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People

Faculty

Louis-Philippe Morency

Louis-Philippe Morency

Louis-Philippe Morency is Assistant Professor in the Language Technology Institute at Carnegie Mellon University where he leads the Multimodal Communication and Machine Learning Laboratory (MultiComp Lab). He was formely research assistant professor in the Computer Sciences Department at University of Southern California and research scientist at USC Institute for Creative Technologies. Prof. Morency received his Ph.D. and Master degrees from MIT Computer Science and Artificial Intelligence Laboratory. His research focuses on building the computational foundations to enable computers with the abilities to analyze, recognize and predict subtle human communicative behaviors during social interactions. In particular, Prof. Morency was the lead investigator for the multi-institution effort that created SimSensei and MultiSense, two technologies to automatically assess nonverbal behavior indicators of psychological distress. He is currently chair of the advisory committee for ACM International Conference on Multimodal Interaction and associate editor at IEEE Transactions on Affective Computing.

Postdocs

Michal Muszynski
Michal Muszynski

Michal Muszynski

Michal Muszynski is a postdoctoral research associate (SNSF fellowship holder) carrying out interdisciplinary research at the intersection of computer science, neuroscience, medicine, and psychology. He received his Ph.D. in Computer Science from the University of Geneva in 2018. His research interests are in the areas of affective computing, affective neuroscience, multimodal deep machine learning, pattern recognition, signal processing, and big data. As part of the MultiComp lab, Michal brings his work experience in physiological and behavioural signal analysis.

Jeffrey Girard
Jeffrey Girard

Jeffrey Girard

Jeffrey Girard is a postdoctoral research associate working in the interdisciplinary space between psychology, medicine, and computer science. He completed his PhD in Clinical Psychology at the University of Pittsburgh in 2018 and has been collaborating with computer scientists at CMU since 2010. He is interested in how internal factors (e.g., emotion, personality, and psychopathology) and external factors (e.g., context, culture, and group processes) influence human behavior. As part of the MultiComp lab, Jeffrey brings expertise in facial computing, statistical analysis, and psychological theory.

Ph.D Students

Chaitanya Ahuja
Chaitanya Ahuja

Chaitanya Ahuja

Chaitanya Ahuja is a doctoral student in Language Technologies Institute in the School of Computer Science at Carnegie Mellon University. His interests range in various topics in natural language, computer vision, computational music, and machine learning. He is currently working on various representation models for multimodal machine learning with Louis-Philippe Morency. Before starting with graduate school, Chaitanya completed his B.Tech at Indian Institute of Technology, Kanpur where he worked on Speech Source Separation and Spatial Audio with Rajesh Hegde.

Volkan Cirik
Volkan Cirik

Volkan Cirik

Volkan is a doctoral student in Language Technologies Institute in the School of Computer Science. His interests range on various topics in natural language, computer vision, and machine learning. Volkan finished a Master’s degree on word vectors for structured prediction at Koç AI Lab with Deniz Yuret. During his second Master’s in Carnegie Mellon, he worked on various topics such as event detection in the biomedical domain, visualization and analysis of neural network models with Teruko Mitamura, Eduard Hovy, and Louis-Philippe Morency. Before starting his graduate studies, he completed his B.S in computer engineering at Boğaziçi University.

Paul Pu Liang
Paul Pu Liang

Paul Pu Liang

Paul is Ph.D. student in the Machine Learning Department at CMU, advised by Louis-Philippe Morency and Ruslan Salakhutdinov. His research is centered around multimodal machine learning, deep learning, natural language processing, computer vision, and reinforcement learning. He is interested in the theoretical foundations of multimodal learning as well as the applications of semi-supervised and unsupervised multimodal learning. He received a M.S. in Machine Learning and a B.S. with University Honors in Computer Science from CMU

Yao-Hung Hubert Tsai
Yao-Hung Hubert Tsai

Yao-Hung Hubert Tsai

Hubert is a Ph.D. student in Machine Learning Department at Carnegie Mellon University under the supervision of Dr. Ruslan Salakhutdinov and Dr. Louis-Philippe Morency. His research interests lie in Deep Learning and its applications, especially on Multi-Modal Learning, Transfer Learning, and Generative Learning. He obtained his B.S. in E.E. from National Taiwan University.

