Postdoc (2020-), <jherasdo(at)andrew.cmu.edu>
I completed my Ph.D’s degree in Theoretical Chemistry at the Universitat Autònoma de Barcelona (UAB). My work consisted in a comparison between RuO2 surfaces and nanoparticles in terms of their catalytic performance for the oxygen evolution reaction (OER) through computational techniques (DFT). I also worked on collaborations with Covalent Organic Frameworks (COF) and Iridium based Single-site Catalysts. Outside of work I enjoy spending time with friends, street photography, music, martial arts and traveling around the world.
Fellow, Science and Research (2020-), <rajeshra(at)andrew.cmu.edu>
Ijointed as Fellow, Science and Research in Ullisi’s group to work on Machine Learning approaches in Catalysis. I will be working on ARPA-E project on surface segregation. Prior to moving to CMU, I had worked as a Assistant Research Scientist/Postdoc at Texas A&M University (College Station, TX and Qatar Campuses) and Visiting Scientist at University of Birmigham, UK.
Honorary group postdoc! (at LBL), <bwood(at)lbl.gov>
Brandon is a NESAP postdoctoral fellow at NERSC who has been working with the Ulissi group on scaling deep learning models in catalysis. This is part of the upcoming Perlmutter supercomputer acquisition and testing.
Postdoc (2020-), <tiant2(at)andrew.cmu.edu>
I obtained my PhD in Chemical Engineering from ETH Zürich, Switzerland using combined theoretical and experimental techniques to model and engineer the properties of two-dimensional materials for real-world applications. I would like to use machine learning methods to study the stucture-property relations of low-dimensional materials in heterogeneous catalysis for which the experimental large-scale screening is impractical. In my leisure time I enjoy classical music, cycling, swimming and model building
Postdoc (2021-), <rtran(at)andrew.cmu.edu>
My research as a PhD student at UC San Diego involved theoretical (DFT) modelling of interfaces (grain boundaries and surfaces) and their influence on mechanical properties when impurities are present. I also investigated metal insulator transition in strongly correlated metal oxides. I hope to continue my research at CMU using machine learning to model the surface properties of strongly correlated metal oxides and other materials in the hopes of discovering novel catalytic materials for a wide variety of applications such as water purication and methanol adsorption. Outside of work I like drawing cartoons, playing video games, and boxing to stay in shape.
PhD (2017-), <apalizha(at)andrew.cmu.edu>
I completed my B.S. in Chemical Engineering at North Carolina State University (Go Pack!). When I was an undergraduate there, I was heavily involved in AIChE and conducted research in Dr. Chase Beisel’s lab for two years. In addition, I was also given opportunities to spend all my summers in the industry. All of these experiences provided me with the privilege to learn from many incredible mentors and inspired me to get into graduate school. CMU was the perfect next step for me (fun fact! My undergraduate academic advisor Dr. Lisa Bullard also graduated from here). Outside of school and research, I am part of the CMU Ballroom Dance Competition Team! It has become a great way for me to de-stress, learn something new, and meet some of the most interesting and unique people outside of my daily routine.
PhD (2017-), <junwoony(at)andrew.cmu.edu>
My research focuses on deep learning and its applications in catalyst screening. I apply recent architectures of state-of-the-art Convolutional Neural Network (CNN) to predict catalyst properties. My goal is to design CNN architectures that can find better representations of atomic structures of materials to yield predictions with DFT-level accuracy. It will enable me to accelerate the catalyst screening process while minimizing the number of DFT calculations conducted. Further, it will also allow for the use of Deep Reinforcement Learning (DRL) for identifying metastable catalysts by providing accurate interatomic potentials for a given atomic configuration. In parallel, I always keep an eye out for (and sometimes buy) stocks for long-term investment. I am also interested in studying stock trading algorithms and strategies using machine learning.
