VMD-CEEMD decomposition algorithm

Variational Modal Decomposition (VMD) is an adaptive signal decomposition algorithm. It can decompose the signal into multiple components, and its essence and core idea is the construction and solution of the variational problem. VMD is commonly used to process non-linear signals and can decompose complex raw data to obtain a series of modal components16.

It can effectively extract the features of runoff data and reduce the influence of its nonlinearity and non-stationarity on the prediction results. The main steps of the VMD algorithm are: (1) The original signal is passed through the Hilbert transform to obtain a series of modal functions u, which are calculated to obtain the unilateral spectrum; (2) Transform the spectrum into the fundamental frequency band and construct the corresponding constrained variational problem by estimating the bandwidth; (3) Converting a constrained variational problem into an unconstrained variational problem17.

The calculated equations are as follows:

$$ L = left( {left{ {u_{k} } right},left{ {omega _{k} } right},lambda } right) = alpha mathop sum limits_{{k - 1}}^{K} left| {partial _{t} left[ {left( {delta left( t right) + frac{j}{{pi t}}} right)*u_{k} left( t right)} right]e^{{ - jomega _{t} t}} } right|_{2}^{2} + left| {fleft( t right) - mathop sum limits_{{k = 1}}^{K} u_{k} left( t right)} right|_{2}^{2} + left[ {lambda left( t right),fleft( t right) - mathop sum limits_{{k = 1}}^{k} u_{k} left( t right)} right], $$

(1)

where ({u}_{k}left(tright)) and ({omega }_{k}) are the modal components and the corresponding center frequencies, respectively, is the penalty function and is the Lagrange multiplier. The results of several experiments show that the decomposition results are better when is taken as 2000, so in this paper, is set to 2000. The k modal components of the VMD are solved by using the alternating direction method of multiplicative operators to find the saddle points of the unconstrained variational problem.

There are some potential features of the VMD decomposed runoff residual sequence. The CEEMD decomposition method is a new adaptive signal processing method. Compared with the commonly used EEMD method, its decomposition efficiency and reconstruction accuracy are higher, and it better exploits the potential features of residual sequences.

The EMD method is a method proposed by Huang et al. for signal time-domain decomposition processing, which is particularly suitable for the analysis of nonlinear and non-stationary time series18. In order to cope with the modal confusion problem of the EMD method, Wu et al.19 proposed an overall average empirical modal decomposition. The EEMD method effectively suppresses the modal aliasing caused by the EMD method by adding white noise to the original signal several times, followed by EMD decomposition, and averaging the EMD decomposed IMFs as the final IMFs20.

CEEMD by adding two Gaussian white noise signals with opposite values to the original signal, which are then subjected to separate EMD decompositions. In ensuring that the decomposition effect is comparable to that of EEMD, CEEMD reduces the reconstruction error induced by the EEMD method. After the original signal x(t) is decomposed by CEEMD, the reconstructed signal can be represented as

$$xleft(tright)=sum_{i=1}^{n}IM{F}_{i}left(tright)+{r}_{n}left(tright)$$

(2)

In Eq.(2), (IM{F}_{i}left(tright)) is the intrinsic modal function component; ({r}_{n}(t)) is the residual term; and n is the number of intrinsic modal components when ({r}_{n}(t)) becomes a monotonic function. The original sequence is finally decomposed into a finite number of IMFs.

In order to accurately predict the runoff sequence, this paper establishes a kernel limit learning machine prediction model based on the kernel function optimised by the nature-inspired BOA algorithm.

In Fig.1, the ELM input weights (omega in {R}^{XY}) (X and Y are the input and hidden layer neural networks, respectively) and biases are randomly generated21. Extreme learning machines require less manual tuning of parameters than BP neural networks, and can be trained on sample data in a shorter period of time, with fast learning rate and strong generalisation ability.

Structure of the KELM model.

