Organized by North America Taiwanese Engineering and Science Association (NATEA), Silicon Valley Chapter and IEEE SCV Computer Chapter.

Theme: Emerging Trends in Applied Data Science 

4:00 PM Keynote: Introductions and Keynote: Active learning and explainable AI for medical imaging informatics – infectious disease outbreak - Dr. KC Santosh

5:00 PM Machine Learning for Misinformation Containment: A Candid Assessment of the State of the Art - Dr. Vishnu S. Pendyala

6:00 PM Yolo4 and Its Applications - Dr. Mark Liao

7:00 PM 3 D Digital Pathology Inspired AI for Precision Diagnosis Dr. Yen-Yin Lin

Introductions and Keynote: Active learning and explainable AI for medical imaging informatics – infectious disease outbreak

Synopsis: When we consider AI for healthcare, infectious disease outbreak is no exception. The talk will begin with machine learning models that help in not only predicting but also detecting abnormalities due to infectious diseases such as Pneumonia, TB, and Covid-19. Prof. KC Santosh will open my talk with infectious disease prediction models and unexploited data, where we will learn that predictive analytical tools are close to garbage-in garbage-out (at least for Covid19). He will then cover multimodal learning and representation based on both shallow learning (handcrafted features) as well as deep learning (deep features) that typically apply on medical imaging tools. Like in computer vision, Prof. KC will open an obvious question, how big data is big in addition to common techniques: data augmentation and transfer learning. With all these facts, as most of models are limited to education and training, he will end the talk with the statement “ML innovation should not be limited to building models.” What we need is #ExplainablableAI in #ActiveLearning framework.

Speaker: Professor KC Santosh is the Chair of the Department of Computer Science (CS) at the University of South Dakota (USD). Prior to that, he worked as a research fellow at the U.S. National Library of Medicine (NLM), National Institutes of Health (NIH). He worked as a postdoctoral research scientist at the LORIA research center, Université de Lorraine in direct collaboration with industrial partner ITESOFT, France. He also served as a research scientist at the INRIA Nancy Grand Est research center (France), where he received his PhD in Computer Science – Artificial Intelligence. His research projects, primarily in Applied AI, are funded (of more than $2m) by multiple agencies, such as SDCRGP, Department of Education, National Science Foundation, and Asian Office of Aerospace Research and Development. He has demonstrated expertise (with 10 books, 220+ research articles, and 20+ journal edited issues, as of Dec. 2021) in artificial intelligence, machine learning, pattern recognition, computer vision, image processing, and data mining with applications such as medical imaging informatics, document imaging, biometrics, forensics, and speech analysis. He completed leadership and training programs for Deans/Chairs (organized by the Councils of Colleges of Arts & Sciences (U.S. 21)) and PELI – President’s Executive Leadership Institute (USD 21). He is highly motivated/interested in academic leadership. To name a few, Prof. Santosh is the proud recipient of the Cutler Award for Teaching and Research Excellence (USD 2021), the President’s Research Excellence Award (USD 2019) and the Ignite Award from the U.S. Department of Health & Human Services (HHS 2014).

5:00 PM Machine Learning for Misinformation Containment: A Candid Assessment of the State of the Art

Synopsis: Misinformation containment has been proven to be NP-hard more than a decade ago. It is undoubtedly a complex problem to solve and appropriately attracted plenty of attention from the research community. A wide variety of machine learning algorithms such as support vector machines and logistic regression, ensemble techniques like random forest and Adaboost, deep learning frameworks such as LSTM and GAN, language models like BOW / TF-IDF and BERT, and many more have been tried out in the attempts to solve the problem. In terms of feature engineering as well, no stone has been left unturned. Manual feature extraction, graph embeddings, and other approaches to representational learning have all been tried. Not just supervised and unsupervised learning, but various other types of learning such as few-shot learning, meta learning, transfer learning, self-supervised learning, semi-supervised learning, reinforcement learning, and active learning have been explored extensively for the problem. Despite the voluminous research literature purporting to solve the problem using machine learning methods, misinformation containment is largely unsolved and is in fact growing by the day. It is therefore pertinent to understand this huge disconnect between what is claimed in the literature and the actual reality. The talk will provide insights into the current state-of-the-art solutions and analyze why they are not helping enough. The talk will present some future directions that in the speaker’s opinion hold the promise and explain why there is hope.

Speaker: Dr. Vishnu S. Pendyala is a faculty member of the Department of Applied Data Science at San Jose State University and the chair of IEEE Computer Society, Silicon Valley Chapter. He has over two decades of experience with software industry leaders like Cisco and Synopsys in the Silicon Valley, USA. Dr. Pendyala served on the Board of Directors, Silicon Valley Engineering Council during 2018-2019. During his recent 3-year term as an ACM Distinguished speaker and before that as a researcher and industry expert, he gave numerous (50+) invited talks. He holds MBA in Finance and PhD, MS, and BE degrees in Computer Engineering from US and Indian universities. Dr. Pendyala taught a one-week course sponsored by the Ministry of Human Resource Development (MHRD), Government of India, under the GIAN program in 2017 to Computer Science faculty from all over the country and delivered the keynote in a similar program sponsored by AICTE, Government of India in 2022. Dr. Pendyala’s book, “Veracity of Big Data: Machine Learning and Other Approaches to Verifying Truthfulness” made it to several libraries, including those of MIT, Stanford, CMU, and internationally.

Dr. Mark Liao, https://scholar.google.com/citations?user=_IXt8boAAAAJ

Topic: Yolo4 and Its Applications

Dr. Yen-Yin Lin

Topic: 3 D Digital Pathology Inspired AI for Precision Diagnosis

Event Details

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