In 2018, US venture capital investments in healthcare-focused companies reached a record $9.6 billion. Medical device investment grew 40% during this time, with categories like surgical robotics surging. Universities continue to be a crucial source of innovation. Here at Pitt, we are licensing technologies and starting new companies at a record pace. Large companies have also realized the benefits of partnering with researcher institutions to feed their pipeline of “external innovations,” creating new opportunities for academic-industry collaborations.
While the process of taking a medical technology to market may seem daunting, the University of Pittsburgh offers a plethora of resources to support its mission of translating discoveries to market for the benefit of society. In this seminar, we will demystify the process of commercialization with a review of the following topics:
Hypersonic flight is a major challenge and substantial
efforts are currently underway to provide the understanding and technology
required to design and operate effectively and safely a hypersonic aircraft for
commercial or military purposes. Ivett Leyva  has recently described the
essence of this challenge in an article in Physics Today, November 2017.
There are several key physical phenomena that can occur in
hypersonic flight that involve fluid/structural/thermal/dynamics interaction
(FSTDI). This talk is an effort to bring some order to this complex
multidisciplinary topic. Issues
involving only two disciplines are identified that need further work as well as
issues that are relatively well understood. Then the discussion moves to issues
involving three disciplines (FSDI) and finally to FSTDI in its full four
To bring some order to the complexity the focus of the talk
will be on (1) response of structures to known turbulent flows, (2) global
dynamic instability of the flow field due to shock wave/ boundary layer
interaction, (3) dynamic instability of
the combined fluid structural system (flutter and limit cycle oscillations),
and (4) effects of thermal fields on the foregoing and vice versa.
The number of non-dimensional parameters that can affect
these physical phenomena is large. These non-dimensional parameters include the
following: Mach Number, Reynolds Number, the ratio of flow dynamic pressure to
structural stiffness, the ratio of fluid mass to structural mass, the ratio of
static pressure loading to structural stiffness, and the ratio of thermal
stress induced by a temperature difference between the flexible structure and
its surrounding structure to flexible structure stiffness as well as the
geometry (e.g. length to width ratio) of the structure. When multiple
disciplines are involved the number of relevant non-dimensional parameters is
indeed daunting. Thus theory and computation are very much needed as a valuable
guide to the design and interpretation of experiments.
Dr. Dowell is an elected member of the National Academy of
Engineering, an Honorary Fellow of the American Institute of Aeronautics and
Astronautics (AIAA) and a Fellow of the American Academy of Mechanics and the
American Society of Mechanical Engineers. He has also served as Vice President
for Publications and member of the Executive Committee of the Board of
Directors of the AIAA; as a member of the United States Air Force Scientific
Advisory Board; the Air Force Studies Board, the Aerospace Science and
Engineering Board and the Board on Army Science and Technology of the National
Academies; the AGARD (NATO) advisory panel for aerospace engineering, as
Presi-dent of the American Academy of Mechanics, as Chair of the US National
Committee on Theoretical and Applied Mechanics and as Chair-man of the National
Council of Deans of Engineering. From the AIAA he has received the Structure,
Structural Dynamics and Materials Award, the Von Karman Lectureship the
Crichlow Trust Prize and the Reed Aeronautics Award; from the ASME he has
received the Spirit of St. Louis Medal, the Den Hartog Award and Lyapunov
Medal; and he has also received the Guggenheim Medal which is awarded jointly
by the AIAA, ASME, AHS and SAE. He has served on the boards of visitors of
several universities and is a consultant to government, industry and
universities in science and technology policy and engineering education as well
as on the topics of his research. Dr. Dowell research ranges over the topics of
aeroelasticity, nonsteady aerodynamics, nonlinear dynamics and structures. In
addition to being author of over three hundred research articles, Dr. Dowell is
the author or co-author of four books, "Aeroelasticity of Plates and
Shells", "A Modern Course in Aeroelasticity", "Studies in
Nonlinear Aeroelasticity" and “Dynamics of Very High Dimensional Systems”.
His teaching spans the disci-plines of acoustics, aerodynamics, dynamics and
structures. Dr. Dowell received his B.S. degree from the University of Illinois
and his S.M. and Sc.D. degrees from the Massachusetts Institute of Technology.
