Upcoming Seminars / Seminars This Week

Mon 2/27/17 11 amVolen 201
Computational Neuroscience Journal Club
Mark Harnett (MIT)
Cellular and Circuit Mechanisms for Associative Computations in Retrosplenial Cortex
Pizza will be served
Hosted by John Lisman

Mon 2/27/17 12 noonRosenstiel 118
Molecular Genetics Journal Club
Anna Kazatskaya (Sengupta Lab)
C. elegans neurons jettison protein aggregates and mitochondria under neurotoxic stress
Ref: Melentijevic I, et al. Nature. 2017 Feb 8. doi: 10.1038/nature21362.
Weijin Xu (Rosbash Lab)
mRNA quality control is bypassed for immediate export of stress-responsive transcripts
Ref: Zander G, et al. Nature. 2016 Dec 12. doi: 10.1038/nature20572.
Hosted by Avi Rodal

Mon 2/27/17 4 pmGerstenzang 121
Chemistry Department Colloquium
Tatyana Polenova (University of Delaware)
Structure and Dynamics of HIV-1 and Microtubule-Associated Protein Assemblies by Integrated MAS NMR, MD, Density Functional Theory
Hosted by Klaus Schmidt-Rohr

Tue 2/28/17 12:30 pmGerstenzang 121
Joint Biology/Neuroscience Colloquium
Bjoern Schwer (Harvard Medical School)
Recurrently breaking genomic loci in neural progenitors
Hosted by Susan Lovett

Tue 2/28/17 1 pmGoldsmith 226
Combinatorics Seminar
Mark Kempton (Harvard University)
Quantum state transfer on graphs
A quantum walk on a graph G describes the evolution of the quantum state of a particle on the graph, and is described by a discrete version of the Schrodinger equation involving a graph Hamiltonian on G.  If u and v are two vertices of a graph, then we say that there is perfect state transfer from u to v if there is some time at which a particle starting at vertex u ends up at vertex v.  Considerable research has been done in recent years on perfect state transfer in graphs, particularly in the case where the graph Hamiltonian is take to be the adjacency matrix.  In addition, one can include an energy potential on the vertex set, which amounts to adding a diagonal matrix to the Hamiltonian.  I will present results showing how the potential can affect whether or not a graph admits perfect state transfer.  In particular, for paths of length greater than 4, there is no potential that can be chosen for which the path admits perfects state transfer.  However, there are infinite families of a graphs where a potential does induce perfect state transfer on the graph.
Hosted by Prof. Olivier Bernardi and Yan Zhuang

Tue 2/28/17 2 pmGoldsmith 317
Topology Seminar
Rose Morris-Wright (Brandeis)
Introduction of Acylindically Hyperbolic groups
Hosted by Prof. Ruth Charney

Tue 2/28/17 4 pmAbelson 131
Physics Department Colloquium
Bjoern Penning (University of Bristol)
How to find dark matter
Refreshments outside Abelson 131 at 3:30
Hosted by John Wardle

Wed 3/1/17 11 amRosenstiel Penthouse
Thesis Seminar (Graduate Program in Biochemistry and Biophysics)
Christopher Wilson (Kern Lab)
Rewinding the Tape

Wed 3/1/17 12 noonRosenstiel 118
Neurobiology Journal Club
Justin Shin (Jadhav Lab)
Gamma frequency entrainment attenuates amyloid load and modifies microglia
Ref: Iaccarino, H., et al. (2016)
Leonardo de Oliveira Royer (Paradis and Van Hooser Labs)
Activity-Dependent Exocytosis of Lysosomes Regulates the Structural Plasticity of Dendritic Spines
Ref: Padamsey, Z. et al. (2017)

Thu 3/2/17 12 noonRosenstiel 118
Psychology Department Brown Bag
Sujala Maharjan (Vision Lab)
Eileen Rasmussen (Aging, Culture & Cognition Lab)

Thu 3/2/17 2 pmAbelson 229
Theory IGERT Seminar ( )
Pavel Chvykov (MIT)
Principle of least rattling: a tale of non-equilibrium systems with disparate timescales
Hosted by Prof. Albion Lawrence

