With a COVID-19 magnified mental health crisis and growing old population (10.7% of population aged over 65 is diagnosed with Alzheimer's disease and 18% is diagnosed with mild cognitive impairment (MCI)) there is an immediate need for developing systems that can better understand and characterize cognitive and mental health (CMH) by tracking various biomarkers from functional magnetic resonance imaging (fMRI), electroencephalogram (EEG), speech, electronic health record (EHR), movement, cognitive surveys, wearable devices, structured, genomic, and epigenomic data. One of the core technical opportunities for accelerating the computational analysis of CMH lies in multimodal (MM) ML: learning representations that model the heterogeneity and interconnections between diverse input signals. MM is particularly important in CMH primarily due to the presence of noisy labels and subjectivity inherent in surveys. The utilization of multiple signals and modalities ofers a potential solution to overcome these challenges. In addition, it is imperative to emphasize the necessity for increased data sharing and enhanced collaboration within the CMH research community. As we endeavor to tackle the multifaceted challenges posed by cognitive and mental health disorders, a collective efort is essential to facilitate access to high-quality datasets and promote collaborative initiatives. By promoting transparency and facilitating the exchange of insights and methodologies, we can accelerate progress and drive innovation in CMH research. This workshop serves as a platform for fostering such collaboration, inviting participants to contribute their expertise and insights towards the shared goal of advancing our understanding and treatment of cognitive and mental health disorders. Together, through open dialogue and shared resources, we can chart a path towards a brighter future for individuals afected by CMH conditions.
CEUR ceur-ws.org
Recently, major progress has been made in pre-trained
deep and MM learning from text, speech, images, video,
signals, and structured data [
working on ML and CMH to learn from each
approaches across these communities, and
real-world CMH impact.
To achieve these goals, this workshop includes a diverse lineup of invited speakers across fields associated with ML and CMH, hosting experts from computer vision (CV), natural language processing (NLP), MM learning, signal processing, human-computer interaction, neuroscience, psychiatry, and psychology. To encourage discussion and further collaboration toward the advancement of ML for CMH, the workshop combines invited talks, contributed papers and posters, and panel 4. real-world deployment concerns such as safety, discussion. In addition, organizers hosted a mentorship robustness, interpretability, and collaboration with various stakeholders, and program with help of mentors from the program committee, similar to mentorship program of ACL-SRW1, 5. extending models to low resource and multilin- in order to increase reach and to help researchers from gual environments.
Machine Learning for Cognitive and Mental Health Workshop (ML4CMH), AAAI 2024, Vancouver, BC, Canada https://winterlightlabs.github.io/ml4cmh2024/ ∗Corresponding author. nEvelop-O across the world who are new to this field to improve the quality of their papers before the submission time.
and increases collaboration between machine learning, psychiatry, psychology, and neuroscience researchers. It
Time 9:00 - 9:05 am encourages collaboration to solve critical CMH tasks and create new datasets and resources to foster CMH research. In addition, it encourages multilingual and multimodal research. The organizers put an efort to invite keynote speakers, panelists, and program committee members from diverse backgrounds, involving both academia and industry. Specifically, organizers made concerted eforts to involve underrepresented groups, so speakers include LGBTQ people, and 50% of female. Moreover, program committee comprises researchers come from 12 countries across 5 continents.
The workshop will take place at Vancouver Convention Centre - West Building, Room 205, on February 26th, 2023. It features six keynote speakers, oral sessions, poster sessions, and panel discussion, and networking lunch. From 20 submitted papers, six were selected for oral and poster presentation and additional nine papers were selected for poster presentation only. Acceptance 2https://scholar.google.com/citations?user=UwQSEOkAAAAJ 3https://scholar.google.com/citations?user=Avse5gIAAAAJ 4https://scholar.google.com/citations?user=pQZaTGEAAAAJ 5https://scholar.google.com/citations?user=E_Ug5tsAAAAJ rate was therefore 75%. See detailed schedule in Table 2. 6https://scholar.google.com/citations?user=QLaCxaoAAAAJ Further details about the workshop can be accessed at 7https://scholar.google.com/citations?user=ar-oFSwAAAAJ https://winterlightlabs.github.io/ml4cmh2024/.
NeurIPS 2020, NAACL 2021, and NAACL 2022, and was a workflow chair for ICML 2019. Program Co-chair. 1. Peter Foltz8, University of Colorado, Boulder, Pro- Jelena Curcic20, Ph.D. is a Senior Data Scientist at fessor, Cognitive Science & Computational Psychi- Novartis Institutes for Biomedical Research with the exatry pertise in development, deployment, and advanced ana2. Paola Pedrelli9, Harvard Medical School, Assis- lytics of digital endpoints and biomarkers in neuroscience tant Professor, ML for Psychology disease area. Her topics of interest are cognition and neu3. Frank Rudzicz10, Dalhousie University, Vector ropsychiatric symptoms in neurodegenerative and mood Institute, CIFAR, Associate Professor, ML for disorders. Publication Chair.
Healthcare Zining Zhu21 is an Assistant Professor at Stevens 4. Jekaterina Novikova11, Winterlight Labs, ML Di- Institute of Technology. He is interested in building inrector, NLP & Speech, ML for CMH terpretable and trustworthy systems with deep neural 5. Vikram Ramanarayanan12, Modality.AI, CSO, networks. His researches apply the developments of deep
Speech & Image Processing for CMH neural network (DNN)-based systems to the detection of 6. Xiaoxiao Li13, University of British Columbia, cognitive impairments using data from multiple modaliUniversity of British Columbia, Trustworthy AI ties. Mentorship Chair.
