cmu-magnus.jpg
Location: Provo, UT, USA
Faculty advisor: Alan Black

Magnus

This team, consisting of PhD and Masters students from Carnegie Mellon University, brings together experience in chatbot interaction strategies, question answering, neural modeling and machine learning.

The team's vision is to build a social bot that can engage users in conversations apart from answering factual queries or performing everyday tasks. In order to facilitate high quality interaction with users, the conversation with the agent should evolve by leveraging mechanisms that establish coherence with user feedback and reactions. In order to engage users in a coherent manner, it would be useful if the agent plans the conversation ahead in time.

Shrimai P. - Team leader

I am a second year Master of Language Technologies student at the School of Computer Science. I am advised by Prof. Alan W. Black and Prof. Carolyn Rose. I am broadly interested in natural language processing and machine learning. Specifically, my research focuses of the various aspects of spoken dialog systems. Currently I am working on language generation for influential style of writing. Before joining CMU, I worked as an Analyst at Goldman Sachs, India on virtualization and operating systems. In my undergraduate program, I worked on building a question answering system for the semantic web.

Abhilasha R.

I am a first year student in the Master's in Language Technologies program. I am currently pursuing research in ubiquitous personal assistants and grounded semantics under Prof. Eric Nyberg. My previous research has been in problems related to parameterized complexity in Ki,j-free graphs and in analyzing social behavior through vision. I also spent a brief time in the industry. I interned at Amazon for six months, and later worked at a Computer Vision startup. My main interests lie in problems related to machine learning, NLP and algorithms.

Akash B.

I'm a graduate student who works at the intersection of Natural Language Processing and Machine Learning. My work in these domains has appeared in top tier conferences such as ICML, EMNLP, AAAI and ITS.

Chaitanya M.

My current research focus is on incorporating external knowledge into neural machine translation. I graduated from Nanyang Technological University, Singapore last year and worked on a range of applied machine learning and NLP projects. A few of these include detecting fraud and stock price changes by building a knowledge graph from entity recognition in financial reports; a context-based recommender system for events; and behavioral modeling for traffic simulation. I am excited about conversational dialog systems and very interested in several aspects such as building context-aware systems, and semantic parsing.

Esha Ashesh K.

I am a graduate student in the Language Technologies Institute in the School of Computer Science, majoring in Computational Data Science (MCDS), Analytics track. My research interests are focused towards natural language processing, dialog systems, and speech processing. I am presently assisting Prof. Alan Black in teaching the graduate level Natural Language Processing course. I also interned at Microsoft, Redmond as a Data Science Intern in Summer 2016. Before starting at CMU, I graduated from the National Institute of Technology, Goa, India with a major in computer science and engineering.

Fadi B.

N/A

Rama Kumar P.

I am currently pursuing a Master's in Language Technology in School of Computer Science, solving exciting problems in ML/NLP applied on large scale datasets! My research interests are in Big Data Analytics, Scalable Machine learning, Convex and Submodular Optimization applied in areas such as Graph mining, text analytics and Social Network Analysis. I pursued my Bachelor's and Master's at the Indian institute of Technology, Madras from the department of Electrical Engineering. I previously worked as a Research Engineer at Big Data Labs, American Express and in the Business Analytics and Math Sciences department, IBM India Research Lab, Bangalore, India.

Shivani P.

I am a Master's student in the LTI department. I work on modelling user trajectories from social media. I have also worked extensively on probabilistic models borrowing from user psychology research.

Tom M.

I am a first year Master's of Language Technologies Student. I graduated from Rensselaer Polytechnic Institute in the Spring of 2016 with a BS in computer science. While there I participated on multiple research projects with both Prof. James Hendler, and Prof. Mei Si in projects related to machine learning, question answering, and knowledge representation. Additionally I have had internships with Pinterest, LinkedIn, Bloomberg LP, and Hover Inc. Currently I am working with Prof. Eric Nyberg at CMU on automated question answering in smart home settings.

Xinrui H.

I'm a first-year graduate student in the M.S. program in Intelligent Information Systems (MIIS) at Language Technologies Institute (LTI). My research interests fall in Information Retrieval, Dialogue System and Machine Learning. I graduated with a Bachelor of Science degree, SUMMA CUM LAUDE from Peking University in 2016.

Zhou Y.

