Maya Banks

Bio: I am a junior math major at Carleton College. I love being outside and I am passionate about activism, problem-solving, and ultimate frisbee. 

SFI MENTOR: Dan Larremore 

In a directed network, we can detect a ranking or hierarchy by assigning `scores' to the vertices based on the connections in the network. These scores can be used to predict the relative importance of different vertices. We are interested in how hierarchy emerges in complex networks, as well as how it evolves over time. In real networks, where the set of edges present in the network may change, we expect that existing hierarchy affects which new edges emerge, with new edges appearing with some probability associated with the ranking of each vertex. At the same time, the ranking of vertices is extracted based on the connections present in the network at a given moment. Using different methods for ranking vertices and rewiring networks according to a given ranking, we explore the dynamics of ranking/hierarchy in directed networks over time. Through computer simulations as well as analytic methods, we investigate limiting degree distributions for different ranking and rewiring algorithms, as well as sudden regime shifts in the emergence of hierarchy in networks.

Final Paper: Emergence of Hierarchy in Complex Networks

Final Presentation: Video

Francis Cavanna

Bio: I am a junior hailing from the University of Dallas. I've majored in physics, but have taken several courses (15+) in the humanities - English, History, Italian, Philosophy, Politics, etc. Outside of my major, my interests include Hamlet, Chess, and the Good, True, and Beautiful. I'm extremely excited for this summer and look forward to meeting you all in person. 

SFI MENTOR: Artemy Kolchinsky 

I will construct a simulation of the Ising model and use it to investigate the thermodynamic properties of computation. An Ising model is a set of discrete elements (representing the spins of atoms) which take a state of “up," or “down," depending on the energy associated with the whole system and the external magnetic field. Above a certain temperature (called the critical temperature), the “spins" of the discrete elements in the Ising model orient themselves in random directions, independent of their neighbors. Below the critical temperature, the spins of the discrete elements in the Ising model preferentially orient themselves along their neighbor's spins. At the critical temperature, the Ising model approximates a mottled surface, with small groups of elements aligning themselves “up" bordered by groups of elements aligning themselves "down". This system can also be changed by an external magnetic field applied to all elements in the model. Our goal is to demonstrate that the work/power/time costs associated with flipping elements for computational tasks are optimized at the critical temperature. To test this hypothesis, we will need to construct a computational Ising model, define work related to the magnetic field, and compile statistics from repeated simulations of this model.

Final Paper: Computational and Theoretical Studies in Minimizing Costs of Computation

Final Presentation: Video

Hamza Chaudhry

Bio: I used to live my life as performance art for God, then the NSA, and now finally just for myself. I'm writing a book filled with memoirs titled Nonfiction Surrealism, which I use as motivation to go out of my comfort zone and put myself into the most absurd, life-affirming situations possible. I don't plan on publishing it; I just use it as a reason to strive. I love meeting people with passion, whether it's for science, philosophy, art, or just life. I look forward to meeting all of you! 

SFI MENTOR: Nihat Ay & Keyan Ghazi-Zahedi 

We will examine various measures of morphological computation in the field of embodied intelligence, a field that that emphasizes the role of the body and environment in how an organism thinks. Morphological computation refers to processes which are conducted by the body and environment that would otherwise be carried out by the brain, implying that a well-chosen morphology can substantially reduce the amount of required cognitive control. We will study embodied agents embedded in a “sensorimotor loop,” comparing a variety of measures that aim to quantify an organism's morphological computation by highlighting specific aspects of the behavior. By first testing these measures against an idealized mathematical model, we hope to develop some intuition in formalizing this philosophical concept and perhaps devise new approaches. We will first reconstruct numerical results from prior research on the “binary model,” then extend this model to its completion by including additional constraints, with an ultimate goal of generalizing these results to more complex models with arbitrary constraints. In the process, we hope to draw analogies between measures of morphological computation in embodied agents and measures of complexity in complex systems (defined in terms of information geometry), with possible applications to computer simulations of biological and robotic movements.

Final Paper: Measuring Morphological Computation

Final Presentation: Video

Cate Heine

Bio: My name is Cate and I'm math major at a little school in Danville, Kentucky called Centre College. I like apples, mountains, crossword puzzles, and my dog. I'm fascinated by cities--the way that they change, grow, and affect peoples' lives. 

