency. Communicate technical material effectively to technical and non-technical audiences.
• Work productively both individually and in teams.
• Recognize the social impact of artificial intelligence and the underlying responsibility to consider the ethical, privacy, moral, and legal implications of artificial intelligence technologies. 

Students who graduate with a bachelors degree in AI, will have the computer science savvy and skills our students are known for, with the added expertise in machine learning and automated reasoning that you'll need to build the AI of tomorrow.

If you're applying to CMU, you need to be accepted into the School of Computer Science. Once you're at CMU and enrolled in SCS, you can declare a BSAI major in the spring of your first year or transfer into the program in your sophomore or junior year. If you are already at CMU but not in SCS, you can apply to transfer into the program after your sophomore year.  Consult with the director or the program administrator of the BSAI program for information.

BSAI majors will take core courses in math and statistics, computer science, artificial intelligence and ethics, along with general education courses in science and engineering, and humanities and arts. 

Math and Statistics

Computer science, artificial intelligence, school of computer science electives, ethics course, science and engineering.

All candidates for the bachelor's degree in Artificial Intelligence must complete a minimum of 36 units offered by the Mellon College of Science and/or the College of Engineering (CIT). These courses offer students an opportunity to explore scientific and engineering domains that can influence their effectiveness as computer scientists upon graduation.

Requirements for this component of the degree are listed under the SCS main page under  General Education Requirements .

Humanities and Arts

All candidates for the bachelor's degree in Artificial Intelligence must complete a minimum of 63 units offered by the College of Humanities & Social Sciences and/or the College of Fine Arts. These courses offer students breadth in their education and perspectives and provide students with a better appreciation of social, artistic, cultural, political and economic issues that can influence their effectiveness as computer scientists upon graduation.

Requirements for this component of the degree are listed under the SCS main page under  General Education Requirements .  SPECIAL NOTE FOR AI STUDENTS: AI majors must satisfy Category 1 of the General Education requirements by taking one of the following Cognitive Studies (Category 1A) courses:   

• 85-211 Cognitive Psychology
• 85-213 Human Information Processing and Artificial Intelligence
• 85-370 Perception
• 85-408 Visual Cognition
• 85-421 Language and Thought

SCS First year seminar

The following course is designed to acquaint incoming students with computer science at CMU :

Computing @ Carnegie Mellon

The following course is required of all students to familiarize them with the campus computing environment:

Free Electives

A free elective is any Carnegie Mellon course. However, a maximum of nine (9) units of Physical Education and/or Military Science (ROTC) and/or Student-Led (StuCo) courses may be used toward fulfilling graduation requirements.

Summary of Degree Requirements

Undergraduate research thesis.

AI majors may use the SCS Honors Research Thesis as part of their degree. The  SCS Honors Undergraduate Research Thesis ( 07-599 )  typically starts in the fall semester of the senior year, and spans the entire senior year. Students receive a total of 36 units of academic credit for the thesis work, 18 units per semester. Up to 18 units can be counted toward SCS elective requirements (9 per semester for 2 semesters maximum).  Students interested in research may also consider using  07-300 Research and Innovation in Computer Science  in their junior year since this course will introduce students to various research projects going on in the School of Computer Science that may lead to a senior thesis. This course leads to a subsequent practicum that allows students to complete a small-scale research study or experiment and present a research poster. Students who use the practicum to start their senior thesis can use these units toward the required 36 units.

For more information about the SCS Honors Research Thesis, refer to the  SCS Honors Research Thesis  section for learning objectives, application requirements and expected outcomes.

The sample given below is for a student who already has credit for introductory programming and introductory calculus. Students with no credit for introductory programming will take 15-112 in their first semester and shift some CS courses to later semesters after consulting with their academic advisor; students with no credit for calculus will take 21-120 in their first semester and shift 21-122 and 21-259 to subsequent semesters. These students should still be able to complete their degree in four years given the light load of their senior year. Students with credit for 21-120 and 21-122 may start with a more advanced math class (e.g.  21-241 ) in their first year. It is recommended that students keep their academic load lighter for their senior fall semester to account for offsite job interviews or for their senior spring semester to account for visits to graduate schools.

