Dr. Sameer Maskey

Dr. 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.

Prof. Dr. Shashidhar Ram Joshi

Dr. Shashidhar Ram Joshi is a full time Professor in Department of Electronics and Computer Engineering, Institute of Engineering, Pulchowk. He was a visiting Professor at South Asian University (SAARC University) in India for one year and a guest lecturer in Japan, Korea and Norway. He was also a research fellow in Osaka Sangyo University, Japan for one year. He received a PhD in Electronics and Computer Engineering from Institute of Engineering, Pulchowk, in 2007 with research works done in Japan, Norway and Nepal. He completed his masters degree from University of Calgary, Canada in 1992 and Bachelors degree in Electrical Engineering with first class first gold medal from REC Surat, India in 1984. He is a member of Subject Committee and Faculty Boards of different Universities in Nepal and has remained in a selection committee member for South Asian University for 4 times. He has worked as a Computer Consultant in several International Agencies, Governmental and Non-Governmental sectors. He has supervised more than 50 Masters Thesis, 2 Ph.D. Thesis and 4 Ph.D.Thesis are in progress. He has published several papers in International and National Journals and has given a number of keynote addresses in international and national conferences. His research fields of interests are: Image Processing, Pattern Recognition, Machine Learning, Artificial Intelligence and Particle Tracking Velocimetry.

Dr. Sunil Aryal

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

Application of machine vision and deep learning in agriculture.

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: https://foraist.com/about-me

Email: a.koirala@cqu.edu.au

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.

Ashok Kumar Pant

Practical Reinforcement Learning

Ashok Kumar Pant is a VP of Engineering and a co-founder at Treeleaf Technologies Pvt. Ltd. His field of interest includes: Deep Learning, Computer Vision, and Natural Language Processing. His focus is on practical machine learning and technology localization. He has published a number of research papers in computer vision and NLP related fields. He completed his Master’s degree in Computer Science and IT from Tribhuvan University, Nepal in 2012.

Personal website: http://ashokpant.github.io/

Reinforcement learning is one of three basic machine learning paradigms along with supervised and unsupervised learning. In this talk, he will discuss about the practical applications of Reinforcement Learning.

Dr. Bal Krishna Bal

Machine Learning Approach to Solving Natural Language Processing Problems

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.

Dr. Bhogendra Mishra:

Methods used for the development of deep learning for remote sensing applications: current status and future direction

Dr. 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.

Dr. Bikash Gyawali

Big Data Processing with PySpark

Dr. 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.

Dr. Binod Bhattarai

Generative Adversarial Networks

Dr. 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).

Dr. Dovan Rai

Causal Modelling

Dr. Dovan Rai has PhD from Computer Science Department at Worcester Polytechnic Institute (WPI), USA. Her dissertation titled ‘Modes and Mechanisms of Game-like Interventions in Intelligent Tutoring Systems’ explored ways we can integrate game elements in Intelligent Tutoring Systems to maximize both rich learning experience and robust learning gains. The research was at the intersection of Educational Psychology, Game Design, Datamining and Artificial Intelligence. She designed and developed three game-like learning environments and used Bayesian and Causal modeling to explore the variable space.

She is passionate about education technology and worked at OLE Nepal, where she designed educational software for public schools in Nepal. She is based in Nepal and running Sujhaab Chautaari, a career counseling platform for youth of Nepal.

Dr. Jhanak Parajuli

Effective Approaches and Machine Learning Algorithms to deal with Time Series Data

Dr. 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: https://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.

Dr. Jwala Dhamala

Applications of Deep Learning to Multi-scale Physics-based Simulators

Dr. 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.

Prof. Dr. Manish Pokharel

Artificial Intelligence (AI) in Education 4.0 during/after Pandemic

Dr. Manish Pokharel is a Professor at the Department of Computer Science and Engineering, Kathmandu University, Nepal. He holds a post doctorate and PhD from Korea Aerospace University, South Korea from 2007-2013. His research interests are: Enterprise Architecture, E-Government, Cloud Computing, Big Data, Internet of Things, Artificial Intelligence and Deep Learning. He taught various graduate level courses in Korea Aerospace University from 2008 - 2010. From the last 25 years, he has been involved with department of Computer Science and Engineering Kathmandu University as a lecturer, Assistant Professor, Head of Department and Professor. He is also appointed as a member of High Level ICT Council under the chairmanship of Prime Minister of Nepal.

In this talk, he will discuss the applications of artificial intelligence in education, especially during and after the COVID pandemic.

Dr. Rakesh Katuwal

Shallow and deep learners for tabular dataset

Dr. Rakesh Katuwal is a machine learning engineer at Fusemachines. Prior to joining Fusemachines, he worked as a project officer at Nanyang Technological University (NTU), Singapore. He received his B. Eng. degree from Kathmandu University (KU) and his doctorate from NTU in 2014 and 2020 respectively. His research interests are in ensemble learning, deep learning and open-set recognition. He has a number of peer-reviewed papers and has served as a reviewer for numerous conferences and journals such as WCCI, IEEE-TPAMI, IEEE-TNNLS, Neural Networks, Pattern Recognition etc.

Personal Website: https://www.linkedin.com/in/katuwal-rakesh/

Tabular dataset, where information is stored in the rows and the columns of the table, is a common dataset found in the real-world. In this talk, he will discuss tabular datasets and provide an overview of several shallow and deep learning algorithms employed for tabular data learning. Shallow learners such as tree based algorithms (Random Forest, Gradient Boosted Trees), and deep learners such as Deep Neural Networks, and the hybrid of them are covered in this talk.

Sarbagya Ratna Shakya

Deep learning for video classification: Application and state-of-the-Art Approach.