Before coming to Duke as Dean of the School of Engineering, serving from
1983-1999, he taught at M.I.T. and Princeton. He has also worked with the
Convective transport has wide applications in oceanography,
weather prediction, and exchange of energy and mass. In this talk, we show that
modern deep learning models, such as generative adversarial networks, can be
used to learn the convective transport without the knowledge of underlying
constitutive equations. We will demonstrate the inference of the coupling
between mass, momentum and energy. Steady-state temperature distribution and
fluid flow-field are predicted with arbitrary geometric domains and boundary
conditions. In contrast to conventional procedure, the deep learning models
learn to generate realistic solutions in a data-driven approach and achieve
state-of-the-art computational performance, while retaining high accuracy.
In the second part, I’ll talk about different ML algorithms
used in molecule/material discovery and present their accuracies for different
tasks. Recently, ML shown to be very effective in material discovery and high
through-put screening of materials. The limitations of these algorithm along
with their interpretability will also be discussed.
Professor Farimani joined the Department of Mechanical
Engineering at Carnegie Mellon University in the fall of 2018. He was
previously a postdoctoral fellow at Stanford University. He received his PhD in
Mechanical Engineering in 2015.
His lab at CMU focuses on the problems
at the interface of Mechanical Engineering, data science and machine learning.
His lab uses the state of the art deep learning and machine learning algorithms
and tools to learn, infer and predict the physical phenomena pertinent to
mechanical engineering. Currently, he is teaching AI and ML to a large class of
graduate students at CMU.
He received the
Stanley I. Weiss best thesis award from the University of Illinois in 2016 and
was recognized as an Outstanding Graduate Student in 2015. During his
post-doctoral fellowship at Stanford, Dr. Barati Farimani has developed
data-driven, deep learning techniques for inferring, modeling, and simulating
the physics of transport phenomena and for materials discovery for energy
Additive manufacturing (AM) describes a class of processes that perform a layer-by-layer “bottom-up” fabrication approach as opposed to traditional top-down, subtractive fabrication such as milling and lathing. Printing-based AM, and in particular micro-scale
AM (µ-AM), has received significant attention in recent years as an enabling technology capable of revolutionizing the way we manufacture electronics, biosensors, and optics in this country. Meso-scale AM is capable of fabricating integrated features
beyond what conventional machining can perform at this length scale. However, µ-AM has yet to demonstrate the fabrication of complex 3D structures at the micro-scale that are not fabricable by traditional micromachining. Limiting this step change
in manufacturing capabilities is the reliance of μ-AM systems on a process monitoring, regulation, and quality control paradigm that is performed post-process and in an ad hoc manner. In this talk, we discuss some recent developments in process modeling,
sensing, and control that aim to break this open-loop paradigm by providing the controls theoretic and process modeling knowledge to develop a robust closed-loop system for measurement and compensatory control.
Kira Barton is an Associate Professor and Miller Faculty Scholar in the Department of Mechanical Engineering at the University of Michigan. She received her B.Sc. in Mechanical Engineering from the University of Colorado at Boulder in 2001. She continued
her education in mechanical engineering at the University of Illinois at Urbana-Champaign and completed her M.Sc. and Ph.D. degrees in 2006 and 2010, respectively. She held a postdoctoral research position at the University of Illinois from Fall 2010
until Fall 2011, at which point she joined the Mechanical Engineering Department at the University of Michigan at Ann Arbor. Kira conducts research in modeling, sensing, and control for applications in advanced manufacturing and robotics, with a specialization
in Iterative Learning Control and micro-additive manufacturing. Kira is the recipient of an NSF CAREER Award in 2014, 2015 SME Outstanding Young Manufacturing Engineer Award, the 2015 University of Illinois, Department of Mechanical Science and Engineering
Outstanding Young Alumni Award, the 2016 University of Michigan, Department of Mechanical Engineering Department Achievement Award, and the 2017 ASME Dynamic Systems and Control Young Investigator Award.
The University of Pittsburgh is co-hosting gradLab this year, which will be held at Penn State University main campus (State College, PA). This free event "provides a wealth of information for underrepresented undergraduates who want to find out what graduate school is all about." This informative Lab will cover how to apply to graduate school, how to fund graduate school, and answer any questions you may have. The event begins at 9:00 a.m. and ends at 3:00 p.m. We will depart from the University at 6:00 a.m., pick up students from Carnegie Mellon University and arrive at Penn State at approximately 9:00 a.m. The bus will depart Penn State at 3:30 p.m. and arrive back to Pitt approximately 6:30 p.m. (depending on traffic). Bus, continental breakfast and lunch will be provided free of charge. Please contact Mary at email@example.com or call 412-624-9842 with any questions. Seats are limited - reserve yours today!!
**Departure times are approximate. Will have definite times closer to date**