Malikeh Ehghaghi22 is a machine learning research scientist at Arcee.ai. She graduated with a Master of Organizers Science in Applied Computing from the University of Toronto. She has over 4 years of research experience Organization Team in applied data science and machine learning, particularly interested in natural language processing, speech Marija Stanojevic14, Ph.D. is an Applied Machine Learn- processing, multimodal machine learning for health, and ing Scientist at Winterlight Labs. She focuses on repre- interpretability. Program Co-chair. sentation learning, multimodal, multilingual, and trans- Ali Akram23 is a Machine Learning Engineer at Camfer learning for cognitive and mental health. She was bridge Cognition, and graduated from the Systems Dea virtual chair of ICLR 2021 and ICML 2021 and main sign Engineering program at the University of Waterorganizer of the 9th Mid-Atlantic Student Colloquium loo. Interested in the eficient orchestration of machine on Speech, Language and Learning (MASC-SLL 2022). learning models, and applications of multimodal machine General chair. learning which leverage speech as the modality of choice.
Elizabeth Shriberg15, Ph.D. specializes in the com- Technical Chair. putational modeling of speech and language. She is currently CSO at Ellipsis Health, a start-up developing speech-based mental health screening technologies for 5. Program Committee clinical applications. She previously held Senior Principal Scientist roles at Amazon, SRI International, and 1) Brandon M Booth, University of Colorado; Microsoft. She is a Fellow of ISCA 16, SRI17, and AAIA18, 2) Kathleen C. Fraser, National Research Council Canada; and has over 300 publications and patents in speech tech- 3) Wilson Y. Lee, HubSpot; nology and related fields. Speaker & Panel Chair. 4) Ashutosh Modi, Indian Institute of Technology Kanpur;
Paul Pu Liang19 is a PhD student at CMU. He re- 5) Albert Ali Salah, Utrecht University; searches foundations of multimodal machine learning 6) Roland Goecke, University of Canberra; with applications in socially intelligent AI, understanding 7) Andreas Triantafyllopoulos, University of Augsburg; human and machine intelligence, natural language pro- 8) Daniele Riboni, University of Cagliari; cessing, healthcare, and education. He organized work- 9) Korbinian Riedhammer, Technische Hochschule Nürnshops on multimodal learning at ACL 2018, ACL 2020, berg; 10) Paula A. Perez-Toro, Friedrich-Alexander Universitat; 11) Torsten Wörtwein, Carnegie Mellon University; 12) Loukas Ilias, National Technical University of Greece; 13) Arun Das, University of Pittsburgh Medical Center; 14) Jingqi Chen, Fudan University; 15) Eloy Geenjaar, Georgia Institute of Technology; 8https://scholar.google.com/citations?user=UwQSEOkAAAAJ 9https://scholar.google.com/citations?user=E_Ug5tsAAAAJ 10https://scholar.google.ca/citations?user=elXOB1sAAAAJ 11https://scholar.google.com/citations?user=C75JskwAAAAJ 12https://scholar.google.com/citations?user=mUm8U2IAAAAJ 13https://scholar.google.com/citations?user=sdENOQ4AAAAJ 14https://scholar.google.com/citations?user=pAyfhIkAAAAJ 15https://scholar.google.com/citations?user=nRZJYPIAAAAJ 16https://www.isca-speech.org/iscaweb/ 17https://www.sri.com/about-us/ 18https://www.aaia-ai.org/ 19https://scholar.google.com/citations?user=pKf5LtQAAAAJ 20https://scholar.google.com/citations?user=Se8a2b8AAAAJ 21https://scholar.google.ca/citations?user=Xr_hCJMAAAAJ 22https://scholar.google.com/citations?user=les29Z8AAAAJ 23https://www.akramsystems.com/
24https://winterlightlabs.com/ 25https://cambridgecognition.com/ We would like to thank you to the following people for their help and support during workshop preparation: 1) Aparna Balagopalan PhD Student at MIT; 2) Thomas Hartvigsen, PhD, Assistant Professor at University of Virginia; and 3) William Jarrold, Trade Desk.
We would like to express our sincere gratitude to Winterlight Labs24, Canada and Cambridge Cognition25, UK companies for their generous support and contribution to the success of this event. We are deeply appreciative of their support and partnership, which has been instrumental in making this event possible. Paper Title [Long] Knowledge-enhanced Memory Model for Emotional Support Conversation [Long] Learning to Generate Context-Sensitive Backchannel Smiles for Embodied AI Agents with Applications in Mental Health Dialogues [Short] A Pretrained Language Model for Mental Health Risk Detection [Short] PMC: Paired Multi-Contrast MRI Dataset at 1.5T and 3T for Supervised Image2Image Translation [Short] Dance of the Neurons: Unraveling Sex from Brain Signals [Abstract] Mental Health Stigma across Diverse Generative Large Language Models - Abstract Paper Title [Long] ConversationMoC: Encoding Conversational Dynamics using Multiplex Network for Identifying Moment of Change in Mood and Mental Health Classification [Short] A Privacy-Preserving Unsupervised Speaker Disentanglement Method for Depression Detection from Speech [Long] Ordinal Scale Evaluation of Smiling Intensity using Comparison-Based Network [Long] Natural Language Explanations for Suicide Risk Classification Using Large Language Models [Long] Deploying AI Methods for Mental Health in Singapore: From Mental Wellness to Serious Mental Health Conditions [Short] Investigating Bias in Afective State Detection Using Eye Biometrics [Long] Towards Remote Diferential Diagnosis of Mental and Neurological Disorders using Automatically Extracted Speech and Facial Features [Short] Prediction of Relapse in Adolescent Depression using Fusion of Video and Speech Data [Long] Toward A Reinforcement-Learning-Based System for Adjusting Medication to Minimize Speech Disfluency