FRESHMAN YEAR:

SOPHOMORE YEAR:

JUNIOR YEAR:

  SENIOR YEAR:

Minimum number of units required for the degree: 360

The flexibility in the curriculum allows many different schedules, of which the above is only one possibility. Some elective courses are offered only once per year (Fall or Spring). AI cluster electives (decision making and robotics, machine learning, perception and language, and human-AI interaction) may be taken in any order and in any semester if prerequisites are met and seats are available. Constrained electives are shown in the specific semesters in the schedule above as an example only. Students should consult with their academic advisor to determine the best elective options depending on course availability, their academic interests and their career goals.   

Students interested in pursuing an additional major in Artificial Intelligence should first consult with the Program Administrator . Students must have all prerequisites completed, 21-112 or 21,120, 15-122, 15-150, one of 15-210, 15-213, or 15-251, as well as 15-281 or 10-315. Students must earn a "B" average in all prerequisite coursework in order to be admitted to the additional major. The additional major requires 6 mathematics courses, 5 computer science courses, 2 artificial intelligence courses, 4 courses from AI cluster areas, 1 course in ethics, and 1 course in human cognition.

Prerequisites

T he following courses are required for the Addition Major in Artificial Intelligence:

Math and Statistics Core 

Computer science core, artificial intelligence core, ai cluster electives, ethics and human cognition.

*Note that Concepts in Artificial Intelligence (07-180) is not required for additional majors, although students interested in the additional major in AI are encouraged to take 07-180 prior to taking 15-281 or 10-315.

Double Counting Restrictions

Students pursuing an additional major in AI can double count at most five courses total , from the Computer Science Core, the Artificial Intelligence Core, and the AI Cluster Electives, towards all other majors and minors they're pursuing. The Mathematics, Ethics, and Human Cognition courses may double count without restriction, except for 36-402 (Advanced Methods for Data Analysis), which is part of the Machine Learning Cluster. Students with majors that overlap substantially with AI should consult with the Program Administrator to review their audit for any potential issues.

Students interested in pursuing a minor in Artificial Intelligence should first consult with the Program Administrator after completion of the prerequisites and 15-281 or 10-301/10-315. Students must earn a "C" average in all prerequisite coursework (including 15-281 or 10-301/10-315) in order to be admitted to the minor. The minor includes 3 required core courses, and 5 courses from AI cluster areas.

The following courses are required for the Minor in Artificial Intelligence: 

Required Core

Technical electives , societal aspects of ai , double counting restriction.

Students pursuing a minor in AI can double count, at most,  two courses  total from the AI course requirements towards all other majors and minors they're pursuing. Students with majors that overlap substantially with AI should consult with the Program Administrator to review their audit for any potential issues.

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Academics   /   Courses   /   Descriptions COMP_SCI 337: Intro to Natural Language Processing

Prerequisites, description.

A semantics-oriented introduction to natural language processing, broadly construed. Representation of meaning and knowledge inference in story understanding, script/frame theory, plans and plan recognition, counter-planning, and thematic structures.

• This course satisfies the AI Breadth & Project Requirement 

REQUIRED TEXTBOOK : None.

REFERENCE TEXTBOOKS:

• ANSI Common Lisp Book

INSTRUCTORS: Prof. Birnbaum COURSE COORDINATOR:  Prof. Larry Birnbaum

DETAILED COURSE TOPICS:

• Language Understanding
• Modeling Human Cognition with Computers
• Psychological Theories and Themes
• Task Driven Software and Artifact Design

HOMEWORK ASSIGNMENTS:

• Assignment 1 - Lisp Intro
• Assignment 2 - Ambiguity
• Assignment 3 - Eliza
• Assignment 4 - Action - Inference.
• Assignment 5 - Inferencer
• Pick one: Paper or Program
• Class discussion - 10%
• Projects and Assignments - 50% (Harder assignments are worth more. Go figure.)
• Final Paper - 30%
• Attendance - 10%

Natural Language Processing

ESG Investing and Natural Language Processing

This project will explore how managers disclose firms' investments in ESG activities using textual analysis and how markets react to this information.

Assistance is needed to collect, clean, and merge data sets. We will also need help with natural language processing tasks, possibly using ChatGPT.

Requisite Skills and Qualifications: Coursework in econometrics or statistics and advanced coding knowledge (Python or R) is required. Knowledge of natural language processing techniques is also desired, but the willingness to self-study before the summer is sufficient.

College of Natural Sciences and Mathematics

News & events, computer science team earns international competition top honors to identify hate speech and offensive language.

