Day 2

November 29

November 29

Jeremiah 33:14-16

14 The days are surely coming, says the Lord, when I will fulfill the promise I made to the house of Israel and the house of Judah. 15 In those days and at that time I will cause a righteous Branch to spring up for David; and he shall execute justice and righteousness in the land. 16 In those days Judah will be saved and Jerusalem will live in safety. And this is the name by which it will be called: "The Lord is our righteousness."


The Lord is Our Righteousness

by Pablo Rivas, Ph.D.

Do you remember what it feels like to take a test for which you prepared? Remember that feeling of confidence when you saw questions to which you knew how to respond? To some extent, you were sure you would not fail; yet nothing was certain until the results were disclosed to you. At that moment, you were sure of that which you suspected.

Being righteous can be similar; to some extent, we can have some certainty that we might be, yet we will not know until it is revealed to us. The prophet’s promise of the Messiah gives us hope that we can know, through Christ, that which is righteous. By being his disciples, we can learn from that which is righteous; by allowing him to guide our lives, we can see up close how his righteousness flows through us. Through Christ, there is an absolute certainty that we can be found righteous through Him, not because of anything we do or do not, but because of his righteousness alone.

For me, the advent season, beyond the desire to spend time with family and friends, serves as a time of renewal of a commitment to be Christ’s disciple. However, this year’s advent season gives me a unique chance of revival after an unprecedented year that cries for the deliverance of a global pandemic and a renewal of what we call normalcy. I look forward to a new beginning where I can see my students’ faces to see if they smile at the unprecedented possibilities of artificial intelligence (AI) models or to see if they share my concerns with the ethical implications of AI. I long for the day when it is safe for all to take off our masks anywhere. But until then, I press on, hoping Christ’s righteousness inspires me and everyone in our institution.

This year felt nothing like coming prepared for a test; on the contrary, it felt like encountering questions that did not look like the ones we observed during our life-long preparation. We had to make educated guesses with the information that we had at hand. We had to rely upon and trust in Christ’s guidance more than ever. And here we are now, in the advent season, waiting for the test results, hoping for no more tests or at least ones for which we feel prepared. We are crying with the people of God, then and now, there and here, come and save us; God, please come and do justice for your people; for the Lord is our righteousness, as Jeremiah prophesied.

May the Lord renew your spirits, my dear colleagues and friends; may the Lord keep our students and their loved ones. May this season of advent serve as a reminder of our calling to become one with Christ and his righteousness. May we be saved; may we live in safety, just as it was promised to God’s people then, through Jeremiah, so be it here and now. Come, Jesus.


Learn More About Our Guest Writer

Pablo Rivas, Ph.D.Pablo Rivas, Ph.D.

Pablo Rivas, Ph.D., is an assistant professor of Computer Science within the School of Engineering and Computer Science. Dr. Rivas’s research and interests are in different aspects of machine learning, data science, big data, and large-scale pattern recognition. Recently, Dr. Rivas has worked with Dr. Greg Hamerly, associate professor of computer science, to accurately detect signs of retinal cancer from photography.

Dr. Rivas’s research and teaching directly support the Data Analytics initiative within Illuminate, Baylor’s strategic plan. Prior to his career in higher education, Dr. Rivas worked for eight years as a software engineer, including an internship at NASA Goddard Space Flight Center where he worked in the detection of a particular kind of atmospheric particle using different machine learning methods.