Work with thought leaders and academic experts in computational theory mathematics

8 years of science and engineering experience integrating multi-omics data to identify biomarkers for cancer studies. Seeking to apply data analytics expertise to develop new diagnosis and treatment strategies.

Edoardo Airoldi is a Professor in the Department of Machine Learning at Temple University. He is also the Director of the Center for Machine Learning and Health. He is a world-renowned expert in the fields of machine learning and artificial intelligence, with a focus on applications to health. Airoldi is a member of the prestigious Association for the Advancement of Artificial Intelligence (AAAI) and the International Machine Learning Society (IMLS). He has published over 200 papers in leading journals and conferences, and his work has been covered by various media outlets including The New York Times, The Wall Street Journal, The Economist, and Wired.

Dr. Justin P. Whalley was educated in the UK (M.Sci. Mathematics, University of Bristol) and France (Ph.D. Bioinformatics, University of Évry). He moved to Spain to work as a postdoc at the National Center for Genomic Analysis (CNAG). During his time there, he ran the Quality Control working group for the Pan-Cancer Analysis of Whole Genomes project to assess the data coming in and reduce batch effects. This involved collaboration with researchers from the Broad Institute of MIT and Harvard, the German Cancer Research Center and the Wellcome Sanger Institute in the UK. He returned to the UK to work as a [Senior Bioinformatician at the University of Oxford](https://www.well.ox.ac.uk/people/jpw/). His time there coincided with the global pandemic and he was deeply involved in the COvid-19 Multi-omics Blood ATlas (COMBAT) consortium as the lead for the Integration (Tensor) working group. Dr. Whalley became a member of the [faculty of the Chicago Medical School at Rosalind Franklin University of Medicine and Science](https://www.rosalindfranklin.edu/academics/faculty/justin-p-whalley/) in January 2023.

Jeffrey Townsend is a Professor of Organismic and Evolutionary Biology at Yale University. He received his Ph.D. from Harvard University in 2002 and his Sc.B. from Brown University in 1994. He has been a teacher at St. Ann's School and an Assistant Professor at the University of Connecticut. He is currently the Elihu Professor of Biostatistics at Yale University.

I am passionate about solving big problems with scientific and computational tools. A highly experienced applied mathematician working in academia (faculty) and industry (consulting), with 15+ years of research and teaching expertise in inverse problems, signal processing, and data science. A highly-skilled software engineer and analyst/architect with 6+ years of experience as a technical lead in professional software development.

My main expertise is in solving computational challenges with an innovative algorithm engineering approach. For more than two decades, I have been studying on such challenges originating from different fields mainly in cryptography and data security, natural language processing, information retrieval, computational biology, data compression and coding, massive data management, and most recently focusing on scalability and security aspects of ML/AI algorithms. I have been devising efficient innovative solutions and/or improving current state-of-art in terms of resource usage, e.g., time, memory, energy, communication costs. I would like to provide a summary of my previous achievements in engineering, research, and administration. Engineering Expertise: After spending around two years on programming point-of-sales devices and regular database programming, I have spent 10+ years in cryptography, where the main focus had been efficient implementation and cryptanalysis of the security&privacy algorithms and protocols both in hardware and software. During those years, despite gaining experience on how to develop programs that run fast and/or with small memory footprint, I had the chance to work with talented mathematicians and hardware engineers, that gave me the opportunity to widen my knowledge on different dimensions, including reverse engineering and FPGA/ASIC design. I also learned a lot on how to develop projects with a team of talent coming from different disciplines. I have observed, and today strongly believe, that theoretical knowledge is vital, but never enough to built efficient systems in practice. The platform that the solution will be executed on and the properties of the input data should always be considered for ground-breaking progress in practical performance. Theory without practice, or vice versa, is akin to trying to fly with one wing. In that sense, the development of the fastest pattern matching solutions and innovating patents that are licensed to companies have been exemplary outcomes of my perspective. Academic Expertise: Following my 15+ years in industry, I joined academia and have been serving as a professor of computer sci- ence. I succeeded to get several research grants and have been also serving in the committees of conferences. Actually, I started publishing in scientific venues when I was with the industry as well. I did my phd on natu- ral language processing, after which I got more engaged with combinatorial algorithms. I mostly published on data compression, combinatorial pattern matching and applications of them on computational biol- ogy/bioinformatics. Most recently, I have been studying scalablity and security aspects in ML/AI systems as well as in information retrieval. I have also experience in massive data management and analysis. I have been teaching courses on algorithms, security/privacy, and related topics. Administrative Expertise: After engineering cryptography for many years, I changed my focus to computational biology, particularly the genomics area. I have served as the deputy director of the National Institute of Genetics and Biotechnology of Turkey for two years, during which I was responsible for the establishment of the first high-throughput DNA sequencing facility of the country. That leadership equipped me with a unique experience of leading an interdisciplinary project with people from computing and life sciences disciplines. The establishment of the lab was supported with more than 2 million dollars grant by the government and was successfully completed in two years. Another leadership experience I had was being the program coordinator of the graduate programs in my university for more than four years. I was responsible by curriculum development and hiring new faculty. I have also served previously as principal investigator in research projects, lead research labs, and delivered project lead positions in industry projects.