Its regression function with output layer weights is:

$$left{begin{array}{c}fleft(xright)=h(x)beta =Hbeta \ {{varvec{H}}}^{T}{left(frac{1}{C}+{varvec{H}}{{varvec{H}}}^{T}right)}^{-1}Tend{array}right.$$

(3)

where: (fleft(xright))-model output; (x) -sample input ({varvec{h}}({varvec{x}})) and ({varvec{H}})-hidden layer mapping matrix; (beta ) -regularisation parameter; T-sample output vector.

Conventional ELM prediction models (solved by least squares) tend to destabilise the output when there is potential covariance in the sample parameters. Therefore, Huang et al.22 used the Kernel Extreme Learning Machine (KELM) with kernel function optimisation. Based on the kernel function principle, KELM can project covariant input samples into a high-dimensional space, which significantly improves the fitting and generalisation ability of the model. In addition, this model does not need to set the number of hidden layer nodes manually, reducing the number of spatial training bits and training time. The model output equation is:

$$fleft(xright)={left[begin{array}{c}K(x,{x}_{1})\ vdots \ K(x,{x}_{N})end{array}right]}^{T}{left(frac{1}{C}+{{varvec{Omega}}}_{ELM}right)}^{-1}$$

(4)

where: K(({x}_{i},{x}_{j}))-kernel function; ({{varvec{Omega}}}_{ELM})-kernel matrix, which is calculated as:

$$left{begin{array}{c}{{varvec{Omega}}}_{ELM}=H{{varvec{H}}}^{T}\ {{{varvec{Omega}}}_{ELM}}_{i,j}=hleft({x}_{i}right)hleft({x}_{j}right)=Kleft({x}_{i},{x}_{j}right)end{array}right.$$

(5)

where: ({x}_{i}) and ({x}_{j})-sample input vectors, i and j are taken as positive integers within [1,N]; K(({x}_{i},{x}_{j}))-kernel function.

KELM determines the implicit layer mapping kernel function in the form of an inner product by introducing a kernel function, and the number of implicit layer nodes does not need to be set; The result is faster model learning and effective improvement of the generalisation ability and stability of the KELM-based runoff prediction model.

Butterfly optimisation algorithm is an intelligent optimisation algorithm derived by simulating butterfly searching for food and mating behaviour23. In the BOA algorithm, each butterfly emits its own unique scent. Butterflies are able to sense the source of food in the air and likewise sense the scent emitted by other butterflies and move with the butterfly that emits a stronger scent, the scent concentration equation is:

where (f)Concentration of scent emitted by the butterfly, (c)Perceived morphology, (l)Stimulus intensity, (a)Power index, taken between [0,1]. When a=1, it means that the butterfly does not absorb the scent, meaning that the scent emitted by a specific butterfly is perceived by the same butterfly; This case is equivalent to a scent spreading in an ideal environment, where the butterfly emitting the scent can be sensed everywhere in the domain, and thus a single global optimum can be easily reached.

In order to prove the above with the search algorithm, the following hypothetical regulations were set up to idealise the characteristics of butterflies: (i) All butterflies can give off some scent, and butterflies attract and exchange information with each other by virtue of the scent. (ii) Butterflies undergo random movements or directional movements towards butterflies with strong scent concentrations.

By defining different fitness functions for different problems, the BOA algorithm can be divided into the following 3 steps:

Step 1: Initialisation phase. Randomly generate butterfly locations in the search space, calculate and store each butterfly location and fitness value.

Step 2: Iteration phase. Multiple iterations are performed by the algorithm, in each iteration the butterflies are moved to a new position in the search space and then their fitness values are recalculated. The adaptation values of the randomly generated butterfly population are sorted to find the best position of the butterfly in the search space.

Step 3: End Phase, In the previous phase, the butterflies move and then use the scent formula to produce a scent in a new location.

The penalty parameter C and the kernel function parameter K in the kernel-limit learning machine are chosen as the searching individuals of the butterfly population, and the BOA-KELM model is constructed to achieve the iterative optimisation of C and K. The specific steps are as follows:

Step 1: Collect runoff data and produce training and prediction sample sets.

