I hold a B.Sc. (Econ.) degree from the National University of Mongolia, as well as an M.Sc. and Ph.D. degrees from Chungbuk National University, South Korea. Throughout my academic career, I have been actively involved in numerous research projects pertaining to deep learning-based time series forecasting, including but not limited to, Infectious Disease Forecasting, Short-term Export and Import Forecasting, and Demand Forecasting for Postal Delivery Service.

My research interests are primarily focused on the development of adaptive deep learning models and their applications across several domains, including financial, medical informatics, and public health informatics. I am passionate about leveraging the power of deep learning techniques to solve complex problems in these domains, and have consistently demonstrated my expertise through various publications in reputable journals and conferences.

I am dedicated to continuously expanding my knowledge and skills in the field of deep learning, and am committed to staying at the forefront of emerging trends and techniques. Through my research and academic pursuits, I strive to make meaningful contributions to the field of data science and to advance our understanding of deep learning and its applications.

EXPERIENCE

Chimege LLC, AI Engineer                       2023-currently

Empasoft Institute of Technology, Lecturer     2023-currently

Trade Development Bank of Mongolia, Data Scientist                          2022-2023

CYAN, quant (part time, remote)                                                                        2022-2023

Chungbuk National University, Post Doctoral Researcher     2022-2023

Trade Development Bank of Mongolia, Machine Learning Consultant        2021-2022

Chiang Mai University, Thailand, Research Intern           2019

Trade Development Bank of Mongolia, Market Risk Analyst                 2012-2016

National University of Mongolia, Research Intern       2012

Global Research Institute of Mongolia, Researcher                 2011-2012

PUBLICATIONS

Refereed Journals 

• Munkhdalai, L., Munkhdalai, T., Hong, J.E., Pham, V. H., Li, M., Ryu, K. H & Theera-Umpon, N. Discrimination Neural Network Model for Binary Classification Tasks on Tabular Data. IEEE Access, 2023. (SCIE) IF 3.367.

• Munkhdalai, L.,  Munkhdalai, T., Pham, V. H., Hong, J.E., Ryu, K. H & Theera-Umpon, N. Neural Network-Augmented Locally Adaptive Interpretable Regression Model for Tabular Data, Sustainability, 2022, (SCIE) IF: 3.889 

• Munkhdalai, L., Munkhdalai, T., Pham, V. H., Li, M., Ryu, K. H & Theera-Umpon, N. Recurrent Neural Network-Augmented Adaptive Interpretable Regression for Multivariate Time-series Forecasting. IEEE Access, 2022. (SCIE) IF 3.367.  

• Munkhdalai, L., Ryu, K. H., Namsrai, O. E., & Theera-Umpon, N. A Partially Interpretable Adaptive Softmax Regression for Credit Scoring. Applied Sciences, 2021, 11(7), 3227. (SCIE) IF 2.679 

• Batbaatar, E., Park, K. H., Amarbayasgalan, T., Davagdorj, K., Munkhdalai, L., Pham, V. H., & Ryu, K. H. Class-Incremental Learning With Deep Generative Feature Replay for DNA Methylation-Based Cancer Classification. IEEE Access, 2020, 8, 210800-210815. (SCIE) IF 3.367.

• Munkhdalai, L., Park, K. H., Batbaatar, E., Theera-Umpon, N., & Ryu, K. H. Deep Learning-Based Demand Forecasting for Korean Postal Delivery Service. IEEE Access, 2020, 8, 188135-188145. (SCIE) IF 3.367.

• Munkhdalai, L., Munkhdalai, T., & Ryu, K. H. GEV-NN: A deep neural network architecture for class imbalance problem in binary classification. Knowledge-Based Systems, 2020, 194, 105534. (SCIE) IF 8.038. code; pdf

• Munkhdalai, L., Munkhdalai, T., Park, K. H., Lee, H. G., Li, M., & Ryu, K. H. Mixture of activation functions with extended min-max normalization for forex market prediction. IEEE Access, 2019, 7, 183680-183691. (SCIE) IF 3.367. code; pdf