PhD (2018-), <mshuaibi(at)andrew.cmu.edu>
My research focuses on graph neural networks and deep learning approaches to catalysis. I strive to develop models and frameworks capable of accelerating atomistic simulations by orders of magnitude while retaining ab-initio level accuracy. I work closely with collaborators at Facebook AI Research (FAIR) to build datasets and models to meet such goals. Additionally, I explore active learning techniques to tackle similar problems in the small-data regime. Outside of work I enjoy spending time with family and friends. I am extremely competitive when it comes to sports and board games – my favorites being volleyball and Catan. My wife and I love exploring cities, parks, and new food places.
PhD (2019-), <ntiwari(at)andrew.cmu.edu>
I obtained my B.S. in Chemical Engineering from Rowan University in Southern New Jersey. I conducted research in the Mixing Group under the direction of Dr. Robert Hesketh, Dr. Arthur Etchells and Dr. John Thomas. Here, I used Computational Fluid Dynamics software (CFD) to simulate stirred-tank systems for industrial application. During my time at Rowan, I was afforded a variety of experiences and connections, both industrial and academic. Each experience helped to shape my own thoughts on my future and research, culminating in my decision to attend Carnegie Mellon University. Through my research, I hope to convey the wide-reaching impact of chemical engineering. Outside of my research, I enjoy almost any physical activity. I run, lift weights, square dance, and play a variety of sports including intramural frisbee, volleyball and flag football.
PhD (2019-), <kbroderi(at)andrew.cmu.edu>
I graduated from Georgia Institute of Technology in May 2019 with a B.S. in Chemical and Biomolecular Engineering. As an undergrad, I analyzed highly multidimensional data and developed machine learning workflows for signal analysis in the Lu Fluidics Group. Undergraduate research afforded me the opportunity to work on fascinating problems alongside a lot of interesting people, leading me to join the Ulissi Group in late 2019. Here, I plan to develop new catalysts for hydrogen evolution and get better at tackling complex problems. Outside of research, I enjoy cooking, reading, and staying active.
PhD (2019-), <usharma(at)andrew.cmu.edu>
I completed my bachelor’s degree in Chemical Engineering from Indian Institute of Technology - Hyderabad (IITH). There I worked with Dr. Praveen Meduri on photocatalytic hydrogen evolution. My aim here was to find or create a material that could photocatalytically split water to generate hydrogenthat could be used as fuel. I also worked with Dr. Phanindra Jampana on Compressed Sensing and System Identification in order to better understand what went into the working of large-scale chemical processes. During my time as an undergraduate student I enjoyed being part of various cultural activities and taking up positions of responsibility in the student council. Here at CMU, as an international student, I discover wonderful new things every day; like cornbread and Halloween, and an amazing work environment. I enjoy reading fiction and fantasy novels, cooking and dancing. I love anime and dogs! My favourite way to procrastinate is playing cards or board games with my siblings.
Visiting PhD, <xfu1(at)andrew.cmu.edu>
Xiaoyan is a visiting PhD student from Jianping Xiao’s group at the Dalian Institute of Chemical Physics.I completed my bachelor’s and master’s at Tianjin University, majoring in chemical engineering. I work with Jianping and focus on catalysts performance prediction and catalysts design. Now I am a visitor and focus on adsorption energy prediction over zeolite.</i>
PhD (2020-), <akolluru(at)andrew.cmu.edu>
I completed my bachelor’s degree in Chemical Engineering from Indian Institute of Technology, Delhi. My research experiences have been broadly related to computational aspects of Chemical Engineering, having completed projects in Deep Learning, Numerical Modeling, MD Simulation and CFD applications. I am currently fascinated with the idea of using efficient machine learning algorithms for accelerating computational catalysis. My goal would be to fill the gap between these two fields. During my undergrad I had been actively involved in various sports and culural activities. I played waterpolo for the institute team and was a part of Debating Club.