Step 2: Initialise the butterfly population searching individuals i.e. penalty parameter C and kernel function parameter K.

Step 3: Initialise the algorithm parameters, including the number of butterfly populations M, the maximum number of iterations .

Step 4: Calculate the fitness value of the individual butterfly population and calculate the scent concentration f. Based on the fitness value, the optimal butterfly location is derived.

Step 5: Check the fitness value of the butterfly population searching individuals after updating their positions, determine whether it is better than before updating, and update the global optimal butterfly position and fitness value.

Step 6:Judge whether the termination condition is satisfied. If it is satisfied, exit the loop and output the prediction result; otherwise, bring in the calculation again.

Step 7:Input the test set into the optimised KELM and output the predictions.

According to the above steps, the corresponding flowchart is shown in Fig.2.

BOA Optimisation KELM Model Flowchart.

In order to improve the accuracy of runoff prediction, this paper designs a runoff prediction framework based on the idea of "decompositionmodeling predictionreconstruction", as shown in Fig.3, and the specific prediction steps are as follows:

VMD-CEEMD-BOA-KELM prediction model framework.

Step 1: Data pre-processing. Anomalies in the original runoff series were processed using the Lajda criterion.

Step 2: VMD-CEEMD decomposition. The raw runoff series was decomposed using the VMD algorithm, and then the data was decomposed quadratically using the CEEMD algorithm to obtain k components.

Step 3: Data preparation. Each component is normalised and divided into a training data set and a test data set.

Step 4: Modelling prediction. A BOA-optimised KELM model is built based on the training dataset for each component and predicted for the test dataset.

Step 5: Reconstruction. The predictions of all components are accumulated to obtain the prediction of the original runoff sequence.

In order to reflect the error and prediction accuracy of the model prediction results more clearly, four indicators, RMSE, MAE, R2, and NSE are used for the analysis, and the equations are calculated as follows:

$${varvec{R}}{varvec{M}}{varvec{S}}{varvec{E}}=sqrt{frac{1}{{varvec{N}}}cdot {sum }_{{varvec{i}}=1}^{{varvec{N}}}{left({{varvec{y}}}_{{varvec{i}}}-{{varvec{y}}}_{{varvec{c}}}right)}^{2}}$$

$${varvec{M}}{varvec{A}}{varvec{E}}=frac{1}{{varvec{N}}}cdot {sum }_{{varvec{i}}=1}^{{varvec{N}}}left|{{varvec{y}}}_{{varvec{i}}}-{{varvec{y}}}_{{varvec{c}}}right|$$

$${{varvec{R}}}^{2}={left[frac{sum left({{varvec{y}}}_{{varvec{i}}}-overline{{{varvec{y}} }_{{varvec{i}}}}right)left({{varvec{y}}}_{{varvec{c}}}-overline{{{varvec{y}} }_{{varvec{c}}}}right)}{sqrt{sum {left({{varvec{y}}}_{{varvec{i}}}-overline{{{varvec{y}} }_{{varvec{i}}}}right)}^{2}}sum {left({{varvec{y}}}_{{varvec{c}}}-overline{{{varvec{y}} }_{{varvec{c}}}}right)}^{2}}right]}^{2}$$

$${varvec{N}}{varvec{S}}{varvec{E}}=1-frac{{sum }_{{varvec{t}}=1}^{{varvec{T}}}{left({{varvec{y}}}_{{varvec{i}}}-{{varvec{y}}}_{{varvec{c}}}right)}^{2}}{{sum }_{{varvec{t}}=1}^{{varvec{T}}}{left({{varvec{y}}}_{{varvec{i}}}-overline{{{varvec{y}} }_{{varvec{i}}}}right)}^{2}}$$

This paper does not contain any studies with human participants or animals performed by any of the authors.

