Tal Kachman, Ph.D Email and Phone Number
Tal Kachman, Ph.D work email
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Tal Kachman, Ph.D personal email
"For a successful technology, reality must take precedence over public relations, for nature cannot be fooled." - Richard FeynmanI am an avid learner and problem solver. I always seek to explore new technologies, being on the cutting edge of research and emerging industries. Currently I am a research staff member in IBM working on deep learning, machine learning, information theory and their applications to a variety of industries such as healthcare, Natural images and vision. I come from both hands on and theoretical strong analytical background following a postdoc in IBM research, a physics Ph.D from Technion IIT and MIT.I have vast research experience in algorithm development, mathematical models, as well as in-depth HPC programming with Python, C, C++, SQL and the full python machine learning stack (skimage, sklearn, numpy etc ..) stemming from almost 8 years of in depth computational work.I am excited about data-driven projects and technological innovation, especially in the realm of machine learning and deep learning.
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Visiting Assistant ProfessorYale School Of ManagementUnited States
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Visiting Assistant ProfessorYale School Of Management Jul 2022 - PresentNew Haven, Ct, Us
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Assistant ProfessorRadboud University Nijmegen May 2021 - PresentNijmegen, Gelderland, Nl -
PrincipalRhizome Works Sep 2018 - PresentRhizome Works is an applied machine learning research and development company. We develop custom solutions and explore ambitious new ideas for a broad range of clients. -
Machine Learning ResearcherAqr Capital Management Jul 2019 - 2021Greenwich, Connecticut, Us -
Research Staff MemberIbm Jul 2017 - Jul 2019Armonk, New York, Ny, UsAs a research staff member, I work and do research in a variety of topics, both theoretical and hands on. My main areas of research and expertise are in deep learning, machine learning, scientific computing, quantum computing and information theory. I work on the mathematical foundations of deep learning both from an optimization point of view and information theory perspective using tools from probability theory and statistical physics.I am particularly interested in one shot learning or learning with limited data, and in the Bayesian interpretation of stochastic backpropagation. In regular machine learning modeling, I work on non Markovian process and their relation to inference and statistical models. I am especially interested in more these kinds of models can be applied to the optimization of healthcare.As part of the “Mathematics of AI” group at IBM, I also work on variety of topics at the interface between classical and quantum computation and machine learning. These include approximation theories for quantum algorithms and their applications for machine learning as well as deep learning methods for quantum machine learning. -
Postdoctoral ResearcherIbm 2017 - 2017Armonk, New York, Ny, UsAs a postdoctoral researcher in the machine learning for healthcare and life sciences group, I developed novel machine learning and deep learning algorithms for text-based and image-based medical data, such as electronic healthcare records and mammography images using Tensorflow, mxnet, and PyTorch. I also developed new Bayesian methods for studying rare statistical events, and researched the effects of memory and non-Markovian processes on learning algorithms. Such as medical time series, and based cohort analysis.
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Visiting ScholarMassachusetts Institute Of Technology (Mit) 2014 - 2017Cambridge, Ma, UsMy Ph.D was centered about fundamental questions of nonlinear dynamics, nonequilibrium statistical physics and their interplay with learning and information theory.I had the great privilege to undertake my Ph.D with Jeremy England at the MIT Center for Physics of Living Systems (PLS).During my time at the PLS I looked at emergent computation in matter driven far from equilibrium. The majority of the work was done using advanced tools from nonequilibrium statistical mechanics, and their numerical implementation. For this purpose we Implemented a molecular dynamics toolbox in C/C++. The simulation was parallelized MPI/openMPI and deployed to a large computational clusters with distributed computing. -
Visiting ScholarMassachusetts Institute Of Technology (Mit) Oct 2013 - Jan 2014Cambridge, Ma, Us
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Data Science FellowInsight Data Science Sep 2016 - Dec 2016San Francisco, Ca, UsI had the great pleasure of being selected as an Insight Data Science fellow. During my time as a fellow, I developed a book categorization and classification method using deep learning. This was done by building a deep learning/machine learning pipeline composing of several steps I first used computer vision detection to parse camera photos of bookshelves, then exported them to a database for bookkeeping (pun intended) and built a recommender system. The project made me engage in hands on experience using SQL/deep learning platforms and computer vision processing tools. -
Phd Candidate And Graduate Student ResearcherTechnion - Israel Institute Of Technology 2013 - Dec 2016Haifa, IlAs part of the nonlinear dynamics group in the technion I performed hand on research into numerical quantum dynamics. Specifically I had a large emphasis on noisy system and chaotic systems.Some of the main aspects of this project were:• Investigated asymptotic behaviour of nonlinear quantum systems and how they evolve over time, using time series analysis partial differential equation analysis with Matlab Mathematica • Developed novel numerically exact methods to deal with quantum and semi ergodic systems.• Build a numerical framework to deal with multi frequency stochastic systems and implemented a multiscale numerical analysis framework in Python and Julia -
Teaching AssistantTechnion - Israel Institute Of Technology Oct 2008 - 2013Haifa, IlAs a teaching assistant I led weekly recitations for a wide range of graduate and undergraduate courses Developed problem sets and administrated course website. Supervised a group of five other teaching assistants.The courses I taught were: Physics 3H(modern physics)Advanced Physical Chemistry Lab : Computational methodsStatistical ThermodynamicsAnalytical Methods 1 In Mechanical EngineeringAnalytical Methods 2 In Mechanical EngineeringClassical DynamicsFluid MechanicsCalculus 1
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ResearcherTechnion - Israel Institute Of Technology May 2012 - Oct 2012Haifa, IlConducted research projects such as aspects of dynamical systems in quantum optics experiments and numerical modeling of nano materials.
Tal Kachman, Ph.D Skills
Tal Kachman, Ph.D Education Details
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Massachusetts Institute Of TechnologyPhysics -
Technion - Israel Institute Of TechnologyTheoretical And Mathematical Physics
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Technion - Israel Institute Of TechnologyMechanical Engineering
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Technion - Israel Institute Of TechnologyPhysics
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Technion - Israel Institute Of TechnologyChemistry
Frequently Asked Questions about Tal Kachman, Ph.D
What company does Tal Kachman, Ph.D work for?
Tal Kachman, Ph.D works for Yale School Of Management
What is Tal Kachman, Ph.D's role at the current company?
Tal Kachman, Ph.D's current role is Visiting Assistant Professor.
What is Tal Kachman, Ph.D's email address?
Tal Kachman, Ph.D's email address is tal.kachman@ru.nl
What schools did Tal Kachman, Ph.D attend?
Tal Kachman, Ph.D attended Massachusetts Institute Of Technology, Technion - Israel Institute Of Technology, Technion - Israel Institute Of Technology, Technion - Israel Institute Of Technology, Technion - Israel Institute Of Technology.
What are some of Tal Kachman, Ph.D's interests?
Tal Kachman, Ph.D has interest in Robotics, Optics, Big Data, Machine Learning, Non Linear Optics, Social Network And Graph Theory, Solid State Physics And Semiconductors, Finance And Mathematical Finance, Numerical Analysis.
What skills is Tal Kachman, Ph.D known for?
Tal Kachman, Ph.D has skills like Physics, Programming, Numerical Analysis, C++, Python, Simulations, Matlab, Algorithms, Machine Learning, Latex, Solid State Physics, Mathematical Modeling.
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