Sameer Maskey is a computer scientist, educator and entrepreneur. He is currently the Founder and CEO at Fusemachines Inc and an Adjunct Associate Professor at Columbia University where he teaches several courses including “Statistical Methods for Natural Language Processing” and “Programming for Entrepreneurs”. He has more than 18 years of experience in artificial intelligence, natural language processing, machine learning, and data science. He attended undergraduate school at Bates College in Maine, USA with degrees in Math and Physics and pursue a PhD in Computer Science at Columbia University in New York City. After completing his PhD, he joined IBM Watson Research Center where he invented various statistical algorithms to improve speech-to-speech translation and question answering systems. He has more than 20 papers published in International Conferences and Journals along with 9 pending/granted patents. He has served as a session chair, a program committee member, and a review committee member of many international conferences including ACL, HLT, ICASSP, Interspeech, NAACL and COLING.
Dr Sunil Aryal is a lecturer in IT at the School of Information Technology, Faculty of Faculty of Science, Engineering and Built Environment, Deakin University, Australia. Prior to joining Deakin University in 2019, he worked as a lecturer at Federation University Australia and sessional teaching staff at various institutions. Before moving to academia, he worked in industry as a data engineer, software developer and IT support officer in Australia and Nepal. He received his PhD from Monash University, Australia. His research is in the areas of Data Mining (DM), Machine Learning (ML) and Artificial Intelligence (AI). He is interested in the applications of DM/ML/AI systems to solve real-world problems in various domains, particularly in healthcare, cyber security, energy and defence. Currently he is focussed on making ML systems robust, fair and explainable.
For more: https://sunilaryal.github.io/
Dr Anand Koirala is working as Postdoctoral Research Fellow at Institute for Future Farming Systems (IFFS), School of Health and Applied Sciences, CQUniversity, Australia. He obtained his PhD in Precision Agriculture- exploration of machine learning approaches, from CQUniversity. He has completed Master of Engineering Science (Electrical and Electronics) from University of Southern Queensland, Australia and Bachelors of Electronics and Communications Engineering form Tribhuvan University, Nepal. He is experienced in researching and developing machine vision solutions (deep learning and image processing) and decision support systems (yield prediction and mapping for farm management and market planning) for several industry partners. With numerous publications in related field, his research outcomes have been implemented in horticulture- for flower and fruit assessment in orchard (classification, detection, counting and sizing) and fisheries- optical grading of live fish. With interest in robotics and automation he has also developed machine vision software for the world’s first mango harvester which is still in developmental phase and was trialled across Australian orchards.
Personal Website: www.foraist.com/about-me/
In this talk, he will explore the application of machine vision and deep learning in Agriculture. Highlights will be object detection and classification using deep learning convolutional neural networks.
Dr. Bal Krishna Bal is Associate Professor & Head of Department of Computer Science & Engineering, Kathmandu University. He did PhD in Computer Science & Engineering from Kathmandu University and Masters and Bachelors in Informatics and Computer Engineering from Volgograd State Technical University, Russia. He is one of the pioneering researchers working with Artificial Intelligence(AI) and Nepali language, embarking in this field as a Natural Language Processing Researcher at Madan Puraskar Pustakalaya in 2005. Besides leading computer science and AI education and research, he is also associated with various innovative organizations such as KEIV Technologies, Language Technology Kendra, and VistaTec.
Personal Website: http://old.ku.edu.np/cse/faculty/bal/
Texts represent a rich source of data for processing and analysis, particularly in the context of their abundant usage on a daily basis in the web and other media. With the advent of advanced technologies like Machine Learning and Machine Intelligence, there is a growing trend towards applying the Machine Learning approaches to different Natural Language Processing (NLP) problems. In the first part of the talk, he will introduce Natural Language Processing as a domain and the standard steps applied to solve generic problems in NLP. Then in the second part of the talk, he will discuss the relevance and applicability of the Machine Learning approach to solving NLP problems focusing on classification and clustering problems in text processing.
Bhogendra Mishra is a spatial data scientist at Science Hub. In addition, he is a visiting faculty in Nepal Open University. His expertise is remote sensing data analysis for diverse applications that include but not limited to land cover change detection, crop monitoring, hydrology, disasters along with others. He completed Master’s degree in Computer Science from Tribhuvan University, Nepal, receiving “Nepal Bidhya Bhusan Kha” for his outstanding performance, and Master of Remote Sensing and GIS from Asian Institute of Technology, and received “The John A. Hones Prize” in recognition of the most outstanding academic performance and Doctorate in Engineering from Kyoto University Japan. He served in a number of development organizations as a subject specialist in Asia Pacific regions and as a researcher in ITC, Netherlands. He has published a number of scientific publications in the highly reputed journals in the field and presented his work in a large number of international conferences and workshops.