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In the present issue of the American Journal of Psychiatry, Smucny et al. suggest that predictive algorithms for psychosis using machine learning (ML) methods may already achieve a clinically useful level of accuracy (1). In support of this perspective, these authors report on the results of an analysis using the North American Prodrome Longitudinal Study, Phase 3 (NAPLS3) data set (2), which they accessed through the National Institute of Mental Health Data Archive (NDAR). This is a large multisite study of youth at clinical high risk for psychosis followed up on multiple occasions with clinical, cognitive, and biomarker assessments. Several ML approaches were compared with each other and with Cox (time-to-event) and logistic regression using the clinical, neurocognitive, and demographic features from the NAPLS2 individualized risk calculator (3), with salivary cortisol also tested as an add-on biomarker. When these variables were analyzed using Cox and logistic regression, the model applied to the NAPLS3 cohort attained a level of predictive accuracy comparable to that observed in the original NAPLS2 cohort (overall accuracy in the 66%68% range). However, several ML algorithms produced nominally better results, with a random forest model performing best (overall accuracy in the 90% range). Acknowledging that a predictive algorithm with 90% or higher predictive accuracy will have greater clinical utility than one with substantially lower accuracy, several issues remain to be resolved before it can be determined whether ML methods have attained this utility threshold.

First and foremost, an ML models expected real-world performance can only be ascertained when tested in an independent sample/data set that the model has never before encountered. ML methods are very adept at finding apparent structure in data that predict an outcome, but if that structure is idiosyncratic to the training data set, the model will fail to generalize to other contexts and thus not be useful, a problem known as overfitting (4). Internal cross-validation methods are not sufficient to overcome this problem, since the model sees all the training data at certain points in the process, even if some is left out on a particular iteration (5). Overfitting is indicated by a big drop-off in model accuracy moving from the original internally cross-validated training data set to an external, independent cross-validation test. Smucny et al. (1) acknowledge the need for an external replication test before the utility of the ML models they evaluated using only internal cross-validation methods can be fully appreciated.

Is there likely to be a big drop-off in accuracy of the ML models reported by Smucny et al. (1) when such an external validation test is performed? On one hand, they limited consideration to a small number of features that have previously been shown to predict psychosis in numerous independent samples (i.e., the variables in the NAPLS2 risk calculator [3]). This aspect mitigates the overfitting issue to some extent because the features used in model building are already filtered (based on prior work) to be highly likely to predict conversion to psychosis, both individually and when combined in a regression model. On the other hand, the ML models employed in the study use various approaches to find higher-order interactive and nonlinear amalgamations among this set of feature variables that maximally discriminate outcome groups. This aspect increases the risk of overfitting given that a very large number of such higher-order interactive effects are assessed in model building, with relatively few subjects available to represent each unique permutation, a problem known as the curse of dimensionality (6). Tree-based methods such as the random forest model that performed best in the NAPLS3 data set are not immune from this problem and, in fact, are particularly vulnerable to it when applied on data sets with relatively small numbers of individuals with the outcome of interest (7).

The relatively low base rate of conversion to psychosis (i.e., 10%15%), even in a sample selected to be at elevated risk as in NAPLS3, creates another problem for ML methods; namely, such models can achieve high levels of predictive accuracy in the training data set simply by guessing that each case is a nonconverter. Smucny et al. (1) attempt to overcome this issue using a synthetic approach that effectively up samples the minority class (in this case, converters to psychosis) to the point that it has 50% representation in the synthetic sample (8). Although this approach is very helpful in preventing ML models from defaulting to prediction of a majority class, its use in computing cross-validation performance metrics is likely to be highly misleading, given that real-world application of the model is not likely to occur in a context in which there is a 50:50 rate of future converters and nonconverters. Rather, the model will be applied in circumstances in which new clinical high risk (CHR) individuals likelihoods of conversion are computed, and those CHR individuals will derive from a population in which the base rate of conversion is 15%. It is now well established that the same predictive model will result in different risk distributions (and, thereby, different thresholds in model-predicted risk for making binary predictions) in samples that vary in base rates of conversion to psychosis (9). Given this, a 90% predictive accuracy of an ML algorithm in a synthetically derived sample in which the base rate of psychosis conversion is artificially created to be 50% is highly unlikely to generalize to an independent, real-world CHR sample, at least as ascertained using current approaches.