March 19, 2024

Social Media Made Safer Through UH Group’s Machine Learning Models

In a constantly changing digital world amid varying political, economic and social climates, the need to promote cybersecurity is growing more crucial to ensure future stability.

University of Houston computer science doctoral students Fatima Zahra Qachfar and Bryan Tuck are taking active roles in this effort.

Under the direction of Rakesh Verma, professor of computer science at the UH College of Natural Sciences and Mathematics, the team earned first place in a competition focused on identifying Arabic and Turkish hate speech and offensive language on social media. The competition was part of the Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024), co-located with the 18th Conference for the European Chapter of the Association for Computational Linguistics held in Malta.

This achievement marks the latest accolade for the group.

In fall 2023, Qachfar, Tuck and research scientist Dainis Boumber in Verma’s lab, earned first place in two categories at the first Arabic Natural Language Processing Conference held with the Conference on Empirical Methods in Natural Language Processing in Singapore. The group earned first place in disinformation detection in Arabic social media as a binary problem and in Arabic social media disinformation categorization into multiple classes.

“I’ve been working in hate speech and persuasion techniques to detect phishing, propaganda, and so on, to help protect others,” said Qachfar. “Bryan has also been working in social media hate speech.”

Tackling a Bigger Challenge

The CASE workshop was a more difficult challenge than what the group experienced in the fall. At CASE, they were limited to only three attempts with their submissions each day of the workshop.

“For this event, they gave us a training set for our machine learning-based model,” said Tuck. “You train your model on the training set, and you evaluate it on different data that it has never seen before.” The test was kept private, and teams had no access to it.

During previous competitions, the team received training and evaluation sets and two test sets for their model.

“We had no need to create an evaluation set in previous competitions, but that was not the scenario for CASE 2024,” said Tuck. “This was problematic and created new challenges.”

The team had the opportunity to test their skills in a more authentic setting.

“The organizers wanted to make it more realistic,” said Verma. “In this competition (CASE 2024), you don’t know how you’re going to test your model.”

The team leveraged advanced machine learning techniques and linguistic analysis to tackle hate speech detection in Arabic and Turkish text. Their approach combined deep learning models with carefully curated training data to achieve robust performance in distinguishing between benign and harmful language.

“Facebook, Instagram and X (Twitter) have filters based on machine learning that can detect potentially harmful language in posts,” said Qachfar. “Online groups find ways around these filters by altering spelling and grammar in their messaging.”

Qachfar is hopeful that machine learning will help shore up these defenses.

A Natural Fit for Participation

Qachfar discovered CASE 2024 over the winter break and contacted Tuck and Verma. “Once I found this workshop and realized our potential given our skillset, it seemed like a natural fit for us to participate,” said Qachfar.

For Tuck, participating in the workshop was an opportunity to build on previous success.

“This was right down our alley, especially coming off a first-place win in the fall semester,” said Tuck.

Identifying Potential Problems

Addressing the spread of harmful content has become increasingly critical in fostering inclusive online communities. The team is actively contributing to the creation of safer digital spaces where diverse voices can thrive without fear of discrimination or harassment.

“We are putting the University of Houston on the map when it comes to finding processes to detect and mitigate hate speech and offensive language in social media,” said Verma. “We need to protect people.”

- Chris Guillory, College of Natural Sciences and Mathematics

Writing Studio

2024 undergraduate creative writing symposium program, schedule-at-a-glance: undergraduate creative writing symposium (wednesday, april 10).

When: Wednesday, April 10, 3:00-6:00 PM | Where: Alumni Hall, 2nd Floor

Follow the links in the schedule below or scroll down for the full program of  presenters, which includes their bios and abstracts.

• 3:00-3:15: Welcome and Opening Remarks
• 3:15-4:10: Spotlight Panel (fiction, nonfiction, and poetry)
• 4:15-5:00: Session 1 – Panel A (fiction) and Panel B (nonfiction)
• 5:15-6:00:   Session 2 – Panel C (poetry)

From 3:00-6:00 pm. all attendees are encouraged to make time to peruse the adjoining Vanderbilt Undergraduate Arts Showcase .

Additional Event Links

• Coming Soon! Read all featured creative writing pieces on the UCWS 2024 Online Creative Writing Gallery
• Coming Soon! Visit the Arts Showcase’s portfolio page to view the incredible works created by undergraduate students.

