Dr. Siamak Sorooshyari | Stanford | United States
Siamak Sorooshyari is an interdisciplinary scholar whose career spans electrical engineering, statistics, and neuroscience. Beginning with rigorous training in communication theory and wireless networks, he later transitioned into integrative biology, where he applied machine learning to explore signatures of brain aging and neural circuit dynamics. His research contributions have illuminated diverse areas, including functional brain connectivity, dopamine signaling, immune–brain interactions, and computational modeling of neural processes. Currently serving as a postdoctoral scholar in statistics at Stanford University, his work continues to push the boundaries of applied mathematics and neuroscience. Recognized through publications in leading journals and a featured journal cover, his scholarship reflects both depth and breadth, making significant impacts across multiple scientific fields.
Profile
Early Academic Pursuits
Siamak Sorooshyari’s academic journey began with a strong foundation in electrical and computer engineering. His undergraduate and master’s training cultivated a deep interest in wireless communications, coding theory, and the mathematical structures underlying information transmission. During these formative years, he developed a keen analytical mindset and produced thesis work that addressed fundamental questions in multicarrier systems and error performance in communication networks. These early explorations into applied mathematics and engineering set the stage for his later transition into interdisciplinary research at the interface of engineering, biology, and neuroscience.
Transition to Interdisciplinary Research
His intellectual curiosity led him to pursue doctoral studies in integrative biology at the University of California, Berkeley. This marked a pivotal shift in his academic path, where he applied his engineering expertise to complex biological systems. His doctoral thesis focused on machine learning approaches to studying signatures of brain aging across multiple modalities of neural recordings, reflecting both his technical rigor and innovative thinking. This transition demonstrated his ability to bridge diverse fields, integrating computational modeling with biological inquiry to address questions of human health and neuroscience.
Professional Endeavors
Following the completion of his doctoral studies, Sorooshyari joined Stanford University as a postdoctoral scholar in statistics, under the guidance of David Donoho. At Stanford, he has continued to advance statistical and computational methods to explore problems at the intersection of brain science, aging, and network connectivity. His work represents a unique synthesis of applied mathematics, neuroscience, and machine learning, positioning him at the forefront of research that blends theory and data-driven discovery. His professional trajectory reflects a consistent drive to expand disciplinary boundaries while addressing some of the most pressing questions in neuroscience.
Research Contributions and Focus
Sorooshyari’s research contributions span multiple domains, from wireless communications and information theory to computational neuroscience and systems biology. His more recent work has focused heavily on understanding brain aging, dopamine signaling, and neural circuit dynamics through advanced machine learning frameworks. He has explored resting-state functional connectivity, brain–immune interactions, sleep and arousal regulation, and computational models of neural circuit activity. Through this work, he has produced a body of scholarship that not only advances theoretical understanding but also carries translational significance for neurological and psychiatric research.
Accolades and Recognition
His scholarship has been recognized through publications in leading journals such as NeuroImage, PLOS ONE, ACS Chemical Neuroscience, and Frontiers in Computational Neuroscience, among others. Notably, his research on dopamine signaling was featured on the cover of a prominent neuroscience journal, underscoring the visibility and impact of his work within the scientific community. Such recognition reflects his ability to address important scientific questions with methodological sophistication and creativity.
Impact and Influence
The impact of Sorooshyari’s work is evident in its breadth and relevance across disciplines. His contributions to communication theory provided insights into wireless networks, while his more recent neuroscience research has opened new avenues for understanding brain function, aging, and disease. His efforts in applying machine learning to neuroscience highlight the potential of computational methods to uncover biological signatures that may guide diagnostics and therapeutics. By integrating engineering precision with biological complexity, he has influenced both fields and established himself as a key contributor to interdisciplinary science.
Legacy and Future Contributions
Looking ahead, Sorooshyari’s trajectory suggests a continued role in shaping the future of computational neuroscience and applied statistics. His interdisciplinary expertise positions him to contribute to both methodological advancements and practical applications, particularly in the context of brain health and disease modeling. His legacy lies in his ability to cross disciplinary borders, uniting engineering, statistics, and biology to generate insights with profound implications for human health. As he continues his work, his contributions are poised to inspire further innovations in the study of neural systems and their applications in medicine and beyond.
Publications
Deconstruction of the beaten Path-Sidestep interaction network provides insights into neuromuscular system development, H Li, A Watson, A Olechwier, M Anaya, SK Sorooshyari, DP Harnett, …, 2017
Power control for cognitive radio networks: Axioms, algorithms, and analysis, S Sorooshyari, CW Tan, M Chiang, 2011
Autonomous dynamic power control for wireless networks: User-centric and network-centric consideration, S Sorooshyari, Z Gajic, 2008
On the generation of correlated Rayleigh fading envelopes for accurate simulation of diversity channels, S Sorooshyari, DG Daut, 2006
Generation of correlated Rayleigh fading envelopes for accurate performance analysis of diversity systems, S Sorooshyari, DG Daut, 2003
A framework for quantitative modeling of neural circuits involved in sleep-to-wake transition, S Sorooshyari, R Huerta, L de Lecea, 2015
Quantitative and correlational analysis of brain and spleen immune cellular responses following cerebral ischemia, Q Liu, SK Sorooshyari, 2021
On maximum-likelihood SINR estimation of MPSK in a multiuser fading channel, S Sorooshyari, CW Tan, HV Poor, 2010
Conclusion
Sorooshyari’s journey exemplifies the power of interdisciplinary research in addressing complex biological and technological challenges. By merging statistical innovation, computational modeling, and biological inquiry, he has advanced our understanding of neural systems and brain health. His ability to transition seamlessly across disciplines demonstrates both intellectual versatility and visionary research leadership. Looking forward, his contributions hold the promise of shaping future developments in computational neuroscience and translational medicine, ensuring his place as an influential figure in the evolving landscape of interdisciplinary science.