About Hume

Alan Cowen is an applied mathematician and computational emotion scientist who has developed new data-driven methods to study human experience and expression.

Prior to founding Hume AI, he was a researcher at Google AI where he helped establish affective computing research efforts. In his paper published in Nature in 2021, Cowen was the first to analyze facial expressions at a sufficient scale and level of precision to draw inferences from their occurrence in everyday life across cultures, analyzing six million natural videos from around the world. In PNAS, Science Advances, and Nature Human Behavior, he has characterized cues of emotion in the face, voice, and music across cultures and even in ancient sculptures. Cowen’s work has given rise to a mathematically-based theory of emotion, semantic space theory, which conceives of emotions as dimensions of a complex, multidimensional space.

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Dacher Keltner is one of the world’s foremost emotion scientists.

He is a professor of psychology at UC Berkeley and the director of the Greater Good Science Center. He has over 200 scientific publications and six books, including Born to Be Good, The Compassionate Instinct, and The Power Paradox. He has written for many popular outlets, from The New York Times to Slate. He was also the scientific advisor behind Pixar’s Inside Out, is involved with the education of health care providers and judges, and has consulted extensively for Google, Facebook, Apple, and Pinterest, on issues related to emotion and well-being.

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Janet Ho is an experienced product and operations manager.

She serves as the Chief Operating Officer of Hume, which she has helped build from the ground up, and was previously the co-managing partner at Aegis Ventures. Previously, she worked on the product teams at The League, Rakuten, and Zynga. She received her B.A. from Harvard in Government and Computer Science.

Lauren Kim is a data scientist and machine learning engineer.

At Microsoft, Facebook, Square, and Harvard she has worked with research and product teams to develop large-scale data processing systems and state-of-the-art machine learning models. She has extensive experience in data engineering, multiple deep learning modalities, analytics, and system design.

Michael Opara is a statistician and software engineer.

At Google and Harvard, he has conducted quantitative research and built robust data pipelines and analytics solutions. He has extensive experience in algorithms and data structures, machine learning, and data visualization.

Dr. Tzirakis is a computer scientist and AI expert with expertise in machine learning, deep learning, and emotion recognition.

He earned his Ph.D. with the Intelligent Behaviour Understanding Group (iBUG) at Imperial College London, where he focused on multimodal emotion recognition efforts. He has published in top outlets including Information Fusion, International Journal of Computer Vision, and several IEEE conference proceedings on topics including 3D facial motion synthesis, multi-channel speech enhancement, the detection of Gibbon calls, and emotion recognition from audio and video.

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Jacob Metrick is an experienced full stack software engineer.

In his eight years of experience at GrubHub and Amazon, he has worked extensively in front and back end web development, Android app and voice assistant development, project management, systems administration, databases, security, and more.

Dr. Brooks is a computational psychologist with expertise in emotion, face perception, and social neuroscience.

He earned his Ph.D. from the Social Cognitive and Neural Sciences Lab at NYU, where he researched the computational and neural mechanisms of emotion perception and social evaluation. His research has been published in the Proceedings of the National Academy of Sciences, Nature Human Behavior, and other leading journals.

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Dr. Baird is an audio researcher with interdisciplinary expertise in machine learning, computational paralinguistics, stress, and emotional well-being.

She earned her Ph.D. at the University of Augsburg, where she was supervised by Dr. Björn Schuller. Her work on emotion understanding from auditory, physiological, and multimodal data has been published extensively in the leading journals and conferences in her field, including Interspeech, ICASSP, IEEE Intelligent Systems, and the IEEE Journal of Biomedical and Health Informatics.

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Chris Gregory is a data scientist and machine learning engineer.

At Microsoft he developed services and open source toolkits for the Azure Machine Learning platform. On the AutoML team he built out training infrastructure for the model selection recommender system and led development of a model evaluation suite for classification and forecasting models. He also helped launch Microsoft's Responsible AI Toolbox, contributing to interpretability methods like feature importance and causal analysis.

Garrett Boseck is a full-stack software engineer.

He has orchestrated, designed, built, and maintained dozens of applications using a wide range of tools and frameworks, including Spring based microservices, micro frontends using Web Components, REST APIs, and a wide range of cloud computing solutions.

Dr. Gagne is an AI researcher and computational psychologist with expertise in reinforcement learning, natural language processing, and clinical psychology.

He earned his Ph.D. at the University of California, Berkeley and completed a postdoctoral fellowship at the Max Planck Institute for Biological Cybernetics. His work centers on computational modeling of human behavior, including reinforcement learning and natural language generation under conditions of stress, uncertainty, emotions, moods, and psychiatric disorders. His research has been published in leading journals, including Journal of Neuroscience, eLife, NeurIPS, and Computational Psychiatry.