Thu 3/2/17 2 pmGoldsmith 317
Everytopic Seminar
Fabian Hayden (Harvard University)
Refined Harder-Narasimhan filtrations in modular lattices and iterated logarithms
I will discuss joint work with Katzarkov, Kontsevich, and Pandit in which we determine the asymptotic of certain ODEs (gradient flow on metrizations of a quiver representation), and conjecturally certain PDEs (non-linear heat flows), using combinatorics in modular lattices in the sense of order theory. One of the consequences is a canonical refinement of any Harder-Narasimhan filtration so that sub-quotients are direct sums of stable objects. The talk will include an introduction to modular lattices and quiver representations.
Hosted by McKee Krumpak, Konstantin Matveev, and Arunima Ray

Fri 3/3/17 10 amVolen 101
Computer Science Seminar (Machine Learning & Data Science Series)
Shiyu Chang (Research Staff, IBM T. J. Watson Research Center)
Similarity Learning in the Era of Big Data
Abstract: The notion of machines that can learn has caught imaginations since the days of the early computer. In recent years, as we face burgeoning amounts of data around us that no human mind can process, machines that can learn to automatically find insights from such vast amounts of data have become a growing necessity. The field of machine earning is a modern marriage between computer science and statistics driven by tremendous industrial demands. The soul behind many applications is based on the so-called "similarity learning". Learning similarities is often used as a subroutine in important data mining and machine learning tasks. For example, recommender systems utilize the learned metric to measure the relevance of the candidate items to target users. Applications of this approach also exist in the context of clinical decision support, search, and retrieval settings. However, the three-V (volume, variety, and velocity) natures of big data make learning similarity for pattern discovery and data analysis facing new challenges. Haw to reveal the truth from massive unlabeled data? How to handle data with multimodality? What if the data consist network structures? Does temporal dynamic effects the process of decision-making? For example, in clinical decision-making, doctors retrieve the most similar clinical pathway for auxiliary diagnosis. However, the sheer volume and complexity of the data present major barriers toward their translation into effective clinical actions. In this talk, I will illustrate some of these challenges with examples from my Ph.D. works on foundations of similarity learning. I will show that with judicious design together with rigorous mathematics for learning similarities, we are able to make various kinds of impact on society and uncover surprising natural and social phenomena.

Speaker Bio: Shiyu Chang is a Research Staff Member at IBM Thomas J. Watson Research Center. Ile recently obtained his Ph.D. from the University of Illinois at Urbana-Champaign (UIUC) under the supervision of Prof. Thomas S. Huang. Shiyu has a wide range of research interests in data explorations and analytics at large-scale. Specifically, his current research directions lie on developing novel machine learning algorithms to solve complex computational tasks in real-world. Shiyu received his B.S. degree at UIUC in 2011 with the highest university honor (Bronze Tablet Award). He graduated front the Department of Electrical and Computer Engineering at UIUC and obtained Iris M.S. degree in 2014. He is a recipient of the Thomas and Margaret Huang Award in 2016 and the Kodak Fellowship Award in 2014. Most of Shiyu' s research has been published in top data mining, computer vision and artificial intelligent venues including SIGKDD, WWW, CVPR, WSDM, ICDM, SDM, IJCAI etc. The paper "Factorized Similarity Learning in Networks" has been selected as the best student paper in ICDM 2014.

Refreshments will be served.

Fri 3/3/17 11:30 amRosenstiel 118
Biochemistry-Biophysics Friday Lunchtime Pizza Talks
Chris Miller (Professor of Biochemistry Brandeis University)
A Weird Ion Channel for a Weird Ion

Fri 3/3/17 12:30 pmGerstenzang 123
Molecular and Cell Biology & Neuroscience Student Seminars
Narendra Mukherjee (Katz Lab)
Danielle DiTirro (Sengupta Lab)

Mon 3/6/17 11 amAbelson 307
String Theory Seminar
Shinobu Hosono (Gakushin University)
Birational geometry from the moduli spaces of mirror CICYs
Hosted by Albion Lawrence

Mon 3/6/17 12 noonRosenstiel 118
Molecular Genetics Journal Club
Daniel Pomeroy (MIT Policy Center)
Organizing Science for Impact in the Trump Era
Hosted by GTG students, Career Development for the Sciences

Mon 3/6/17 2 pmAbelson 229
Theory IGERT Seminar
Srikanth Sastry (Jawaharlal Nehru Centre for Advanced Scientific Research)
Hosted by Prof. Bulbul Chakraborty