I am a PhD student at the Language Technology Institute under School of Computer Science, working with Prof. Alan W Black and Prof. Alexander I. Rudnicky in LTI. In Summer 2015 and 2016 I interned with Prof. David Suendermann-Oeft in ETS San Francisco Office working on cloud based mulitmodal dialog systems. In Fall 2014, I interned with Dan Bohus and Eric Horvitz in Microsoft Research working on situated multimodal dialogue systems.

Alan Black - Faculty advisor

Professor of Computer Science in the Language Technologies Institute in the School of Computer Science. I am a world leader in speech synthesis, spoken dialog and speech-to-speech translations. I have over 200 referred papers, and am one of the highest cited authors in the speech community (Interspeech).

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US, MA, N.reading
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US, CA, Sunnyvale
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US, WA, Seattle
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US, WA, Bellevue
Amazon LEO is Amazon's low Earth orbit satellite network. Our mission is to deliver fast, reliable internet connectivity to customers beyond the reach of existing networks. From individual households to schools, hospitals, businesses, and government agencies, Amazon LEO will serve people and organizations operating in locations without reliable connectivity. The Amazon LEO Global Business Operations (GBO) team drives data-driven decision-making across sales, marketing, operations, product, engineering, finance, and legal functions. We build scalable business intelligence solutions and data infrastructure to solve complex, ambiguous problems with LEO-wide impact. We are looking for a talented Research Scientist to contribute to LEO's long-term vision and strategy for capacity simulations and inventory optimization. This effort will be instrumental in helping LEO execute on its business plans globally. As one of our valued team members, you will be obsessed with matching our standards for operational excellence with a relentless focus on delivering results. Key job responsibilities In this role, you will: Collaborate with product, business development, sales, marketing, operations, finance, and various technical teams (engineering, science, R&D, simulations, etc.) to support the implementation of capacity simulations and inventory optimization solutions. Develop and prototype scalable solutions to optimization problems for operating and planning satellite resources. Support technical roadmap definition efforts by building models to predict future inventory availability and key operational and financial metrics across the network. Design experiments and simulations to evaluate optimization improvements and understand how they interact with each other. Analyze large amounts of satellite and business data to identify simulation and optimization opportunities. Communicate insights and recommendations to technical and non-technical audiences to support decision-making across LEO. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.
US, CA, Sunnyvale
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Our organization is building world-class teams with deep expertise in large-scale recommender systems. This role sits at the intersection of AI research and direct business impact, where recommendation quality directly influences business outcomes and customer satisfaction. You'll be joining a team focused on foundational models for recommender systems and working on production systems that serve millions of customers and shape the future of personalized entertainment experiences. We're seeking talent who can deliver measurable impact on our core business metrics while advancing the state-of-the-art in personalization and recommendation technology. Key job responsibilities - Develop AI solutions for various Prime Video Search & Recommendation systems using Deep Learning, Reinforcement Learning, Optimization Methods, and most importantly, GenAI - Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end - Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses - Effectively communicate technical and non-technical ideas with teammates and stakeholders - Stay up-to-date with advancements and the latest modeling techniques in the field - Publish your research findings in top conferences and journals About the team The Prime Video - Personalization & Discovery Science team owns science solution to power search experience on various devices, from sourcing, relevance, & ranking (to name a few). We are on a mission to deliver an AI-first customer experience. At the heart of this transformation are our recommendation systems -- core, customer-facing components that serve as primary drivers of engagement & growth.
CA, ON, Toronto
Are you interested in shaping the future of Advertising and B2B Sales? We are a growing team with an exciting AI-first charter and need your passion, innovative thinking, and creativity to help take our products to new heights. Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products are strategically important to our businesses driving long term growth. We break fresh ground in product and technical innovations every day! Within the Advertising Sales organization, we are building a central AI/ML team and are seeking top Applied Science talent to help us build new, science-backed services that drive success for our customers. Our goal is to transform the way account teams operate by creating AI agents that help optimize their end-to-end workflows, and developing actionable insights and recommendations they can share with their advertising accounts As an Applied Scientist on the team with a specific focus on creating autonomous AI agents that can operate accurately at large scale, you will bring deep expertise in Natural Language Processing (inc. tokenization, syntactic parsing, named entity recognition (NER), sentiment analysis, text classification), Large Language Models (inc. foundation model