Dan Tulpan Position/Title: Associate Professor email: [email protected] Phone: (519) 824-4120 ext. 52482 Office: ANNU 127 Google Scholar Research Gate LinkedIn

Dan Tulpan’s research interests range from computational biology and bioinformatics to mathematical modelling and computer vision (using computers to extract and process data from images).  His expertise has application across a broad range of topics, but at U of G, Dan is applying his skills to livestock breeding and other areas of plant and animal science.  Previously, Dan was a research officer in the Scientific Data Mining Team, Digital Technologies Research Center of the National Research Council in Moncton, New Brunswick.  There, he headed the NRC Atlantic Bioinformatics Laboratory. He has held numerous research and academic positions across Canada and worked in the software industry internationally.

Academic history

• Postdoctoral Fellow, Simon Fraser University, Department of Molecular Biology and Biochemistry, 2006-2007
• Ph.D., University of British Columbia, Department of Computer Science, 2000-2006
• B.Sc./B.Eng., Politechnic University of Bucharest, Department of Engineering Sciences and Computers, 1995-2000

Affiliations and Partnerships

• Adjunct Professor, Department of Animal Science and Aquaculture, Faculty of Agriculture, Dalhousie University
• Association of Computing Machinery (ACM)

Selected Awards and Honours

• 2018: NRC Instant Award, For significant effort and support in hosting the Next Generation Sensors workshop and developing the subsequent proposal in support of potential Challenge programs.
• 2015: ISMB - BioVis Design Contest Award (1st Place), The Circular Secondary Structure Uncertainty Plot (CS2-UPlot) - Visualizing RNA Secondary Structure with Base Pair Binding.
• 2015: Breakthrough of the Year - NRC ICT Award Project, An Enrichment Model for Wheat Gene Annotations Using Phylogeny, Orthology and Existing Gene Ontologies

Teaching

• AGR*3200: Computing for Bioscientists, Winter semester
• ANSC*6330: Topics in Computational Biology and Bioinformatics, Fall semester
• ANSC*6100: Special Project - Machine Learning Modelling, Winter semester

Research Impact

Dan is relatively new to U of G, and he’s enthused about building an innovative research program. His focus is on advanced computing and information technology to provide solutions to challenges in animal agriculture such as automatic animal identification, tracking and phenotype acquisition. He has published over 50 peer-reviewed articles, edited a book, co-authored scientific software for computational biology and bioinformatics and contributed to the formation of several highly qualified personnel such as students and programmers. Dan serves on several international conferences and journal editorial boards, co-organizes the ACM Symposium of Applied Computing Track on Bioinformatics and maintains strong research collaboration with researchers in Brazil, Switzerland and other countries.

Research Themes and Projects

• Theme 1: Development of an automatic real-time data acquisition platform for phenomics, physiological and environment information for livestock and poultry. The platform should be capable to capture and store information in real-time and periodically based on a variety of on-animal (e.g. accelerometers, magnetometers, gyroscopes, visual, audio, location), off-animal (e.g. cameras, thermal imaging, walk-over scales, LIDAR lasers) and/or in-animal (e.g. RFID chips) sensors. Ideally, the platform will be able to interface with existing acquisition systems developed by other research groups.

• Theme 2: Development of bioinformatics and computer vision data integration pipelines capable to efficiently select, filter, model and process the acquired information. The original data will be transformed into ready-to-analyze information and will be combined when needed with complementary external information resources (e.g. environment data, GPS locations, maps, other omics sources)

• Theme 3: Development of real-time interactive analytic and visualization tools to turn high-throughput phenomics data into testable hypotheses and actionable results and facilitate genotype-phenotype association studies.

(Under)Graduate Student Information

Dan’s research is truly interdisciplinary and has a strong bioinformatics and biology component. Therefore, graduate students should have good computing skills and be keenly interested in learning new tools and applying their knowledge to biological problems. Supervising, mentoring, guiding and working with talented, hard working students is a passion for Dan and he is enthused about helping students reach their research potential and expand their knowledge with each project they contribute to.

Current openings

• I am seeking MSc coursework students who are eager to develop computing and modeling skills, with a focus on applying the latest advancements in artificial intelligence and machine learning to animal science research and industry.