Mon 3/6/17 4 pmGerstenzang 121
Chemistry Department Colloquium
Sharon Hammes-Schiffer (University of Illinois, Urbana-Champain)
Proton-Coupled Electron Transfer in Catalysis and Energy Conversion
Hosted by Judith Herzfeld

Tue 3/7/17 12:30 pmGerstenzang 121
M.R. Bauer Colloquium Series
Leonardo Belluscio (National Institutes of Health)
Using the Olfactory System to Study Neurodegeneration
Hosted by Don Katz

Tue 3/7/17 4 pmAbelson 131
Physics Department Colloquium
David Keith (Harvard University)
Assessing and Reducing the Risks of Solar Geoengineering
Refreshments outside Abelson 131 at 3:30pm
Hosted by W. Benjamin Rogers

Wed 3/8/17 12 noonRosenstiel 118
Neurobiology Journal Club
Claire Symanski (Jadhav/Van Hooser Lab)
A hippocampal network for spatial coding during immobility and sleep
Ref: Kay, K., et al (2016)
Lauren Tereshko (Sengupta Lab)
Activity-Dependent Exocytosis of Lysosomes Regulates the Structural Plasticity of Dendritic Spines
Ref: Padamsey, Z. et al. (2017)

Wed 3/8/17 1 pmVolen 201
Safety Training
Andrew Finn (Environmental Health & Safety)
Fire Safety Topics

Thu 3/9/17 12 noonRosenstiel 118
Psychology Department Brown Bag
Veronica Flores (Behavior, Learning & Electrophysiology Lab)
John Ksander (Aging, Culture & Cognition Lab)

Fri 3/10/17 11:15 amPearlman 113
Biochemistry-Biophysics Friday Lunchtime Pizza Talks
First-Year BCBP PhD Students
First-Year Biochemistry & Biophysics Doctoral Student Rotation Talks

Fri 3/10/17 12:30 pmGerstenzang 123
Molecular and Cell Biology & Neuroscience Student Seminars
Weijin Xu (Rosbash Lab)
Justin Shin (Jadhav Lab)

Mon 3/13/17 12 noonVolen 101
Computer Science Seminar (Machine Learning & Data Science Series)
Wei Cheng (NEC Research Labs Princeton, NJ)
Integrating Multiple Networks for Big Data Analysis Monday
Abstract: In many big data applications, data with complex structures can usually be modeled as network data. Usually, for one data mining problem, we have multiple networks. For one thing, data about the same object can be obtained from various. For another, the different objects may have complex structures and can be interrelated in a complex way. Integration of different network data is valuable for reaching a more accurate decision and discovering novel patterns. The task is challenging because of the inherent characteristics of the networks: 1) variety (e.g., complex structures, heterogeneous types and data sources); and 2) poor quality; 3) massive volume. In this talk, I will present our research efforts to use big data technologies to integrate multiple networks for both supervised and unsupervised data mining problems. First, I will begin by presenting the work of integrative analyzing multi-domain heterogeneous data for graph clustering. Next, I will present the work on anomaly detection by considering temporal and dynamic network and robust sparse regression algorithm that integrates multi-source heterogeneous networks.

Speaker Bio: Wei Cheng is a Researcher at NEC Labs America. He received his Ph.D from Department of Computer Science, UNC at Chapel Hill in 2015. During his Ph.D period, he visited Department of Computer Science of UCLA from 2013 to 2015. Before that, he received a Master's and Bachelor' s degree from Tsinghua University and Nanjing University, in 2010 and 2006, respectively. His research interests include data science, data mining, machine learning, and bioinformatics. His research has been published in top tier conferences including SIGKDD, ISMB, IEEE ICDM, CIKM, SDM and journals including Nature, Nature Genetics, TKDE, TKDD, Bioinformatics, BMC Bioinformatics, KRIS and so forth. His research results received Best Paper Runner-Up Award at SIGKDD 2016 and were nominated for the Best Paper Award at ICDM 2015 and SDM 2012 respectively. He has also served as a program committee member for several top tier conferences including SIGKDD'14, IJCAI' 15, SDM'17, etc. Previously, he also conducted research at Microsoft Research and IBM Research as an intern.

Refreshments will be served.