Full Schedule: Undergraduate Creative Writing Symposium (Wednesday, April 10)

3:00-3:15 : opening remarks by major jackson , professor of english & director of creative writing gertrude conaway vanderbilt chair in the humanities, 3:15-4:10 : spotlight panel (alumni hall, room 206).

• Faculty Panel Chair: Justin Quarry (English)
• Panelists: Liam Betts  ’24  (poetry), Elyse Sparks  ’24  (nonfiction), Avery Fortier  ’24  (fiction)

Back to top

Spotlight Panel - Abstracts and Author Bios

Liam betts ’24:  the waves of light.

• Presenter Bio :  Liam Betts is a senior double majoring in computer science and english. He is originally from Portugal, but now lives in Pleasanton, California. He is the president of VandyWrites and prose editor for The Vanderbilt Review. His story The Waves of Light was selected as First Runner-Up for The Dell Magazines Award for Undergraduate Excellence in Science Fiction and Fantasy Writing in 2024.
• Abstract: The Waves of Light is a neo-Victorian story that reimagines Charles Darwin’s voyage aboard The Beagle to include his two young children, William and Anne. When circumstances thrust both siblings into an odyssey from the Atlantic to London, Anne is forced to reckon with a strange metamorphosis. While William performs street magic to keep them alive, Anne studies and experiments, dreaming of becoming a natural philosopher in nineteenth century England, a world where every door is closed to her. The story is told in the form of a letter from Anne to her father.

Elyse Sparks ’24:  The Golden Child

• Presenter Bio : Elyse Sparks in a member of the class of 2024.
• Abstract: The Golden Child is centered around my mental health struggles, sexuality, and my relationship with my pastor parents. I explore how my mom, despite her religious views that seemingly contradict loving a gay child, has stood by my side in a decade-long fight with major depression. Through coming out and hospitalizations and hard conversations, I have watched my mother grow into my biggest advocate.

Avery Fortier ’24: A Clean Mind

• Presenter Bio: Avery is a member of the class of 2024.
• Abstract: This is a piece of fictional prose meant to prompt consideration of mental health experiences across contexts and roles. I wanted to reflect the importance of protecting those responsible for treating others’ health as well as those who more obviously fall into the role of “patient.”

4:15-5:00 : Session 1

• Faculty Panel Chair: Fatima Kola (Medicine, Health, and Society)
• Panelists: Sawyer Sussner  ’24 , Shadhvika Nandhakumar  ’24 , Claire Marie Tate  ’24 , Sanat Malik  ’24
• Faculty Panel Chair:  Sandy Solomon (English)
• Panelists: Molly Buffenbarger  ’24, Franklin Udensi ’27 , Sarah Wermuth ’27 , and TaMyra Johnson ’27

Panel A - Abstracts and Author Bios

Sawyer sussner ’24: power to the players.

• Presenter Bio : Sawyer is a member of the class of 2024.
• Abstract: On her last shift as an employee at the failing gaming giant Game Stop, seventeen year old Twitch streamer Cass must navigate uncomfortable conversations with leering customers along with the impossible expectations of her boss, the washed up manager known to customers only as “The Bobcat,” determined to save his failing store. In a reflection of the gaming world’s treatment of women, Power to the Players explores misogynistic cycles of behavior and how to leave them behind.

Shadhvika Nandakumar ’24:  circles

• Presenter Bio:  Shadhvika is a member of the class of 2024.
• Abstract: This realistic fiction short story discusses the experiences of a young girl who finds out that her dad has had a heart attack. Told from the perspective of someone looking back over time, it is filled with various musings about the nature of life and relationships.

Claire Marie Tate ’24: Ocular Mistrust

• Presenter Bio: Claire Marie Tate is a member of the class of 2024 from Baton Rouge, LA. She is studying Neuroscience and Medicine, Health, and Society and will begin medical school this fall. In her free time, she enjoys running, dancing, discovering new music, reading, and, more recently, writing as a creative outlet.
• Abstract: “Ocular Mistrust” is a short piece which was inspired by the notion of the eye as the window to the soul and the unreliable nature of the visual pathway. This piece puts artistic themes of eyes in conversation with the physiology of visual processing.

