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Conferences & Symposia

TMLS Conferences & Symposia are designed to uplift the goals of the initiative for its members and researchers with common interests in building theoretical frameworks for understanding living systems through presentations and discussion-driven panel formats.

Emory TMLS Virtual Workshops

With the cancelation of all in-person meetings due to the pandemic, TMLS is stepping in to help retain a sense of community amongst all of us studying questions at the interface of physical and life sciences. With TMLS Virtual Symposia, we gather speakers around a provocative set of questions on this interface, ask them to think about where their field is and what the future holds, and to present these thoughts in a short, punchy talk, accessible to a broad biophysics community, to be followed by an extensive discussion session. We aim to attract new trainees to the field and to expand the reach of the field to the parts of the globe where access to traditional in-person scientific conferences has been limited. Recordings of past TMLS Virtual Workshops can be found on our YouTube Channel .

2021 Virtual Workshops


Identification of stereotyped features of movements and their changes in health and disease have been a common step towards understanding and modeling movements and mechanisms of motor control that give rise to them. However, we can often recognize individuals by the way they move, and differences in motor control and movement are influenced by prior experience, training, personality, and disease mechanisms.

In other words, stereotypy is not that stereotyped! What is to be gained by looking past regularities in movement across individuals and understanding aspects of movement and motor control unique to individuals? To what extent are data considered stereotypical through one lens, but variable through another can help us in understanding movements and their neural generation? What are the techniques that allow us to identify and characterize stereotypy versus individuality to help us understand normal and impaired movement?

Our speakers will explore these and other related questions using methods from biomechanics, neural control of movement, and machine learning, and by addressing basic science, technology development, and clinical questions. Each speaker will deliver a 10 minute bold, provocative talk to sketch the current boundaries of the field and chart its future developments. Ample time will be set aside for moderated questions and discussion.



Thursday, March 18, 2021 10am to 1pm EST


We expect to live stream the recording through the Emory TMLS YouTube Channel . Please register at the link below to be updated of any changes. 


Selected Speakers:

  • Monica Daley, PhD (University of California, Irvine)
  • Gordon Berman, PhD (Emory University)
  • Gelsy Torres-Oviedo, PhD (University of Pittsburgh)
  • Nidhi Seethapathi, PhD (University of Pennsylvania)
  • Jose L. Contreras-Vidal, PhD (University of Houston)
  • Mackenzie Mathis, PhD (Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland)


Dr. Lena Ting (Emory University and Georgia Institute of Technology)
Dr. Trisha Kesar (Emory University) 


Emory Theory and Modeling of Living Systems 
Neural Engineering Center (NEC)
Synergy/Nexus II

The SARS-CoV-2 pandemic is awash in data, including daily, spatially-resolved COVID case data, virus sequence data, patients `omics data, and mobility data. Journals are now also awash in studies that make use of quantitative modeling approaches to gain insight into the geographic spread of SARS-CoV-2 and its temporal dynamics, as well as studies that predict the impact of control strategies on SARS-CoV-2 circulation. Some, but by no means all, of these studies are informed by the massive amounts of available data. Some, but by no means all, of these studies have been useful — in that their predictions revealed something beyond simple back of the envelope calculations.

To summarize some of these findings, in this symposium, we will address questions such as: What do we want from models of disease spread? What can and should be predicted? Which data are the most useful for predictions? When do we need mechanistic models? What have we learned about how to model disease spread from unmet and/or conflicting predictions? The workshop speakers will explore these questions from different perspectives on what data need to be considered and how models can be evaluated. 

As in other TMLS workshops, each speaker will deliver a 10-minute talk with ample time set aside for moderated questions/discussion. We expect the talks to be provocative and bold, while respecting different perspectives.

When: Thursday, January 21, from 10am to 2pm EST

Where: We expect to live stream the recording through the Emory TMLS YouTube Channel . Please register at the link below to be updated of any changes. 