fundamentals, post-training, reward modeling, RAG, transformer architecture), Deep Learning, Reinforcement Learning and/or Recommender Systems. You have the scientific and technical skills to build and refine models that can be implemented in production and you continuously measure the performance of your system to drive continuous improvements. You will contribute to chart new courses with our ad sales support technologies, and you have the communication skills necessary to explain complex technical approaches to a variety of stakeholders and customers. You will be part of a team of fellow scientists and engineers taking on iterative approaches to tackle big, long-term problems. You are fluently able to leverage the latest Generative AI systems and services to accelerate and improve your work while maintaining high quality in your work outputs. Key job responsibilities Scientific Modeling - Conceptualize and lead state-of-the-art research on new Reinforcement Learning, Deep Learning, NLP, LLM, (Generative) Artificial Intelligence and Recommender System solutions to create AI agents and optimize all aspects of the Ad Sales business - Lead the technical approach for the design and implementation of successful models and algorithms in support of expert cross-functional teams delivering on demanding projects - Run regular A/B experiments, gather data, and perform statistical analysis - Improve the scalability, efficiency and automation of large-scale data analytics, model training, deployment and serving - Publish scientific findings in reports and papers that can be shared internally and externally Product Development Support - Partner with software engineering and product management teams to support product and service development, define success metrics and measurement approaches, and help drive adoption of innovative new features for our services. - Lead requirements gathering sessions with product teams and business stakeholders - Maintain scientific documentation and knowledge for product initiatives Collaboration & Communication - Work closely with software engineers to deliver end-to-end solutions into production - Translate complex scientific findings into actionable business recommendations for stakeholders and senior management - Provide clear, compelling reports and presentations on a regular basis with respect to your models and services - Communicate with internal teams to showcase results and identify best practices. About the team Sales AI is a central science and engineering organization within Amazon Advertising Sales that powers selling motions and account team workflows via state-of-the-art of AI/ML services. Sales AI is investing in a range of sales intelligence models, including the development of advertiser insights, recommendations and Generative AI-powered applications throughout account team workflows.
CA, ON, Toronto
Are you interested in shaping the future of Advertising and B2B Sales? We are a growing team with an exciting AI-first charter and need your passion, innovative thinking, and creativity to help take our products to new heights. Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products are strategically important to our businesses driving long term growth. We break fresh ground in product and technical innovations every day! Within the Advertising Sales organization, we are building a central AI/ML team and are seeking top Applied Science talent to help us build new, science-backed services that drive success for our customers. Our goal is to transform the way account teams operate by creating AI agents that help optimize their end-to-end workflows, and developing actionable insights and recommendations they can share with their advertising accounts As an Applied Scientist on the team with a specific focus on creating autonomous AI agents that can operate accurately at large scale, you will bring deep expertise in Natural Language Processing (inc. tokenization, syntactic parsing, named entity recognition (NER), sentiment analysis, text classification), Large Language Models (inc. foundation model fundamentals, post-training, reward modeling, RAG, transformer architecture), Deep Learning and/or Reinforcement Learning . You have the scientific and technical skills to build and refine models that can be implemented in production and you continuously measure the performance of your system to drive continuous improvements. You will contribute to chart new courses with our ad sales support technologies, and you have the communication skills necessary to explain complex technical approaches to a variety of stakeholders and customers. You will be part of a team of fellow scientists and engineers taking on iterative approaches to tackle big, long-term problems. You are fluently able to leverage the latest Generative AI systems and services to accelerate and improve your work while maintaining high quality in your work outputs. Key job responsibilities Scientific Modeling - Conceptualize and lead state-of-the-art research on new NLP, LLM and (Generative) Artificial Intelligence solutions (inc. post-training, fine-tuning, reward modeling) to optimize all aspects of the Ad Sales business - Lead the technical approach for the design and implementation of successful models and algorithms in support of expert cross-functional teams delivering on demanding projects - Run regular A/B experiments, gather data, and perform statistical analysis - Improve the scalability, efficiency and automation of large-scale data analytics, model training, deployment and serving - Publish scientific findings in reports and papers that can be shared internally and externally Product Development Support - Partner with software engineering and product management teams to support product and service development, define success metrics and measurement approaches, and help drive adoption of innovative new features for our services. - Lead requirements gathering sessions with product teams and business stakeholders - Maintain scientific documentation and knowledge for product initiatives Collaboration & Communication - Work closely with software engineers to deliver end-to-end solutions into production - Translate complex scientific findings into actionable business recommendations for stakeholders and senior management - Provide clear, compelling reports and presentations on a regular basis with respect to your models and services - Communicate with internal teams to showcase results and identify best practices. About the team Sales AI is a central science and engineering organization within Amazon Advertising Sales that powers selling motions and account team workflows via state-of-the-art of AI/ML services. Sales AI is investing in a range of sales intelligence models, including the development of advertiser insights, recommendations and Generative AI-powered applications throughout account team workflows.