When developing the NAPLS2 risk calculator, the investigators made purposeful decisions to allow the resulting algorithm to be applied validly in scaling the risk of newly ascertained CHR individuals (3). Key among these decisions was to avoid using the NAPLS2 data set to test different possible models, which would then necessitate an external validation test. Rather, a small number of predictor variables was chosen based on their empirical associations with conversion to psychosis in prior studies, and Cox regression was employed to generate an additive multivariate model of predicted risk (i.e., no interactive or non-linear combinations of the variables were included). As a result, the ratio of converters to predictor variables was 10:1 (helping to create adequate representation of the scale values of each predictor in the minority class), and there was no need to use a synthetic sampling approach given that Cox regression is well suited for prediction of low base rate outcomes. The predictor variables chosen for inclusion are ones that are easily ascertained in standard clinical settings and have a high level of acceptability (face validity) for use in clinical decision making. It is important to note that the NAPLS2 model has been shown to replicate (in terms of area under the curve or concordance index) when applied to multiple external independent data sets (10).

Nevertheless, two issues continue to limit the utility of the NAPLS2 risk calculator. One is that it will generate differently shaped risk distributions on samples that vary in conversion risk and in distributions of the individual predictor variables, making it problematic to apply the same threshold of predicted risk for binary predictions across samples that differ in these ways (9, 11). However, it appears possible to derive comparable prediction metrics across samples with differing conversion risks when considering the relative recency of onset or worsening of attenuated positive symptoms at the baseline assessment (11). A more recent onset or worsening of attenuated positive symptoms defines a subgroup of CHR individuals with a higher average predicted risk and higher overall transition rate and in whom particular putative illness mechanisms, in this case an accelerated rate of cortical thinning (12), appear to be differentially relevant (11).

The second rate-limiting issue for the utility of the NAPLS2 risk calculator is that its performance in terms of sensitivity, specificity, and balanced accuracy, even when accounting for recency of onset of symptoms, is still in the 65%75% range. Although ML methods represent one approach that, if externally validated, could conceivably result in predictive models at the 90% or higher level of accuracy, such models would continue to have the disadvantage of being relatively opaque (black box) in terms of how the underlying predictor variables aggregate in defining risk and for that reason may not be used as readily in clinical practice. Alternatively, it may be possible to rely on more transparent analytic approaches to achieve the needed level of accuracy. It has recently been demonstrated that integrating information on short-term (baseline to 2-month follow-up) change on a single clinical variable (e.g., deterioration in odd behavior/appearance) improves the performance of the NAPLS2 risk calculator to >90% levels of sensitivity, specificity, and balanced accuracy; i.e., a range that would support its use in clinical trial design and clinical decision-making (13). Importantly, although the Cox regression model aspect of this algorithm has been externally validated, the incorporation of short-term clinical change (via mixed effects growth modeling) requires replication in an external data set.

Smucny et al. (1) are to be congratulated on a well-motivated and well-executed analysis of the NAPLS3 data set. It is heartening to see such creative uses of this unique shared resource for our field bear fruit, reinforcing the value of open science. As we move forward toward the time and place in which prediction models of psychosis and related outcomes have utility for clinical decision making, whether those models rely on machine learning methods or more traditional approaches, it will be crucial to insist on external validation of results before deciding that we are, in fact, there.

Clark L. Hull Professor of Psychology and Professor of Psychiatry, Yale University, New Haven, Conn.

Dr. Cannon reports no financial relationships with commercial interests.

