Adrian E. Roitberg
Full Professor
Department of Chemistry @ University of Florida 

  • Email: roitberg@ufl.edu
  • Twitter: @adrian_roitberg
  • Curriculum Vitae: PDF
  • Citations: Google Scholar
  • Office: 440 Leigh Hall
  • Phone: 352-392-6972
  • Address: 440 Leigh Hall, University of Florida, Gainesville, FL. 32611-7200
  • Education
    • 1987: Licenciado, University of Buenos Aires, Argentina
    • 1992: Ph.D., Chemistry, University of Illinois at Chicago
  • Teaching: Link

Honors and Awards

2016 Ulam Scholar. Center for Non-linear Sciences. Los Alamos National Laboratory. New Mexico.
2016 University of Florida Research Foundation Professor.
2014 Elected Fellow of the American Chemical Society.
2014 Elected Fellow of the American Physical Society.
2012 Ulam Fellow. Los Alamos National Laboratory.
2011 Raices Prize. Ministry of Science and Technology of Argentina.
2007 Howard Hughes Medical Institute Distinguished Mentor Award. University of Florida.
2006 Fulbright Fellow (research place in Argentina).
2006 Invited Professor. University of Buenos Aires. Argentina.
2004 Invited Professor. University of Buenos Aires. Argentina

Research Experience

2016-2019 University of Florida Research Foundation Professor, Department of Chemistry, University of Florida.
2011-Present Full Professor, Department of Chemistry, University of Florida.
2011-Present Affiliate Full Professor. Department of Physics, University of Florida
2013-2014 Colonel Allan R. and Margaret G. Crow Term Professor. College of Liberal Arts and Sciences, University of Florida.
2004-2011 Associate Professor, Quantum Theory Project and Department of Chemistry, University of Florida.
2005-2011 Affiliate Associate Professor. Department of Physics, University of Florida
2003-2003 Associate Scientist, Quantum Theory Project and Department of Chemistry, University of Florida.
2001-2003 Assistant Scientist, Quantum Theory Project and Department of Chemistry, University of Florida.
1998-2000 Affiliate Assistant Professor. Computational Sciences Institute, George Mason University.
1996-2000 Guest Research Scientist. Biotechnology Division, National Institute of Standards and Technology.
1992-1995 Postdoctoral Fellow. Chemistry Department, Northwestern University. Advisor: Prof. Mark Ratner.

Invited Talks

listed only since 2016

2019

  • Machine Learning in Chemistry: The end of Quantum and the rise of the machines?. March 18th. Boston University. Chemistry Department. Boston, MA.
  • Machine Learning in Chemistry: The end of Quantum and the rise of the machines?. March 6th. Open Eye CUP meeting. Santa Fe, NM
  • Neural networks learning quantum chemistry: The rise of the machines. February 21st. Sanibel Symposium. St Simons Island. GA

2018

  • ANAKIN-ME: Using deep learning to develop a fully-transferable and chemically accurate GPU-accelerated potential. October 25th. Schrödinger Users Group Meeting. Boston, MA
  • ANAKIN-ME: Using deep learning to develop a fully-transferable and chemically accurate GPU-accelerated potential. October 20th. NESS 2018: Biomolecular Design and Structure Prediction. University of Connecticut. CT
  • ANAKIN-ME: Using deep learning to develop a fully-transferable and chemically accurate GPU-accelerated potential. October 11th. Italian Institute of Technology. Genoa, Italy.
  • ANAKIN-ME: Using deep learning to develop a fully-transferable and chemically accurate GPU-accelerated potential. October 9th. Artificial Intelligence in Chemical Research. Stein, Switzerland
  • ANI strikes again. New results from a grown-up Machine learning method for organic systems. September 28-29. The Future of Enzyme Modeling. Stockholm, Sweden
  • Molecular dynamics with machine learning potentials. From gas phase to solution chemistry, at low cost and high accuracy. September 3-7th. 10th International meeting on Photodynamics and related aspects. Cartagena. Colombia.
  • ANI strikes again. New results from a grown-up Machine learning method for organic systems August 19-23. 256th ACS National Meeting in Boston, MA
  • Machine Learning for chemical entities. June 13th. East China Normal University, Shanghai, China
  • Modeling biological systems at constant pH. May 28th. Departmento de Quimica Biologica. FCEyN. Universidad de Buenos Aires. Buenos Aires. Argentina
  • Desarrollo y uso de redes neuronales para estudiar reacciones químicas. May 16th. Instituto de Calculo. FCEyN. Universidad de Buenos Aires. Buenos Aires. Argentina
  • Active learning in chemical space for the automatic improvement of the ANI deep learned potential with an application to reaction profiles. March 19-22. 255th ACS National Meeting. New Orleans, LA
  • Molecular dynamics with machine learning potentials. From gas phase to solution chemistry, at low cost and high accuracy. March 19-22. 255th ACS National Meeting. New Orleans, LA
  • Machine learning in quantum chemistry: the hype and the data. Is it time for the rise of the machines?. Feb 5th, Ohio State University, Columbus, OH
  • Using advanced molecular dynamics to understand IM/MS data. January 27th. ASMS Sanibel meeting. Tampa, FL