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Zach Krall is a multidisciplinary artist and creative technologist.

He received his MFA in Design and Technology from Parsons School of Design at The New School and his BFA from School of Visual Arts. He's helped startups implement interactive UI for large-scale video and e-commerce projects, performed at live code events at a wide range of conferences. He teaches data visualization at Parsons School of Design.

Yini Shi is an experienced front-end engineer.

From her time at Amazon Alexa, AWS, and Conversant, she has experience building scalable front-ends and leading engineering teams. At Amazon Alexa AI, she led front-end development and UX design for a natural language understanding platform.

Nathan Leung is a data scientist and machine learning engineer.

Previously, he led the account security team at Robinhood, where he developed services and machine learning models to detect, prevent, and mitigate account takeovers. At Robinhood as well as at Apple and Uber, he has worked across backend engineering, data engineering, data science, and security. He received his BASc in Mechatronics Engineering from the University of Waterloo.

Franc Camps-Febrer is an applied research scientist, frontend developer, and data visualization engineer.

He most recently led the development of data visualization tools at the New York Times. Prior to that, he led efforts to build research and data visualization tools at Obsidian Security, Forensic Architecture, and Knewton. He has a Master's in Information Experience Design, a Master's in Developmental Neuroscience, and a B.S. in Physics.

Jake Marlo is a skilled startup operator.

Prior to joining Hume, he served in multiple product and business operations roles at other startups. He also has investment banking and private equity experience. He received his B.A. from Harvard in Economics.

Moses Oh is a machine learning engineer and applied AI researcher.

He previously worked at Google, where he developed and deployed conversational AI models. Prior to that, he received his B.S. in Data Science from the University of California, San Diego and spent multiple years working as a software engineer at Spatial and NASA.

James Cha-Earley is Head of Developer Relations at Hume.

He has spent over a decade building teams that develop, support, and evangelize developer platforms. At Bill.com he led the team that rebuilt the developer platform, from design to engineering. At Clover Network, he managed the developer relations team, leading initiatives to engage, support, and educate developers who brought their applications to the Clover App Market.

Zachary Greathouse is an experienced software engineer with a focus on developer relations and front-end web development.

In his five years at Clover and Bill.com, Zach learned how to efficiently build and scale developer platforms. At Clover, he helped a wide range of organizations develop and launch applications on the Clover App Market. At Bill, his work spanned strategic partner integrations, feature development for the core product, UI development, and internal engineering initiatives.

Matt Forte is an experienced host, producer, designer, and marketing strategist.

Prior to joining Hume, he spent 10 years at Apple, where he managed the Retail Marketing team's special events across the globe. Then he hosted, produced, and marketed the “BUILDseries” at Verizon. He's also worked closely with Disney, Netflix, Uber, Yahoo!, American Express, (RED) and more on all aspects of content creation. He began working with Hume at its inception in 2021, helping create and launch “The Feelings Lab” podcast series.

Brenton McMenamin is an experienced data engineer.

For the last eight years, he has built and maintained data pipelines and machine learning models at OkCupid, Enigma Technologies, and Spotify. He has a PhD in computational affective neuroscience and developed methods for analyzing emotion-related responses in large-scale functional neuroimaging data.

Mary Jiang is a UX engineer and product designer.

At Google, she developed prototypes for large-scale consumer products and search features. Previously, she has worked on design systems, open-source software, and social platforms with both established UX teams and startups. Mary graduated from Duke University with a B.A. in Computational Media, minoring in Psychology.

Lydia Schooler is a skilled UX researcher and brand strategist.

She was the first UX Researcher at Snackpass and worked in Brand Strategy at Apple. She has worked in both Iceland and Malawi, working for their governments and contributing to public-private partnerships focused on business development and global health, respectively. Previously, she majored in Cognitive Science at Yale and contributed to research projects at MIT Sloan School of Management. Fluent in German and skilled in several programming languages, she is adept at navigating complex technical and cultural landscapes.

Brian Kitano is a skilled software engineer and machine learning application developer.

His tenure at Amazon involved significant contributions to Amazon SageMaker, including launching it in multiple AWS regions and developing customer-facing features. Brian was a founding engineer at Rutter, where he led scaling and reliability. He graduated Yale with a B.S. in Statistics and Data Science, where he served as Teaching Assistant for advanced statistics and computer science courses. In his spare time he writes and teaches about deep learning and large language models.