Torsten Wörtwein
Torsten Wörtwein

Torsten Wörtwein

Torsten Wörtwein is a Ph.D. student in Language Technologies Institute. His research interests include affective computing, multimodal behavior analysis, and healthcare analytics with a focus on symptoms of depression and psychotic disorders. He is currently focusing on emotion recognition and on technical challenges including uncertainty estimation and personalization. Previously, he worked on public speaking anxiety and public speaking performance assessment as well as on several computer vision projects. He received his B.Sc. and M.Sc. in Informatics from the Karlsruhe Institute of Technology in Germany.

Alexandria Vail
Alexandria Vail

Alexandria Vail

Alexandria K. Vail is a Ph.D. student in Human-Computer Interaction. Her research interests include user modeling, affective computing, and multimodal behavior analysis, particularly within the context of healthcare and clinical decision support technologies. Her work recently received the Best Paper Award at the International Conference on User Modelling, Adaptation, and Personalization; this work has also been recognized with distinction at the International Conference on Intelligent Tutoring Systems and the International Conference on Educational Data Mining. Before joining CMU, she received the B.S. degree in Computer Science and the B.S. degree in Mathematics from North Carolina State University, with minor concentrations in Cognitive Science and Physics.

Amir Zadeh
Amir Zadeh

Amir Zadeh

Amir Zadeh is an Artificial Intelligence Ph.D student at Carnegie Mellon University. His research is focused on Multimodal Deep Learning both theory and applied. From theoretical perspective he is interested in building foundations of multimodal machine learning. From application perspective, he is keen on giving computers capability to understand human communication as a multimodal signal involving language, gestures and voice. His research spans different areas of natural language processing, computer vision and speech processing. He started his Ph.D in January 2015. Prior to that he received his Bachelors of Science from University of Tehran, ECE department, where he was a member of Advanced Robotics Laboratory.

Master Students

Research Engineer

Visiting Scholar

Lab Manager

Nicki Siverling
Nicki Siverling

Nicki Siverling

Nicki is the MultiComp Lab’s Coordinator. She has 12 years of experience in managing social/behavioral research labs and is currently providing support on several grants. Before joining CMU, she served as Coordinator of the Affect Analysis Group at the University of Pittsburgh. Nicki received her B.A. from the University of Pittsburgh.

Staff

Graduate Research Assistants

  
Muqiao Yang

Muqiao Yang

Muqiao is a graduate student at CMU. His research interest is about understanding real-world problems and mechanisms with machine learning techniques. Prior to joining CMU, he received his bachelor degree from The Hong Kong Polytechnic University.

  
Tianjun (TJ) Ma

Tianjun (TJ) Ma

TJ is currently a fifth year master’s student in the school of computer science, advised by Louis-Philippe Morency. His research interests include multimodal signal processing, assistive technologies, and visual QA.

  
Martin Ma

Martin Ma

Martin Q. Ma is a second-year master student in the School of Computer Science. He has been working on deep learning and multimodal learning. His previous work lies in topics including multimodal learning, seq-to-seq learning, domain adaptation, zero-shot learning, interpretable models, etc. Martin received his Bachelor’s degree in computer science and mathematics from Brandeis University.

  
Tejas Srinivasan

Tejas Srinivasan

Tejas is a second-year Master’s student in the Language Technologies Institute at CMU. Tejas is currently working on Multimodal Co-learning to build models that are robust to missing modalities during inference time. He has worked on a number of research projects in the space of NLP, including multimodal speech recognition, dialog, and machine translation. Prior to joining CMU, he worked on end-to-end speech translation at the Indian Institute of Technology, Bombay.