• Munkhdalai, L., Munkhdalai, T., Park, K. H., Amarbayasgalan, T., Batbaatar, E., Park, H. W., & Ryu, K. H. An end-to-end adaptive input selection with dynamic weights for forecasting multivariate time series. IEEE Access, 2019, 7, 99099-99114 code; pdf

•  Munkhdalai, L., Munkhdalai, T., Namsrai, O. E., Lee, J. Y., & Ryu, K. H. An empirical comparison of machine-learning methods on bank client credit assessments. Sustainability, 2019, 11(3), 699. (SCIE) IF: 3.251. code; pdf

• Musa, I., Park, H. W., Munkhdalai, L., & Ryu, K. H. Global research on syndromic surveillance from 1993 to 2017: bibliometric analysis and visualization. Sustainability, 2018, 10(10), 3414. (SCIE) IF 3.251

• Li, D., Park, H. W., Batbaatar, E., Munkhdalai, L., Musa, I., Li, M., & Ryu, K. H. Application of a mobile chronic disease health-care system for hypertension based on big data platforms. Journal of Sensors, 2018. (SCIE) IF 2.137

• Yang, E., Park, H. W., Choi, Y. H., Kim, J., Munkhdalai, L., Musa, I., & Ryu, K. H. A simulation-based study on the comparison of statistical and time series forecasting methods for early detection of infectious disease outbreaks. International journal of environmental research and public health, 2018, 15(5), 966. (SCIE) IF: 3.390.

Conference and Workshop Papers

• Park, K. H., Munkhdalai, L., & Ryu, K. H. Feature Selection Deep Learning Model considering Time Series Prediction. In Proceedings of the Korea Information Processing Society Conference, 2021, pp. 509-512. Korea Information Processing Society.

• Davagdorj, K., Park, K. H., Amarbayasgalan, T., Munkhdalai, L., Wang, L., Li, M., & Ryu, K. H. BioBERT Based Efficient Clustering Framework for Biomedical Document Analysis. In International Conference on Genetic and Evolutionary Computing, 2021, pp. 179-188. Springer, Singapore.

• Munkhdalai, L., Davagdorj, K., Pham, V. H., & Ryu, K. H. Adaptive Softmax Regression for Credit Scoring. In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 2021, pp. 409-417. Springer, Singapore.

• Munkhdalai, L., Pham, V. H., & Ryu, K. H. Bayesian Meta Regression. In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 2021, pp. 52-59. Springer, Singapore. (Best Student Paper Award)

• Amarbayasgalan, T., Park, K. H., Munkhdalai, L., & Ryu, K. H. Attention-Based Deep Neural Network for Coronary Heart Disease Risk Prediction. In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 2021, pp. 401-408. Springer, Singapore.

• Park, K. H., Pham, V. H., Davagdorj, K., Munkhdalai, L., & Ryu, K. H. A subtype classification of hematopoietic cancer using machine learning approach. In Asian Conference on Intelligent Information and Database Systems, 2021, pp. 113-121. Springer, Singapore.

• Munkhdalai, L., Li, M., Theera-Umpon, N., Auephanwiriyakul, S., & Ryu, K. H. VAR-GRU: A hybrid model for multivariate financial time series prediction. In Asian Conference on Intelligent Information and Database Systems, 2020, pp. 322-332. Springer, Cham. (Best Student Paper Award)

• Munkhdalai, L., Lee, J. Y., & Ryu, K. H. A Hybrid Credit Scoring Model Using Neural Networks and Logistic Regression. In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 2020, pp. 251-258. Springer, Singapore.

• Munkhdalai, L., Wang, L., Park, H. W., & Ryu, K. H. Advanced neural network approach, its explanation with lime for credit scoring application. In Asian Conference on Intelligent Information and Database Systems, 2019, pp. 407-419. Springer, Cham.

• Munkhbat, K., Munkhdalai, L., Batbaatar, E., & Ryu, K. H. Advisor application for child healthcare. 한국콘텐츠학회 ICCC 논문집, 2018, pp. 47-48.

• Munkhdalai, L., & Ryu, K. H. Korean Stock Market Prediction using Recurrent Neural Networks with Extended Min-Max Normalization, Korea BI Data Mining Spring Conference, 2018.

