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"Chemical Engineers in Computing and Systems Technology, AIChE" <[log in to unmask]>
=?windows-1252?Q?Aditya_Tulsyan?= <[log in to unmask]>
Tue, 22 May 2018 17:11:57 -0400
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=?windows-1252?Q?Aditya_Tulsyan?= <[log in to unmask]>
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Dear Colleagues, 

We are organizing a one-day workshop on  "Advanced Tools for Process Data Analytics" 
at the 10th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM 
2018)  in Shenyang, China from July 25-27, 2018. The workshop overview and agenda 
are given below.


We are currently at the cusp of what is considered the fourth industrial revolution. This 
revolution is driven by the ubiquitous cyber-physical systems, algorithmic 
developments  in artificial intelligence, gargantuan computing power, inexpensive 
memory and the  gigantic volumes of data that are being collected. The process 
industries are in  possession of treasure troves of heterogeneous data that is gravely 
underutilized. The  competitive global environment and the ever-increasing demands on 
energy, environment  and quality are subjecting these industries to a high level of 
economic pressure. The incredible volumes of data that they already possess are 
poised to provide a level of automation and efficiency never seen before and thus 
alleviate the economic and  competitive pressures.

Process industries have been using data analytics in various forms for more than three 
decades. In particular, statistical techniques such as principal component analysis 
(PCA),  partial least squares (PLS), canonical variate analysis (CVA) and time series 
methods for modeling such as maximum likelihood estimation, prediction error methods 
have been  extensively applied on industrial data. The recent developments in machine 
learning and  artificial intelligence provide a new opening for using process data on 
large scale  problems. However, in order to successfully apply machine learning 
methods to process  data, researchers require not only a high-level understanding of 
the algorithms but also strong programming knowledge in packages such as Python, 
TensorFlow, Keras and Jupyter.


Starting with an elementary introduction to statistics and probability, we will develop 
various regression, classification, dimensionality reduction and advanced learning 
algorithms that are of interest to engineers. In addition, various widely-used machine 
learning software packages will be introduced. Registrants will solve exercises and 
receive takeaway software code to implement these algorithms. The course will start 
with  basics of probability and statistics, under-fitting, overfitting and bias-variance 
tradeoff. After that, the course will consist of the following:

1.  Big Data Analytics (Guest Lecture 1)

2.  Classification Algorithms
a. k-nearest neighbors algorithm
b. k-means algorithm
c. Support Vector Machines
d. Naive Bayes' Classifier and Decision Trees

3. Regression Algorithms
a. Linear Least Squares
b. Non-linear Least Squares
c. Kernel Regression

4. Dimensionality Manipulation Algorithms
a. Principal Component Analysis (PCA)
b. Principal Least Squares (PLS)
c. Isometric Mapping (ISOMAP)
d. Local Linear Embedding (LLE)
e. Canonical Correlation Analysis
f. Multidimensional Scaling (MDS)

5. Alarm Management (Guest Lecture 2)

6. Advanced Learning Algorithms
a. Artificial Neural Networks
b. Deep Learning
c. Gaussian Processes
d. Bayesian Networks
e. Deep Reinforcement Learning

a. Prof.  Bhushan Gopaluni, University of British Columbia, Canada
b. Dr Aditya Tulsyan, Senior Engineer, Amgen Inc., USA
c. Yiting Tsai, University of British Columbia, Canada
d. Lee Repon, University of British Columbia, Canada

Guest Speakers: 
a. Prof. Richard D. Braatz, Massachusetts Institute of Technology, USA
b. Prof. Sirish L. Shah, University of Alberta, Canada

Workshop Website:
Registration Website:

Please feel free to contact me if you require any additional information. 



Aditya Tulsyan, PhD.,
Senior Engineer, 
Digital Integration and Predictive Technologies,
Amgen Inc.,
Email: [log in to unmask]