School of Humanities and Sciences
Showing 141-150 of 184 Results
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Siamak Sorooshyari
Postdoctoral Scholar, Statistics
BioMy research lies at the intersection of AI/ML, statistics, biology, and engineering. I was initially trained as an electrical engineer, with a focus on signal processing and statistical algorithms. I then pursued my PhD in a neuroscience laboratory studying stress and the blood-brain barrier, where I gained experience with biological systems, experimental design, instrumentation, and data collection. My current work brings these perspectives together as I develop computational and statistical methods to better understand and predict biological processes.
A major focus of my research is understanding how aging affects the brain and how these changes are reflected across biological scales and measurement modalities. I have studied signals recorded from individual brain regions, communication between brain networks, and changes in functional connectivity across the lifespan. An important question in this work is whether quantitative properties of biological signals, such as monotonicity, exhibit consistent relationships with age. I am particularly interested in determining how different modalities capture changes associated with aging in both healthy and diseased systems, and what these measurements reveal about the underlying biological processes. This perspective can also provide insight into the reliability and interpretability of different recording modalities as measures of biological aging. I have recently begun extending these questions beyond the brain to the brain-gut-heart axis in healthy humans. By examining relationships and coordinated changes among measurements from multiple organs, I aim to develop a more integrated understanding of healthy aging and, ultimately, of how these relationships are altered in disease, specifically neurodegeneration. This work represents a broader effort to study aging as a multidimensional biological process rather than as a phenomenon confined to a single organ or measurement modality.
In parallel, I develop statistical methods for assessing the reliability and reproducibility of unsupervised learning results. In particular, I am interested in understanding how methodological choices - including the clustering algorithm, model parameters, and the number of clusters - can affect the conclusions drawn from noisy, high-dimensional datasets. This work has led to ERICA (evaluating replicability via iterative clustering assignments), a framework for evaluating whether clustering structure can be reproduced under repeated analyses without requiring a predefined ground truth. I am applying this framework to biological datasets, including cancer and neurodegenerative diseases, where clustering is frequently used to identify molecular or phenotypic subgroups. More broadly, this work seeks to develop rigorous statistical tools that can help distinguish reproducible structure from patterns that may depend strongly on methodological choices. -
Navin Sridhar
Postdoctoral Scholar, Physics
Current Research and Scholarly InterestsElectromagnetic and multi-messenger signals powered by plasma processes around compact objects.
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Daniel Stack
Associate Professor of Chemistry
BioResearch in the Stack group focuses on the mechanism of dioxygen activation and the subsequent oxidative reactivity with primarily copper complexes ligated by imidazoles or histamines. Specifically, the group is interested in substrate hydroxylations and full dioxygen reduction. The remarkable specificity and energy efficiency of metalloenzymes provide the inspiration for the work. Trapping and characterizing immediate species, primarily at low temperatures, provide key mechanistic insights especially through substrate reactivity along with spectroscopic and metrical correlation to DFT calculations. Our objective is to move these efficient enzymatic mechanisms into small synthetic complexes, not only to reproduce biological reactivity, but more importantly to move the oxidative mechanism beyond that possible in the protein matrix.
Daniel Stack was born, raised and attended college in Portland Oregon. He received his B.A. from Reed College in 1982 (Phi Beta Kappa), working with Professor Tom Dunne on weak nickel-pyrazine complexes. In Boston, he pursued his doctoral study in synthetic inorganic chemistry at Harvard University (Ph.D., 1988) with Professor R. H. Holm, investigating site-differentiated synthetic analogues of biological Fe4S4 cubanes. As an NSF Postdoctoral Fellow with Professor K. N. Raymond at the University of California at Berkeley, he worked on synthesizing new, higher iron affinity ligands similar to enterobactin, a bacterial iron sequestering agent. He started his independent career in 1991 at Stanford University primarily working on oxidation catalysis and dioxygen activation, and was promoted to an Associate Professor in 1998. His contributions to undergraduate education have been recognized at the University level on several occasions, including the Dinkelspiel Award for Outstanding Contribution to Undergraduate Education in 2003.
Areas of current focus include:
Copper Dioxygen Chemistry
Our current interests focus on stabilizing species formed in the reaction of dioxygen with Cu(I) complexes formed with biologically relevant imidazole or histamine ligation. Many multi-copper enzymes ligated in this manner are capable of impressive hydroxylation reactions, including oxidative depolymerization of cellulose, methane oxidation, and energy-efficient reduction of dioxygen to water. Oxygenation of such complexes at extreme solution temperatures (-125°C) yield transient Cu(III) containing complexes. As Cu(III) is currently uncharacterized in any biological enzyme, developing connections between the synthetic and biological realms is a major focus.