SFI MENTORS: Geoffrey West and Chris Kempes 

As our world becomes more and more urbanized, understanding the intricacies of the human city becomes increasingly important. Much work has been done to understand general urban growth patterns: researchers like Luis Bettencourt and Geoffrey West have identified nearly universal urban scaling laws. However, the underlying structure of that growth is less clear. Do the substructural elements of cities—communities such as neighborhoods or school districts—scale in similar ways? How does the substructure of a city and the interaction between its elements contribute to its overall productivity? Does the mixing of a city's population between neighborhoods inform its over- or underperformance in terms of more general scaling laws? By answering these questions, I hope to nuance my understanding of urban growth and shed light on what it means for a city to be successful and productive.

Final Paper: Scaling & City Substructure

Final Presentation: no video

Xiaofan Liang

Bio: I am Xiaofan from China. I have a degree background in Sociology, and now a rising Junior at Minerva Schools at KGI, majoring in Computational Science. What fascinate me are how complexity science, computer science, social science, and education come together to 1) create a (cognitive/social) system that maximizes collective output and efficient adaptation, 2) uncover human behavioral and cultural insights to facilitate synergies in groups and 3) make all these tools accessible to the public. 

SFI MENTOR: Marion Dumas & Chris Kempes 

ABSTRACT:  Scaling Properties of Universities in the United States

Final Paper: Scaling Properties of Universities in the United States

Final Presentation: no video

Phuc Nguyen

Bio: I'm a junior from Vietnam, studying applied mathematics/statistics and computer science. I'm interested in computational mathematics and its applications in social sciences. I enjoy arts of all forms but particularly visual art. In my free time, I like to cook or explore new places to eat. 

SFI MENTOR: Daniel Larremore, Caterina De Bacco, Cris Moore 

Underlying hierarchies govern interaction patterns in many systems. Consequently, observed interactions between members of a system can reveal their positions in the hierarchy. For instance, within a tournament, teams that have similar rankings tend to play against one another more often. The outcomes of these interactions, i.e. which team wins a game, also affect the rankings in the hierarchy. A proposed physics-inspired model formalizes the two aforementioned assumptions to infer hierarchies from network data, and gives real-valued rankings to nodes. We would like to analytically quantify the uncertainty in the inferred ranking from this model. That is, if we were to use the inferred ranking to make a bet on the next game, how confident could we be of our bet? Or is a completely different ranking just as likely to exist?

Final Paper: Inferring Hierarchy Structure from Rankings Uncertainty

Final Presentation: Video

Erick Oduniyi

Bio: I am currently a third-year student at the University of Kansas studying computer engineering with a minor in mathematics. I am passionate about design, storytelling (any form of human communication), and helping individuals to showcase their stories through technology. Additionally, I like producing music, drawing/painting, and listening (to people's stories). 

SFI MENTOR: Vanessa Ferdinand, Elly Power, Dan Larremore 


Final Paper: Analyzing the Role of Emotion in Cultural Transmission

Final Presentation: Video

Brooke Taylor

Bio: I am a junior at Whitman College in Washington whose passion is mathematics and its applications, including computer science and physics. Outside the science realm, I really enjoy writing creative fiction and playing volleyball and badminton. I am also a pluviophile. 

SFI MENTOR: Brendan Tracey and Vanessa Ferdinand 

Are today's neologisms indicating that we are all a bunch of pessimists? New words are being produced at a rapid rate of about 15 per day and then shared easily online. Some words last only a week in a small circle of friends while others making their more permanent residence in the dictionary. But as with the words that came before them, they are subject to the force of language evolution. Using natural language processing techniques, including sentiment analysis, we track a set of neologisms from their origin to today to see how their semantics and their sentiments may have changed, perhaps steadily becoming more positive, suffering a drastic plummet to negativity, or remaining perfectly constant. We bring into question how closely semantics and sentiments are intertwined in these words, and ultimately if we can predict what types of words are likely to alter their emotional and lexical meanings in their lifetime.