Personal Website: https://sciencehub.org.np/team/bhogendra-mishra-phd/
In this talk, he will assess the deep learning development methods based on taxonomies for the available journal papers that were published between 2010 and 2020 and their applications. Specifically, it includes methods used for determining model inputs, the approaches to subset dataset, model calibration and validation methods and best model structures for the different applications that includes but not limited to clustering, information retrieval, reconstruction and prediction of different thematic application such as urban, agriculture, forestry, hydrology, disasters, etc.
Bikash Gyawali is a Postdoctoral Researcher at the Open University, UK working in the Big Scientific Data and Text Analytics Group. His research domain is Natural Language Processing and he has published several research papers related to text mining, data analytics, natural language understanding and generation. He is currently working on big data systems for text mining of scientific documents in CORE.
In this talk, he will introduce the PySpark framework for big data processing and provide examples of real world application usage. This talk will be structured to meet the needs of both newcomers and experienced users - a walkthrough of environment setup and explanation of key concepts in big data processing will be presented followed by experiments and questionnaire session.
Binod Bhattarai is a Postdoctoral Research Associate in Imperial Computer Vision and Research Lab at Imperial College London. Before joining Imperial College London, he worked as a Data Scientist at Telenor Group in Bangkok. He obtained his doctoral degree from Normandie Universite, Caen, France in and Masters degree from Saarland University, Germany. His main research interests are in Natural language processing and computer vision. He has a number of papers published in high impact international conferences and journals such as CVPR, ECCV, ICASSP. He is involved as Industry co-ordinator in NAAMII, Nepal has been a program chair in the first and second Nepal winter school in Artificial Intelligence.
Personal Website: https://sites.google.com/view/bbinod
In this talk, he will explain and provide overview on a class of machine learning framework where for the given training data, the technique learns to develop new data with the same statistics as the training set. This framework is called Generative adversarial network (GAN).
Jhanak Parajuli is a researcher and data scientist, currently working in DHL express Germany. He received his doctoral degree in computer science and electrical engineering from Jacobs University Bremen in 2016 and has worked in different companies in Germany since 2017. His research works are related to interference management in wireless communications using non-linear manifold learning. He has experiences working with different machine learning and deep learning techniques and building the whole industrial pipeline to bring the model into production. He is also one of the founding members of machine learning and data science network (MLDSN) Nepal.
Personal Website: www.databigyan.com.
In this talk, he will explore the effective approaches and related machine learning algorithms to deal with time series data. Highlights will be Trend- seasonality Decomposition, Regression approaches, Boosting algorithms, Long Short Term Memory (LSTM) etc.
Jwala Dhamala is Research Scientist at Amazon Alexa NLU, Cambridge, US. Her current research interests are on fairness, accountability, and transparency in machine learning models with a specific focus on natural language processing applications. Prior to this role, Jwala received a doctoral degree from the College of Computing and Information Sciences at the Rochester Institute of Technology and a Bachelors of Computer Engineering from Pulchowk Campus, Tribhuvan University. During her PhD, she worked on Bayesian active learning models and generative models in the healthcare domain. Several of her research works have been published in top-tier conferences and journals (MICCAI, IPMI, IEEE TMI, MedIA, etc.). She was also the finalist for young scientist award for two years in succession at MICCAI 2018 and MICCAI 2019. During her PhD, she has also worked as a research intern at Philips research in the summer of 2018. She is an active member of the research community and has participated in workshop organization and reviewing in various conferences and journals.
Personal Website: http://jwaladhamala.com/
In this talk, she will elaborate on how modern machine learning and deep learning models can be applied for the personalization and uncertainty quantification of complex Multiscale Physics-based Simulations of Cardiac Electrophysiology.
Riwaj Sapkota is a co-founder of dstack.ai and also works as a senior product manager in Giesecke+Devrient Mobile Security, Germany. He has several years of both startup and large enterprise experience in Internet of Things (IoT) and data science. He has received MBA degree from University of California, Berkeley, Haas School of Business and Technical University of Munich, Germany. He has strong knowledge in Entrepreneurship and Entrepreneurial Thinking in Management.
Personal Website: https://www.linkedin.com/in/riwaj-sapkota/
In this talk, he will discuss on how to manage data science artifacts as a data scientist and also as a manager and start-up entrepreneur.
Tej Bahadur Shahi is an Assistant Professor at the Central Department of Computer Science and Information Technology, TU. Currently, He is pursuing his higher degree by research study at CQ University, Australia with RTP scholarship (On study leave). His research interest includes application of machine learning techniques in Natural Language Processing, Environmental Modelling, Crop Management and remote sensing. He has published a number of papers on Nepali language processing and carried out significant research in computer science at the University. He has served Nepal Government for more than four years as an information technology officer, before joining the University.
Personal Website: https://tejshahi.github.io/
In this tutorial, he will talk about the fundamental steps in supervised machine learning techniques, evaluation metrics and practical applications.