Abbas Alameer is an Assistant Professor of Bioinformatics at Kuwait University. He received his PhD in Bioinformatics and Molecular Modelling from the University of Leicester in 2014 and his MRes in Bioinformatics and Computational Biology from the University of Leeds in 2006. He has over 10 years of experience in bioinformatics related research and teaching. His research focuses on the computational analysis and modelling of biological molecules, and the development of novel algorithms and Bioinformatics tools. He has published several articles in leading journals in the field and has presented his work at international conferences.

**Results-driven AI leader with 20+ years of success spearheading model development and optimization initiatives in the energy industry and academia. Proven track record in leveraging computational data science, scientific machine learning, and AI to drive breakthrough physics-data solutions and deliver tangible business value. Adept at translating complex scientific concepts into robust AI models. Skilled in numerical simulation, scientific machine learning, and bilingual communication to optimize project outcomes.**

Dr. Denys Dutykh initially comes from the broad field of Applied Mathematics. He did his Master's degree in numerical methods applied to the problems of Continuum Mechanics and a Ph.D. thesis at Ecole Normale Supérieure de Cachan (France) on the mathematical modeling of tsunami waves. After this, he was hired as a permanent research scientist at the Institute of Mathematics (INSMI) at the Centre National de la Recherche Scientifique (CNRS). His research activities have been conducted in the following years at the picturesque University Savoie Mont Blanc (USMB, France) in the field of mathematical methods applied to the modeling and simulation of nonlinear waves (mostly in Fluid Dynamics). The Habilitation thesis of Dr. Dutykh was defended there on the topic of the mathematical methods in the environment. Since then, his research interests have significantly broadened to include the Dimensionality Reduction methods in Machine Learning, modeling of PV panels, and even some more theoretical questions in the Number Theory.

Dr Fadekemi Janet Osaye is a mathematician whose primary research interest is in graph theory and network modeling. In particular, she is interested in distance measures in graphs and their applications to solving many real-world problems. Her interest in discrete mathematics was inspired by her research project carried out at AIMS Ghana. She has published several articles in reputable journals and has presented in several conferences across the globe. In 2019, she became the first black female to be awarded a PhD in Mathematics by the University of Johannesburg, South Africa, in its 116 years of its existence. Since June 2021, she has been an Assistant Professor of Mathematics at Alabama State University and was previously a Visiting Assistant Professor at Auburn University. She is the founder of GirlsMatics Foundation, a STEM non-governmental organisation for girls in Nigeria which was motivated by her involvement with AIMS Ghana’s outreach programs for high school students in Biriwa, Ghana. She is also the co-founder of FadNna Partners, an analytics and management firm based in Lagos, Nigeria.

Dr. Anindya Ghose is the Heinz Riehl Chair Professor of Business at New York University Stern School of Business. His research interests are in the areas of IT-enabled platforms, services and products, including mobile apps, social media, digital marketplaces, and big data analytics. He has also done work on understanding consumer behavior in digital environments. He has published over 130 papers in academic journals and has been honored with several best paper awards. He has also served on the editorial boards of several journals, including Management Science, Information Systems Research, and MIS Quarterly. He is a Fellow of the Institute for Electrical and Electronics Engineers (IEEE) and the Association for Computing Machinery (ACM).

Dr. Suhang Wang is an Assistant Professor of Computer Science and Engineering at Pennsylvania State University. He received his PhD in Computer Science from Arizona State University in 2018, and his Master's degree in Electrical Engineering: Systems from the University of Michigan in 2013. Before joining Penn State, Dr. Wang was a postdoctoral researcher at the University of California, Santa Barbara. His research interests include natural language processing, artificial intelligence, and machine learning. He was recognized for his work at the International Conference on Knowledge Discovery and Data Mining in 2017 and the Fifth ACM International Conference on Web Search and Data Mining in 2016.