Final Paper: The Evolution of Neologisms

Final Presentation: Video

Ryan Taylor

Bio: I'm Ryan, a sophomore studying in sustainability at Arizona State University. I'm fascinated by complex adaptive systems science in general, especially systems of global development: how we shape them, and how they shape us. I want to develop a systems view of modern civilization that is more than fanciful, but useful, insightful, and true to the beautiful world around us. Also, I'm a native of Albuquerque, New Mexico, and I love the Land of Enchantment! Looking forward to meeting everyone. 

SFI MENTOR: Marion Dumas & Chris Kempes 


Final Paper: Scaling Properties of Universities in the US

Final Presentation: no video

William Thompson

Bio: I am a freshman at Saint John's College in Santa Fe. Although my interests lie in computer science and math, I am pursuing a liberal arts degree. In my free time I love reading and hiking. 

SFI Mentor: Mirta Galesic 

Abstract: Link

Final Paper: Quantitative Analysis of Choral Interaction in Attic Tragedy

Final Presentation: Video

Milo Trujillo

Bio: I am a junior at RPI, dual majoring in computer science and science, technology, and society. I have a keen interest in computer security and privacy, and have spent the past several years developing anti-surveillance software and teaching offensive security. I am fascinated by self-organized and decentralized systems, and want to build a model for developing robust social or technical structures that are efficient and resistant to damage. 

SFI MENTOR: Justin Grana 

Optimizing social structure for arbitrary organization types - A novel application of deep neural-networks for designing social hierarchy models that maximize communication speed, information distribution, redundancy, or fault-tolerance. These models can help demonstrate why different groups exhibit different organizational hierarchies, subject to the availability of resources and the cost of different communications mediums. Among a wide range of potential applications, these models can be used to analyze social organizations such as corporate hierarchies, computer networks, and military command structures.

Final Paper: Optimizing Social Hierarchy

Final Presentation: Video

Sara Vanovac

Bio: I am a sophomore from Bosnia and Herzegovina, triple majoring in Physics, Mathematics and Computing and Applied Mathematics at Furman University. In my free time I love to listen to spoken work poetry, paint or draw. I am a great fan of TED talks, food, and Richard Feynman. Professional skills comprise of speaking incredibly fast, saying the most random things ever and never saying no to a challenge. 

SFI MENTOR: Cris Moore 

ABSTRACT: We address the question of finding topics in a corpus of documents using spectral methods. Our model is similar to probabilistic latent semantic analysis (PLSA). We regard the document as ‘bag of words’ and we care only about the frequency of the unique words in the documents. Using the Poisson factorization with expectation-maximization (EM) algorithm we can detect overlapping communities. By doing the stability analysis of the area around the trivial fixed point of the EM, we gain new insight into what the right linear operator for spectral clustering is. We try to answer the question of finding the number of communities beforehand, by looking at singular values of this new linear operator.

Final Paper: Spectral Methods in Topic Modeling

Final Presentation: Video

Taylor Wehrs

Bio: I’m a Junior at Doane University, pursuing a dual degree in Math and Physics with minors in Computational Science and Psychology. Three of the things I’m truly passionate about are cooking, dancing and learning. I also love to travel. I’ve been to nearly every state, in addition to traveling to Spain, Morocco, and Costa Rica. I have a natural curiosity that drives my desire to explore new things. I’m excited for this summer and to meet all of you! 

SFI Mentor: Eric Libby 

Multicellular organisms can generate complex morphologies. However, there are many multicellular organisms that do not generate such complexity. For example slime molds form very simple shapes such as mobile slug bodies and dispersal structures. In general, it is observed that complex shapes start from single cells or affixed clonal groups. In contrast, when cells aggregate or are not clonal they seem to be limited in the complexity they can produce. To test whether these starting points constrain the organisms morphology or ability generate complex shapes, we use the digital evolutionary software Avida. Avida is an artificial life software platform with self-replicating and evolving computer programs. Through the use of this system, we evolve the shape of an organism. An analysis of the complexity of these structures is given by the speed of replication and the success of the organism. These findings will help us understand how the structure of specific organisms came to be.

Final Paper: Morphology of Multicellular Organisms

Final Presentation: Video