Tue 3/14/17 12:30 pmGerstenzang 121
Pepose Award Lecture
Frank Werblin (UC Berkeley)
Hosted by John Lisman

Tue 3/14/17 3 pmGerstenzang 121
Chemistry Department Colloquium
Anne McNeil (University of Michigan)
Synthesizing Conjugated Polymers with Sequence Control & Engaging Students through Authentic Research Experiences
Hosted by Irving Epstein

Tue 3/14/17 4 pm
Physics Department Colloquium
No colloquium (APS Meeting)

Wed 3/15/17 12 noonRosenstiel 118
Neurobiology Journal Club
Chelsea Groves-Khunle (Van Hooser and Turrigiano Labs)
Lina Ni (Garrity Lab)

Thu 3/16/17 12 noonGerstenzang 124
Career Development Seminar
Colin Brenan (Founder/CCO HiFiBiO BV)
Reflections on Entrepreneurship and Innovation: Lessons Learned by an Entrepreneurial Scientist
Hosted by Career Development for the Sciences

Thu 3/16/17 12 noonLurias, Hassenfeld Conference Center
Psychology Department Colloquium (NIGMS Brain, Body & Behavior Training Grant NIA Cognitive Aging in Social Context Training Grant)
Dr. Helen Tager-Flusberg (Boston University)
Hosted by Paul DiZio

Fri 3/17/17 10 amVolen 101
Computer Science Seminar (Machine Learning & Data Science Series)
Bo Li (Postdoctoral Research Fellow Electrical Engineering and Computer Science University of Michigan)
Secure Learning in Adversarial Environments
Abstract: Advances in machine learning have led to rapid and widespread deployment of software-based inference and decision making, resulting in various applications such as data analytics, autonomous systems, and security diagnostics. Current machine learning systems, however, assume that training and test data follow the same, or similar, distributions, and do not consider active adversaries manipulating either distribution. Recent work has demonstrated that motivated adversaries can circumvent anomaly detection or classification models at test time through evasion attacks, or can inject well-crafted malicious instances into training data to induce errors in classification through poisoning, attacks. In addition, by undermining the integrity of learning systems, the privacy of users' data can also be compromised.

In this talk, I will describe my recent research addressing evasion attacks, poisoning attacks, and privacy problems for machine learning systems in adversarial environments. The key approach is to utilize game theoretic analysis and model the interactions between an intelligent adversary and a machine learning system as a Stackelberg game, allowing us to design robust learning strategies which explicitly account for an adversary' s optimal response. I'll briefly discuss human subject experiments that support the results of mathematical models, and I will also introduce a real world malware detection system deployed based on adversarial machine learning analysis.

Speaker Bio: Dr. Bo Li is a postdoctoral research fellow in the department of Electrical Engineering and Computer Science at University of Michigan, and is a recipient of the Symantec Research Labs Graduate Fellowship in 2015. Her research focuses on both theoretical and practical aspects of machine learning, security, privacy, game theory, social networks, and adversarial deep learning. She has designed several robust learning algorithms, a scalable framework for achieving robustness for a range of learning methods, and a privacy preserving data publishing system. She is also active in adversarial deep learning research for training generative adversarial networks (GAN) and designing robust deep neural networks against adversarial examples. Her website is

Refreshments will be served.

Fri 3/17/17 11:15 amRosenstiel 118
Biochemistry-Biophysics Friday Lunchtime Pizza Talks
Philip J. Kranzusch (Harvard Medical School)
Ancient cGAS-STING pathways reveal new mechanisms of human innate immune activation
Hosted by Maria-Eirini Pandelia

Fri 3/17/17 12:30 pmGerstenzang 123
Molecular and Cell Biology & Neuroscience Student Seminars
Claire Symanski (Jadhav/VanHooser Labs)
Laura Laranjo (Lovett Lab)

Tue 3/21/17 12:30 pmGerstenzang 121
Joint Biology/Neuroscience Colloquium
Alla Grishok (Boston University )
Nuclear roles of RNAi in gene regulation: lessons from C. elegans
Hosted by Nelson Lau

Tue 3/21/17 3:30 pmGerstenzang 121
Chemistry Department Colloquium
Ramesh Giri (University of New Mexico)
Cross-Coupling as a Platform for Olefin Dicarbofunctionalization
Hosted by Li Deng

Tue 3/21/17 4 pmAbelson 131
Physics Department Colloquium
Refreshments at 3:30pm, outside Abelson 131

Tue 3/21/17 4 pmAbelson 131
Physics Department Colloquium
John Bush (MIT)
Hosted by Professor W. Benjamin Rogers