1. Smucny J, Davidson I, Carter CS: Are we there yet? Predicting conversion to psychosis using machine learning. Am J Psychiatry 2023; 180:836840 Abstract,Google Scholar

2. Addington J, Liu L, Brummitt K, et al.: North American Prodrome Longitudinal Study (NAPLS 3): methods and baseline description. Schizophr Res 2022; 243:262267Crossref, Medline,Google Scholar

3. Cannon TD, Yu C, Addington J, et al.: An individualized risk calculator for research in prodromal psychosis. Am J Psychiatry 2016; 173:980988Link,Google Scholar

4. Cawley GC, Talbot NLC: On over-fitting in model selection and subsequent selection bias in performance evaluation. J Mach Learn Res 2010; 11:20792107 Google Scholar

5. Arlot S, Celisse A: A survey of cross-validation procedures for model selection. Statist Surv 2010; 4:4079 Crossref,Google Scholar

6. Hughes G: On the mean accuracy of statistical pattern recognizers. IEEE Trans Inf Theor 1968; 14:5563 Crossref,Google Scholar

7. Peng Y, Nagata MH: An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data. Chaos Solitons Fractals 2020; 139:110055Crossref, Medline,Google Scholar

8. Chawla NV, Bowyer KW, Hall LO, et al.: SMOTE: Synthetic Minority Over-Sampling Technique. J Artif Intell Res 2002; 16:321357 Crossref,Google Scholar

9. Koutsouleris N, Worthington M, Dwyer DB, et al.: Toward generalizable and transdiagnostic tools for psychosis prediction: an independent validation and improvement of the NAPLS-2 risk calculator in the multisite PRONIA cohort. Biol Psychiatry 2021; 90:632642Crossref, Medline,Google Scholar

10. Worthington MA, Cannon TD: Prediction and prevention in the clinical high-risk for psychosis paradigm: a review of the current status and recommendations for future directions of inquiry. Front Psychiatry 2021; 12:770774Crossref, Medline,Google Scholar

11. Worthington MA, Collins MA, Addington J, et al.: Improving prediction of psychosis in youth at clinical high-risk: pre-baseline symptom duration and cortical thinning as moderators of the NAPLS2 risk calculator. Psychol Med 2023:19Crossref, Medline,Google Scholar

12. Collins MA, Ji JL, Chung Y, et al.: Accelerated cortical thinning precedes and predicts conversion to psychosis: the NAPLS3 longitudinal study of youth at clinical high-risk. Mol Psychiatry 2023; 28:11821189Crossref, Medline,Google Scholar

13. Worthington MA, Addington J, Bearden CE, et al.: Dynamic prediction of outcomes for youth at clinical high risk for psychosis: a joint modeling approach. JAMA Psychiatry 2023:e232378Google Scholar

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Learn about and apply for artificial intelligence careers in the United States

Artificial intelligence (AI) is one of the most rapidly growing and in-demand fields in the United States, with job openings expected to grow 33% by 2030, much faster than the average for all occupations. AI careers offer competitive salaries, exciting opportunities to work on cutting-edge technologies, and the chance to make a real impact on the world. In this article lets explore the available AI careers in the United States.

Role: AI/ML Developer

Qualifications: Expertise in Python libraries for machine learning, natural language processing, image preprocessing, and databases, as well as experience with machine learning toolkits and frameworks, deep learning concepts, and applying ML algorithms effectively.

Responsibilities: Design, develop, and train machine learning systems to optimize performance and build self-learning applications. Stay up-to-date with the latest developments in the field and use GPU, pyspark, and parallel compute libraries in Python. Understand how components and processes work together using library calls, REST APIs, queueing/messaging systems, and database queries. Design systems to avoid bottlenecks and scale well with increasing volumes of data.

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Role: AI/ML EngineerQualifications: Data science, statistics, or machine learning education, with strong programming skills in Python, R, or Java. Experience with machine learning frameworks and setting up machine learning problem spaces and solutions. Ability to evaluate the performance and efficacy of machine learning solutions and interest in advanced machine learning concepts.

Responsibilities: Collaborate with data scientists, machine learning engineers, software engineers, and QA engineers to explore and prototype data and machine learning opportunities. Design, develop, test, and support services to deploy resulting models and cognitive solutions in production.