  
Chengfeng Mao

Chengfeng Mao

Chengfeng Mao is a second-year master student in Computer Science at Carnegie Mellon University. He is passionate about machine learning and applying it to solve real-world problems. During his study at CMU, he has also been working on multimodal machine learning applied to video sentiment analysis and multimodal generation with Professor L.P. Morency. Prior to attending CMU, he had worked at Yahoo, Cask, and Google Cloud as a software engineer, specializing in big data infrastructure development. He received his bachelor’s degree in Computer Engineering from the University of Illinois at Urbana-Champaign.

  
Atabak Ashfaq

Atabak Ashfaq

Atabak Ashfaq is a first-year Masters student at the Language Technologies Institute at the School of Computer Science at CMU. He is working on active learning techniques to optimize annotation collection process. His research interests lie in the field of active learning and natural language processing. Prior to CMU, he was working at a financial giant to automate production management using time series modelling and active learning.

  
Victoria Lin

Victoria Lin

Victoria is a Masters student in the Language Technologies Institute. Her research interests include affective computing, multimodal behavior analysis, and applied statistics in the context of healthcare and medical diagnostics. With Jeffrey Girard, she is currently exploring models of emotional expressiveness, with applications in the diagnosis of psychotic disorders. Prior to joining CMU, Victoria was a researcher in the Program for Causal Inference at the Harvard School of Public Health, where she worked on the implementation of time-varying causal models for complex longitudinal data. She received her joint A.B. in Statistics and in Molecular and Cellular Biology from Harvard University.

  
Ying Shen

Ying Shen

Ying is a second-year master student in the Language Technologies Institute of School of Computer Science at Carnegie Mellon University. Her research interests lie in deep learning, multimodal machine learning, natural language processing and computer vision. In particularly, she is interested in understanding human multimodal language and develop agents that feature a more human-like intelligence with the help of deep learning. Prior to joining Carnegie Mellon, she received the Bachelor’s Degree in Software Engineering from Fudan University.

Undergraduate Research Assistants

  
Dong Won Lee

Dong Won Lee

Don is a undergraduate student in Carnegie Mellon University pursuing Statistics and Machine Learning. He is workingto build models for human communication pose generation via audio-visual modalities. His interests lie in the practical application of deep learning and multimodal machine learning techniques.

  
Gayatri Shandar

Gayatri Shandar

Gayatri Shandar is an undergraduate in the School of Computer Science at Carnegie Mellon University. She works with Dr. Girard where her current research interests include developing machine learning models to further facial computing and the understanding of human emotion and its influences on facial behavior.

  
Holmes Wu

Holmes Wu

Holmes Wu is an undergraduate student in Carnegie Mellon University. His current research project involves convolutional neural networks in 3D. His hope to gain in-depth understanding of the underlying principles of machine learning, especially deep learning.

  
Michael Chan

Michael Chan

Michael Chan is an undergraduate student at Carnegie Mellon University. His current work concerns using artifical intelligence to identify qualities of human social interaction through body language and speech from video clips. Michael hopes to learn more about machine learning overall and intends to use his knowledge to challenge the artistic and creative capabilities of machine learning models.

Interns

  
Simon Hessner

Simon Hessner

Simon Hessner is a research intern at the Multicomp lab in the Language Technologies Institute at Carnegie Mellon University. His main research interest is the field of Computer Vision, especially problems related to facial image analysis. Together with Amir Zadeh he is working on facial landmark detection and variational auto-decoders to model face shapes. He received his bachelor’s and master’s degrees in computer science from Karlsruhe Institute of Technology (KIT). From November 2018 to April 2019 he was a visiting scholar at CMU and worked on his master’s thesis on facial landmark detection. He returned to CMU in September 2019 as a research intern.

Previous Members