Title: "Engineering protein synthesis modulators to understand the neural basis of emotional behaviors"
Emotions are crucial features that allow us to react adaptively to internal and external stimuli in a manner conducive towards self-preservation. Emotional regulation, the ability to recognize, evaluate, and control one’s emotions and recognize others’ emotions, strongly influences learning abilities, and other daily activities. Emotionally salient experiences are rapidly transformed into long-term memories, and they serve as useful model to study the biological basis of memory consolidation, the process of stabilizing a memory trace. In this presentation, Dr. Shrestha will discuss how nascent protein synthesis and regulatory pathways for protein synthesis modulate innate and learned emotional behaviors. Her lab has developed advanced genetic tools to control protein synthesis in specific neuronal populations with various degrees of temporal precision. She will present her team’s ongoing work on elucidating the molecular players underlying consolidation of emotional memories and how disruption of protein synthesis regulatory pathways can lead to pathological anxiety in neurodevelopmental disorders.
Biography:
Prerana Shrestha is an Assistant Professor in the Department of Neurobiology & Behavior at the Renaissance School of Medicine. Her research is focused on understanding the neurobiological basis of emotional behaviors using novel tools engineered to manipulate nascent protein synthesis. She received her PhD in Life Sciences from the Rockefeller University, following which she did her postdoctoral work at the Center for Neural Science in NYU. She is a recipient of several accolades including Alfred P Sloan Research fellow and Kavli fellow.
Date: Tuesday, September 24, 2024
Time: 1pm-2pm
Location: Special Collections Seminar Room, E-2340, second floor of the Melville Library
Title: "Can a machine learn chemistry?"
We live in a new scientific paradigm: the Big Data era, in which a lot of data is available for almost anything. In this new paradigm, the driving force is to use data directly to learn about chemical and physics systems employing artificial intelligence. This paradigm has proven helpful in simulating realistic physical, biological, and chemical models, yielding impressive results. Similarly, the insight gained in these situations can be used to improve our understanding of fundamental processes. In that regard, we want to answer the question: Can a machine learn chemistry? The answer to this question is still debatable, but we will show our ideas and methods to find the answer. Our group uses a bottom-up approach, starting with simple systems and then increasing the level of complexity. The simplest molecules, diatomics (two atoms bound to each other), will be the first system under consideration. Specifically, we will show that it is possible to predict molecular properties of diatomic molecules (spectroscopic constants and dipole moments) from atomic ones. Indeed, the level of accuracy is nearly as good as the gold standard in theoretical chemistry. Next, we will discuss our results on predicting atom-diatom reactions, showing that it is possible to predict the outcome of a reaction across the chemical space. Finally, we will discuss other avenues and work in progress in our group.
Biography:
Jesús Pérez Ríos was born and educated in Spain, where he got his Ph.D. in physics in 2012 from Universidad Complutense de Madrid. Then, after a postdoc period in different world-recognized institutions, he joined the Fritz Haber Institute of the Max Planck Society as a group leader in the Department of Molecular Physics (2019). Finally, he joined Stony Brook University in early 2022. Jesús Pérez Ríos has authored a book entitled “An introduction to cold and ultracold chemistry: atoms, molecules, ions and Rydbergs,” published by Springer in 2020. Besides, he is an editorial board member of the few-body systems journal and one of the leading editors of the Cambridge Elements on Physics beyond the standard with atomic and molecular systems.
Date: Tuesday, October 29, 2024
Time: 1pm-2pm
Location: Special Collections Seminar Room, E-2340, second floor of the Melville Library
Title: “Human-Centered Large Language Modeling”
To truly understand human language, we must look at words in the context of the human generating the language, i.e., who is speaking, where and in what situation, when they are speaking, and to whom it is addressed. For example, a person feeling exhilarated on a hike would complete the statement “I am feeling..” differently as compared to when they are feeling dejected during a break-up. Factors such as demographics, personality, modes of communication, and emotional states have also shown to play a crucial role in NLP models pre-LLMs (large language modeling) era. Advances in language modeling yielded in Transformer-based LLMs as the base of most current NLP systems. However, traditional language modeling views words or documents independent of the aforementioned human context. To address this, we have taken first steps of mathematically defining the inclusion of human context in language modeling, and empirically comparing the effects of including different types of human contexts in language modeling on downstreams tasks.
Biography:
Nikita is a PhD student at Stony Brook University co-advised by H. Andrew Schwartz and Niranjan Balasubramanian. Her vision is to drive Natural Language Processing (NLP) towards unbiased and true understanding of human language by making models aware of the ``generators'' of language (humans) without being limited to the context of words. Her research focuses on developing NLP models based on (human) language modeling that include a rich human context, encompassing personal, social, situational, and environmental attributes. The purpose of her research is to enable an AI-future centric world to maintain the focus on the growth of humans. She has been on the program committee of multiple top-tier NLP conferences (ACL, NAACL, EMNLP) and workshops (NLP+CSS, ICWSM). She is the lead organizer of the workshop on human-centered large language modeling.
Date: Tuesday, November 19, 2024
Time: 1pm-2pm
Location: Special Collections Seminar Room, E-2340, second floor of the Melville Library
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