Sanat Malik ’24:  Ishak’s

• Presenter Bio:  Sanat Malik is a Senior at Vanderbilt University. He was born in Hong Kong, spent some years in his native India, but primarily grew up in Singapore. Sanat is an Economics and English double major who has a passion for short story writing and journalism. He writes mainly about cultural topics with which he has personal experiences and perspectives. After college, Sanat will be working in an Investment Bank as a Raid Defense Consultant. He hopes to continue to grow in his career as a writer beyond college, and ideally would like to pursue investigative journalism in the future.
• Abstract: Ishak’s is a fictional piece about Ishak, an Indian Immigrant who has recently moved to New York to start an Indian fine-dining restaurant with his friend, Jai. Vying to win customers, Ishak creates an open kitchen in hopes that the smells spill onto the streets and draw in customers. In exploring Ishak and Jai’s pursuit of success in the culinary world, the story explores themes of immigration, assimilation, the pursuit of excellence, and the relationship between meticulous Ishak and laid-back Jai.

Panel B - Abstracts and Author Bios

Molly buttenbarger ’24:  night watch.

• Presenter Bio:  Molly is a member of the class of 2024.
• Abstract: I wrote this memoir about the night I spent alone in the hospital with my mother, when I was in sixth grade. After my mother completed chemotherapy for breast cancer, she underwent a double mastectomy and reconstruction. However, her reconstructed implant got infected, which meant she ended up hospitalized after emergency surgery.

Franklin Udensi ’27: The Igbo Anglican Church

• Presenter Bio: Franklin Udensi, a budding author from Lagos, Nigeria, finds deep inspiration in the works of his favorite author, Chimamanda Ngozi Adichie and his piece, “The Igbo Anglican Church,” reflects this influence. Beyond literature, Franklin enjoys diving into the immersive worlds of anime and manga, getting swept in the melodies of Jon Bellion, and delighting in the ever-changing landscape of construction sites, where the promise of unfinished structures sparks his imagination. With each stroke of his pen, he blends his varied influences into narratives that speak to the human experience.
• Abstract: This essay explores the author’s encounter with the Igbo Anglican church, unraveling the intricacies of cultural pride, identity, and the pursuit of connection in a diasporic community. Through reflections on language, tradition, and the clash of two worlds, the piece captures a unique narrative that invites readers to contemplate the dynamics of immigrant experiences and the dialogue between belonging and the complexities of assimilating to a new cultural landscape.“The Igbo Anglican Church” is a piece I wrote based on my own experiences navigating the United States upon my arrival during the summer before Vanderbilt. What began as pent-up emotions that I couldn’t quite explain ended up as a short story narrating my observations and cultural clashes with a segment of the Igbo (an ethnic group in Nigeria) diaspora in the US. Writing this piece showed me that my unique perspective as a literary observer could serve as a platform to explore fresh ideas surrounding cultural crossroads, immigrant perspectives, and the complexities of belonging while strengthening confidence in my storytelling abilities. This process enabled me to think critically about my own sentiments and express these thoughts in both personal and universally relatable ways. The piece engages in dialogue by presenting a narrative that resonates with individuals with similar experiences within immigrant communities. It figuratively converses with the present by exploring contemporary themes like cultural integration and identity. Additionally, it contributes to a broader discourse on the immigrant experience, belonging in a foreign land, and the intricate dance between tradition and assimilation, inviting readers to reflect on their encounters with such cultural crossroads.

Sarah Wermuth ’27: I’m Not (Wilmeth) Smart

• Presenter Bio: Sarah is a member of the class of 2027 majoring in Political Science with minors in Gender Studies and Creative Writing.
• Abstract: In 2023, I took a creative nonfiction English class at Vanderbilt, and an essay prompt was: “Write a personal essay exploring one way your identity has developed in opposition to your family of origin.” As a result, I wrote “I’m Not (Wilmeth) Smart.” It tells the story of how growing up in a family of brilliant individuals while simultaneously struggling in school made it hard for me to see myself as smart despite getting into Vanderbilt, one of the top universities in America.

TaMyra Johnson ’27: Racial Imposter Syndrome: Personal Experience + Interviews

• Presenter Bio: TaMyra Johnson is a part of the class of 2027 from Louisville, Kentucky. She plans on double majoring in Communications and Culture Advocacy Leadership with a minor in film.
• Abstract: This piece talks about my personal experience with racial imposter syndrome. Racial imposter syndrome can be described as being unconnected or feeling inauthentic to parts of their racial identity and culture or as when a person feels internally connected to a racial identity that is not perceived by others which causes doubt in their racial self perception.