Wed 3/22/17 12 noonRosenstiel 118
Neurobiology Journal Club
Linnea Herzog (Jadhav and Katz Labs)
Gonzalo Budelli (Garrity Lab)

Wed 3/22/17 3:30 pmGerstenzang 123
Rosenstiel Award Lecture
Multiple Speakers
The Remarkable Scientific Life of Susan Lindquist
Hosted by Jim Haber

Thu 3/23/17 12 noonRosenstiel 118
Psychology Department Brown Bag
Brandon Hager (Social Interaction & Motivation Lab)
Nadya Greenberg (Social Interaction & Motivation Lab)

Thu 3/23/17 4:30 pmGoldsmith 317
Joint Mathematics Colloquium
Richard Kenyon (Brown University)
Refreshments at 4pm in Goldsmith 100
Hosted by Prof. An Huang

Fri 3/24/17 10 amVolen 101
Computer Science Seminar (Machine Learning & Data Science Series)
Minwoo "Jake" Lee (Computer Science Dept. Colorado State University)
Sparse Bayesian Reinforce for Robust and Efficient Lea
Abstract: An intelligent agent often fails to learn when it encounters a new environment because it forgets what it has learned. This impacts the stability of learning process. One instance of this is well-known "catastrophic forgetting" of neural networks. Biased input data used for neural network training cause the networks to lose the learned knowledge as new data arrive from outside the old data space. To overcome this, sparse Bayesian reinforcement learning proposes a way to gradually gain knowledge about the domain with a sparse learning model for efficiency. Robust learning can be achieved through a knowledge augmentation framework. With the proposed heuristics, we claim that the sparsity of the domain is maintained. The Bayesian model makes learning more efficient by providing a way to make a correct decision depending on the confidence of estimation. The proposed framework has been successfully applied to analytical learning and efficient search for transfer learning and efficient fine control. This will be demonstrated in application to control of a simulated octopus arm.

Speaker Bio: Minwoo Lee is a PhD candidate in Computer Science at Colorado State University. He received his M.S. and B.S. from Korea Aviation University in 2002. His research interests focus on machine learning, with, emphasis on reinforcement learning, transfer learning, sparse learning, multiagent learning, fine control based policy development, knowledge representation and robust knowledge augmentation. He received REUSSIINSF fiends for research with Imia, France in 2012 and investigated robust high dimensional clustering models for biomedical data. His recent research about pretraining deep networks for reinforcement learning agents received the best paper award from IJCNN 2015. He has served as a referee for journals such as IEEE Transactions on Neural Networks and Learning Systems (TNNLS) and conferences such as IEEE Conference of Decision and Control (CDC), IEEE International Joint Conference on Neural Networks (IJCNN), and IEEE International Conference on Tools with. Artificial Intelligence (ICTAI).

Refreshments will be served.

Fri 3/24/17 11:15 amRosenstiel 118
Biochemistry-Biophysics Friday Lunchtime Pizza Talks
Dr. Myles Akabas (Albert Einstein College of Medicine)
Malaria Purine Transporter: Novel Target for Development of New Antimalarial Drugs
Hosted by Chris Miller

Fri 3/24/17 12:30 pmGerstenzang 123
Molecular and Cell Biology & Neuroscience Student Seminars
Brenda Lemos (Haber Lab)
Chelsea Groves Kuhnle (VanHooser/Turrigiano Labs)

Mon 3/27/17 12 noonRosenstiel 118
Molecular Genetics Journal Club
Sebastian Kadener (Brandeis University )
Rounding up the circle, unraveling molecular and physiological functions of circRNAs
Hosted by Avi Rodal

Mon 3/27/17 4 pmGerstenzang 121
Chemistry Department Colloquium
Gang Han (University of Mass., Worcester)
Hosted by Bing Xu

Tue 3/28/17 12:30 pmGerstenzang 121
M.R. Bauer Distinguished Guest Lecture Series
Casper Hoogenraad (Utrecht University)
Cytoskeleton-based mechanisms underlying the biology and diseases of the nervous system
Hosted by Avital Rodal and Suzanne Paradis

Tue 3/28/17 4 pmAbelson 131
Physics Department Colloquium (Joint Quantitative Biology / Physics Department Colloquium)
Steve Harvey (University of Pennsylvania)
Hosted by Michael Hagan

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