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Peter SchuppView profile

Professor

• Mathematical Physics
• Theoretical Physics
• Classical and Quantum Gravity
• +7 more

Peter Schupp is a full Professor of Physics in the School of Science at Constructor University in Bremen, Germany, a position he has held since 2002. He previously served as visiting fellow and lecturer at Princeton University (1997-1999) and as assistant professor at the University of Munich (1994-1997, 1999-2002). His office is located in Research III, Room 65 on the Constructor campus. Education: PhD (Physics), University of California at Berkeley, 1990–1993 MSc (Physics), California Institute of Technology, 1989–1990 Vordiplom (Physics), University of Heidelberg, 1986–1989 Habilitation, University of Munich, 2001 Research Interests span a broad range of topics in mathematical and theoretical physics, including: Classical and quantum gravity Quantum field theory and string theory Generalized, graded and non-commutative geometry Coherent states, entropy and quantum information Non-geometric fluxes and nonassociative structures Cosmological data analysis (CMB anisotropy & non-Gaussianity) His publications reveal a steady focus on non-commutative and nonassociative extensions of geometry and gravity, as well as their implications for quantum field theory and cosmology. Recent work couples sophisticated algebraic techniques (graded Poisson algebras, Hopf algebras) to concrete physical problems such as CMB entropy measures and axion-gravity interactions. Scientific Awards & Fellowships : Heisenberg Fellowship, Deutsche Forschungsgemeinschaft (DFG), 2002 Studienstiftung des deutschen Volkes, 1986–1992 Max-Kade-Foundation Fellow, 1997–1998 Theodore S. Brown & Edith N. Brown Fellowship / Ephraim Weiss Scholarship, 1991–1992 Dora Garibaldi Scholarship, 1990–1991 Earl C. Anthony Fellowship, Caltech, 1989–1990 University Fellow, Renker GmbH & Co. KG, 1989–1991 International Physics Olympiad Silver Medal (Portorož 1985, 7th overall) International Physics Olympiad Bronze Medal (London 1986) Contact & Collaboration : Prof. Schupp welcomes inquiries at pschupp@constructor.university or by phone at +49 421 200-3224. While the text does not list specific PhD advisees, his long tenure and extensive publication record indicate active supervision of graduate researchers in theoretical and mathematical physics.

Thomas VillmannView profile

Professor

• Computational Intelligence
• Machine Learning
• Vector Quantization
• +7 more

Thomas Villmann is a Professor of Computational Intelligence and Techno-Mathematics at Mittweida University of Applied Sciences in Germany. He serves as Deputy Spokesperson for the Mathematics Department, AI Coordinator at the university, Director of the Saxon Institute for Computational Intelligence and Machine Learning (SICIM), and President of the German Chapter of the European Neural Network Society (GNNS). His academic credentials include the German 'Dr. rer. nat. habil.' designation, indicating both doctoral and habilitation qualifications in natural sciences. Professor Villmann's research focuses on computational intelligence with particular emphasis on vector quantization, learning vector quantization, neural networks, and interpretable machine learning. His work spans theoretical developments in mathematical foundations of machine learning algorithms as well as practical applications in bioinformatics, remote sensing, medical diagnostics, and autonomous systems. He has developed mathematically sound methods for data-based analysis (clustering), decision support systems, data-based prediction models, and visualization of complex data. His recent publication record demonstrates a strong trend toward interpretable and explainable AI, with emphasis on vector quantization techniques applied across diverse domains including medical diagnostics (particularly breast cancer detection), fairness in machine learning, satellite remote sensing, and autonomous vehicle systems. Villmann's work consistently bridges theoretical mathematics with practical applications, maintaining a focus on making machine learning models more transparent, reliable, and ethically sound. As Director of SICIM and leader of the Computational Intelligence Research Group, Professor Villmann oversees an integrated research ecosystem focused on problem-oriented intelligent data analysis. His institutes aim to stimulate interest in computational intelligence among students and young researchers while supporting public institutions, authorities, and companies in data analysis both regionally and internationally. His leadership extends to organizing academic events and workshops, including serving as president of the German Chapter of the European Neural Network Society.