5:15-6:00: Session 2

• Panel Chair:  TBD
• Panelists:  David Lemper ’27 , Nicole Reynaga ’26 , Ilana Drake ’25 , and Eli Apple ’24

Breakout Panel C - Abstracts and Author Bios

David lemper ’27: shakespeare rap.

• Presenter Bio: David is a member of the class of 2027.
• Abstract: This rap was written for an assignment in which students had to cast a scene of a Shakespeare play into rap lyrics. The concept was inspired by Shakespearean rap lyrics from Margaret Atwood’s “Hagseed,” a modern retelling of Shakespeare’s “The Tempest.” Rap as a genre—specifcally an African-American born genre—calls back to the theme of freedom, which is a very prominent theme within both Shakespeare’s “The Tempest” and “Romeo and Juliet,” so using this genre to express these narratives evokes the theme of freedom.

Nicole Reynaga ’26:  In one breath, we escaped together

• Presenter Bio:  Nicole is a member of the class of 2026.
• Abstract:  For this workshop’s penultimate poem, we were tasked with writing a prose poem (a poem not split into verse lines). As prose poems typically lack any rules of poetic form and do not visually appear as poetry, they heavily rely on the use of other poetic elements and metaphorical language. The theme of my piece falls into a more personal/self-aware realm.

Ilana Drake ’25: on rapid decline

• Presenter Bio: Ilana Drake is a junior studying Public Policy Studies and English, and she is a student activist and writer. She serves as a United Nations UNA-USA Global Goals Ambassador for SDG 10 (Reduced Inequalities), and she was selected as a Clinton Global Initiative University Fellow in 2023. This year, Ilana was appointed to the Inaugural Student Advisory Board for the Vanderbilt Project on Unity and American Democracy. Ilana was recognized as one of the forty undergraduate changemakers on Vanderbilt’s campus last year, and she is a Delegate for the 68th Session of the Commission on the Status of Women. Ilana’s writing has been published in Insider ,  Ms.  Magazine, and  The Tennessean , among others, and she has been quoted in  The New York Times ,  The Washington Post , and  Teen Vogue . Her poetry has been published internationally in literary magazines and zines. In her free time, she enjoys swimming, exploring Nashville with friends, and searching for the best iced coffee.
• Abstract: This poem is about the importance of time and health. I wrote this piece following my grandmother’s death in November 2023.

Eli Apple ’24:  Autoimmune (Selected Poems)

• Presenter Bio: Eli Apple is a writer of fiction and poetry. He has lived his whole life in Tennessee and is currently a senior at Vanderbilt University, where he is studying English, Spanish, and Portuguese. In addition to writing, he loves reading, traveling, and going on walks with his dog.
• Abstract: My submission includes eight poems that will appear in my English Honors thesis. My thesis, entitled Autoimmune, is a poetry collection that investigates literal and metaphorical illnesses and their effects on the body. These poems belong in Part Two of the collection, which examines homosexuality and internalized homophobia as illnesses together with the continuing effects of the AIDS epidemic on American society.

Access the UCWS 2024 Online Gallery

Coming Soon! Visit the UCWS 2024 Online Gallery of Creative Writing to read each of this year’s featured works along with a reflection from its author.

Special Thanks and Acknowledgements

The Writing Studio offers special thanks to all those who helped make our event possible and have contributed to its success.

Our Event Co-Host and Partner

The Office of Experiential Learning and Immersion Vanderbilt

Our Event Co-Sponsors

The Martha Rivers Ingram Commons

The Jean and Alexander Heard Libraries

Our Invited Creative Writing Reviewers from the MFA Program in Creative Writing

Langston Cotman

Ajla Dizdarević

Sydney Mayes

Our Writing Studio and Tutoring Services team members

Beth Estes (Assistant Director), Lead Symposium Coordinator

Lucy Kim (Academic Support Coordinator), Assistant Symposium Coordinator

Drew Shipley (Academic Support Coordinator), Assistant Symposium Coordinator

Cameron Sheehy (Peabody), Graduate Assistant Symposium Coordinator

Tim Donahoo, Administrative Specialist for the Writing Studio and Tutoring Services

all Writing Consultants Events Committee Members and all consultants present to support the event today

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