Prof. Dr. Daniel Walter

Prof. Walter forscht an mathematischen Optimierungsmethoden für strukturierte Probleme in Banach-Räumen und deren Anwendung auf optimale Steuerung dynamischer Systeme. Sein aktueller Schwerpunkt liegt auf proximalen Gradientenmethoden für nichtglatte Minimierungsprobleme sowie auf Optimalsteuerung mit partiellen Differentialgleichungen und Maßräumen. Die Arbeiten adressieren Konvergenzgarantien und effiziente numerische Implementierungen für komplexe Optimierungsaufgaben. Für Industrie und öffentliche Hand relevant sind Anwendungen in der automatisierten Steuerung dynamischer Systeme (z. B. autonome Baumaschinen) und in inversen Problemen des Machine Learning, wo strukturierte Optimierung zentral ist.

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• Thematic Einstein Semester on Mathematical Optimization for Machine Learning

  Quelle ↗

  Förderer: Einstein Zentrum Zeitraum: 04/2023 - 09/2023 Projektleitung: Prof. Dr. Andrea Walther, Prof. Dr. Daniel Walter

• Bewegliches Sensor- und Aktuator-Design für dynamische Systeme

  Quelle ↗

  Förderer: DFG Exzellenzstrategie Cluster Zeitraum: 10/2025 - 09/2028 Projektleitung: Prof. Dr. Daniel Walter

• Thematic Einstein Semester on Mathematical Optimizationfor Machine Learning

  Quelle ↗

  Förderer: Einstein Zentrum Zeitraum: 04/2023 - 09/2023 Projektleitung: Prof. Dr. Andrea Walther, Prof. Dr. Daniel Walter

• Attention Deficit/Hyperactivity Disorder in Children and Adolescents With Autism Spectrum Disorder

  2009

  Journal of Attention Disorders · DOI

  OBJECTIVE: This study aims to evaluate ADHD-like symptoms in children with autism spectrum disorder (ASD) based on single-item analysis, as well as the comparison of two ASD subsamples of children with ADHD (ASD+) and without ADHD (ASD-). METHODS: Participants are 83 children with ASD. Dimensional and categorical aspects of ADHD are evaluated using a diagnostic symptom checklist according to DSM-IV. RESULTS: Of the sample, 53% fulfill DSM-IV criteria for ADHD. The comparison of the ASD+ and the ASD- samples reveals differences in age and IQ. Correlations of ADHD and PDD show significant results for symptoms of hyperactivity with impairment in communication and for inattention with stereotyped behavior. Item profiles of ADHD symptoms in the ASD+ sample are similar to those in a pure ADHD sample. CONCLUSION: The results of our study reveal a high phenotypical overlap between ASD and ADHD. The two identified subtypes, inattentive-stereotyped and hyperactive-communication impaired, reflect the DSM classification and may theoretically be a sign of two different neurochemical pathways, a dopaminergic and a serotonergic.

• Comparison of drinking patterns measured by daily reports and timeline follow back.

  2000

  Psychology of Addictive Behaviors · DOI

  The major objective of this study was to compare near real-time daily alcohol consumption data over the course of 366 consecutive days with retrospective reports by means of the timeline follow-back (TLFB). Participants (N = 33) responded for 366 days on an interactive voice response (IVR) system by entering alcohol consumption data daily using the touch-tone pads of their telephones. In-person interviews were conducted every 13 weeks during which participants were administered the TLFB. The correlations between the IVR and TLFB for amount consumed, drinking days, and heavy drinking days were modest. There was a wide variability across participants in their individual correlations for these variables. Participants who were diagnosable with a lifetime DSM-IV alcohol disorder at baseline significantly underreported their drinking compared with participants who were not diagnosable. The authors were unable to ascertain variables that influenced accurate reporting on the TLFB.