Charles L. KaneView profile

Professor

• Theoretical Condensed Matter Physics
• Topological Insulators
• Quantum Spin Hall Effect
• +3 more

Charles L. Kane is a renowned theoretical condensed matter physicist and the Christopher H. Browne Distinguished Professor of Physics at the University of Pennsylvania. He earned his B.S. in physics from the University of Chicago (1985) and his Ph.D. from the Massachusetts Institute of Technology (1989). His research focuses on topological insulators, quantum spin Hall effect, and novel quantum phases in condensed matter systems. Dirac Prize (2012) Oliver E. Buckley Prize (2012) Breakthrough Prize in Fundamental Physics (2019) His work has revolutionized the understanding of topological materials, particularly through predicting the quantum spin Hall effect in graphene and pioneering concepts in topological insulators. Recent research explores topological density correlations, multipartite entanglement, and nonlinear transport phenomena. Kane's scientific contributions have been recognized by numerous prestigious awards, including the Franklin Medal (2015) and the Frontiers of Knowledge Award (2018). He maintains active collaborations and continues to advance frontiers in theoretical condensed matter physics.

Mahdi VasighiView profile

Assistant Professor

• Bioinformatics
• Structural Classification of Proteins
• Protein-Protein Interaction
• +9 more

Mahdi Vasighi is currently serving as an Assistant Professor at the Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS) in Zanjan, Iran, a position he has held since February 2012. Prior to this, he was a Post-doc Researcher at the same institution from February 2011 to February 2012. He has also served as a Visiting Researcher at the Milano Chemometrics and QSAR Research Group, University of Milano - Bicocca, Milan, Italy from September to October 2009, and as a Guest Lecturer at the Pasteur Institute, Tehran, Iran since September 2016. Dr. Vasighi earned his educational qualifications from the Institute for Advanced Studies in Basic Sciences (IASBS) in Zanjan, Iran, where he completed his Ph.D. in Chemometrics in May 2010 and his M.Sc. in Analytical Chemistry between 2002 and 2005. His undergraduate education was in Pure Chemistry at Imam Khomeini International University, Qazvin, Iran, from 1998 to 2002. Dr. Vasighi's primary research interests lie at the intersection of bioinformatics, machine learning, and data analysis. His work focuses on structural bioinformatics, particularly on modeling relationships between biological sequences and their corresponding structure or function. He has made significant contributions to the field of self-organizing maps with dynamic structure, developing innovative approaches like the Directed Batch Growing Self-Organizing Map (DBGSOM) that enhance topology preservation and visualization of high-dimensional data. His research spans multiple domains including protein structural classification, cancer diagnostics using fluorescence spectroscopy, and drug discovery for diseases like COVID-19. Dr. Vasighi's publication record demonstrates a strong trajectory in applying machine learning techniques to solve complex problems in bioinformatics and medical diagnostics. His recent work shows an increasing focus on applying computational approaches to healthcare challenges, including cancer detection, protein analysis, and drug discovery for viral diseases. He has successfully bridged the gap between theoretical machine learning advancements and practical applications in biology and medicine, with a particular emphasis on developing interpretable models that can be used by domain experts. Dr. Vasighi has actively contributed to the academic community through teaching and conference organization. He has served as Local Chair for the International Conference on Contemporary Issues in Data Science 2019 (CiDaS 19) and as Scientific Committee Member and Organizing Chair for previous CICIS conferences. His teaching portfolio includes graduate courses in Artificial Neural Networks, Computational Data Mining, Bioinformatics, Statistical Pattern Recognition, and Multimedia Systems. Dr. Vasighi has supervised numerous MSc students, with over twenty graduated students and nine current students listed in his profile. His research has been supported through collaborations with institutions like the Pasteur Institute, where he worked on projects related to nuclear magnetic resonance-based screening of thalassemia and determination of coronary heart disease risk using NMR spectra of plasma lipoproteins. Through his Directed Batch Growing Self-Organizing Map (DBGSOM) package and other software contributions, Dr. Vasighi has made his research tools accessible to the broader scientific community. His work continues to push the boundaries of how machine learning can be applied to solve challenging problems in bioinformatics and medical diagnostics.