Student information

Currently Dan supervises or co-supervises the following undergraduate and graduate students and PDF:

	Student	Academic level	Co-advisors
	Ahmed, Zohair Syed	Undergraduate Research Assistant, ANSC	Dr. Katrina Merkies
	Aricibasi, Harry	M.Sc., ANSC
	Dr. Saeed Shadpour	Post-Doctoral Fellow	Dr. Christine Baes (Animal Biosciences) Dr. Flavio Schenkel (Animal Biosciences)
	Sahar, Maureen	Ph.D., ANSC	Dr. Jennifer Ellis (main advisor, Animal Biosciences)
	You, Jihao	Postdoctoral Scholar, ANSC	Dr. Jennifer Ellis (main advisor, Animal Biosciences)
	Yang, Ji (Ben)	M.Sc., ANSC

Alumni

	Student	Academic level	Co-advisors	Year	Position/Employer
	Adams, Sarah	Ph.D., ANSC	Dr. Jennifer Ellis (main advisor, Animal Biosciences)	2022
	Ahmed, Syed	B.Sc., CS (URA, Work Study Student)		2022	Software developer - intern, Magnet Forensics
	Aricibasi, Harry	B.Sc. Bio-medical science  (Contract)	Dr. Renee Bergeron (main advisor, Animal Biosciences)	2024	MSc - University of Guelph
	Chan, Esther	M.Sc., ANSC		2022	Wildlife Rehabilitator at Toronto Wildlife Centre
	Harvie, Julia	M.Sc., BINF+AI specialisation	Dr. Dirk Steinke (main advisor, Integrative Biology)	2022
	Lopes, Lucas	Ph.D., ANSC	Dr. Christine Baes (main advisor, Animal Biosciences)	2024
	Raffington, Jennien	M.Sc., BINF	Dr. Dirk Steinke (main advisor, Integrative Biology)	2020	Data analyst, Bonfire
	Rodriguez, Kaitlyn	M.Sc., BINF		2024
	Saljay, Niela (Charis)	B.Sc., Neuroscience (URA)		2022	Student, University of Guelph
	Sorkin, Ryan	B.Sc., CS (URA, Work Study Student)		2020	Software developer, BMO
	Vinden, Nicholas	B.Sc. Computer science  (Contract)		2023	M.Sc., University of Guelph;
	Wang, Zhuoyi (Elena)	M.Sc., ANSC		2023
	You, Jihao	Postdoctoral Scholar, ANSC	Dr. Jennifer Ellis (main advisor, Animal Biosciences)	2024	Postdoctoral Scholar, University of Guelph

Featured Publications

• J. Fraser, H. Aricibasi, D. Tulpan, R. Bergeron. A computer vision image differential approach for automatic detection of aggressive behavior in pigs using deep learning. Journal of Animal Science, 101, skad347, 2023.
• S. Shadpour, T. Chud, D. Hailemariam, G. Plastow, H. Oliveira, P. Stothard, J. Lassen, F. Miglior, C. Baes, D. Tulpan, F. Schenkel. Predicting methane emission in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks. Journal of Dairy Science, 105(10):8272-8285, 2022.
• J. You, D. Tulpan, M. Malpass, J. Ellis. Using machine learning regression models to predict the pellet quality of pelleted feeds. Animal Feed Science and Technology, 293:115443, 2022.
• Z. Wang, S. Shadpour, E. Chan, V. Rotondo, K.M. Wood, D. Tulpan. Applications of machine learning for livestock body weight prediction from digital images. Journal of Animal Science, 99(2):1-15, skab022, 2021.
• M. Yoosefzadeh-Najafabadi, D. Tulpan, M. Eskandari. Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits. Plos One, 16(4):1-18, e0250665, 2021.
• JL. Ellis, M. Jacobs, J. Dijkstra, H. van Laar, J.P. Cant, D. Tulpan, N. Ferguson. Synergy between mechanistic modelling and data-driven models for modern animal production systems in the era of big data. Animal, 14(S2):s223–s237, 2020.
• S. Nayeri, M. Sargolzaei, D. Tulpan. A review of traditional and machine learning methods applied to animal breeding. Animal Health Research Reviews, 20(1): 31-46, 2019.

For a more comprehensive list of publications please visit Dr. Tulpan's Google Scholar page.

Featured Bioinformatics Software and Data Sets

• The Circular Secondary Structure Base Pair Probabilities Plot, 2019.
• The Plant Orthology Browser, 2017 [not available, server maintenance ended].
• Dataset for Sense and Avoid Collision Detection for Drones, 2016 [not available, server maintenance ended].
• MetaboHunter - identification of individual metabolites in 1H-NMR spectra of complex mixtures, 2011 [not available, server maintenance ended].