Prof. Dr. Daniel Walter

Profil

Zusammenfassung

Prof. Dr. Daniel Walter forscht an der Schnittstelle von mathematischer Optimierung, Maschinellem Lernen und der Gestaltung von Sensoren und Aktuatoren für dynamische Systeme. Seine Expertise liegt darin, komplexe Optimierungsprobleme zu formulieren und zu lösen, die in modernen datengesteuerten Anwendungen und in der Steuerung physischer Systeme entstehen.

Skills

Name

Prof. Dr. Daniel Walter

Titel

Prof. Dr.

Fakultät

Mathematisch-Naturwissenschaftliche Fakultät

Institut

Institut für Mathematik

Arbeitsgruppe

Nichtglatte Optimierung

E-Mail

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HU-FIS-Profil

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Zuletzt gescrapt

28.6.2026, 01:14:19

• 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 Optimization for Machine Learning

  Quelle ↗

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

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

  2009

  Journal of Attention Disorders · 220 Zitationen · 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. ( J. of Att. Dis. 2009; 13(2) 117-126)

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

  2000

  Psychology of Addictive Behaviors · 131 Zitationen · 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.

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

  2000

  Psychology of Addictive Behaviors · 114 Zitationen · DOI

• Freie Universität Berlin

  Thematic Einstein Semester on Mathematical Optimization for Machine Learning

  university

• Technische Universität Berlin

  Bewegliches Sensor- und Aktuator-Design für dynamische Systeme

  university

• Zuse-Institut Berlin

  Thematic Einstein Semester on Mathematical Optimization for Machine Learning

  research_institute