Sarbagya Ratna Shakya is currently a Ph.D. student in school of computing sciences and computer Engineering, University of Southern Mississippi, USA. He has received his B.E. in Electronics Engineering from National College of Engineering, Tribhuvan University and M.E. in Computer Engineering from Nepal College of Information Technology, Pokhara University.His research interests includes Machine Learning, Deep learning, Internet of things(IoT) and High Performance Computing. He is currently working on recognizing Human activity in real time with video data using Deep learning Algorithms.

personal website: https://www.linkedin.com/in/sarbagya-shakya-b2a30716a/

In this talk he will discuss on different deep learning approaches that has been implemented in recent years for video classification along with its its application in real world problems.

Tej Bahadur Shahi

Supervised Machine Learning Pipeline: A Step by Step Tutorial

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.

Dr. Bhoj Raj Ghimire

Dr. Bhoj Raj Ghimire, Assistant Professor at Nepal Open University is a researcher, an academician, a system analyst, and a technology enthusiast accumulating professional experience in national and international institutions for a decade and a half. His field of expertise includes but not limited to ICT project management, digital governance and transformation, geospatial data analysis and application

Er. Jnaneshwar Bohara

Jnaneshwar Bohara is working as a computer engineer in the Government of Nepal. He is a gold medalist from Tribhuvan University, Institute of Engineering in Masters of Computer Systems and Knowledge engineering. He has more than 10 years of experience in IT field and has been working on National ID project from its beginning. Before joining Government of Nepal, he worked in Verisek Information Technology for more than 5 years in the capacity of Senior Software engineer and Technical lead. His core expertise is Java and Big Data tools from Hadoop ecosystem like MapReduce, Hbase, MongoDB, spark. His research entitled "MapReduce based approach to longest common sequence in BioSequences" is published as a book from Lambert publication Germany and is available in Amazon as ebook.

Er. Shaligram Parajuli

Shaligram Parajuli is a multi dynamic personality. He is working as a Deputy Manager in a big Nepal Telecom as well as a Lawyer with the specialization of Cyber Law and Corporate Law with License for arbitration and consultation from Nepal Council of Arbitration (NEPCA). He possesses a strong problem-solving ability, excellent verbal and written communication skills, and has experience with a wide range of computer systems and security tools, Management and leadership ability, Flexibility and the ability to multi-task in a fast-paced atmosphere and integrity.

Suresh Gautam

Suresh Gautam is the Chief Executive Officer and Co-Founder at ExtensoData. Prior to ExtensoData, he was Senior Solution Architect at Verscend Technology, where he scaled & architected workflow framework for US healthcare data ingestion and ETL process, it increases the platform stability and realize operational efficiencies in a data warehouse workflow, and also establish industry standard process in the software development and maintenance team.

Before joining Verscend, Suresh has been CTO at eSewa where he leads digital mobile wallet development team, and Engineering Manager at VeriskHealth where he directs the DBA and DevOps team and drives multi-tera bytes production data warehouse infrastructures and internal tools development team. Suresh holds Masters of Computer Application from Purvanchal Vishwavidyalaya, Bsc. IT from Sikkim Manipal University, and gained several certifications, specifically in the database, development process, big data, and data science domain.

Surya Basnet (Moderator)

Surya Basnet is a Chief Executive Officer and Co-Founder of Mountech Solutions, organizing committee member of NWMLDS, Head Of Department at Bachelor in Information Management, KIST College Kamalpokhari Kathmandu.

He is also the Faculty member of The British College Kathmandu Affiliation with Leeds Beckett University UK and he is also the visiting Faculty Member of Amrit Science Campus. He has more than 15 years of experience in the field of Computer Science in education as well as industry and his interests are Oracle database cloud, Blockchain Technology , Cloud 'Computing and Machine Learning and Data Science'.

Amir Rajak

Amir Rajak is a graduate from Asian Institute of Technology (AIT), Thailand in 2019 with M. Sc. in Data Science and Artificial Intelligence. Currently, he is working as a Research Engineer at the AI Center of AIT. His research is primarily focused towards deep learning based computer vision systems for face detection, facial analysis, face recognition and human behavior analysis. He enjoys working on any data science or AI projects involving image processing, NLP and machine learning. Prior to pursuing his masters degree, he worked for around 5 years in Nepal in the field of data analysis and data warehousing in companies like Deerwalk Services, Verscend Technologies and LIS Nepal.

Website: https://taureanamir.github.io/profile/

Bal Krishna Nyaupane

Bal Krishna Nyaupane, is a full time Assistant Professor in Department of Electronics and Computer Engineering, Institute of Engineering, Paschimanchal Campus, Pokhara. He completed his masters degree from Malardalen University, Sweden and Bachelors degree in Computer Engineering with Distinction from Institute of Engineering, Pulchowk. He has more than 12 years of experience in the field of Computer Engineering in education as well as industry and his interests are Machine Learning and Data Science, Deep learning, Big Data Analytics and Natural Language Processing.

Poshan Pandey

Poshan Pandey is a machine learning and data science enthusiast with strong background knowledge on programming and mathematics. He is also working as a software developer at Ekbana Solutions.

Ramit Sharma

Ramit Sharma is studying Masters in Autonomous systems at Bonn-Rhein-Sieg University of Applied scinces, Germany with specialization in Artificial intelligence. He has worked as Machine Learning intern at Insoro GmbH and has also worked in Audi, Germany as digitization for industrial engineering intern. His research interests are: Application of Deep learning in the field of Natural Language Processing and has done a number of projects in this domain.

Dr. Remun Koirala

Remun Koirala obtained his Masters in communication, systems and Electronics from Jacobs University Bremen Germany and his PhD in the topic 5G Localization and communication from CEA Leti, Grenoble France. Remun possesses deep understanding of mathematics and machine learning algorithms.