More Info: will be posted at


  • Rachel Baker (Princeton University)
  • Caroline Buckee (Harvard University)
  • Sarah Cobey (University of Chicago)
  • Nigel Goldenfeld and Sergei Maslov (UIUC)
  • Ruian Ke (LANL)
  • Stephen Kissler (Harvard University)
  • Lauren Ancel Meyers (University of Texas)
  • Isabel Rodriguez-Barraquer (UCSF)
  • Sam Scarpino (Northeastern University)
  • Michael Worobey (University of Arizona)
  • Joshua Weitz (Georgia Institute of Technology)

Organizers: Katia Koelle, Daniel Weissman, Rustom Antia

Emory TMLS: Ilya Nemenman, Tiera Ward 

2020 Virtual Workshops

The 2020 virtual program included 8 workshops, detailed below. We invite you to download the official  2020 Emory TMLS virtual workshops series publication here.

Description: We both agreed that typical workshop formats wouldn’t necessarily translate well to the virtual domain and thought to organize around one question:

“Now that we can track (most) everything, what can we do with the data?”

Given the recent dramatic advances in technology, we now have behavioral data sets with orders of magnitude more accuracy, dimensionality, diversity, and size than we had even a few years ago.  That being said, there is still little agreement as to what theoretical frameworks can inform our understanding of these data sets and suggest new experiments we can perform.  We hope that after this workshop we’ll see a variety of new ideas and perhaps gain some inspiration.


Organizers: Gordon Berman (Emory University), Greg Stephens (Vrije Universiteit Amsterdam, Okinawa Institute of Science and Technology)

Speakers: Andre Brown (Imperial College), William Bialek (Princeton University), Ann Kennedy (California Institute of Technology), Ugne Klibaite (Harvard University), Alexander Mathis (Harvard University), Nicholas Ouellette (Stanford University), Samuel Reiter (Okinawa Institute of Science and Technology), Ariana Strandburg-Peshkin (University of Konstanz)

Description: There has been a surge of publications on using machine learning (ML) on experimental data from physical systems: social, biological, statistical, and quantum. However, can these methods discover new physicsIt can be that their biggest impact is in better data preprocessing, while inferring new physics is unrealistic without specifically adapting the learning machine to find what we are looking for — that is, without the “intuition” — and hence without having a good a priori guess about what we will find.

Is machine learning a useful tool for physics discovery? Which minimal knowledge should
we endow the machinewith to make them useful in such tasks? How do we do this?
Eight speakers below will anchor the workshop, exploring these questions in contexts of diverse systems (from quantum to biological), and from general theoretical advances to specific applications. Each speaker will deliver a 10 min talk with another 10 minutes set aside for moderated questions/discussion. We expect the talks to be broad, bold, and provocative,
discussing where the field is heading, and what is needed to get us there.

Organizers: Ilya Nemenman (Emory University)

 Speakers: Aleksandra Walczak (Centre national de la recherche scientifique/École Normale Supérieure Paris), David Schwab (City University of New York), Sam Greydanus (Google Brain), Max Tegmark (Massachusetts Institute of Technology), Bryan Daniels (Arizona State University), Andrea Liu (University of Pennsylvania), Roger Melko (University of Waterloo), Lucy Colwell (Cambridge University)

Organizers: Samuel Sober (Emory University), Gordon Berman (Emory University)

Speakers: Amy Bastian (Kennedy Krieger Institute), Rui Costa (Columbia University), Amy Orsborn (University of Washington), Chethan Pandarinath (Emory University and Georgia Tech University), Abigail Person (University of Colorado), Andrew Pruszynski (Western University), Flip Sabes (University of California San Francisco and Neuralink), Lena Ting (Emory University /Georgia Tech University)

Gordon Berman (Emory University)
Megan Carey (Champalimaud Centre for the Unknown)       
Ilya Nemenman (Emory University)   
Sam Sober (Emory University) 

Organizers: Jennifer Rieser (Georgia Tech University), Daniel Sussman (Emory University)

Speakers: Michael Brenner (Harvard University), Eleni Katifori (University of Pennsylvania), Cristina Marchetti (University of California, Santa Barbara), Chase Broedersz (Ludwig Maximilian University), Joshua Shaevitz (Princeton University), Michael Murrell (Yale University), Symone Alexander (Georgia Tech University), Orit Peleg (University of Colorado, Boulder)

Session 1: What Is Linked Selection Doing To Populations?

