Areas of Expertise (4)
Computational Systems Biology
Autism Spectrum Disorder
Modeling and Control
Juergen Hahn is professor and head of the Department of Biomedical Engineering and holds an appointment in the Department of Chemical and Biological Engineering. Hahn’s research interests include systems biology and process modeling and analysis, with over 140 peer-reviewed publications in print.
Hahn received a Fulbright scholarship (1995-96), the Best Referee Award 2004 from the Journal of Process Control, and the CPC 7 Outstanding Contributed Paper Award in 2006. He was named Outstanding Reviewer by the journal Automatica in 2005, 2006, and 2007. Hahn was the 2010 CAST Outstanding Young Researcher. He was elected as an AIMBE fellow in 2013, an AIChE fellow in 2020, and a fellow of BMES in 2022. He served on the IEEE CSS Board of Governors in 2016 and has been a CACHE Trustee since 2014. He is currently serving as deputy editor-in-chief for the Journal of Process Control and as associate editor for Control Engineering Practice, Journal of Advanced Manufacturing and Processing, and Journal of Personalized Medicine.
Hahn earned his bachelor’s degree in engineering from RWTH Aachen, Germany, in 1997. He earned his master’s and doctoral degrees in chemical engineering from the University of Texas, Austin, in 1998 and 2002, respectively. He was a post-doctoral researcher at the Chair for Process Systems Engineering at RWTH Aachen, Germany, before joining the Department of Chemical Engineering at Texas A&M University, College Station, in 2003. Hahn has been with Rensselaer since 2012.
University of Texas at Austin: Ph.D., Chemical Engineering 2002
University of Texas at Austin: M.S., Chemical Engineering 1998
RWTH Aachen, Germany: Diploma Degree, Engineering 1997
Media Appearances (5)
New Blood Test Could Revolutionize Autism Diagnosis
Verywell Health online
The current method of diagnosing the disorder “is purely observational, which makes it time-consuming,” lead study author Juergen Hahn, PhD, a professor and head of the Department of Biomedical Engineering at Rensselaer Polytechnic Institute, tells Verywell. “One result of this is while ASD can be diagnosed by 18 to 24 months, the average age of diagnosis is around four years of age. There is often a long waiting period involved between when concerns regarding ASD are noted and when an actual diagnostic observation is scheduled."
Medical conditions may mark distinct autism subtypes
An autistic child in one group generally has few conditions that overlap with those of a child in a different group, says lead researcher Juergen Hahn, professor of biomedical engineering at Rensselaer Polytechnic Institute in Troy, New York.
Using Big Data To Evaluate Autism Treatments
The Academic Minute online
Juergen Hahn is the department head of the Department of Biomedical Engineering at Rensselaer Polytechnic Institute in addition to holding an appointment in the Department of Chemical & Biological Engineering. He received his Diploma degree in engineering from RWTH Aachen, Germany, in 1997, and his MS and Ph.D. degrees in chemical engineering from the University of Texas, Austin, in 1998 and 2002, respectively.
Experimental blood test could speed autism diagnosis: U.S. study
Developers of an experimental blood test for autism say it can detect the condition in more than 96 percent of cases and do so across a broad spectrum of patients, potentially allowing for earlier diagnosis, according to a study released on Thursday.
Experimental autism blood test for children looks promising
Researchers say an experimental blood test has shown promise as a novel way to diagnose autism in children. The test appears to be nearly 98 percent accurate in kids between the ages of 3 and 10, the researchers claimed. “The test was able to predict autism, regardless of where on the spectrum an individual was,” said study co-author Juergen Hahn, referring to varying degrees of autism severity.
Maternal risk factors vary between subpopulations of children with autism spectrum disorderAutism Research
Genevieve Grivas, Richard E Frye, Juergen Hahn
2022 Previous work identified three subgroups of children with ASD based upon co-occurring conditions (COCs) diagnosed during the first 5 years of life. This work examines prenatal risk factors, given by maternal medical claims, for each of the three subgroups: children with a High-Prevalence of COCs, children with mainly developmental delay and seizures (DD/Seizure COCs), and children with a Low-Prevalence of COCs. While some risk factors are shared by all three subgroups, the majority of the factors identified for each subgroup were unique; infections, anti-inflammatory and other complex medications were associated with the High-Prevalence COCs group; immune deregulatory conditions such as asthma and joint disorders were associated with the DD/Seizure COCs group; and overall pregnancy complications were associated with the Low-Prevalence COCs group. Thus, we have found that the previously identified subgroups of children with ASD have distinct associated prenatal risk factors. As such, this work supports subgrouping children with ASD based upon COCs, which may provide a framework for elucidating some of the heterogeneity associated with ASD.
Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent AdvancementsSeminars in Pediatric Neurology
Troy Vargason, Genevieve Grivas, Kathryn L Hollowood-Jones, Juergen Hahn
2020 An ever-evolving understanding of autism spectrum disorder (ASD) pathophysiology necessitates that diagnostic standards also evolve from being observation-based to include quantifiable clinical measurements. The multisystem nature of ASD motivates the use of multivariate methods of statistical analysis over common univariate approaches for discovering clinical biomarkers relevant to this goal. In addition to characterization of important behavioral patterns for improving current diagnostic instruments, multivariate analyses to date have allowed for thorough investigation of neuroimaging-based, genetic, and metabolic abnormalities in individuals with ASD. This review highlights current research using multivariate statistical analyses to quantify the value of these behavioral and physiological markers for ASD diagnosis. A detailed discussion of a blood-based diagnostic test for ASD using specific metabolite concentrations is also provided. The advancement of ASD biomarker research promises to provide earlier and more accurate diagnoses of the disorder.
Clustering of co-occurring conditions in autism spectrum disorder during early childhood: A retrospective analysis of medical claims dataAutism Research
Troy Vargason, Richard E Frye, Deborah L McGuinness, Juergen Hahn
2019 Individuals with autism spectrum disorder (ASD) are frequently affected by co-occurring medical conditions (COCs), which vary in severity, age of onset, and pathophysiological characteristics. The presence of COCs contributes to significant heterogeneity in the clinical presentation of ASD between individuals and a better understanding of COCs may offer greater insight into the etiology of ASD in specific subgroups while also providing guidance for diagnostic and treatment protocols. This study retrospectively analyzed medical claims data from a private United States health plan between years 2000 and 2015 to investigate patterns of COC diagnoses in a cohort of 3,278 children with ASD throughout their first 5 years of enrollment compared to 279,693 children from the general population without ASD diagnoses (POP cohort).
Multivariate techniques enable a biochemical classification of children with autism spectrum disorder versus typically-developing peers: A comparison and validation studyBioengineering & Translational Medicine
Daniel P Howsmon, Troy Vargason, Robert A Rubin, Leanna Delhey, Marie Tippett, Shannon Rose, Sirish C Bennuri, John C Slattery, Stepan Melnyk, S Jill James, Richard E Frye, Juergen Hahn
2018 Autism spectrum disorder (ASD) is a developmental disorder which is currently only diagnosed through behavioral testing. Impaired folate-dependent one carbon metabolism (FOCM) and transsulfuration (TS) pathways have been implicated in ASD, and recently a study involving multivariate analysis based upon Fisher Discriminant Analysis returned very promising results for predicting an ASD diagnosis. This article takes another step toward the goal of developing a biochemical diagnostic for ASD by comparing five classification algorithms on existing data of FOCM/TS metabolites, and also validating the classification results with new data from an ASD cohort.
Classification and adaptive behavior prediction of children with autism spectrum disorder based upon multivariate data analysis of markers of oxidative stress and DNA methylationPLoS Computational Biology
Daniel P Howsmon, Uwe Kruger, Stepan Melnyk, S Jill James, Juergen Hahn
2017 The number of diagnosed cases of Autism Spectrum Disorders (ASD) has increased dramatically over the last four decades; however, there is still considerable debate regarding the underlying pathophysiology of ASD. This lack of biological knowledge restricts diagnoses to be made based on behavioral observations and psychometric tools. However, physiological measurements should support these behavioral diagnoses in the future in order to enable earlier and more accurate diagnoses.
Modeling regulatory mechanisms in IL‐6 signal transduction in hepatocytesBiotechnology and Bioengineering
Abhay Singh, Arul Jayaraman, Juergen Hahn
2006 Cytokines like interleukin‐6 (IL‐6) play an important role in triggering the acute phase response of the body to injury or inflammation. Signaling by IL‐6 involves two pathways: Janus‐associated kinases (JAK) and signal transducers and activators of transcription (STAT 3) are activated in the first pathway while the second pathway involves the activation of mitogen‐activated protein kinases (MAPK). While it is recognized that both pathways play a major role in IL‐6 signal transduction, a majority of studies have focused on signaling through either one of the pathways...
An improved method for nonlinear model reduction using balancing of empirical gramiansComputers & Chemical Engineering
Juergen Hahn, Thomas F Edgar
2002 Nonlinear model predictive control has become increasingly popular in the chemical process industry. Highly accurate models can now be simulated with modern dynamic simulators combined with powerful optimization algorithms. However, computational requirements grow with the complexity of the models...
Automatic control in microelectronics manufacturing: Practices, challenges, and possibilitiesAutomatica
Thomas F Edgar, Stephanie W Butler, W Jarrett Campbell, Carlos Pfeiffer, Christopher Bode, Sung Bo Hwang, KS Balakrishnan, Juergen Hahn
2000 Advances in modeling and control will be required to meet future technical challenges in microelectronics manufacturing. The implementation of closed-loop control on key unit operations has been limited due to a dearth of suitable in situ measurements, variations in process equipment and wafer properties, limited process understanding, non-automated operational practices, and lack of trained personnel. This paper reviews the state-of-the-art for process control in semiconductor processing, and covers the key unit operations of lithography, plasma etching, thin film deposition, rapid thermal processing, and chemical–mechanical planarization...