PhD (2020-), <xiaoxia3(at)andrew.cmu.edu>
I graduated from Rose-Hulman Institute of Technology with a bachelor’s degree in Chemical Engineering. When I was an undergrad, I worked with Dr. Heather Chenette on polymer degradation in simulated marine environment. I also conducted research on developing 3D-printed filter press with Dr. Marissa Tousley and Dr. Daniel Anastasio. I was fascinated by the idea of employing machine learning method to facilitate catalyst discovery, which leads me to the Ulissi Group. In my free time, I enjoy baking, watching movies, and playing with my cat. I love taking photos to capture the best moments of my life.
PhD (2020-), <bwander(at)andrew.cmu.edu>
I graduated from the University of California, Los Angeles with a B.S. in Chemical Engineering. At UCLA, I conducted experimental research in both the Chemistry and Chemical Engineering departments. After graduating, I worked as a Process Engineer at Apeel Sciences for three years. Outside of work, I enjoy cooking, playing games, and painting!
PhD (2020-), <jmus(at)cmu.edu>
I graduated from Iowa State University with a B.S. in Chemical Engineering and a minor in computer science. I am very interested in the application of machine learning methods to computational catalysis, in particular the acceleration of DFT calculations through active learning. In my free time I love to run, bike, hike, cook, play games, and read fantasy novels. I also enjoy casually observing U.S. politics and the stock market.
PhD (2021-), <firstname.lastname@example.org>
I received my Sc.B. in Chemical Engineering and A.B. in Economics from Brown University in 2016. Throughout my academic and professional journey, I have wanted to contribute to the development of sustainable forms of energy for worldwide electrification. During my undergraduate years at Brown, I worked with Professor Peterson and Alireza Khorshidi, helping with the development of models that can return on-the-fly electronic energies. Following graduation, I wanted to approach the problem of sustainable energy production through the lens of project and structured finance. Having spent over 4 years working at a FinTech start-up developing software for renewable energy investments, I have returned to academia with a view to contributing to more accurate and scalable models using democratized deep learning frameworks, HPC/GPUs and world-class datasets. In my free time, I enjoy playing squash, looking for good restaurants and working on commercializing technologies from academia. I am currently working with a friend of mine to productize state-of-the-art shape analysis algorithms, with use cases like Optical Character Recognition (OCR).
UG (Summer 2021), email@example.com
Outside of school, I’m a sprinter on the track team and I’m also a part of a Christian fellowship, Klesis CMU. Over the summer, I studied copper hydride nanoclusters using a density functional theory based genetic algorithm. I researched Cu4 clusters with varying copper to hydrogen ratios. With these different copper to hydrogen ratios, I used computational methods to find the geometry with the global minimum energies associated with each structure. I also analyzed the thermodynamic stability and factors affecting the stability of each nanocluster.
UG (Summer 2021), <aarongar(at)andrew.cmu.edu>
Over the summer, I trained machine learning models from the Open Catalyst Project on the Transition Metal Quantum Mechanics dataset (tmQM), with the objective of predicting interesting properties of new transition metal complexes, to potentially be applied in the catalysis field. Outside of classes, I enjoy playing racquetball and squash, as well as playing the French horn, tuba, and trombone.
UG (Summer 2021), <ketongc(at)andrew.cmu.edu>
As a student researcher in the Open Catalyst Project, I implemented similarity assessment among catalyst surfaces for data mining purpose. My method helps improve efficiency and accuracy of active learning through selecting highly similar surfaces as training inputs. Outside of school, I spend lots of time on music. When I am free, I sing and I play the piano, the guitar, the ukulele, and the drum. I also enjoy travelling and photography.
Role: PhD (2016-2021). Now working as a software developer in Schrödinger’s materials science division.
Role: UG(-2021). Now pursuing a PhD at Georgia Tech.
Role: MS(2019-2020), RA (2021). Now pursuing a PhD at UC Davis.
Role: MS (2019-2020)
Role: Post-doc 2018-2020, now faculty at Sogang University in Korea.
Wen Zhong, MS, 2018
Zong Qian Yu, 2018
Tanmay Raj, 2019
Qingyang (Ernest) Zhang, 2019
Shiv Rekhi, 2019
Hyukjae Kwark, 2018