• Munkhdalai, L., Namsrai, O., & Ryu, K. H. Credit scoring with deep learning. In 4th International Conference on Information, System and Convergence Applications, 2018, pp. 1-5.

• Munkhdalai, L., Namsrai, O. E., & Ryu, K. H. A hybrid approach based on long short-term memory networks and vector autoregression for stock market price prediction. In Proceedings of the FITAT, 2017, pp. 1-4.

ACHIEVEMENTS

o   3rd Place Mongolian Students' Statistical Olympiad 

o   1st and 3rd  Place Mongolian Students' Mathematical Olympiad 

 o   We participated in IDAO 2020.  We got 92.88 score on Track 1 and 92.61 score on Track 2.  Our solution is available.

o   We got the Best Student Paper Award in ACIIDS 2020 (12th Asian Conference on Intelligent Information and Database Systems).

o   We participated in Data Nomads competition. The problem was to predict the arrival time at bus stops. Our solution scored third place out of 414 entries.

o   We participated in OSIC Pulmonary Fibrosis Progression on Kaggle platform. The aim of this competition was to predict a patient’s severity of decline in lung function based on a CT scan of their lungs.  Our solution scored 36th place (silver medal) out of 39,082 entries.  Our solution is available

o   We participated in Mongolian Plate Number Recognition on Kaggle platform. The task was to recognize plate number from images. Our solution scored 2nd place out of 326 entries.  Our solution is available

o   We participated in Mongolian Air Pollution Forecasting competition on Kaggle platform. The task was to forecast hourly air quality index from the historical weather and climate data. Our solution scored 2nd place out of 1504 entries. Our solution is available

o   We participated in IDAO 2021. Our solution scored 4th place in 1st round and 5th place in 2nd round.

o   We participated in Shopee - Price Match Guarantee competition on Kaggle platform. The aim of this competition is to build a model that predicts which items are the same products. Our solution scored 96th place (silver medal) out of 51,077 entries. Our solution is available

o   We participated in MLB Player Digital Engagement Forecasting competition on Kaggle platform. The aim of this competition is to forecast four different measures of engagement for a subset of MLB players who are active in the 2021 season. Our solution scored 42th place (silver medal) out of 660 entries. Our solution is available

o   We participated in CommonLit Readability Prize competition on Kaggle platform. The aim of this competition is to build algorithms to rate the complexity of reading passages for grade 3-12 classroom use. Our solution scored 63th place (silver medal) out of 72,150 entries. Our solution is available

o   We participated in NUM Challenge competition on Kaggle platform. The aim of the competition is to build a model that extracts the wordnet translations from a bilingual dictionary. Our solution scored 3rd place out of 327 entries. Our solution is available

o   We participated in Optiver Realized Volatility Prediction competition on Kaggle platform. The aim of this competition is to build models that predict short-term volatility for hundreds of stocks across different sectors. Our solution scored 236th place (bronze medal) out of 3,852 teams. Our solution is available

o  We participated in Mongolian Ger Detection competition on Kaggle platform. The aim of the competition is to build a model that detects Mongolian ger from satellite images. Our solution scored 1st place out of 425 entries. Our solution is available.

o  We participated in Women in Data Science (WiDS) Datathon 2023 on Kaggle platform. The aim of the competition is to build a model for forecasting sub-seasonal temperatures (temperatures over a two-week period, in our case) within the United States. Our solution scored 2nd place out of 22,074 entries and 697 teams. Our solution is available.

o  We participated in Bengali.AI Speech Recognition on Kaggle platform. The aim of the competition is to recognize Bengali speech from out-of-distribution audio recordings. We built a model trained on the first Massively Crowdsourced (MaCro) Bengali speech dataset with 1,200 hours of data from ~24,000 people from India and Bangladesh. Our solution scored 1st place out of 12,710 entries and 753 teams. Our solution is available.

PROFESSIONAL ACTIVITIES (paper reviewing)

IEEE Access

PLOS One  

BMJ Open 

Journal of Flood Risk Management  

IEEE Transactions on Neural Networks and Learning Systems 

The program committee of ACIIDS 2021 (13th Asian Conference on Intelligent Information and Database Systems)      

The program committee of ACIIDS 2022 (14th Asian Conference on Intelligent Information and Database Systems)