Eva Casanova is a dynamic growth leader with a track record of helping early- to mid-stage companies grow into market leaders.

As Head of Growth at Shibuya.xyz, she played a foundational role in leading business development, partnerships, and marketing. During her tenure at Galaxy Digital, Eva served as the Director and Head of Brand Management and Marketing. Her experience also includes a role at Day One Ventures, where she collaborated with portfolio founders on go-to-market strategies. Eva's is adept at communicating and strategically planning the launch and widespread adoption of transformative technologies.

Jeremy Hadfield is a technical product manager with wide-ranging experience in AI risk analysis, data analytics, software engineering, technical writing, and product management.

He has worked in both early-stage startups and large enterprises. At Elicit, Jeremy improved the AI research assistant's data extraction feature and developed robust product analytics. At the Alignment Research Center, he identified safety vulnerabilities in AI systems, including red-teaming GPT-4. At Qualia Research Institute, he studied the computational neuroscience of happiness. As a software engineer at Legrand, he developed automated tests for industrial lighting. As a technical writer at the Center for AI Safety, he contributed to a textbook on AI risk. In college, he cofounded and helped grow the mental health peer support app Unmasked. Jeremy graduated from Dartmouth with a B.A. in Computational Cognitive Science and Philosophy and a Master of Engineering Management. He writes essays & fiction (Imaginaries) and enjoys exploring nature.

Serena Wang is an experienced strategic operator and financial analyst.

At Aegis Ventures, she played a key role in conducting financial analyses for capital deployment to portfolio companies and developing the strategic vision for shared services, identifying areas for resource reallocation to improve value creation. At Optain Health, she provided support to the portfolio company through customer and investor-facing pitches and FDA clinical trial support. Serena graduated from the University of Michigan, Ross School of Business with a Bachelors in Business Administration.

Ian Kelk is an experienced developer relations engineer with expertise in data visualization and natural language processing.

Previously at Clarifai, he championed AI/ML innovation by creating technical content, running hackathons, and serving as the technical ML and generative AI expert. Ian's research contributions include papers on automatic fake news detection and adversarial studies on face recognition. He was also a teaching fellow in reinforcement learning at the Harvard Extension School. Ian also founded Virmuze, an online museum platform. Ian's broad skill set spans languages like Python and C++, machine learning models, and frameworks like PyTorch and TensorFlow.

Shahbaz Mogal is a software engineer with a robust background in API development, data science, and artificial intelligence.

His previous tenure at Flexport Inc. saw him architecting and implementing critical API functionalities, enhancing system efficiency, and optimizing performance monitoring. Prior to that, he worked with Microsoft's Language Understanding research team and had multiple internships across AI and finance sectors. Shahbaz's passion for STEM education and accessibility also led him to significant roles with Girls Who Code, TUMO Armenia, and initiatives like Code for Palestine and Code for Albania, where he developed curricula and taught with the aim of democratizing access to STEM for young women.

Steven Lai is a seasoned software engineer specializing in Cloud Infrastructure and Site Reliability Engineering.

At Gusto, he led the design and implementation of cloud infrastructures using Terraform, AWS, Kubernetes, and Python. His expertise includes disaster recovery automation, platform migration to Kubernetes, and tools for self-service provisioning of cloud resources. At Postmates, Steven spearheaded the development of payment processing services and played a pivotal role in migrating financial logic to scalable microservices. Stevens holds a B.A. in Philosophy from Stony Brook University.

Trevor Tsue is an experienced AI research scientist and software engineer.

He previously co-founded flairlabs.ai, a YCombinator-backed startup that reached $40k MRR with its LLM copilot for lead generation. With extensive experience in large-scale ML technologies across AWS, Azure, and GCP, Trevor has trained and deployed a range of AI models. In the healthcare space, he worked on a semi-autonomous AI to interpret mammograms at Whiterabbit.ai and developed the openAPS artificial pancreas ML algorithm, resulting in multiple patents and publications.

Dylan Chow is a Machine Learning Engineer with experience in AI research, large-scale distributed systems, and software engineering.

At Amazon, he made significant contributions to SageMaker Model Inference, helping to launch features that enable customers to run generative AI workloads on AWS, and improving the performance and scalability of the platform. A University of California, Berkeley graduate in Computer Science, Dylan has engaged in notable AI research at the UC Berkeley RISE Lab and Berkeley Artificial Intelligence Research (BAIR). Beyond his engineering experience, Dylan was a mentor at the Jacobs Institute for Design Innovation, where he supported innovative projects at the intersection of design and technology.