US, WA, Bellevue
Alexa+ is Amazon’s next-generation, AI-powered virtual assistant. Building on the original Alexa, it uses generative AI to deliver a more conversational, personalized, and effective experience. As an Applied Scientist II on the Alexa Sensitive Content Intelligence (ASCI) team, you'll be part of an elite group developing industry-leading technologies in attribute extraction and sensitive content detection that work seamlessly across all languages and countries. In this role, you'll join a team of exceptional scientists pushing the boundaries of Natural Language Processing. Working in our dynamic, fast-paced environment, you'll develop novel algorithms and modeling techniques that advance the state of the art in NLP. Your innovations will directly shape how millions of customers interact with Amazon Echo, Echo Dot, Echo Show, and Fire TV devices every day. What makes this role exciting is the unique blend of scientific innovation and real-world impact. You'll be at the intersection of theoretical research and practical application, working alongside talented engineers and product managers to transform breakthrough ideas into customer-facing experiences. Your work will be crucial in ensuring Alexa remains at the forefront of AI technology while maintaining the highest standards of trust and safety. We're looking for a passionate innovator who combines strong technical expertise with creative problem-solving skills. Your deep understanding of NLP models (including LSTM and transformer-based architectures) will be essential in tackling complex challenges and identifying novel solutions. You'll leverage your exceptional technical knowledge, strong Computer Science fundamentals, and experience with large-scale distributed systems to create reliable, scalable, and high-performance products that delight our customers. Key job responsibilities In this dynamic role, you'll design and implement GenAI solutions that define the future of AI interaction. You'll pioneer novel algorithms, conduct ground breaking experiments, and optimize user experiences through innovative approaches to sensitive content detection and mitigation. Working alongside exceptional engineers and scientists, you'll transform theoretical breakthroughs into practical, scalable solutions that strengthen user trust in Alexa globally. You'll also have the opportunity to mentor rising talent, contributing to Amazon's culture of scientific excellence while helping build high-performing teams that deliver swift, impactful results. A day in the life Imagine starting your day collaborating with brilliant minds on advancing state-of-the-art NLP algorithms, then moving on to analyze experiment results that could reshape how Alexa understands and responds to users. You'll partner with cross-functional teams - from engineers to product managers - to ensure data quality, refine policies, and enhance model performance. Your expertise will guide technical discussions, shape roadmaps, and influence key platform features that require cross-team leadership. About the team The Alexa Sensitive Content Intelligence (ASCI) team owns the Responsible AI and customer feedback charters in Alexa+ and Classic Alexa across all device endpoints, modalities and languages. The mission of our team is to (1) minimize negative surprises to customers caused by sensitive content, (2) detect and prevent potential brand-damaging interactions, (3) build customer trust through generating appropriate interactions on sensitive topics, and (4) analyze customer feedback to gain insight and drive continuous improvement loops. The term “sensitive content” includes within its scope a wide range of categories of content such as offensive content (e.g., hate speech, racist speech), profanity, content that is suitable only for certain age groups, politically polarizing content, and religiously polarizing content. The term “content” refers to any material that is exposed to customers by Alexa (including both 1P and 3P experiences) and includes text, speech, audio, and video.
US, CA, Palo Alto
The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. About the team The SPB-Agent is the central agent that interfaces with advertisers in Ads Console, Selling Partner portals (Seller Central, KDP, Vendor Central), and internal Sales systems across all agentic experiences (conversational and others). SPB Agent team's vision is to build a highly personalized and context-aware agentic advertiser guidance system that seamlessly integrates Large Language Models (LLMs) with sophisticated tooling, operating across all experiences. We identify high-impact opportunities spanning from strategic product guidance to granular optimization and deliver them through personalized, scalable experiences grounded in state-of-the-art agent architectures, reasoning frameworks, sophisticated tool integration, and model customization approaches including fine-tuning, MCP, and preference optimization. This presents an exceptional opportunity to shape the future of e-commerce advertising through advanced AI technology at unprecedented scale, creating solutions that directly impact millions of advertisers.