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Role: Machine Learning Researcher

Qualifications: 3+ years of experience in machine learning for computer vision applications, with a strong understanding of machine learning algorithms and a proven track record of developing and deploying high-quality computer vision algorithms. Excellent software design, problem-solving, and debugging skills. Fluency in Python and another language (C/C++, Go, Rust), as well as experience with relevant deep learning software packages. Great technical skills, a drive for high-quality software, and the ability to innovate creative solutions. Excellent communication and the flexibility to learn new technologies.

Responsibilities: Youll be involved in all stages of model development, from data analysis and prototyping to testing and deployment.

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Role: AI Research Scientist

Qualifications: Masters degree in Electrical Engineering, Computer Engineering, Computer Science, or related field, with 3+ years of experience in neural architecture search (NAS) and machine learning for training deep neural networks.

Responsibilities: This role is available as a fully home-based and generally would require you to attend Intel sites only occasionally based on their business need. This role may also be available as our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site. In certain circumstances, the work model may change to accommodate business needs.

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Role: Senior Artificial Intelligence Scientist

Qualifications: Experience in machine learning research, evidenced by at least one first-author publication in a scientific journal, or equivalent, and experience with search and/or optimization-based sequence design algorithms.

Responsibilities: You will join a growing team of AI/ML experts and work closely with scientists across gRED to develop and deploy machine learning methods for the analysis of single-cell genomics datasets and biological sequences. You will help build and scale machine learning techniques to handle massive datasets and deploy novel machine learning algorithms.

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AUSTIN, TX November 7, 2023 SandBox Semiconductor, a pioneer in applying physics-based, AI-enabled modeling to accelerate the development of semiconductor manufacturing processes, will participate in the2023 AVS International Symposium and Exhibitiontaking place November 5-10, 2023 at the Oregon Convention Center in Portland, OR. Dr. Sebastian Naranjo, Computational Engineer at SandBox Semiconductor will give a presentation on Thursday discussing how advances in machine learning, analysis, and visualization technology are helping to accelerate recipe and process modeling optimization in semiconductor manufacturing.

The fact that AVS is dedicating a full session to AI and machine learning is a testament to the growing importance of technology in the semiconductor manufacturing process, said Dr. Meghali Chopra, SandBox Semiconductor CEO & Co-founder. Were looking forward to discussing how it can be applied to recipe optimization for deposition to accelerate process development, get new products to market faster, and reduce costs.

WHO: Dr. Sebastian Naranjo, Computational Engineer,SandBox Semiconductor

WHAT:Rapid Optimization of Gap-Fill Recipes Using Machine Learning

WHEN: Thursday, November 9, 2023 at 3:40 pm PT

WHERE: Oregon Convention Center, 777 NE Martin Luther King Jr Blvd, Portland, OR 97232

WHY: Semiconductor manufacturing is a complicated process that involves up to 1,200 steps. Currently, process development for each unit process is performed sequentially, one step after another. SandBox Studio AI combines physics-based modeling with AI to allow engineers to streamline process development to shrink timelines and decrease dependency on extensive physical experiments. The AVS presentation will discuss how machine learning can enable process engineers to co-optimize multiple unit processes at a time and dramatically alleviate the cost, time and complexity issues associated with recipe development. The presentation focuses on a gap-fill process flow.

About SandBox Semiconductor

Founded in 2016, SandBox Semiconductor is a pioneer in developing AI based software to accelerate process development for semiconductor manufacturing. Its fully integrated no-code AI tool suite gives process engineers the ability to build their own physics-based, AI-enabled models to solve challenges during process definition, ramp-up, and high-volume manufacturing.

Using SandBoxs physics-based models and machine learning tools, engineers can virtually simulate, predict, and measure process outcomes. Even with small sets of experimental data, SandBoxs tools can extract valuable insights and patterns, helping engineers to gain a deeper understanding of manufacturing processes and to make informed decisions about recipe adjustments. SandBox leverages expertise in numerical modeling, machine learning, and manufacturing optimization to develop its proprietary toolsets, which are used by the worlds leading chip manufacturers and semiconductor equipment suppliers. SandBox is based in Austin, Texas. More information can be found here:www.sandboxsemiconductor.com.

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