Natural selection affects not only selected alleles, but also indirectly affects all alleles linked to selected sites. An increasing body of evidence suggests that this linked selection is an important driver of allele frequency dynamics throughout the genomes of many species, implying that we need to substantially revise our basic understanding of molecular evolution and the tempo and mode of adaptation. This session brings together early-career researchers working towards a quantitative understanding of linked selection, bringing together data from many different systems as well as models of different forms of linked selection

Session 2: What Determines Microbial Diversity?

Microbes make up the vast majority of the tree of life. While we know very little about most microbial species, large-scale sequencing is giving us glimpses of the diversity that exists both within species and in ecosystems.The challenge now is to find the patterns in this diversity and understand them. This session features provocative talks on attempts to meet that challenge. 

Organizers: Daniel B. Weissman (Emory University)

Speakers: Christelle Fraïsse (Institute of Science and Technology Austria/Centre National de la recherche scientifique), Derek Setter (University of Edinburgh), Kimberley Gilbert (University of Lausanne/University of Bern), Ivana Cvijovic (Stanford University), Erik van Nimwegen (University of Basel), Jacopo Grilli (International Centre for Theoretical Physics), Maitreya Dunham (University of Washington), Nandita Garud (University of California, Los Angeles)

Description: Which features of biological data are predictable? Should theorists interested in quantitative predictions focus on different questions, not typically asked by biologists? Which types of models are best suited to making quantitative predictions in different fields? Why do theories work, when there’s no general reason for them to? Do large, multidimensional datasets make theories (and which theories?) more or less likely to succeed? This will be an unapologetically theoretical physics workshop — we won’t focus on a specific subfield of biology, but will explore these questions across the fields, hoping that the underlying theoretical frameworks will help us find the missing connections. 

Organizers: Ilya Nemenman (Emory University)

 Speakers: Ned Wingreen (Princeton University), Jane Kondev (Brandeis University), Arvind Murugan (University of Chicago), Boris Shraiman (Kavli Institute for Theoretical Physics / University of California, Santa Barbara), Greg Stephens (Vrije University Amsterdam/Okinawa Institute of Science and Technology), Thierry Mora (ENS/CNRS), Leennoy Meshulam (University of Washington), Audrey Sederberg (Emory University), Eve Marder (Brandeis University)

Description: When was the last time you ran into a giant? Chances are never. Almost 100 years ago, JBS Haldane posed an outwardly simple yet complex question—what is the most optimal size (fora biological system)? The living world around us contains a huge diversity of organisms, each with its own characteristic size. Even the size of subcellular organelles is tightly controlled. Inabsence of physical rulers, how do cells and organisms truly “know” how large is large enough? What are the mechanisms in place to enforce size control?

Organizers: Sandeep Choubey, (Max Planck Institute for the Physics of Complex Systems), Shashank Shekhar (Emory University)

 Speakers: Dan Fletcher (University of California, Berkeley), Kinneret Keren (Technion), Karl Niklas (Cornell University), Lishibanya Mohapatra (Rochester Institute of Technology), Marija Zanic (Vanderbilt), Stephanie Weber (McGill University), Wallace Marshall (University of California San Francisco), Ken Andersen (Technical University of Denmark)

Organizers: Chethan Pandarinath (Emory University/Georgia Tech University)

Speakers: Adrienne Fairhall (University of Washington), Mehrdad Jazayeri (Massachusetts Institute of Technology), John Krakauer (John Hopkins), Francesca Mastrogiuseppe (Gatsby / University of College London) Abigail Person (University of Colorado, Boulder), Abigail Russo (Princeton University), Krishna Shenoy (Stanford University), Saurabh Vyas (Columbia University)