2017

  • Replica Exchange and other advanced sampling techniques. Protein Simulation Symposium. UNAM. October 11th. Mexico City, Mexico
  • Machine learning in quantum chemistry: the hype and the data. Is it time for the rise of the machines?. Computational Advances in Drug Discovery. September 4th. Lausanne, Zwitzerland.
  • Machine learning in quantum chemistry: the hype and the data. Is it time for the rise of the machines?. DE Shaw Research. June 14 New York, NY
  • Machine learning in quantum chemistry: the hype and the data. Is it time for the rise of the machines?. Schrodinger CO. June 13 New York, NY.
  • Conformations and protonations: A day in the life of a protein. Telluride Science Research Center. June 26-30. Telluride, CO.
  • Machine learning in quantum chemistry: the hype and the data. Is it time for the rise of the machines?. JCUP. May 25-26. 2017. Tokyo. Japan.
  • Machine learning in quantum chemistry: the hype and the data. Is it time for the rise of the machines?. IX Congreso Internacional de Formación y Modelación en Ciencias Básicas. 3-5 May. 2017. Medellin. Colombia.
  • Machine learning in quantum chemistry: the hype and the data. Is it time for the rise of the machines?. NIH. April 26. 2017.
  • Machine learning in quantum chemistry: the hype and the data. Is it time for the rise of the machines?. NSF workshop on Big Data. April 17-19. 2017. Arlington, VA
  • ANAKIN-ME: A general purpose and chemically accurate deep learned potential. 253rd ACS National Meeting in San Francisco, California, April 2-6. 2017.
  • Computing with international students. Workshop on promoting undergraduate chemical and biological computing. March 9-11. New Orleans. Lousiana.

2016

  • Pushing the envelope in Molecular Dynamics of Biological Systems: Speedups and New Ensembles Center for Non-Linear Sciences. Los Alamos National Laboratory. Los Alamos. New Mexico. November 2016.
  • Cheap and Accurate Energies through Machine Learning. OpenEye co. Santa Fe. New Mexico. November 2016.
  • Redes Neuronales aplicadas a la quimica teorica. XV reunion Mexicana de Fisico-Quimica Teorica. Merida, Mexico. November 2016.
  • Excited state energy transfer in conjugated oligomers. University of New Mexico. Alburquerque. New Mexico. October 2016
  • Things that move, from electrons, to protons to molecules, and what that motion says about function. Frontiers of Computational Chemistry. UNAM. Mexico City, Mexico. August 2016
  • Extending the Range of Simulations with Constant pH Methods. Gordon Research Conference in Computational Chemistry. PGA Catalunya Business and Convention Centre. Girona, Spain. July 2017
  • Things that move, from electrons, to protons to molecules, and what that motion says about function. Universite Pierre et Marie Curie. July 2017
  • Enhanced sampling methods in drug design. 251th ACS National Meeting. San Diego. CA. March 2016.
  • Constant pH simulations in biomolecular systems. 251th ACS National Meeting. San Diego. CA. March 2016.
  • Accounting for pH effects in biomolecular dynamics and reaction mechanisms through computational approaches.. Merck Co. New Jersey. February 2016